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Measuring Non-Medical Prescription Drug Use and Associated Risk-Taking Behaviors among Adolescents

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 Title Page
 Acknowledgement
 Table of Contents
 List of Tables
 List of Figures
 Abstract
 Introduction
 Review of the literature
 Materials and methods
 Results
 Summary, discussion, implications,...
 Appendix A: Santa Rosa County final...
 Appendix B: List of expert panel...
 Appendix C: Expert panel cover...
 Appendix D: Abstract of the proposed...
 Appendix E: Preliminary items for...
 Appendix F: Expert panel review...
 Appendix G: Expert panel comments...
 Appendix H: Application to the...
 Appendix I: Santa Rosa County School...
 Appendix J: University of Florida...
 Appendix K: Parental consent...
 Appendix L: Teacher protocol for...
 References
 Biographical sketch
 

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MEASURING NON-MEDICAL PRESCRIP TION DRUG USE AND ASSOCIATED RISK-TAKING BEHAVIORS AMONG ADOLESCENTS By MELISSA CHRISTINA MORRIS A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2006

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Copyright 2006 By Melissa Christina Morris

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iii ACKNOWLEDGEMENTS To be a philosopher is not merely to have subtle thoughts, nor even to found a school, but so to love wisdom as to live accord ing to its dictates, a life of simplicity, independence, magnanimity, and trust. Henry David Thoreau, Walden, 1854 I would like to express my sincere gratitu de for all of the assistance and support I have received from my family, friends and pr ofessors, throughout my tenure as a doctoral student especially during the time I was writing this dissertation. I would first like to thank Dr. Robert M. Weiler for serving as my academic advisor, dissertation chair, mentor, and friend. It was Dr. Weiler that helped instill in me a sense of self-confidence in my research ab ilities and provided me with the guidance to see this project through to the end. His en couragement and enthusiasm for research and scholarship have undoubtedly made for a rewarding academic experience over the past three years. I am deeply indebted not onl y to Dr. Weiler but to my entire dissertation committee for creating a supportive learning environment throughout the dissertation process Dr. R. Morgan Pigg, Jr., Dr. Virg inia J. Dodd, Dr. Dennis Thombs, and Dr. David Miller. Their directi on and thoughtful criticism thr oughout the inception, review, and revision of my dissertati on are greatly appreciated. I am grateful to the members of my expert panel for taking the time to carefully examine the items constructed for my research and evaluate them for content validity. In particular, I would like to thank Dr. Davi d HaddoxVice President of Risk Management and Health Policy at Purdue Pharma for his extended conversation regarding modification of the items.

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iv I wish to acknowledge the individuals in Santa Rosa County who made it possible for me to conduct my study. I would especi ally like to thank Mrs. Sharon MeyersMongue, for helping me coordinate and execu te my project. For her assistance, along with the cooperation of the school principals teachers, and students involved with the study, I extend my heartfelt thanks. Throughout my education, I have encount ered numerous other individuals who helped shape my experiences as a student, re searcher, and professional. My graduate studies would not have been the same wit hout the social and academic challenges and diversions provided by my student -colleagues, the staff, and professors who comprise the Department of Health Education and Behavior Special thanks go to my fellow doctoral students and friends, Erin a nd Jeff Wight, for opening up their home to me on my jaunts to Gainesville during my last semest er as a doctoral student. I want to especially tha nk my mother and father who have always showered me with emotional guidance, love, and support to pursue my interests. Their unwavering faith in my abilities has instilled in me a conviction that I can accomplish almost anything. Thanks go to my big brother, Ed, who has also provided me with support and understanding during the long ye ars of my education. Finally, I wish to thank my best frie nd and my voice of reason, Doug, for his compassion, endless patience, and encourag ement when it was needed the most.

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v TABLE OF CONTENTS page ACKNOWLEDGEMENTS...............................................................................................iii LIST OF TABLES...........................................................................................................viii LIST OF FIGURES..........................................................................................................xii ABSTRACT.....................................................................................................................xi ii CHAPTER 1 INTRODUCTION TO THE STUDY...........................................................................1 Statement of the Research Problem..............................................................................2 Purpose of the Study.....................................................................................................7 Significance of the Study..............................................................................................8 Research Questions.......................................................................................................9 Delimitations...............................................................................................................10 Limitations..................................................................................................................10 Assumptions...............................................................................................................11 Definition of Terms....................................................................................................11 2 REVIEW OF THE LITERATURE............................................................................15 Nonmedical Prescription Drug Use Defined..............................................................15 Classes of Prescription Drugs Used Nonmedically....................................................18 Opioids................................................................................................................18 Stimulants............................................................................................................20 Depressants..........................................................................................................21 Epidemiology of Nonmedical Prescription Drug Use................................................23 Initiation..............................................................................................................24 Prevalence............................................................................................................25 Subgroup Trends.................................................................................................27 Indicators of the Problems Seriousness..............................................................28 The Youth Risk Behavior Survey...............................................................................30 Correlates of Adolescent Prescription Drug Abuse....................................................35 Tobacco Use........................................................................................................35 Alcohol Use and Binge Drinking........................................................................37 Marijuana Use.....................................................................................................38

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vi Other Illicit Drug Use..........................................................................................38 Driving Under the Influence of Alcohol.............................................................41 Violence Related Behaviors................................................................................42 Sexual Experience and Risk Taking....................................................................44 Depression and Suicide Ideation.........................................................................45 Decreased Physical Activity................................................................................46 Use of Problem Weight Loss Practices...............................................................48 3 MATERIALS AND METHODS...............................................................................50 Research Design.........................................................................................................50 Research Variables.....................................................................................................52 Study Population.........................................................................................................53 Instrumentation...........................................................................................................54 Item Development...............................................................................................56 Content Validity..................................................................................................58 Human Subjects...................................................................................................63 Pilot Study...........................................................................................................63 Item Reliability....................................................................................................68 Data Collection...........................................................................................................71 Data Analysis..............................................................................................................72 Summary.....................................................................................................................74 4 RESULTS...................................................................................................................75 Participant Demographics...........................................................................................75 Profile of Non-Medical Pres cription Drug Use Behaviors.........................................77 Chi-Square Analysis...................................................................................................83 Research Question One...............................................................................................86 Research Question Two..............................................................................................91 Research Question Three............................................................................................97 Research Question Four............................................................................................102 Research Question Five............................................................................................108 Research Question Six..............................................................................................114 Research Question Seven.........................................................................................119 Research Question Eight...........................................................................................124 Summary...................................................................................................................127 5 SUMMARY, DISCUSSION, IMPL ICATIONS, AND CONCLUSION................129 Summary...................................................................................................................129 Discussion.................................................................................................................133 Implications..............................................................................................................149 Recommendations for Research........................................................................150 Recommendations for Practice..........................................................................152 Conclusion................................................................................................................154

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vii APPENDIX A SANTA ROSA COUNTY FI NAL QUESTIONNAIRE..........................................156 B LIST OF EXPERT PANEL REVIEW ERS FOR INSTRUMENT ASSESSMENT170 C EXPERT PANEL EMAIL COVER LETTER.........................................................172 D ABSTRACT OF THE PROPOSED STUDY...........................................................174 E PRELIMINARY ITEMS FOR EXPERT PANEL REVIEW...................................178 F EXPERT PANEL REVIEW ITEM ASSESSMENT SURVEY..............................181 G EXPERT PANEL COMMENTS FOR REVISIONS...............................................196 H APPLICATION TO THE UNIVERSIT Y OF FLORIDA INSTITUTIONAL REVIEW BOARD....................................................................................................200 I SANTA ROSA COUNTY SCHOOL BOARD LETTER OF INITIATION...........204 J UNIVERSITY OF FLORIDA INTIUTIONAL REVIEW BOARD PROJECT EXEMPTION NOTICE............................................................................................206 K PARENTAL CONSENT FORM..............................................................................208 L TEACHER PROTOCOL FOR SURVEY ADMINISTRATION............................210 REFERENCES................................................................................................................215 BIOGRAPHICAL SKETCH...........................................................................................236

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viii LIST OF TABLES Table page 1. Benchmarks for Interpretation of th e Kappa Statistic (Landis & Koch, 1977)........68 2. The Santa Rosa County Health Behavior Survey Pilot Study: Measures of TestRetest Reliability for Prescripti on Drug Abuse Items Using Kappa ( ) Statistics..70 3. Santa Rosa County Health Behavior Survey: Participation Rate by High School...76 4. Santa Rosa County Health Behavior Survey: Demogra phic Characteristics of Participants (expresse d in percentages)*..................................................................77 5. Santa Rosa County Health Behavior Survey: Lifetime, Past 30 Day, and Past 30 Day Non-Medical Use on School Property of any Prescription Drug.....................78 6. Santa Rosa County Health Behavior Survey: Measures of Non-Medical Prescription Drug Use Prevalence by Drug Class (expressed in frequencies and percentages)..............................................................................................................79 7. Lifetime, 30-Day, and 30-Day on School Property Prevalence of Selected Drug Classes (expressed in percentages)..........................................................................80 8. Santa Rosa County Health Behavior Survey: Self-Reported Age at First Use, Drug Source, and Motivations for NonMedical Use by Drug Class (expressed in frequencies and percentages)...............................................................................82 9. Chi-square Analysis of Demographi c Variables by Lifetime, Past 30 Day, and Past 30 Day on School Property Prevalence of any Non-Medical Prescription Drug Use..................................................................................................................84 10. Chi-square Analysis of Demographic Variables by Lifetime, Past 30 Day, and Past 30 Day on School Property Preval ence of Non-Medical Prescription Pain Reliever Use.............................................................................................................84 11. Chi-square Analysis of Demographic Variables by Lifetime, Past 30 Day, and Past 30 Day on School Property Preval ence of Non-Medical Prescription Depressant Use........................................................................................................85

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ix 12. Chi-square Analysis of Demographic Variables by Lifetime, Past 30 Day, and Past 30 Day on School Property Preval ence of Non-Medical Prescription Stimulant Use...........................................................................................................85 13. Odds of Lifetime, Past 30 Day, and Past 30 Day on School Property Prevalence of Prescription Drug Abuse by De mographic Characteristics.................................86 14. Illicit Substance Use Behaviors Associ ated with Lifetime and Past 30-Day NonMedical Use of Prescripti on any Prescription Drug.................................................87 15. Illicit Substance Use Behaviors Associ ated with Lifetime and Past 30-Day NonMedical Use of Prescription Pain Relievers ............................................................89 16. Illicit Substance Use Behaviors Associ ated with Lifetime and Past 30-Day NonMedical Use of Prescription Depressants ................................................................90 17. Illicit Substance Use Behaviors Associ ated with Lifetime and Past 30-Day NonMedical Use of Prescription Stimulants ...................................................................91 18. Tobacco Use Behaviors associated with Lifetime and Past 30-Day Non-Medical Use of any Prescription Drug...................................................................................93 19. Tobacco Use Behaviors Associated with Lifetime and Past 30-Day Non-Medical Use of Prescription Pain Relievers ...........................................................................94 20. Tobacco Use Behaviors Associated with Lifetime and Past 30-Day Non-Medical Use of Prescription Depressants ..............................................................................95 21. Tobacco Use Behaviors Associated with Lifetime and Past 30-Day Non-Medical Use of Prescription Stimulants .................................................................................96 22. Alcohol Use Behaviors Asso ciated with Lifetime and Past 30-Day Non-Medical Use of any Prescription Drug...................................................................................97 23. Alcohol Use Behaviors Asso ciated with Lifetime and Past 30-Day Non-Medical Use of Prescription Pain Relievers ...........................................................................99 24. Alcohol Use Behaviors Asso ciated with Lifetime and Past 30-Day Non-Medical Use of Prescription Depressants ............................................................................100 25. Alcohol Use Behaviors Asso ciated with Lifetime and Past 30-Day Non-Medical Use Prescription Stimulants ...................................................................................101 26. Sexual Risk Behaviors Associated with Lifetime and Past 30-Day Non-Medical Use of any Prescription Drug.................................................................................103 27. Sexual Risk Behaviors Associated with Lifetime and Past 30-Day Non-Medical Use of Prescription Pain Relievers .........................................................................105

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x 28. Sexual Risk Behaviors Associated with Lifetime and Past 30-Day Non-Medical Use of Prescription Depressants ............................................................................106 29. Sexual Risk Behaviors Associated with Lifetime and Past 30-Day Non-Medical Use of Prescription Stimulants ...............................................................................107 30. Violence-Related Behaviors Associat ed with Lifetime and Past 30-Day NonMedical Use of any Prescription Drug...................................................................110 31. Violence-Related Behaviors Associat ed with Lifetime and Past 30-Day NonMedical Use of Prescription Pain Relievers ..........................................................111 32. Violence-Related Behaviors Associat ed with Lifetime and Past 30-Day NonMedical Use of Prescription Depressants ..............................................................112 33. Violence-Related Behaviors Associat ed with Lifetime and Past 30-Day NonMedical Use of Prescription Stimulants .................................................................113 34. Feelings of Depression and Suicide Ideat ion Associated with Lifetime and Past 30-Day Non-Medical Use of any Prescription Drug..............................................115 35. Feelings of Depression and Suicide Ideat ion Associated with Lifetime and Past 30-Day Non-Medical Use of Prescription Pain Relievers .....................................116 36. Feelings of Depression and Suicide Ideat ion Associated with Lifetime and Past 30-Day Non-Medical Use of Prescription Depressants .........................................117 37. Feelings of Depression and Suicide Ideat ion Associated with Lifetime and Past 30-Day Non-Medical Use of any Prescription Stimulants .....................................118 38. Indicators of Physical Inactivity Asso ciated with Lifetime and Past 30-Day NonMedical Use of any Prescription Drug...................................................................120 39. Indicators of Physical Inactivity Asso ciated with Lifetime and Past 30-Day NonMedical Use of Prescription Pain Relievers ..........................................................121 40. Indicators of Physical Inactivity Asso ciated with Lifetime and Past 30-Day NonMedical Use of Prescription Depressants ..............................................................122 41. Indicators of Physical Inactivity Asso ciated with Lifetime and Past 30-Day NonMedical Use of Prescription Stimulants .................................................................123 42. Problem Weight Loss Strategies Asso ciated with Lifetime and Past 30-Day Non-Medical Use of any Prescription Drug...........................................................124 43. Problem Weight Loss Strategies Asso ciated with Lifetime and Past 30-Day Non-Medical Use of Prescription Pain Relievers ..................................................125

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xi 44. Problem Weight Loss Strategies Asso ciated with Lifetime and Past 30-Day Non-Medical Use of Prescription Depressants ......................................................126 45. Problem Weight Loss Strategies Asso ciated with Lifetime and Past 30-Day Non-Medical Use of Prescription Stimulants .........................................................127

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xii LIST OF FIGURES Figure page 1. Instructions and Items Regarding Pres cription Pain Relievers Used for the Expert Panel Review................................................................................................58 2. Screen Capture of the Web-based Expert Panel Review Form................................60

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xiii Abstract of Dissertation Presen ted to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy. MEASURING NON-MEDICAL PRESCRIP TION DRUG USE AND ASSOCIATED RISK-TAKING BEHVIORS AMONG ADOLESCENTS By Melissa Christina Morris December 2006 Chair: Robert M. Weiler Major: Health and Human Performance Although there is considerab le research on adolescent s ubstance use, limited data exist on the non-medical use and abuse of pr escription drugs. The purposes of this investigation were to design reliable and valid measures of non-medical prescription drug use to be incorporated into a comprehensiv e health risk behavior assessment and to describe the relationships between non-medical prescription drug use and additional health risk behaviors. The studys methodol ogy took place in three stages: 1) the design, expert panel review, and revision of instrument items, 2) the pilot testing of these items among a convenient sample of 209 students in grades 9-12 at a Northwest Florida Public High School, and 3) the administration of th e final questionnaire to 485 12th grade students at two Northwest Flor ida High Schools (different from the pilot study). Results of the study revealed that lifetime prevalence of non-medical use of any prescription drug use was relatively high among participants at 31.9%. When reported by class of abusable prescription drug, participants most readily reported having used pain relievers non-

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xiv medically in their lifetime (28.6%), followed by depressants (12.7%) and stimulants (9.0%). Multiple logistic regression analyses we re used to assess the covariance of nonmedical prescription drug use with selected heal th risk behaviors relevant to adolescents. Results indicated that non-medical prescripti on drug use was significantly associated with multiple health risk behaviors including tob acco, alcohol, and illicit drug use; violence; depression and suicide ideation; sexual risk-t aking; and problem we ight loss strategies. These findings suggest that nonmedical prescription drug use behaviors are not isolated events in the lives of youth. Rather, the beha viors covary with other health risk behaviors suggesting that non-medical pr escription drug use is linked to a syndrome of problem behavior. The studys findings may be a pplied to improve youth risk behavior surveillance systems and to better inform policy, prevention, and tr eatment initiatives related to the non-medical use and abuse of prescription drugs.

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1 CHAPTER 1 INTRODUCTION TO THE STUDY Throughout the history of modern medi cine, prescription drugs have made tremendous positive changes in the health of the United States and the world, and today they play an increasingly important role in restoring, maintaining, and improving health and well-being. Additionally, for the millions of Americans that rely on prescription drugs for therapeutic purposes, they can lead to dramatic improvements in the quality of life. In 2003, 3.4 billion prescriptions were filled in the U.S., a 70% increase in number of prescriptions filled just 10 years prior (2.0 billion). Wh en compared to the U.S. population growth of 13% during that same time period, the average number of prescriptions per capita in creased from 7.8 to 11.8 (Kai ser Family Foundation, 2004). Clearly, the use of prescription medication ha s increased in the U.S. as a result of demands by an emerging older population that re lies on drug therapy to stay healthy, and to advances in medical technology which ha ve led to hundreds of new drug approvals by the U.S. Food and Drug Administration (FDA) in the past 10 years (U.S. Food and Drug Administration [U.S. FDA], 2005). With an understanding that indicated prescription drug use generally improves health, most people who take prescription drugs take them responsibly. National general population surveys of prescription drug use cons istently find that the vast majority of prescribed use of psychotherapeutic drugs is conservative, therap eutically appropriate, and generally limited to short periods of time. Furthermor e, when taken properly for

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2 medical purposes, the benefit-to-risk ratio of for these substances is generally positive with little evidence that a busive use of prescribed drugs is common (Peterson, 1993). Yet, when abused or used for non-medi cal purposes, many prescription drugs can have effects that hinder, rath er than enhance lives. The non-medical use of prescription drugs is defined as using a prescription drug th at was not prescribed for you or that was taken only for the experience or feeling it caused (Substance Abuse and Mental Health Services Administration [SAMHSA], 2004a). According to the National Institute on Drug Abuse (NIDA), the non-medical use or ab use of prescription dr ugs is a serious public health concern (2001b, p. 1) and several indicators suggest that it is increasing in magnitude in the U.S., particularly am ong young people (Johnston, OMalley, Bachman, & Schulenburg, 2005b). Just as with the abuse of alcohol or illicit drugs such as marijuana, cocaine, or heroin, prescription drug abuse and non-medi cal use may lead to addiction, serious financial, emotional, social, and legal pr oblems, and in extreme cases may lead to disability or death (Zacny et al., 2003). Although prescription drug abuse is not a new phenomenon in this country, it is one that de serves renewed research attention. This investigation will increase the understanding of non-medical prescription drug use among adolescents and, enhance surveillance measures currently available to monitor this type of drug use among youth. Statement of the Research Problem While the rates of illicit dr ug, alcohol, and tobacco use have declined among teens in recent years, the non-medical use and a buse of controlled prescription drugs has been steadily rising (Johnston et al., 2005b). When placed into perspectiv e relative to other types of youth drug abuse, the prevalence of non-medical prescription drug use ranks

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3 fourth, after alcohol, cigarettes, and mar ijuana respectively (J ohnston et al., 2005b). While many types of prescription drugs can be abused, the three ma jor classes of drugs used by youth for their mood altering propertie s include opioid pain relievers, central nervous system (CNS) stimulants, and CNS de pressants. According the National Survey of Drug Use and Health (SAMHSA, 2004a ), between 1992 and 2003 the rate of nonmedial use of controlled prescription drugs among 12to 17y ear olds increased 212 percent. Additionally, new use of prescription opioids among teens is up 542 percent, more than four times the rate of in crease among adults (SAMHSA, 2004a). Indicators of the problems seri ousness include drug-related emergency department visits and mortality. The DAWN Report (SAMHSA, 2003b), a publication monitoring drug-related emergency department hospital visits and deaths, noted that benzodiazepines, analgesics, and antidepre ssants represented n early 30% of drugs referenced in emergency department reports in 2001. Yet well-known illicit drugs (e.g., cocaine, heroin, and marijuana) continue to re ceive most of the rese arch attention in drug abuse research and remain the primary focus of most prevention efforts. Furthermore, according to the Florida Office of Drug Control, one-third of all drugrelated deaths in the state during 2002 were the result of prescrip tion drug abuse (Chun, 2003). Adolescence is a developmental peri od of rapid physical, psychological, sociocultural, and cognitive changes (DiClemente, Hanse n, & Ponton, 1996). While it marks the transition from childhood to adulthoo d, it is characterized by efforts to confront and surmount challenges and to establish a sense of identity and autonomy. It also remains a period associated with high rates of substance abuse, including that involving

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4 prescription drugs due to biological and envi ronmental factors (e.g., psychosocial, family, peer) (Kaminer & Bukstein, 1998). Both theory and research suggest that drug taking behavior s are not isolated events in the lives of adolescents. Probl em Behavior Theory (Jessor & Jessor, 1977) contends that adolescent probl em behaviors (i.e., alcohol, marijuana abuse, delinquency, precocious sexual activity) are associated with one another and are inversely related to conventional behaviors (i.e., church attend ance, school performance) (Donovan, Jessor, & Costa 1988, 1991). The theory postulates that the covariation of problem behaviors is related to an underlying common factor that reflects unconv entionality, and represents a syndrome of problem behavior (Donovan & Jessor, 1985). While the present study will not attempt to explain the underlying cau ses or antecedents of prescription drug abuse, the idea that risk be haviors tend to cluster in vul nerable adolescents is the grounding framework for this study. Data from national population surveys of drug use suggest that teens who use prescription drugs for recreational purposes are twice as likely to use alcohol, five times more likely to use marijuana, 12 times more likely to use heroin, 15 times more likely to use ecstasy, and 21 times more likely to use cocaine, compared to teens who do not use such drugs (SAMHSA, 2004a). National data also report that high school seniors who use prescription opioids for their psychoactive effects are more likely to be involved in other deviant behaviors (i.e., school expulsion, skipping school, bringing drugs to school, getting high at parties, ha ving friends who use marijuan a) (McCabe, Boyd, & Teter, 2005). Studies conducted with smaller samples confirm national report s that prescription adolescents using prescripti on drugs non-medically report hi gher rates of cigarette

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5 smoking, alcohol, marijuana, ecstasy, and cocaine use, and other problem behaviors that lead to unintentional inju ry (i.e., drove after binge drinking, passenger with a drunk driver) (Boyd, McCabe, & Teter, 20 06; McCabe & Boyd et al., 2005). Findings from empirical studies suggest that adolescent risk behaviors are correlated; that is, engaging in one behavi or may indicate an increased likelihood for engaging in other behaviors or patterns of ri sk behavior (Dryfoos, 1990; Jessor, Donovan, & Costa, 1991). Specifically, numerous studies confirm that substance abusing behaviors in adolescence are not only related to each other (i.e., tobacco, alcohol, and illicit substance abuse) (Abdel-Ghany & Wang, 2003; Everett, Giovino, Warren, Crossett, & Kann, 1998) but have also been correlated with behaviors that put adol escents at risk for unintentional injury (McCabe, 2005; McCabe, Teter, & Boyd, 2004), aggressive and violent behaviors (O rpinas, Basen-Engquist, Grunba um, & Parcel, 1995; Valois, McKeown, Garrison, & Vincent, 1995; S ilverman, Raj, Mucci, & Hathaway, 2001), decreased physical activity (Pate, Heath, Dowda, & Trost, 1996; Winnail, Valois, McKeown, Saunders, & Pate, 1995), problem wei ght loss tactics (Neumark-Sztainer, Story, Dixon, & Murray, 1998; Rafiroiu, Sargen t, Parra-Medina, Drane, & Valois, 2003), sexual experience and risk taking (Shrier & Crosby, 2003; Shrier, Emans, Woods, & DuRant, 1996; Valois, Oeltmann, Waller, & Hussey, 1999) and depression/suicide ideation (Barrios, Everett, Simon, & Bre nner, 2000; Garrison, McKeown, Valois, & Vincent, 1993). Collectively, these facts logically suggest th at prescription drug abuse, a subset of substance abuse, may also be related to a syndrome of risk taking. However, because items related to non-medical prescription drug use are not present on surveys assessing multiple risk be haviors (i.e., CDCs Youth Risk Behavior

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6 Survey [YRBS]) we know little about whether th is type of drug use is also associated with other risk behaviors leadi ng to social and health problem s in adolescents. Although substance use correlates of recreational prescription drug use have been examined (McCabe, Teter, & Boyd, 2005, 2004; McCabe Teter, Boyd, & Guthrie, 2004), no previous research has been conducted to investigate rela tionshipsif anyprescription drug abuse may have with additional adolescent risk behaviors such as involvement in violence, risky sexual behavior, depression a nd suicide ideation, physical inactivity, or problem weight loss practices. Furthermore, when non-medical pres cription drug use behaviors and their correlates are reported in research studies, gene rally only one class of drugs is reported at a time (i.e., opioids, stimulant s, or depressants) ((McCab e, Teter, & Boyd, 2005, 2004; McCabe, Teter, Boyd, & Guthrie, 2004). While the unauthorized use of each of these classes of drugs can compromise health, th ey each have unique effects on the nervous system (NIDA, 2001b) and differ with respect to peer norms regarding the drugs, beliefs about the dangers of the drugs, and perceived availability by adolescents (Johnston et al., 2005b). No previous studies have made an atte mpt to report the risk behavior correlates of all three classes of abusable prescrip tion drugs concurrently. The existing body of knowledge would therefore be enhanced if a study were to report all three classes of prescription drugs when assessing co-morbidity so that differential levels of risk can be considered. The YRBS is one of three federally f unded surveys assessing adolescent drug use prevalence; Monitoring the Future (MTF), and the National Survey of Household Drug Use and Health (NSDUH) are the others. Although the design, implementation, and

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7 documentation of all three surveys have been deemed of high quality (Cowan, 2001), the YRBS is the only one that does not include measures of non-medical prescription drug use (other than steroid use), although psyc hoactive prescription drug abuse is a major contributor to drug-related morbidity in teen s. Adding non-medical prescription drug use items to the YRBS will only serve to enhan ce the federal surveillance methods used to estimate prevalence and trends of substance use among youth. This study will expand upon the CDCs Y RBS one of the most widely used surveys monitoring behaviors which place youth at risk for injury and disease by adding items that measure non-medical pres cription drug use. By assessing the prevalence and correlates of this type of drug use among adolescents, the study may provide policy-makers, health behavior resear chers, and health educ ators with a better understanding of the scope of non-me dical prescription drug use. Purpose of the Study This investigation examined the rela tionship between non-medical prescription drug use and other risk behaviors among adol escents using a modified version of the YRBS, the Santa Rosa County Health Behavior Survey. The purposes of this study were to (1) design reliable and valid measures of adolescent prescription drug use to be incorporated into the YRBS ; (2) describe the relati onships between non-medical prescription drug use and other adolescent ri sk behaviors; (3) imp rove monitoring and tracking systems; (4) raise aw areness among public health pr ofessionals about risks of non-medical prescription drug use among adol escents; (5) inform policy, prevention, and treatment measures related to non-medical prescription dr ug use and abuse; and (6) encourage inclusion of prescription drug it ems on surveys that monitor drug use and youth risk behaviors.

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8 Significance of the Study Although there is considerab le research on adolescent s ubstance use, limited data exist on the non-medical use of prescription dr ugs. To better inform policy, prevention, and treatment initiatives, it ha s been suggested that nationa l surveys of drug use should include specific items that measure prescr iption drug use and abuse behaviors (National Center on Addiction and Substance Abuse [NCA SA], 2005). One such national survey is the CDCs YRBS (Centers for Disease Cont rol and Prevention [CDC], 2005). By adding items relating to non-medi cal prescription drug use to the YRBS, federal agencies will be able to inform policy by improvi ng monitoring and tracking systems. Healthy People 2010 (Office of Disease Prevention and Health Promotion [ODPHP], 2000) a report of the nations health objectives for the coming decade, dedicated a chapter to substance abuse as a leading health indicator, identifying the nations goal as to: Reduce substance abuse to protect the health, sa fety, and quality of life for all, especially children (p. 26-3). However, in outlining the issues and trends, the report highlights alcohol first, and then turn s to illicit drugs, id entifying marijuana, cocaine, heroin, acid, inhalants, and me thamphetamine, in arguing the serious consequences of substance use and abuse. Although Healthy People 2010 (ODPHP, 2000) includes objectives related to reduci ng adverse consequences of substance use (e.g., drug-induced deaths, drug-related hospi tal emergency department visits) and identifies substance-free youth (p. 26-11) as a goal, prescription drugs are never explicitly identified as part of the problem. Considering prescription drugs are the third most commonly used class of psychoac tive drugs for recreational purposes among adolescents, the findings from this investiga tion may alert public health agencies to the importance of including prescription drug abuse measures on federally funded surveys.

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9 As a precursor to making associations betw een variables of interest on a survey, it is necessary to first establish validity and reliab ility of the items assessing those variables. The primary importance of this study lies in the assertion that it will generate items providing adequate estimates of prescripti on drug use behaviors among adolescents. Furthermore, this study is informed by Problem Behavior Theory, which provides evidence for a syndrome of problem behavior (Jessor et al., 1991; Jessor & Jessor, 1977). The present study will also help prov ide additional evidence for the concept that risk taking behaviors do not o ccur in isolation and that pr escription drug abuse may be part of a constellation of risk taking among adolescents. Only afte r fully understanding the scope and severity of non-medical pr escription drug use am ong adolescents will health education and prevention specialists be able to tailor interventions that address prescription drugs as part of the larg er problem of drug use and abuse. Research Questions The eight research questions for this st udy were formulated for a sample of 12th grade students. They included: 1. Is there a relationship between illicit drug use (i.e., marijuana, cocaine, inhalants, heroin, methamphetamine, ecstasy, and steroids) and non-medical prescription drug use? 2. Is there a relationship between tobacc o use and non-medical prescription drug use? 3. Is there a relationship between alcoho l use and non-medical prescription drug use? 4. Is there a relationship between sexual ri sk behaviors and non-medical prescription drug use? 5. Is there a relationship between viol ence-related behaviors and non-medical prescription drug use? 6. Is there a relationship between depre ssion and suicide idea tion and non-medical prescription drug use?

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10 7. Is there a relationship between decr eased physical activity and non-medical prescription drug use? 8. Is there a relationship between the use of problem weight loss tactics and nonmedical prescription drug use? Delimitations The following delimitations should be cons idered when interp reting the results from this investigation: 1. This study enlisted a cross-sectiona l census survey design using a group administered paper-pencil questionnaire. 2. Participants in this study included 12th grade students enrolled in two senior high schools during the spring of 2006. 3. Participants included 12th grade students attending cl ass on the day the survey was administered in the selected schools during the spring of 2006. 4. Participants included 12th grade st udents from the study population who voluntarily agreed to pa rticipate in the study. 5. Participants included only 12th grade students who obtained passive parental consent. 6. Participants were able to read and understand the direc tions, the questions, and the respective response options necessary to complete the questionnaire. 7. Findings from this study were based on self-report data co llected by using the Santa Rosa County Health Behavior Survey. Limitations The following limitations should be consider ed when interpreting the results from this investigation: 1. Data collected from cross-sectional surv ey designs only reflect responses from participants at a specific point in time and therefore ca nnot establish causation. 2. Findings from this study cannot be generalized to other populations of adolescents.

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11 3. Students who were absent on the day of data collection were excluded from the study. 4. Students who did not voluntar ily agree to participate were excluded from the study. 5. Students who do not obtain passive parent al consent were excluded from the study. 6. Variations in student reading ability a nd comprehension may have affected study results. 7. The study design relied on self-reported da ta, limiting the ability to determine the extent of over-reporting or under-reporting data. Assumptions For the purposes of this study, the following assumptions were made: 1. The students who participated in the st udy adequately represented the students enrolled in the schools. 2. The students who participated in the st udy were able to read and understand the directions, the questions, and provide the response options necessary to complete the questionnaire. 3. The students who agreed to participate in the study completed the Santa Rosa County Health Behavior Survey honestly. 4. The Santa Rosa County Health Behavior Survey was a valid and reliable instrument for collecting health be havior data from adolescents. 5. The research design was considered appr opriate for the purpose of the study. Definition of Terms Addiction : A chronic, relapsing disease, charac terized by compulsive drug seeking and use and by neurochemical and molecular changes in the brain. (NIDA, 2001b) Adolescence : The period of transition from late childhood to early adulthood corresponding roughly to the period between the ages of 10 and 19. (World Health Organization [WHO], 1986)

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12 Adolescent : In this study, twelfth grade students, with an approximate age range of 1718, were referred to as adolescents. Barbiturates : A sub-class of CNS depressant drugs prescribed for anxiety, insomnia, and convulsions, sometimes referred to as sedatives They are older and less frequently prescribed than barbiturates. (Julien, 2001) Benzodiazepines : A sub-class of CNS depressant drugs with with hypnotic, anxiolytic, anticonvilsant, amnestic, and muscle relaxant properties sometimes often referred to as tranquilizers. (Julien, 2001) Central nervous system (CNS) : The brain and spinal cord. (NIDA, 2001b) CNS depressants : A class of drugs that slow CNS f unction, some of which are used to treat anxiety and sleeping diso rders; includes barbiturates and benzodiazepines. (Julien, 2001) CNS stimulants : Drugs that enhance the activity of th e brain and lead to increased heart rate, blood pressure, and respirat ion; medically used to treat disorders such as narcolepsy and attention-deficit hyperac tivity disorder. (Doweiko, 2002) Controlled drug : A drug whose distribution and use is closely monitored by the Federal Drug Enforcement Agency (DEA) because of pot ential for abuse or risk. Drugs with the highest abuse potenti al are placed in Schedule I, and those with th e lowest abuse potential are in Schedule V. (U.S. Drug Enforcement Administration [U.S. DEA], n.d.) Dependence : An adaptive physiological state that can occur with regular drug use and results in withdrawal when drug use is discontinued. (NIDA, 2001b) Dopamine : A neurotransmitter present in regions of the brain that regulate movement, emotion, motivation, and feelings of pleasure. (NIDA, 2001b)

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13 Health risk behaviors : The variety of behaviors that pu t adolescents at risk for injury and disease. In this study, health risk be haviors include tobacco, alcohol, and other drug use; behaviors that compromise personal sa fety including driving under the influence of alcohol and violence; sexual be haviors; depression and suicid e ideation; lack of physical activity; and problem weight loss practices. (CDC, 2005) Non-medical prescription drug use : The use of prescription therapeutic drugs without a physicians permission or taken only fo r the experience or feeling it caused. (SAMHSA, 2004a) Norepinephrine : A neurotransmitter present in some areas of the brain and the adrenal glands; decreases smooth muscle contraction and increases h eart rate; often released in response to low blood pressure or stress. (NIDA, 2001b) Opioids : Controlled drugs or narcotics most of ten prescribed for the management of pain; natural or synthetic chemicals based on opium's active component morphine that work by mimicking the actions of pain-reli eving chemicals produced in the body. (NIDA, 2005d) Polydrug abuse : The abuse of two or more drugs at the same time, such as CNS depressant abuse accompanied by abuse of alcohol. (Rosenthal & Levounis, 2005) Prescription drug abuse : A maladaptive pattern of pr escription drug use leading to clinically significant impairment or distress as manifested by one or more behaviorally based criteria. These criteria include failure to fulfill major role obligations at work, school, or home; recurrent substance use in si tuations in which it is physically hazardous; recurrent substance-related legal problems; and continued use despite persistent or recurrent social problems caused by effects of the substance (American Psychiatric

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14 Association [APA], 2000) Psychotherapeutics : Drugs that have an effect on the function of the brain and that often are used to treat psychiatric disorders; can include opioids, CNS depressants, and stimulants. (NIDA, 2001b) Tolerance : A condition in which higher doses of a drug are required to produce the same effect as experienced initially. (NIDA, 2001b) Withdrawal : A variety of symptoms that occur after chronic use of some drugs is reduced or stopped. (NIDA, 2001b)

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15 CHAPTER 2 REVIEW OF THE LITERATURE Nonmedical Prescription Drug Use Defined To understand the nature of nonmedical prescription drug use among adolescents, it is meaningful to first illustrate the various ways this type of drug use is referred to in the literature. Three terms used widely to i ndicate the use of prescription drugs in ways other than therapeutically intended include misuse nonmedical use, and abuse Though these terms are commonly used interchang eably, they have distinct meanings and attention should be given to the way in which they are defined. Misuse generally refers to a patients incorr ect use of a medication. This includes taking it for other than the in tended use, taking other than the prescribed amount, and taking it more frequently or for longer than prescribed. However, physician behavior may also lead to misuse when a medicati on is prescribed for the wrong ailment, at improper doses, or for longer than is n ecessary (Ling, Wesson, & Smith, 2005). Abuse is perhaps, the term used most wi dely and with the widest range of definitions with reference to prescription dr ugs. The American Psychiatric Associations (APA) Diagnostic and Statis tical Manual (2000) defines abuse as a maladaptive pattern of substance use leading to cl inically significant impairment or distress as manifested by one or more behaviorally based criteria. Th ese criteria include fa ilure to fulfill major role obligations at work, school, or home; recu rrent substance use in situations in which it is physically hazardous; recurrent substance -related legal problems; and continued use despite persistent or recurre nt social problems caused by e ffects of the substance (APA,

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16 2000). The U.S. Drug Enforcement Ad ministration (U.S. DEA) defines abuse as the nonmedical use of drugs that are regulated under existing fe deral law into one of five schedules. These five sche dules are based upon the subs tance's medicinal value, harmfulness, and potential for abuse or addicti on (U.S. DEA, n.d.). It is apparent that the term drug abuse holds different meanings for different organizations. However, these differences are not trivial, as the definitions may be each tailored to address specific or policies concerned with the organization. The medical pr ofession, concerned primarily with clinical issues, and law enforcement agen cies, concerned with street diversion, have clearly adopted definitions rele vant to their own practices. Research agencies also have their own definitions for abuse, which are often broader in scope to reach a wider audience. The Institute of Me dicine (1996) defines abuse as any harmful use, irrespective of wh ether the behavior constitutes a disorder in the DSM diagnostic nomenclature (p. 3). The Substance Abuse and Mental Health Services Administration defines abuse as th e nonmedical use of a substance for psychic effect, dependence, or suicide attempt or gesture (SAMHSA, 2003a). Although NIDA does not have an official de finition of drug abuse, the agency often uses the phrase nonmedical use of a substance in thei r educational material s (Zacny et al., 2003, p. 217). Nonmedical use has been defined as using a ps ychotherapeutic drug even once, that was not prescribed for you, or that you took only for the feeling it caused (SAMHSA, 2004b). Some researchers distingu ish between nonmedical use and abuse. For example, Zacny et al. (2003) refer to a ny problematic prescription drug use (that has

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17 not shown to meet DSM-IV criteria for substance abuse disorder) as non-medical whereas they define abuse as that which meets DSM-IV criteria. However, as evidenced by the IOM and SAMHSA definitions above, nonmedical use is also frequently used interchangeably with abuse In fact, overlapping of all three terms within the same institution, is not unc ommon. In a single research report for example, NIDA cited, the nonmedical use or abuse of prescription drug remains a serious public health co ncern (p.1), and the misuse of prescribed medications may be the most common form of drug abuse among the elderly (p. 6) (NIDA, 2001b). A related document also equates misuse with any nonmedical use when citing the National Household Survey on Drug Use and Health (NIDA, 2005d). In an attempt to define the use of prescription drugs in ways other than therapeutically intended, orga nizations have provided us with diverse yet, sometimes convergent definitions. This presents a poten tially confusing situati on for the researcher trying to select one definition that is more correct than a nother. Because there is no universally agreed upon meaning for any of these terms, indicators of problem prescription drug use will need to be cl assified in a systematic manner. For the purpose of this review of the literature, misuse will be defined as the improper use of a drug (with regards to dosage, indication, timing, etc.). In this case, it is an inadvertent action that can sometimes be perpetuated by the provider. In their definitions of nonmedical use and abuse agencies often include any use of prescription drugs without a doctors orders (SAMHSA, 2004a ). However, clinicians generally view this as misuse if it is for the medically intended purpose (Ling et al., 2005).

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18 Nonmedical use and abuse will not be used interchangeably as in some research studies. Public health ag encies often recognize both abuse and nonmedical use as any potentially problematic use that is inten tional and deliberate, and has no corresponding medical benefit (SAMHSA, 2004a; NIDA, 2001b). Yet, the medical community generally acknowledges abuse only when impairme nt or distress presen ts itself on one or more forms (APA, 2000). Thus, the wi dely used definition provided by SAMHSA (2004a) taking of prescription drug without a doctors orders or on ly for the feeling it causedwill be used in characterizing nonmedi cal prescription drug use, but not abuse, which tends to have much more serious implications. Among young people, nonmedical use is most commonly associated with re creational or mood altering purposes but may also be associated with a suic ide attempt, appetite suppressi on, or increased attentiveness and focus (Meadows, 2001; SAMHSA, 2002). Classes of Prescription Drugs Used Nonmedically While virtually all prescription drugs can be misused, three major classes of drugs are most commonly used nonmedically for th eir psychotherapeutic properties. The public health, prevention, and law enforcem ent communities express particular concern for the use of opioids, central nervous syst em (CNS), stimulants, and CNS depressants (National Drug Intelligence Center, 2002; NIDA, 2001b; SAMHSA, 2004a). Many prescription drugs in these three classes are controlled drugs, that is are regulated by the federal government under the Controlled Subs tances Act of 1970, because of their abuse potential, addictive properties, and high risk for physical dependence (U.S. DEA, n.d.). Opioids Also referred to as narcotics, analgesics, pain killers, or pain relievers, opioids are most commonly prescribed in the clinical setting for their pain relieving properties but

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19 may also be used to treat severe coughs and diarrhea (NIDA, 2001b). The family of opioids (meaning like opiates) includes true opiates like morphine, derived from the opium poppy, as well as synthetic compounds w hose chemical properties and biological actions resemble those of true opiates (Julien, 2001). According to archaeological evidence, much of the ea rly history of opium took place before the advent of written records. It is thought that ancient Man ingested the dried resin of the opium poppy to control pa in, and treat children suffering from colic (Doweiko, 2002). It was not until the early 1800s however, that an ac tive agent, a pure alkaloid base, was chemically isolated from opium to produce morphine (Doweiko, 2002). After the invention of the hypodermic n eedle in 1857 and the widespread use of morphine and opium on Civil War battlefields it did not take long before a massive outbreak of opiate addiction took place in the U.S. (Doweiko, 2002). Throughout the history of modern medicine, the medical co mmunity has struggled to strike a balance between risk management strategies to preven t abuse, and the need for patients to have appropriate access to opioids (Zacny et al., 2002). Opioids are prescribed for three types of pain: acute or short-lived pain, chronic malignant (cancer) pain, and chronic nonmali gnant pain (Portenoy, 1992). They act by attaching to proteins called opioid receptors found in the brain, spinal cord, and gastrointestinal tract. When these drugs at tach to opioid receptors, they block the transmission of pain messages in the brain (Julien, 2001). Opioids are thus, an important component of pain management in the clinical setting. However, like illicit opioids (e.g., heroin) they additionally affect regions of the brain that mediate what we perceive as pleasure, thus resulting in an initial sens e of euphoria for the us er (Julien, 2001).

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20 Side effects of opioids include unc lear thinking, dizziness, drowsiness, constipation, itching, and depending on the am ount taken, depressed breathing. A large enough single dose could cause severe respirat ory depression resulting in death (NIDA, 2001b). Some of the more commonly abused opioids include mor phine, codeine (e.g., Tylenol 3) hydrocodone (e.g., Vicodin, Lort ab), oxycodone (OxyContin, Percocet), hydromorphone (e.g., Dilaudid), propoxyphene (e.g., Darvon, Dar vocet), and meperidine (e.g., Demerol) (NIDA, 2001b; Johnston et al., 2005b). Because of their moderate to high addictive properties, opioids are usually classified as Schedule II or III drugs (U.S. DEA, n.d.). Stimulants As with opioids, the use of central ne rvous system (CNS) stimulants, also known as psychostimulants or simply stimulants, date s back several thousa nds of years. In ancient Rome for example, Gladiators were thought to use stimulants to counter the effects of fatigue, gain energy, and fight longer. It has be en common practice in modern combat for soldiers and aviators to use stimulants to induce wakefulness (Doweiko, 2002). Stimulants have chemical structures si milar to key brain neurotransmitters called monoamines, which include norepinephrin e and dopamine. Increasing levels of norepinephrine in the brain and body increa ses blood pressure, heart rate, and blood glucose; constricts blood vessels; and opens up the pathways of the respiratory system. An increase in dopamine is associated with a sense of euphoria that can accompany the use of these drugs (Julien, 2001; NIDA, 2001b). The modern therapeutic uses of stimul ants focus on treating narcolespsy, a sleep disorder, and attention deficit (hyperactiv ity) disorder (AD[H]D), although they were widely prescribed as recently as the 1980s for weight loss, depression, and asthma

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21 (Julien, 2001; Doweiko, 2002). Though several families of drugs are classified as stimulants, prescription stimulants are ge nerally grouped into the amphetamines or amphetamine-like drugs. Some of the co mmonly prescribed stimulants include methyphenidate (e.g., Ritalin, Concerta), amphetamine-dexatroamohetamine (e.g., Adderall), dextroamphetamine (Dexedrine), and sibutramine hydrochloride monohydrate (e.g., Meridia) (NIDA, 2001b; SAMHSA, 2004a). Consequences of stimulant drug abuse incl ude feelings of host ility or paranoia for the user. Also, taking high doses of a s timulant may result in dangerously high body temperatures and irregular heartbeat. Th ese physiological effect s increase a persons potential for cardiovascular failur e and fatal seizures (NIDA, 2001b). Prescription stimulants have high addictiv e potential resulting in a majority of them being classified as Sche dule II drugs (U.S. DEA, n.d.). In recent years, drug abuse researchers have placed part icular attention on the drugs used to treat AD[H]D (e.g., Ritalin, Aderall, Concerta). There has been so me debate as to whether prescribed use of the drugs leads to substance abus e later on in life. However, e xperts agree that this is not the case (Farone & Wilens, 2003). Mo st young people who use these drugs nonmedically, do so to party, stay awake, st udy, or suppress appetite and often times do not attain them through their own prescrip tions (Klein-Schwartz & McGrath, 2003). Depressants Central nervous system (CNS) depressant s, also known as sedative-hypnotics, are substances that slow normal brain function. Because of their calming effects, depressants are often prescribed to treat epilepsy, anxiety, and sleep disorders (Julien, 2001). Depressants work in the brain affecting le vels of gamma-aminobutyric acid (GABA), a neurotransmitter which facil itates communication between brai n cells. When GABA is

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22 in an excited state, it causes a person to feel sedation, reli ef from anxiety, anticonvulsant effects, and dilation of the blood vessels (Julien, 2001; NIDA, 2001b). Two main types of drugs comprise the pr escription depressants, barbiturates and benzodiazepines. Barbiturates sometimes referred to as sedatives, have been used therapeutically to induce anes thesia, relieve anxiety, and tr eat seizures. Examples of barbiturates include mephoba rbital (e.g., Mebaral) and pentobarbital sodium (e.g., Nembutal), (NIDA, 2001b). Benzodiazepines, so metimes referred to as tranquilizers, are newer and more widely used than barbiturates They have largely replaced barbiturates because of their greater efficacy, lower abuse potential, and lower risk of inducing coma or fatal intoxication (Julien, 2001). Examples of benzodiazepines often prescribed to treat anxiety and panic disorder include include diazepam (e.g., Valium), alprazolam (e.g., Xanax), chlordiazepoxide HCl (e.g., Librium), clonazepam (e.g., Klonopin), and lorazepam (e.g., Ativan). Benzodiazepines with more sedating effects including triazolam (e.g., Halcion) and estrazolam (e.g., ProSom) are more commonly used to treat sleeping disorders (Julien, 2001). Because of their addictive potential, depr essants are classified as Schedules II-IV drugs. Short-acting, high potency benzodiazepin es (e.g., Valium) are more likely to lead to addiction than slowerand longer-acting benzodiazepines (e.g., Librium), thus placing them at a higher schedule according to the DEA (U.S. DEA, n.d.). Young people who use prescription depressants nonmedically, gene rally do so for their calming and sedating psychoactive effects. Nonmedical users of de pressants may also use them to counter the stimulatory effects of drugs such as cocaine or enhance the sedative effects of drug such as alcohol (Sheehan, Sheehan, Torres, Coppola, & Francis, 1991).

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23 Long-term use of depressants leads to to lerance to the drug. If cessation occurs abruptly rather than thro ugh medically supervised tape ring-off, the individual may experience serious withdrawal symptoms, incl uding seizures, hallucinations, and in some cases, death. Barbiturates have a particularly high risk of inducing severe and potentially life-threatening withdrawal sy mptoms. In contrast, taking benzodiazepines rarely leads to death except when the drugs are used in combination with other drugs including alcohol. Epidemiology of Nonmedical Prescription Drug Use While rates of illicit drug use for the most part have been stable or declining in recent years, the abuse of controlled prescription drugs has been steadily increasing, particularly among young people (SAMHSA, 2004a; Johnson et al, 2005b). Evidence of this increase comes from a number of epidemiological databases including the National Household Survey on Drug Use and H ealth (NSDUH) (SAMHSA, 2004b), the Monitoring the Future (MTF) project (Johns ton et al., 2005b), the Drug Abuse Warning Network (SAMHSA, 2003a), and the Treatment Episode Data Set (SAMHSA, 2004d). When investigating an emerging trend in drug use, such as the nonmedical use of prescription drugs, the most important epidemio logical findings are likely to be those concerning the changes that take place over time. Important questions surveillance surveys ask likely are, What drugs are rising or declining in popularity? How fast are these changes occurring? and What subgroups are most affected? The answers to these questions have important implications fo r the efforts of health educators, prevention specialists, law enforcement, and public polic y makers and deserve further attention. Reports indicate that 13.7% of youth ages 12-17 have abused prescription drugs in their lifetime; 4.0% currently use prescription drugs, as indicated by use in the month

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24 preceding a national survey (SAMHSA, 2004a). Each year close to half (44%) of new prescription drug abusers are under the age of 18 (SAMHSA, 2004a). When placed into perspective relative to other types of youth drug abuse, th e prevalence of prescription abuse use ranks fourth, after alcohol, cigare ttes, and marijuana re spectively (Johnston et al., 2005b). Controlled prescription drugs most likely to be used for their psychotherapeutic properties by young people include opioid pain re lievers (i.e., opioids), stimulants and depressants. Anabolic-androge nic steroids such as Anabol or Equipose are also use nonmedically by young people, however primar ily for purposes of body sculpting and athletic performance (NIDA, 2000). Although the medical community and public health agencies express concern for steroid abuse s adverse effects on h ealth, especially among young people (NIDA, 2000), this study concentrat es on the three classes of controlled psychotherapeutic drugs opioid pain relieve rs, stimulants, and depressants because they are the fastest growing categories of drugs used nonmedically for recreational purposes and have received relativel y little research attention. Initiation The National Household Survey of Drug Us e and Health shows that the initiation of nonmedical prescription dr ug use occurs primarily am ong persons age 12 to 25, and that new use has been increasing among young people (SAMHSA, 2004a). The annual number of new users of pain relievers ha s been rising since the mid-1980s, from about 400,000 initiates to 2.4 million in 2004. New users of stimulants increased from approximately 200,000 in 1991 to 793,000 in 2004 (SAMHSA, 2004a, 2003c). New users of tranquilizers have been increasing since the mid-1980s, but the largest increase has been recently, from more than 700,000 ne w users in 1999 to almost 1.2 million users

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25 in 2004 (SAMHSA, 2004a, 2003c). The number of new users of sedatives remained around 100,000 per year between 1988 and 1994. Starting in 1995 however, the number rose from 111,000 to 240,000 in 2004 (SAMHSA, 2004a, 2003c). Between 1998 and 2004, there were more new users both opioids and stimulants among 12 to 17 year olds than among 18 to 25 year olds (SAMHSA, 2004a, 2003c). Prevalence Nonmedical use rates for each class of abusable prescription drugs have also generally increased each year during the past decade (Johnston et al., 2005b). Opioids, most commonly prescribed for pain relieving properties, ha ve gained a great deal of attention in recent years because they ar e the most widely abused prescription medications; up to 70% of non-medical prescr iption drug users report abusing opioids. (SAMHSA, 2004a). From 1992 to 2000 annual prevalence of opioid abuse more than doubled among 12th graders (3.3% to 7.0%); non-medical opioid use continued to steadily increase until reaching p eak levels in 2004 (9.5%). Specific drugs in this class including Vicodin, codeine, Percocet, a nd OxyContin are among the drugs most commonly mentioned by high school seniors in recent years (Johnston et al., 2005b). In 2002, free-standing questions were incl uded for two of those drugs in the Monitoring the Future StudyVicodin and OxyC ontin because of considerable public comment on the diversion of these medications That year, both Vicodin and OxyContin attained surprisingly high prevalence rates, especially among 12th graders, suggesting that they very likely help to account for the upturn in the use of the general class of opioids. Annual prevalence in 2002 for Vi codin and OxyContin were 9.6% and 4.6%, respectively among 12th graders. In 2004, thos e rates shifted sli ghtly, to 9.3% for Vicodin and 5.0% for OxyContin (Johnston et al., 2005b).

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26 Stimulants (i.e., amphetamines) follow opi oids with the next highest rates of nonmedical use. Since 1992, annual prevalen ce has veered upward, rising from 7% in 1992 to 10% in 1997, before peaking in 2002 (11%) among high-school seniors (Johnston et al., 2005b). Annua l prevalence of amphetamine s however, decreased to 10% in 2004. Ritalin has become the single most widely abused stimulant in recent years; its use increased among high sc hool seniors from an annual prevalence of 0.1% in 1992 to 2.8% in 1998, and declined only sligh tly to 2.2% in 2000. Th is statistic rose significantly in 2004 to 3.9% (Johnston et al., 2005b). Because of increasing concern for Ritalin abuse, a newer stand-alone question was integrated into Monitoring the Future in 2001. While the newer question yielded some what higher absolute prevalence levels (believed to be a more accurate).1% in 2001, 4.0% in 2002, and 2003, and 5.1% in 2004researchers hold that the trend Johnston et al., 2005b). Depressants are grouped in to two subcategories: barbiturates (sedatives) and benzodiazepines (tranquilizers) Although barbiturates such as Seconal (secobarbital) and Nembutal (pentobarbital sodium) ar e still on the pharmaceutical market, benzodiazepines such as Valium (diazepam) and Xanax (alprazolam) are more widely prescribed than barbiturates and are therefor e more available for abuse (Julien, 2001). Though barbiturates were widely used in th e mid 1970s to mid 1980s, prevalence for the drug in this class declined dramatically in the early 1990s. By 1992, annual prevalence of use for 12th graders (2.8%) was less than one-third of the 1 975 level (10.7%). Over the past decade, annual prevalence of tranquilizer use among 12th graders has been increasing steadily; from 2.8% in 1992 to 7.7% in 2002 about where it remains in 2004 (7.3%) (Johnston et al., 2005b).

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27 Subgroup Trends Gender Most of the gender differences fo r abuse of individual classes of prescription drugs have remained fairly paralle l in the past 30 years that is, any trends in prevalence have remained stable with re gard to gender (Johnst on et al., 2005a). In general, adolescent males repor t higher rates of all types of drug use alth ough there are some exceptions when it comes to prescription drugs. Females report slightly higher rates of amphetamine and tranquilizer abuse in the lower (8th and 10th) grades (Johnston et al., 2005a). Geographic Region. The U.S. is frequently divided into four regions Northeast, South, North Central, and West for the purpose of monitoring geographic trends. In general, the South, which spans as far west as Texas and as far north as Maryland has the lowest rates of use of any illicit drug use. Rates very somewhat with respect to the different classes of abusable pr escription drugs. Prevalen ce is highest in the West for opioids when it is presented as a co llective drug class, much of which is likely accounted for by the high rates of Vicodin us e in that region. Amphetamine abuse is highest in the South and lowest in the West. Greatest abuse of both sedatives and tranquilizers also takes place in th e South (Johnston et al., 2005a) Population Density. For the purpose of assessing differences in drug use according to population density, communities are classified into one of three strata Large Metropolitan Statistic al Areas (LMSAs) (i.e., the 16 largest population areas according to the 1990 census), Other MSAs (i .e., cities with a population larger than 50,000 but not classified as Large MSAs), and Non-MSAs (i.e., towns that do not have a population of at least 50,000). Other MSAs tend to have the highest and Large MSAs tend to have the lowest levels of any type of illicit drug use. This tendency

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28 generally holds true for prescription drug abuse as with one exception; amphetamine abuse is highest among Non-MSAs (Johnston et al., 2005a). Socioeconomic Status When parental educatio n is used as a proxy for socioeconomic status (SES), little systematic relationship exists for prescription drug abuse. However, it seems that a slight curvilinear relationship exists for opioids, amphetamines, and sedatives, with the hi ghest and lowest SES groups being least susceptible to abuse. However, one excepti on is that in the lower grades, lowest SES groups report abuse of tranquilizers more than any other group (Johnston et al., 2005a). Race/Ethnicity While the three major racial/ethnic frequently groups cited Whites, African Americans, and Hispanics ha ve different levels of use of some drugs, they have similar trends in almost all pres cription drug classes. Abuse of opioids has consistently been highest among White studen ts, considerable lower among Hispanics, and lowest among African Americans. In re cent years, the rate s have particularly increased among Whites. These data on gene ral opioid use are reported only for 12th graders. Hispanics consistently fall in th e middle for use of amphetamines, sedatives, tranquilizers as well with one exception. In th e 8th grade, the Hispanics have the highest prevalence of amphetamine use (Johnston et al., 2005a). Indicators of the Problems Seriousness In spite of public misperceptions that nonmedical use of prescription drugs is comparatively safer than illicit drug use, indicators of the problems seriousness are reflected in high numbers of young people re quiring prescription drug-related treatment and emergency health care (SAMHSA 2004b, 2003b). Drug-related morbidity and mortality affects more young people than the abuse of hallucinogens, methamphetamine, ecstasy, inhalants, and heroin combined (SAMHSA, 2003b). The Drug Abuse Warning

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29 Network (DAWN), reports that anxiolytics a nd sedative hypnotics (i.e ., depressants) are the most frequent psychotherapeutic agen ts motioned in drug related emergency department (ED) visits, representing 11% of total ED mentions (SAMHSA, 2003b). Closely following are the narc otic analgesics (i.e., opioi ds), comprising 10% of drug related mentions in ED visits. Prescription s timulants are mentioned in less than 2% of drug-related ED visits. Alcohol, marijuana, and cocaine are the only primary drugs of abuse mentioned in greater frequency th an prescription drugs (SAMHSA, 2003b). The dangers of prescription drug abuse ar e further exacerbated when more than one drug is present and are illu strated by the fact that threequarters of all ED visits related to both prescription opioids and benzodiazepines (7 8%) involve more than one drug (SAMHSA 2004b, 2004c). The most frequent substances found in combination with both these drugs are alcohol, other opioid pain relievers, and cocaine (SAMHSA 2004b, 2004c). Approximately 30% of all DAWN emergency department cases involve patients aged 25 and under (SAMHSA, 2002). In addition to ED visits, substance abus e treatment admissions further shed light on the problem. Prescription drugs were the primary substances of abuse for approximately 4% of the 1.9 million treatm ent admissions reported to the Treatment Episode Data Set (TEDS) in 2002 (SAMHSA, 2004d). More than half (55%) of those admissions were attributed opioids. Stimul ants, tranquilizers, and sedatives comprised approximately 28%, 10%, and 6% of prim ary substances mentioned in treatment admissions, respectively. It is important to note that polydrug abuse is more common among treatment admissions than the abuse of a single substance. Prescription drugs are

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30 also frequently mentioned as secondary or te rtiary substances of abuse, thus further increasing their presence among treatme nt admissions (SAMHSA, 2004d). The Youth Risk Behavior Survey The Youth Risk Behavior Survey (YRBS ) is a component of the Centers for Disease Control and Preventions (CDC) Y outh Risk Behavior Surveillance System (YRBSS). Additional components of the Syst em include the National College Health Risk Behavior Survey (NCHRBS), the Nati onal Health Interview Survey (NHIS), a middle school version of the YRBS, and vari ous special population surveys that have been conducted periodically (Brener et al., 2004). The CDC initiated the design of the su rveillance system in 1988 upon review of the leading causes of mortality among yout hs and young adults. Over 70 percent of adolescent mortality is due to preventable causes incl uding motor vehicle crashes (32.3%), other unintentional injuries (11.7%), homicide (15.1%), a nd suicide (11.7%). Furthermore, substantial morbidity and soci al problems result from substance use, unintended pregnancies and sexually tran smitted diseases, and risk factors for cardiovascular disease (Grunbaum et al., 2004). These beha viors are often established during youth, and extend into adulthood. In 1989, CDC took further steps to develop the instrument by convening a panel of experts to delineate priority behaviors and devise questions to measure those be haviors (Brener et al., 2004). Subsequent revisions led to the first and second nationwide administra tion of the YRBS in the spring of 1990 and 1991, respectively (Kann, Kolbe & Collins, 1993). However, it was soon determined that because behavioral change in populations typi cally occurs gradually, administrations of the survey every two years would be suffici ent to assess health risk-behaviors among adolescents (Brener et al., 2004).

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31 The YRBS therefore, has been administ ered biennially, in every odd year, since 1991 to measure the prevalence of six priority health risk behavi or categories: (a) behaviors that contribute to unintentional injuries; (b) tobacco use; (c) alcohol and other drug use; (d) sexual behavior s that contribute to unintended pregnancy and sexually transmitted diseases; (e) unhealthy dietary beha viors; and (f) physical inactivity (Brener et al., 2004; Kann, Kolbe & Collins, 1993b). In focusing on these specific behaviors, relevant agencies are able to monitor the le ading preventable causes of adolescent death, disability, and social problems to assess whet her these behaviors increase, decrease, or remain the same over time, and examine the co -occurrence of health-ris k behaviors. In addition, the YRBS provides a means to monito r relevant national health objectives, first for the year 2000 (Public Health Service, 1991), and now for the year 2010 (ODPHP, 2000). The YRBS includes national, state, and lo cal school-based surveys of high school students (Brener et al., 2004). The sampling frame for the most recent national YRBS consisted of all public and priv ate schools with students in grades 9-12 in the 50 states and the District of Columbia. A threestage cluster design was used to produce a nationally representative sample from public and private high school s (Grunbaum, et al., 2004). State and local administrations of the YR BS also rely on cluster sample designs to obtain representative samples of high school st udents in the partic ipating jurisdictions (Grunbaum et al., 2004). All students complete the YRBS during a regular class period (approximately 50 minutes) by recording their responses directly on a computer scannable questionnaire booklet or answer sheet. Student partic ipation is voluntary and anonymous, yet

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32 methodologies may vary slightly with respec t to local parental consent procedures. Although the use of passive parental consent yi elds higher participati on rates than active consent, type of parental consent generally does not affect prevalence estimates for risk of self-report behaviors on the YRBS (E aton, Lowry, Brener, Grunbaum, & Kann, 2004). In addition to differing consent procedures, stat es and cities may modify the 87-item core questionnaire to meet individual needs under th e conditions that they conserve at least two-thirds of the core questions from the st andard YRBS; limit response options to eight mutually exclusive categories, and; do not use skip patterns or grid formats (Grunbaum et al., 2004; Brener et al., 2004). Refraining from the use of grids a skip patterns helps safeguard privacy because comparable amount s of time are required to complete the survey regardless of risk stat us (Brenner et al., 2002). Because data from the YRBS are used to develop policies and prevent health risk behaviors among youth (Everett, Kann, & Mc Reynolds, 1997), it is important that researchers have confidence that it contai ns reliable and valid measures for youth behaviors. Two CDC sponsor ed test-retest reliability studies have been conducted throughout the life of the surveillance syst em and in both cases, the survey was subsequently modified as a result (Brener et al., 2002; Brener, Collins, Kann, Warren, & Williams, 1995). The most recent test-retest reliability assessment of the survey was conducted in 2000 on a convenience sample of 4,619 high school students. Results for the test-retest were reported in the form of kappa statistics and 95% confidence intervals. Kappa statistics, wh ich provide a measure of agreement among items in the two administrations correcting for chance, reveal ed that nearly half (47.2%) of the items had at least subs tantial reliability (kappas 61%) and almost all (93.1%)

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33 had at least moderate reliability (kappas 41%). Furthermore, based on nonoverlapping 95% confidence intervals, 22.2% of the items had significantly different prevalence estimates at Time 1 vs. Time 2. Examining reliability of the measures by behavior category revealed that items pert aining to certain beha viors elicited higher reliability than others. For example, items related to tobacco use, alcohol and other drug use, and sexual behavior demonstrated signifi cantly higher reliability than items related to dietary behaviors, physical activity, and other health-related topi cs (e.g., ever been taught about HIV/AIDS in school, saw a dentis t during past 12 months, rarely or never use sun screen, etc.). Based on the 2000 test-retest, study item s have been revised or deleted from subsequent versions of the YRBS. Speci fically, 11 items have been modified or removed. These include items related to pre gnancy and pregnancy prevention; the use of a helmet when riding a motorcycle; 30-day prev alence of heroin and inhalant use; most recent check-up visit to a health care pr ovider (i.e., nurse, doctor, dentist); strength training; sources of cigare ttes; providing proof of ag e when buying cigarettes; and frequency of wearing sunscreen (CDC, 2005, 1999). Since the 2000 test-retest study, seven items have also been added. These in clude items related to lifetime ecstasy use; seatbelt use in a car; having property stolen or damaged at school; being diagnosed with asthma and year prevalence of asthma att acks; a measure of total weekly physical activity; and a personal assessment of ove rall health (CDC, 1999; CDC 2005). The 2005 YRBS consequently, is compri sed of 87 items divided into twelve major sections. The first section contains st andard demographic questions and the last section asks students about other health topics including as thma diagnosis and

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34 HIV/AIDS education. The remaining health topics are divided across 10 behavioral areas: (1) personal safety; (2 ) violence; (3) sadness, suicid e ideation, and attempts; (4) alcohol use; (5) marijuana use; (6) cocaine and other drug us e; (7) sexual behaviors; (8) body weight; (9) dietary behaviors; and (10) physical activit y (CDC, 2005). Although reliability is a necessary characteristic of a valid measure, demonstration of the YRBS reliability does not ensure validity. The truthfulness and accuracy of self-report questionnaires lik e the YRBS may be compromised because health-risk behaviors are often difficult to recall and some are so sensitive that respondents may not feel comfortable repor ting them (Brener, Billy, & Grady, 2003). Specifically, validity allows res earchers to answer to pivotal question, Can we trust the data from this questionnaire? Despite the importance of establishing validity, no study has validated all selfreport behaviors included on the YRBS. CDC has discussed difficulties in executing that task given the lack of objective measures for many behaviors of interest (Brener et al., 2003). In fact, validation of specific YRBS items has been limited to two studies. A small study by McDermott and colleagues veri fied that YRBS questions related to alcohol are valid measures of youth drinking behavior (McDer mott, Sarvela, Caravella, & Gast, 1995). However another study conducted by CDC to assess the validity of height and weight measures, demonstrated that students tend to over-report height and underreport weight, leading to underestimations of overweight (Brener et al., 2003). In spite of the relative absence of validity estimates of YRBS self-report measures, CDC officials maintain YRBS items likely accu rately assess many youth-risk behaviors, especially those related to drug, alcohol, and to bacco use. This belief is based on studies

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35 of measures similar to YRBS items that ha ve demonstrated valid ity of self-reported alcohol and other drug use, tobacco use, suicidal ideat ion, sexual behavior, dietary behaviors, and physical activity (Brener et al., 2002). Correlates of Adolescent Prescription Drug Abuse A substantial body of evidence suggests that adolescent risk behaviors do not occur in isolation, but rath er, cluster in vulnerable i ndividuals (Bell & Bell, 1993; Donohew, Palmgreen, Zimmerman, Harrington, & Lane, 2003; Dryfoos, 1990; Jessor et al., 1991). Also, over time, involvement in one type of risk beha vior increases the likelihood of becoming involved in other ri sk behaviors (Osgood, Johnston, OMalley, & Bachman, 1988). Singly, any risk behavior and its associated health outcomes can represent a serious threat to an adolescents health. Ho wever the tendency for risk behaviors to cluster, or covary further exacerbates the health threat. Studies of nonmedical prescription dr ug use in adolescents suggest that prevalence is not only associated with other substance abusing behaviors, but is also correlated with an array of academic and personal problems, and selected problem behaviors (Boyd et al., 2006; Mc Cabe & Boyd et al., 2005). Although limited data exist on the health-risk correlates of prescription drug abuse, the literature is beset with studies indicating that substance abuse is associat ed with multiple risk-taking behaviors in adolescents (Bell & Bell; 1993; DiClemente et al., 1996). Because prescription drug abuse is a subset of drug abus e, many of these correlates may also have particular relevance to prescription drug abuse. Tobacco Use The onset and development of cigarette smoking and tobacco use usually occurs in adolescence (Kopstein, 2001). Tobacco use deserves special atte ntion because it is

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36 highly addictive and remains the chief preven table cause of death in the U.S. (U.S. Department of Health and Human Services [U .S. DHHS], 2004). It can not only lead to negative short term effects in youth but also has long-term implications for adults, especially for those who initia te tobacco use at young ages (Perry & Stufacker, 1996). Smoking during childhood and adolescence appears to increase the risk for developing chronic obstructive pulmonary disease; acute respiratory illness; heart disease; and stroke; and can lead to several types of cancers including those of the lung, larynx, oral cavity, pharynx, pancreas, and cervix (U.S. DHHS, 2004). Recent research on a nationally representa tive sample of adolescents found that high school seniors who used abused prescription opioids were approximately seven times more likely to have smoked cigarettes in their lifetime as compared to non users. OxyContin users in particular, were almost nine times more likely to have smoked cigarettes in the past 30 days (McCabe & Boyd et al., 2005). Smoking has also been correlated with stimulant abuse. In a seconda ry analysis of nationa l data, McCabe, Teter, Boyd, & Guthrie (2004) found that illicit methylph enidate (Ritalin) users in grades 8, 10, and 12, were significantly more likely than non-users to report cigarette smoking in the past month (64% vs. 18%). McCabe, Tete r, and Boyd (2004), found similar results from a survey conducted on 1,536 students in a Midw estern school district where one in every two illicit stimulant users reported cigarett e smoking as opposed to less than 10% among non-stimulant users or prescribed stimula nt users. Although no studies report the correlation between tobacco and prescripti on depression abuse in adolescents, a secondary analysis of the Harvard School of Public Health College data suggests tobacco use is a correlate of nonmedical prescrip tion benzodiazepine (e.g., Xanax, Valium) use

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37 among college undergraduates. It is impor tant to note that a strong dose dependent relationship between tobacco use and multiple substance use behaviors has also been found among a national sample of ad olescents (Everett et al., 1998). Alcohol Use and Binge Drinking Alcohol use remains extremely widespread among young people. The majority of adolescents (77%) have consum ed alcohol by the end of high school and more than half (60%) of 12th graders report havi ng been drunk at least once in their life (Johnston et al., 2005b). High levels of adolescent alcohol use ha ve been associated with the three most frequent forms of mortality among adolescents that is, accidental deaths (especially automobile accidents), homicides, and suicides (Mokdad, Marks, Stroup, & Gerberding, 2004). Higher levels of adolesce nt alcohol use are also associ ated with incr eased risk of contracting HIV and other sexually transmitte d diseases, teenage pregnancy, poor school performance, and school dropout (Wi ndle, Shope, & Bukstein, 1996). According to national surveillance data, teens who admit to abusing controlled prescription drugs are twice as likely to ha ve used alcohol (SAMHSA, 2004a). High school seniors who nonmedically use opioids (specifically Vicodin and OxyContin), are over six times more likely to report alcohol use in the past month and heavy episodic drinking in the past two weeks (McCabe & Boyd et al., 2005). Am ong a national sample of 8th, 10th, and 12th grade students, those who abused prescripti on stimulants (i.e., Ritalin) were significantly more likely to report heavy episodic drinking than nonabusing students (61% vs. 20%). Alt hough no study reports the relationship between adolescent depressant and alcohol use, a nationwide st udy of undergraduate students illustrated that nonmedical tranquilizer us e (e.g., Valium, Librium, Xanax, Ativan,

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38 Klonopin) significantly increased students odds of frequent binge drinking by four times (McCabe, 2005). Marijuana Use Among the illicit drugs, use of marijuana is the most prevalent (Johnston et al., 2005b). Among adolescents who use drugs, approximately 60% use only marijuana (SAMHSA, 2004a). Marijuana shares with tobacco, smoking as the usual route of administration, and as a result, may lead to many of the same adverse affects to the respiratory and cardiovascular systems (Sidney, Beck, Tekawa, Quesenberry, & Friedman, 1997). Research has shown that can nabis can produce a subtle impairment of attention, memory, and the organization a nd integration of complex information. Prolonged use may even lead to changes in the brain permanently affecting learning and mood (Block & Ghoneim, 1993). Results from the National Household Surv ey on Drug Use and Health suggest that teens who abuse prescription drugs are fi ve times more likely to use marijuana (SAMHSA, 2004a). When broken down into sp ecific drug class, adolescents age 10-18 who abuse opioids are approximately six times more likely to engage in past months marijuana use. High school seniors who abuse the specific opioids Vicodin and OxyContin are 10 and 14 times more likely to engage in past year marijuana use, respectively. Students using the prescripti on stimulant Ritalin report much higher rates of marijuana use (80% vs. 25%) than t hose who do not abuse the stimulant drug. (McCabe, Teter, Boyd, & Guthrie, 2004). Other Illicit Drug Use Illicit drug use is greater among youth in the U.S. than has been documented in any other developed country in the world (B lanken, 1993). To name some of the more

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39 prominent consequences, illicit drug use is associated with suicide, homicide, motorvehicle injury, transmission of sexually tr ansmitted diseases including HIV infection, pneumonia, unwanted pregnancy, delinquency, vi olence, mental illness, and hepatitis (Mokdad et al., 2004; SAMHSA, 2004a). Alth ough national trends suggest illicit drug use is decreasing in the U.S., substance use is nonetheless a problem with serious health and legal implications. Each class of illicit drugs has unique health implications. Cocaine for example, acts as a stimulant and short-te rm effects can incl ude excitability; increased temperature and blood pressure; erratic behaviors; paranoia ; and in large doses, heart failure. Longterm use of cocaine can lead to psychosis mood swings, weight loss, and insomnia. (NIDA, 1999). As an opiate, heroin has sedative effects, th us slowing vital functions such as higher brain activity, breathing and heart rate and may lead to death. Other heroin-related health risks include infection, associated with syringe use (NIDA, 2005a). Ecstasy a hallucinogenic stimulant, emerged as a popular drug among youth during the mid to late 1990s. Many of the side effect s users encounter with ecstasy are similar to those found with cocaine. However, a majo r risk to health from taking ecstasy is hyperthermia, or heatstroke, leading to cardiac failure (NIDA, 2004a). Though not necessarily illicit drugs, inhalants including solvents, aeros ols, and nitrates, are sometimes used illicitly to get high. This type of substance use is often seen among younger students because inhalants are readily available and relatively inexpensive (NIDA, 2005b). Nonetheless, inhalants can cause bronchitis, skin problems, memory impairment vital organ damage, asphyxiation, an d are potentially lethal (NIDA, 2005b). Steroids represent another class of substances th at are potentially abused by adolescents

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40 and should be mentioned. Unlike the other illic it drugs, steroids are not used for their psychoactive properties but ra ther, for their body building and sculpting potential. Consequences of steroid use in adolescence include severe acne, mood disruptions, and can interfere with growth through growth ha lted prematurely through premature skeletal maturation and accelerated puberty changes (NIDA, 2000). The literature frequently describes p olydrug abuse or polysubstance abuse when referring to populations who concurrently use multiple substances whether licit or illicit (Rosenthal & Levounis, 2005). Polydr ug use can be dangerous due to a whole host of effects that can occur by combining drugs in ways that are not physician approved putting them at risk for adve rse interactions and overdose (NIDA, 2001b). In addition, successful treatment of polydrug abuse may be difficult, as treatment programs may not be designed to effectively address multiple drugs (SAMHSA, 2004a) Adolescent users are a subgroup who have been identified as high risk for polysubstance use and compared to older groups, younger users in treatment settings are more likely to report multiple drugs (SAMHSA, 2004a). Among teens ag es 12 to 17, 79.8% of prescription drug abusers are polysubstance abusers. National data also indicate that adolescents who abuse prescription drugs are at risk for hard illicit drug use at a mu ch higher rate than they are for marijuana use when compared to adolescents who do not abuse such drugs. They are 12 times more likely to use heroin, 15 times more likely to use ecstasy, and 21 times more likely to use co caine (SAMHSA, 2004a). Correlations between abuse of the three specific classe s of prescription drugs and selected illicit drug have been made among young people. For example, middle and high school students who abuse methylphenidate, a prescription stimulan t, are significantly

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41 more likely than nonabusing students (43.2% vs 1.5%) to use ecstas y, another potent yet illicit stimulant drug (McCabe, Teter, & Boyd, 2004). Prescription pain medications have also been correlated with illicit drug use other than marijuana among adolescents. In a Detroit-area public school district, in f act, students who used opioids nonmedically experienced a nine-fold increased risk of illic it drug use (i.e., cocaine, inhalants, ecstasy, inhalants, and psychedelics including LS D) (Boyd et al., 2006). Young people who abuse prescription depressants including Xa nax, Librium, and Valium also use illicit drugs at increased rates. Specifically, non medical use of depressants increases the tendency for college students to also use cocaine and ecstasy by 14 and 16 times, respectively (McCabe, 2005). Although the use of heroin, st eroids, and inhalants has not been reported in studies correlating prescrip tion drug abuse with illicit drug abuse, the literature suggests that one type of drug abus e increases an adolescents risk for another (Abdel-Ghany, & Wang, 2003; Bukstein, 1995); therefore, implying that use of these types of drugs may also be exacerbated by prescription drug use. Driving Under the Influence of Alcohol Motor vehicle crashes are the leading cause of death for U.S. teenagers, accounting for nearly 40% of fatalities (Na tional Center for Injury Prevention and Control [NCIPC], 2005). Traffic crashes are mo re likely to result in death or injury if alcohol is involved (i.e., thos e in which the driver or pedestrian had a blood alcohol concentration (BAC) above zero). Accordi ng to the National Highway Traffic Safety Administration (2003), 41% of people fatally in jured in traffic cras hes were in alcohol related crashes and 35% were crashes i nvolving someone with a BAC of 0.08% or higher. Although 14% of driver s in alcohol-related fatal cras hes are between the ages of

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42 16 and 20, this age group represents only 7% of the population, t hus suggesting that young people are at particular ri sk (Hingson & Winter, 2003). The nonmedical use of prescription drugs among adolescents and young adults has been shown to significantly increase risk of driving under the influence and riding with an intoxicated driv er (McCabe, 2005; McCabe, Teter, & Boyd, 2004; McCabe, Teter, Boyd, Knight, & Wech sler, 2005). For example, among high school students who partake in the nonmedical us e of prescription stimulants, 66.7% re port riding with a driver who has had at least one drink and 44.4% report riding w ith an intoxicated driver. These rates are both significantly higher than rates in the non-abusing population (McCabe, Teter, & Boyd, 2004). Although stud ies have not correlate d the abuse of the other two readily abused cl asses of prescription drugsdepressants and opioidswith driving under the influence among adolescen ts, correlations have been made among college undergraduate students. Young adults who abuse prescription benzodiazepines or opioids are approximately four times more likely to report driving a car after binge drinking and almost six times more likely to report being a passenger with a drunk driver (McCabe, 2005; McCabe, Teter, Boyd & Knight et al., 2005). Violence Related Behaviors CDCs YRBS includes acts such as phys ical fighting, carrying weapons to school, feeling unsafe or threatened at school, school thefts, and forced sexual intercourse in its definition of violence-related behaviors (C DC, 2005). Although most violent behavior produces relatively minor injuries, long-term disability and death are also frequent consequences, especially when a weapon is present. Approximately 90% of homicide victims in the U.S. are killed with a weapon, that is, a gun, knife, or club (Anderson et al., 2001). Homicide is the second leading cause of death among a ll adolescents and it is the

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43 leading cause of death among Black youth ag ed 15-19 (NCIPC, 2005). Forced sexual intercourse is also an important problem in adolescence, as it has also been associated with poorer physical and mental health among women (Brener, McMahon, Warren, & Douglas, 1999). The association between violence and al cohol and drug use is well established. A large scale study in a Texas school district a ssessed the co-occurre nce of youths violent behavior and engagement in other risk beha viors including substance use using the YRBS (Orpinas et al., 1995). Although prevalence of fighting and weapon carrying vary greatly by gender (males engage significantly more in both), associations were demonstrated between fighting and carrying a weapon, and heavy alcohol, tobacco, and other drug use among both boys and girls. Highest correlati ons however, were found between frequency of weapon carrying and frequenc y of drinking alcohol and bei ng inebriated (Orpinas et al., 1995). In another regional study of correla tes of adolescent violent and aggressive behaviors, alcohol was signifi cantly associated with fighting among White female, while binge drinking with fighting among White male s only (Valois et al., 1995). Significant relationships were also found for illicit drug use (i.e., marijuana, cocaine, intravenous drug use) among Whites but not among Blacks. For carrying a weapon, the strongest predictors also included illegal drug use among Whites bu t not among Blacks (Valois et al., 1995). Youth exposure to violence and victimi zation through indirect means has also been shown to relate to a dolescent drug use. In add ition to being the victim or perpetrator of a violent act, students who have knowledge of violence (i.e., know victims of violence) or witness viol ence have higher a tendency for substance abuse (Albus,

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44 Weist, & Perez-Smith, 2004). Forced sexual in tercourse, is a topic largely neglected in the literature. However, Howard & Wang ( 2005) recently demonstrated that substance use behaviors including smoking, binge drinking, cocaine and inhalant use, and drinking or using drug before sex are related to being forced to have sex. Although more pronounced for females, the relationships also exists among males (Howard & Wang, 2005). Sexual Experience and Risk Taking Younger age at first interc ourse and greater numbers of sexual partners are associated with increased risk for unwanted pregnancy and sexually transmitted infections (STIs) including HIV (Abma & S onenstein, 2001). Research shows that pregnancy often limits the educational ach ievements and vocational opportunities among adolescents, especially for females, thus leading to social and economic problems (Stevens-Simon & McNarney, 1996). One of the most immediate and si gnificant risks to the health of adolescents is acquiring a sexua lly transmitted infection. From an economic and social standpoint, these infections take a large toll on adolescents and ultimately society. In terms of health outcomes, sexually transmitted infections can be potentially devastating. The potential impact of STIs is incredibly significant for example, in the case of HIV infection, which can ultimately result in fatal illness, AIDS. Studies using the YRBS to assess the co rrelates of sexual risk taking have focused on sexual experience (Shrier & Crosby, 2003); condom use (Richter, Valois, McKeown, & Vincent, 1992; Shrier et al., 1996); early in itiation of sexual interc ourse, (Coker et al., 1994); multiple HIV virus-related sexual be haviors (Lowry et al., 1994); number of sexual intercourse partners (V alois et al.,1999; Shrier et al., 1996) or have employed composite scores of the various behaviors.

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45 In each of these studies, substance use was significantly associated with one or more measures of sexual risk taking. Lowr y et al. (1994) for example, found that students who reported no substance use, were least likely to re port having had sexual intercourse, having had four or more sex part ners, and not having us ed a condom at last sexual intercourse. Furthermore, sexual risk behaviors were greatest among students who had used marijuana, cocaine, or other illicit drugs. Stude nts who only used alcohol or cigarettes had smaller but still significant increases in the like lihood of HIV-related behaviors (Lowry et al., 1994). Studies have further confir med the significant association between drug use (i.e., alcohol, tobacco, and marijuana use) and increased number of sexual partners in assessing a cluster of risk behaviors placing adolescents at risk for unintended pregnancy, HIV, and other sexually transmitted infections (Shrier et al., 1996; Valois et al., 1999). Among alternative high school students si milar results have also been found. Shrier & Crosby (2003) found that sexually experienced studen ts in alternative schools were close to 50% more likely to engage in cocaine, inhalant, and needle use, and almost three times as likely to use alcohol and marijuana than their sexually abstinent counterparts, though they did not show highe r rates of tobacco use. This may be attributed to the fact that smoking is tw ice as prevalent among youth in alternative schools and it may have been difficult to de tect small differences in smoking between sexually experienced and abstinent youth in this study (Shrie r & Crosby, 2003). Depression and Suicide Ideation The relationship between depression a nd sadness and suicide attempts is well established (Wulsin, Vaillant, & Wells, 1999). Empirical studies have indicated that major depression is most highly correlated with suicide for girls (increasing risk 20-fold)

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46 followed by a previous suicide attempt. Among boys, a previous attempt is the most highly correlated factor (incre asing risk 30-fold) followed by depression, alcohol, or drug use, and disruptive behavior (Lewinsohn, Rohde, & Seeley, 1996). Fortunately, only a fraction of youth who have attempted suicid e are successful; less than one in 50 suicide attempts result in death (Cohen, Spirito, & Brown, 1996). Nonetheless, suicide remains the third leading cause of death amo ng adolescents (NCIPC, 2005) and studies identifying risk factors warra nt further attention. Although a number of different correlates or predictors of suicidal thoughts, plans, and attempts have been addressed in th e literature, drug use has perhaps, received the most attention (Crumley, 1990; Garrison et al., 1993; Hallfors et al., 2004). In a study of South Carolina youth, Garris on et al. (1993) confirm that illicit drug use is a highly influential factor for suicidal thoughts, plans, and attempts, second only to female gender. When broken down into specific drugs, the largest effects for suicide attempts are seen for harder drugs, that is, cocaine use, other drug use (i.e., LSD, PCP, ecstasy, mushrooms, speed, heroin) and intravenous drugs although alcohol use and marijuana also increase risk (Garrison et al., 1993). Data from the National Longitudinal Study of Adolescent Health (Add Health) further illustrate that cluste rs of students who engage in illegal drug use (including mar ijuana, and other drug use) ha d highest odds of depression, suicidal ideation, and su icide attempts (Hallfors et al., 2004). Decreased Physical Activity The benefits of physical activity have been well documented. Participation in regular physical activity helps control weight, strengthen bon es, build lean muscle, and reduce body fat content. Exercise also redu ces feelings of depr ession and anxiety, and promoting psychological well-being (U.S. D HHS, 1996). Over time, regular physical

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47 activity decreases the risk of premature deat h and disability from causes including heart disease, high blood pressure, diabetes, and certain cancers (U.S. DHHS, 1996). Despite the benefits of exercise, hi gh school students, particular ly girls, generally do not participate in sufficient levels of vigorous physical activity (Grunbaum et al., 2004). Though no empirical studies have examined the association between prescription drug abuse and physical activity in adol escents, research has shown significant relationships among physical activity levels and other drug abuse behaviors including those involving tobacco and marijuana (P ate et al., 1996; Winnail et al., 1995). In comparing physically inactive (re porting fewer tan 2 days a w eek of light exercise) with highly active adolescents (repor ting 6 or more days exerci se) Pate et al., (1996) found that physical activity level wa s inversely related to cigare tte and marijuan a use but not alcohol use. In fact, students who consumed one or more alcoholic beverages in the past month were more likely to be in the high activity group (Pat e et al., 1996). In a similar study, Winnail et al. (19 95) grouped a physical activity variable on the South Carolina YRBS into three levels lower, moderate, and higher to find that higher level of physical act ivity served as a protective fa ctor for White males in the use of cigarettes, smokeless tobacco, and mar ijuana. The relationship however, did not hold for Blacks or females. Although th e study suggested a relationship among White males, it was not based on a nationally repres entative sample, and results should therefore be interpreted with caution. Evidence suggests that as adol escents age, not only do their levels of physical activity decline but their substance use tends to increase (Grunbaum et al., 2004). However, given the relative absen ce of studies examini ng the covariation of

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48 physical activity and drug use and the mixed results of the studies that have been conducted, it is apparent that further research needs to be conducted on the topic. Use of Problem Weight Loss Practices Studies have shown high rates of body dissatisfaction and dieting among adolescents. This is especially pronoun ced among females, many of which engage in unhealthy weight control behaviors such as fasting and self-induced vomiting. (Neumark-Sztainer & Hannan, 2000). It is estimat ed that up to 8% of females in the U.S. suffer from anorexia nervosa and/or bulimia during their lifetime (APA, 2004). Both these disorders are associated with high ra tes of comorbidity. Extreme dieting and disordered eating behaviors among youth are of concern in that they have the potential to adversely affect nutrient intake, mental health status, and long-term health outcomes such as osteoporosis (Neumark-Sztainer & Hannan, 2000). Although problem weight loss tac tics items on the YRBS can not be used to define eating disorders, they help assess behavior s that are potentially problematic. Although prescription stimulan ts are now rarely prescribed to treat obesity and overweight, it is widely known that these drugs prevent weight gain. Studies correlating problem weight loss practices with drug use have confirmed that adolescents who are willing to engage in unhealthy weight loss be haviors such as restricting and purging, are also likely to use prescription stimulants to lose weight. In a study of 854 adolescent girls and young women age 12 through 23, 9.5% reported using stimulant drugs to control their weight. Many of those same females also reported dieting, fasting, and vomiting for weight control (Moore, 1988). A recent study on 101 female adolescents clinically diagnosed with an eating disorder (i.e., meeting DSM-IV criteria) found that both anorexic and bulimic patients were sign ificantly more likely to abuse stimulant

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49 drugs than those young women in a compar ison group (Stock, Goldberg, Corbett, & Katzman, 2002). It is also important to note that several st udies of high school populations, including those using the YRBS, have demonstrated an association with problem weight loss tactics and the use of ci garettes, alcohol, or marijuana, suggesting that they may cluster with substance abus e behaviors (Neumark-Sztainer et al., 1998; Rafroiu et al., 2003). The relationship among adolescent drug us e and various health risk behaviors demonstrates the importance of developing a deeper understanding of prescription drug abuse exposure and involvement within the broa der context of risk. These associations provide compelling support that the abuse of pres cription drugs may represent a part of the pattern of polydrug abuse and a larg er cluster of problem behaviors.

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50 CHAPTER 3 MATERIALS AND METHODS In this study, I examined the relation ship between non-medical prescription drug use and other risk behaviors among adolescen ts using a modified version of the 2005 Youth Risk Behavior Survey referred to hereafter as the Santa Rosa County Health Behavior Survey (SRCHBS). In this chapte r, the methods that I used to conduct the research project are described. A description of the research design, research variables, population, instrumentation, data collection proce dures, and data analyses are presented. Research Design According to Creswell, research designs are the specific procedures involved in the last three steps of the re search process: data collec tion, data analysis, and report writing (p. 51). Specifically, research designs allow investigators to answer our research questions (Cottrell & McKenz ie, 2005). A studys design is therefore important in determining whether ones findings are scientifically sound. This study employed a correlational rese arch design using specific survey measures for data collection. Cottrell a nd McKenzie (2005) desc ribe correlational research as non-experimental research that examines re lationships between or among variables (p. 7). In utilizing a correlati onal design, the researcher must be careful to refrain from concluding a cause and effect relationship between factors. Causal relationships can only be esta blished by using experimental design (Cottrell & McKenzie, 2005).

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51 A correlational study assumes that the rese archer can first desc ribe (by measuring or observing) each of the variables she is trying to relate (Trochim, 2001). The most widely used method to collect descriptive and behavioral data in health education is the survey research design. According to Alreck and Settle (2004), a survey is a research technique where information requirements ar e specified, a population is identified, a sample is selected and systematically questio ned, and the results analyzed, generalized to the population, and reported to meet the info rmation needs (p. 449). A surveys value depends on both the amount of resources devo ted to it and the care and expertise that goes into the work. Surveys frequently take the form of questionna ires (e.g., paper-andpencil, electronic) or inte rviews (e.g., one-on-one, fo cus group, telephone) (Alreck & Settle, 2004). The myriad strengths of survey research explain why this method is so popular among social scientists. As cited earlier, surveys can be administered through several media and can be tailored to measure a wide ra nge of characteristics from a sample. In addition, surveys employ a standardized method of data collection and can be designed to collect a large amount of information in a relatively short period of time (McDermott & Sarvela, 1999). Despite the usefulness of this type of research, survey methods also have some disadvantages. One limitation of surveys is th e potential of a low response rate, as only those respondents who are accessible and motivat ed to complete the survey can become sources of data. This is especially true for self-administered surveys, where the investigator is not present to motivate the re spondent or clarify any sources of confusion (McDermott & Sarvela, 1999). Surveys are al so limited in that they depend on direct

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52 responses from the study sample. If sensitive items are included in a survey, respondents may skip these items because they feel embarra ssed or threatened by them or may tend to overor under-report behaviors (Alreck & Settle, 2004). Othe r disadvantages of survey research include lack of a comparison group and absence of a pre-test for assessing change scores (Aday, 1997). This study used a cross-sectional groupadministered census survey. Using a version of the 2005 Youth Risk Behavior Surv ey (YRBS) that was modified to include items on prescription drug abuse, I assessed he alth behaviors of 12th grade students from two senior high schools in Northwest Florida. Cross sectional denotes that data collection took place at a single point in time (Cottrell & McKenzie, 2005). A census attempts to acquire data from each member of a population and was appropriate for this study because the known population of interest was relatively small and readily accessible (Gay, 1996, p. 252). The survey was administered in the group setting of the schools respective cafeterias during an assembly for convenience by the researcher. Research Variables When conducting correlational studies, researchers sometimes refrain from using the terms independent variable and dependent variable They may instead refer to the correlation of two variables in keeping with the principl e that correlations measure relationships among variables rather than m easure the extent to which the independent variable influences the depe ndent variable (Creswell, 2005) For this study however, I have defined dependent and independent va riables. The dependent variable was nonmedical prescription drug use, measured by the prescription drug items in the SRCHBS. The dependent variable was measured using a composite score of prevalence items that correspond to the three major classes of abusable pres cription drugs: opioid pain

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53 relievers, central nervous system stimulants, and central nervous system depressants. The composite score was then dichotomized to indi cate whether a student has or has not used prescription drugs non-medically. The studys independent variables include d tobacco, alcohol, and marijuana use; other illicit drug use includi ng cocaine/crack, inhalants, heroin, methamphetamines, ecstasy, or heroin; violence-related behavior s; sadness, suicide ideation and attempts; sexual risk taking, and the use of problem we ight loss tactics. These independent variables were measured by using the corres ponding items found in the 2005 YRBS. The independent variables were selected because they have each been correlated with adolescent substance abuse behaviors in prev ious research (DiClemente et al., 1996). Study Population Sampling involves selecting individu als from a population in a way that generalizes results to the popul ation from which the units were chosen (Trochim, 2001). Researchers often enlist sampling methods to determine how specific study participants will be selected. The alternative to sampli ng is enumeration (also referred to as a census), that is, counting the entire populat ion (Alreck & Settle, 2004). A researchers ability to conduct a census depe nds upon the number of potentia l participants, researcher accessibility to participants, a nd data collection resources (C ottrell & Mckenzie, 2005). Because the numbers of 12th graders in the two public high schools selected for the study were relatively small and read ily accessible, a census was employed. Therefore, the population for this study include d all 12th grade students enrolled in two high schools in Santa Rosa County in Northwes t Florida during the spring of 2006. Santa Rosa County is home to six public senior high schools ranging in approximate size from 295 to 1850 students. The first school had a student body of approximately 1650

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54 students, 387 of which were seniors. The second school had approximately 1600 students, 340 of which were seniors. T hus, the study population in cluded 727 senior high 12th graders. Students who attended the seni or student assemblies at the two schools on May 1, 2006, who received parental permissi on, and who volunteered to complete the group-administered, paper-pencil questionnaire served as the study participants. Instrumentation I collected data using a modified versi on of the 2005 YRBS. The YRBS is part of the Youth Risk Behavior Surveillance Sy stem administered by the CDC. The 2005 YRBS contains 87 items designed to monitor si x categories of priority health behaviors among youth (1) behaviors that co ntribute to unintentional injuries and violence, (2) tobacco use, (3) alcohol and other drug use, (4) sexual behaviors that contribute to unintended pregnancy and sexually transmitted diseases, (5) unhealthy dietary behaviors, and (6) physical inactiv ity (CDC, 2005). The YRBS has been conducted biennially (in odd numbered years) since 1991, on representative samples of U.S. students in grades 912 (Kann, Kolbe, & Collins, 1993; Brener & Kann, et al., 2004). Reliability measures the extent to which an instruments measurement is consistent, dependable, and stable (McD ermott & Sarvela, 1999, p. 346). In 2000, CDC conducted a test-retest re liability study of the questionnaire (Brenner et al., 2002). In that study, the 1999 version of the questionnaire was administered to a convenience sample of high school students on two occasions, approxima tely two weeks apart. Approximately one of five items had significantly differe nce prevalence estimates for the first and second administrations. The CDC used Kappa coefficients, a widely used method for testing whether agreement on the first and sec ond administration exceeds chance levels. Kappa coefficient determination is a strai ghtforward approach to calculate simple

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55 agreement on two observations (Streine r & Norman, 1995, p. 116). Ten items (14%) had both kappas <61% and signi ficantly different time-1 and time-2 prevalence estimates, indicating that the reliability of these items is questionable. Based on the 2000 reliability study, certain items have been revised or de leted from subsequent versions of the questionnaire (Brenner et al., 2002). Validity is the degree to which an in strument measures what it purports to measure (Cottrell & McKenzie p. 143). Although no study has been conducted to assess the validity of all self-re ported behaviors that are incl uded in the YRBS, in 2003 CDC conducted a review of existing empirical lite rature to assess co gnitive and situational factors that might affect the validity of adolescen t self-reporting of behaviors measured by the questionnaire. Brener et al. (2003) disc ussed difficulties in determining validity of self-report data. However, CDC had determ ined that for the most part, behaviors are reported accurately, especially when they meas ure substance use (Bre ner et al., 2003). The 2005 YRBS was modified by first excluding three supplemental items regarding HIV/AIDS prevention and asthma, a nd two items regarding height and weight. These items did not concern variables of inte rest for the study. Items which were added included one demographic item regarding school site and 19 items regarding prescription drug abuse. Eighteen of the prescription drug items (six each representing the three drug classes of interest) largely pa ralleled the behavior anchors and response options of the items in the YRBS that are designed to m onitor marijuana use. The other measured prescription drug abuse as a we ight loss tactic. Instructions for the completion of the newly added items were also written. Thus the final questionnaire contained 102 items (see Appendix A).

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56 Item Development The 19 items assessing non-medical pres cription drug use were designed by (a) reviewing literature via database searches (e.g., PubMed, PsycInfo) regarding prescription drug use and abuse in adolescents (b) reviewing existi ng prescription drug use and abuse-related items on questionnaires appropriate for adolescents, (c) reviewing the general drug abuse-related items on the 2005 YRBS, and (e) asking an expert panel to critique the new items usi ng specified guidelines. For my study, I modified an existing inst rument and therefore had to first assess validity of any new items I incorporated. The validity of a survey reflects the degree to which items are free from both random error and systematic bias (Alreck & Settle, 2004). Validity is differentiated into three types: content, construct, and criterion. Content validity is generally considered a prerequisite for other forms of validity (e.g., construct, criterion-related, etc.) and is especially im portant for measures of social behaviors (McDermott & Sarvela, 1999). Content validity of survey items relies on judgments about whether the questions chosen are repres entative of the concepts they are intended to reflect (Aday, 1997, p. 57). The use of asse ssments that lack cont ent validity can lead to invalid conclusions as well as the mi sapplications of findings (Vogt, King, & King, 2004). According to Aday (1997), a research er can ensure a r easonable amount of validity by first reviewing questi ons or variables on the same t opic that have been used in other studies. Specifically, I referred to it ems found in the 2004 Monitoring the Future Study (Johnston et al., 2005b) and the 2004 Na tional Survey on Household Drug Use and Health (Research Triangle Institute, 2003) becau se they are both considered reliable and

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57 valid measures of drug use, and are administ ered at the national level annually (Cowan, 2001). Based upon my review of the literature a nd existing questionnaire items, I initially constructed 12 items and corres ponding instructions for comp letion in a format analogous to those monitoring marijuana use on the 2005 YRBS. Each major class of abusable prescription drugs was represented by four pa rallel items for the purpose of consistency across the measures. Specifically, the questi ons addressed lifetime prevalence; age at first use; 30 day prevalence; and 30 day prevalence on school property. Figure 1 provides the instructions a nd the four items regarding prescription pain relievers presented to the expert panel in the form of a text document that accompanied the Webbased review form. Parallel items were also constructed for CNS depressants and stimulants.

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58 Section A: Prescription Pain Relievers The next 4 questions are about pain relievers, wh ich doctors sometimes prescribe after surgery or when someone is in a lot of pain. People are only supposed to take prescription pain relievers if they have a prescription from a doctor for them. Prescription pain relievers are sometimes called Pain pills, Oxies, Percs, Hi llbilly heroin, or Demmies. They include drugs such as: Vicodin, OxyContin, Percocet, Percodan, Codeine (Tylenol 3 & 4), Darvon, Demerol, Dilaudid, Morphine, Methadone. In the answers you provide, we are onl y interested in your use of prescription pain relievers if the drug was NOT prescribed to you or you took the drug only for the experience or feeling if caused. We are not interested in your use of over-the-counter pain relievers such as aspirin, Tylenol, or Advil that can be bought in drug stores or grocery stores without a doctors prescription. 1. During your life, how many times have you used a prescription pain reliever that was not prescribed to you or you took only for the feeling it caused? A. 0 times B. 1 or 2 times C. 3 to 9 times D. 10 to 19 times E. 20 to 39 times F. 40 to 99 times G. 100 or more times 2. How old were you when you first tried a prescription pain reliever not prescribed to you or you took only for the feeling it caused? A. I have never used a prescription pain reliever in a way not prescribed for me by a doctor. B. 8 years old or younger C. 9 or 10 years old D. 11 or 12 years old E. 13 or 14 years old F. 15 or 16 years old G. 17 years old or older 3. During the past 30 days, how many times did you use a prescription pain reliever that was not prescribed to you or you took only for the feeling it caused? A. 0 times B. 1 or 2 times C. 3 to 9 times D. 10 to 19 times E. 20 to 39 times F. 40 or more times 4. During the past 30 days, how many times did you use a prescription pain reliever not prescribed to you or you took only for the feeling it caused on school property? A. 0 times B. 1 or 2 times C. 3 to 9 times D. 10 to 19 times E. 20 to 39 times F. 40 or more times Figure 1. Instructions and Items Regarding Prescription Pain Relievers Used for the Expert Panel Review Content Validity The most important element related to content validity is representativeness That is, the instrument must examine all the cont ent areas adequately (McDermott & Sarvela,

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59 1999). Specifically, the ques tion, do the items adequate ly represent non-medical prescription drug use prevalence? needed to be answered. After developing the prescription drug abuse items in a format based upon the review of existing items, I enlisted a panel of national experts (see Appendix B) who used guidelines to further establish content valid ity of the survey items. Thes e experts asked to conduct the review included professionals with e xpertise in the area of study, measurement specialists, and those familiar with the target audience as suggested by McDermott & Sarvela (1999). Specifically, the experts co nsisted of two professors of pharmacy, both with research interests in prescription dr ug abuse surveillance and prevention; the vicepresident of risk management and health policy at a major pharmaceutical manufacturing firm; an adjunct professor of behavioral biology at a school of medicine, and vice president for research and health policy at a consulting firm specializing in public health and addiction treatment policy; a behavioral scientist and expert in youth risk behaviors from the Division of Adolescent and School H ealth at the Centers from Disease Control and Prevention who has worked with the YRBS since its inception in 1991; a professor in the department of community and family h ealth at a college of public health with research interests in health behavior, adolescent health, and measurement; an assistant professor specializing in statistics, measurem ent, and psychometrics from a college of education; and a professor in the department of applied health scie nce at a college of health, physical education a nd recreation with research and teaching interests in measurement and evaluation of health education programs. Once members agreed to serve on the panel through electronic mail (e-mail) correspondence, they were sent a follow-up e-mail message consisting of a cover letter

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60 and an embedded hyperlink directing them to the Web-based item review instrument on February 2, 2006 (see Appendix C). An ab stract of my propo sed research study (Appendix D) and a text document containing the items for review were also attached to the e-mail (see Appendix E). When expert pa nelists were ready to conduct the review, they simply selected the link which connect ed them to the ques tionnaire and contained explicit directions for the asse ssment of the items, their inst ructions, and response options (Appendix F). Perseus Survey Solutions Express, Web-based survey software, was used to design the expert panel review instru ment and subsequently manage responses as they were submitted by the reviewers. A screen capture of the Web-based survey instructions is provided in Figure 2. Figure 2. Screen Capture of the Webbased Expert Panel Review Form

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61 The criteria for item assessment were based upon six important attributes of survey instructions and questi ons (i.e., focus, brevity, clarity readability, assurance, and adequacy of response options) adapted from Weilers (1997) expert panel review. The panelists were also asked to provide their professional opinio n regarding the currency of slang or street terms for prescription dr ugs commonly used by il licit users. Because some of the panelists chosen were not expe rts in prescription dr ug abuse, but rather, experts in measurement or youth risk behaviors, all were not expected to respond to these items. Finally, the panel members were enc ouraged to provide additional comments and recommendations regarding any aspect of the survey. Overall, the response to the expert pa nel review was positive. However, the experts did offer numerous co mments for revision (see Appendix G). Some of the more substantive suggestions included the need to evaluate the usefulness of the operational definition of prescription drug abuse used in the survey; condensing the instructions in order to improve readability; the addition or deletion of selected drug (either trade or slang) names based on current trends; the ad dition of a response option for the items addressing 30 day prevalence; and the integr ation of two additional items one assessing where the students are obtaining the drugs and the other, asking the st udents to indicate reasons for taking the drugs. In addition, a small number of formatting suggestions were offered, such as boldfacing or ital icizing selected terms. Upon review of the responses, it became cl ear that further dialogue with one or more of the panelists who had expertise in prescription drug use and abuse would be helpful to come to an agreement as to which items should be modified and to what extent they should be changed. I first arranged a phone conference with th e vice-president of

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62 risk management and health policy at a pharm aceutical firm. We discussed the validity of the operational definition of non-medical pr escription drug use as taking a prescription drug that was not prescribed to you or you took only for the feeling it caused, frequently used on federally-sponsored surv eys (i.e., National Survey on Drug Use and Health). This definition has been criticized as imprecise because it may not only capture intentional illicit use but also therapeutic use (e.g., a pain reliever offered to them by their parent for an appropriate reas on analgesia). We discusse d strategies to validate the definition by including additional items. These additional items would ask respondents to choose from reasons for use (i.e., the expe riences or feelings sought from using the drugs non-medically). This would allow me to conserve the definition used on Federal surveys for the sake of comparison. It woul d also allow me to determine the students who may not truly be abusing the drugs, but rath er, using for the therapeutic purpose, just without a doctors permission. During th e phone conversation, we also discussed the inclusion of items exploring where the students get the prescription drugs for nonmedical use. Based on that conference and reviewer co mments, I then modified the initial set of items by: significantly minimizing the leng th of instructions pertaining to each drug classcompletely removing the second, fifth, and sixth sentences from each prompt; adding a single item regarding prescription dr ug abuse as a weight loss tactic; adding two items for each drug class addressing sources of the drugs (see items 52, 57, and 63) and reasons for use (see items 52, 58, a nd 64); adding the response option G. 100 or more times to the items pertaining to 30day prevalence; and boldfacing the terms on school property. Upon revision, the new se t of items was e-mailed to the four content

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63 reviewers for assessment, none of which had furt her suggestions. Thus, as a result of the expert panel review process, the 19 pr escription drug abuse-related items were subsequently modified for the pilot study. Human Subjects Before initiation of any study componen ts involving human participants, an application to the University of Florid a Institutional Review Board (UFIRB) was submitted (Appendix H). The UFIRB reviews all University of Florida research projects involving human subjects. Because Sant a Rosa County agreed to oversee the administration of parental consent forms and survey materials and maintain anonymity of the research participants, the researchers role was classifi ed as secondary data analyst (Appendix I). Thus the study, project num ber 2006-U-0164, was deemed exempt from further university review in accordance with the Federal Office for Human Research Protections (OHRP) code number 45 CFR 46.101b(4) (see Appendix J). Pilot Study A pilot study can be thought of as a dre ss rehearsal for the larger research study in which methods, materials, and procedures to be used are tested for feasibility and appropriateness among the target population (McDermott & Sarvela, 1999). The pilot study often involves the administ ration of a preliminary survey on a small, convenience sample, permitting the researcher to obtain vital feedback regarding data collection instruments, instructional material, data collection procedures, and data analysis procedures (Alreck & Settle, 2004). In the case of a survey, the pilot study is crucial, in that it allows the researcher to estimate the length of time needed to complete the survey. Any problems detected in the pilot study can therefore be corrected before the project

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64 details are finalized and the larger, form al survey conducted (McDermott & Sarvela, 1999). The pilot study served not only to test the procedures used for administration of the SRCHBS but also allowed me to assess test -retest reliability of the newly constructed items. Test-retest reliability is concerned w ith the extent to which responses on survey items are similar when administered at two points in time (McDermott & Sarvela, 1999). This type of reliability estimat es the stability of repeated measures and is often conducted on items that assess surveillance data (McDer mott & Sarvela, 1999; Bren ner et al., 2002). In this approach, respondents complete th e instrument twice and a coefficient is computed for the relationship between the sc ores on the first and second administrations (Di lorio, 2005). Four teachers from a moderately large sized high school in Northwest Florida were recruited for the pilot study. They were selected in a conveni ently sampled fashion, by the school principal to facilitate the survey in two of their classes. Each of the four teachers taught a sample of students represen ting one of four grade levels: 9, 10, 11, and 12. One of the teachers taught 9th grade heal th and the remaining three teachers taught English (grades 10, 11, and 12.) A total of 209 students were registered for the classes recruited for the pilot study. Two weeks before study initiation, on Febr uary 21, 2006, students received parental consent forms and were instructed to present th em to their parents or guardians and return them to their teachers no later than March 10, 2006 (see Appendix K). No student in the sample returned a form in which a parent or guardian refused consent for their childs participation in the study.

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65 Because they served as data collecti on assistants for the pilot study, the four teachers who agreed to participate received a brief instructional session on March 20, 2006, describing the study and the protocol de signed for collecting data (Appendix L). The teachers were instructed not to deviate from the protocol wh en administering the survey. These steps were taken to ensure uni formity in conducting the survey. At the instructional meeting, particip ating teachers were also pres ented with a small token of appreciation in the form of a thank-y ou card and a $50.00 gift certificate to a local restaurant. Data collection procedures employed by Br ener et al. (2002) were used for the pilot study, as this test-retest technique has b een used successfully in previous studies and adequately safeguards students privacy. Befo re the first survey ad ministration, a unique number was assigned to two Scantron answer sheets. Each set of identically numbered answer sheets was placed inside a separate questionnaire booklet: one marked with the letter A in the upper right-ha nd corner, and the other marked with the letter B. These two surveys each consisted of the same 102 co re items and differed only in that survey A prompted students to offer written co mments about any items they felt were confusing or unclear. This provision allowe d students the opport unity to express any concerns about the newly added items. Both pairs of Scantron sheet/booklet sets were placed in a large envelope. On March 21, 2006 the students were give n a class period to complete the first test of the SRCHBS. St udents were instructed to remove and use only answer sheet/booklet set A during the first admini stration. Answer sheet/booklet set B was to remain in the envelope which was then sealed by the student. Students were

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66 subsequently instructed to write their name across the seal. Survey start time; time the first student finished; and time the last student finished the 102-item survey instrument was recorded during the survey administrati on by the teachers. The mean completion time (and standard deviation), for the fast est students was 21.25 (SD=5.39) minutes, and was 38.00 (SD=9.62) minutes for the slowest stud ents. Completed answer sheets were then placed in a large envelope and sealed by the teacher in full view of the students. Of the 209 students enrolled for the classes par ticipating in the pilo t study, 23 students were absent and two students chose not to participate in the su rvey on the first testing day, therefore providing 188 survey responses. On April 4, 2006, 14 days from the firs t survey administration, students were asked to re-take the SRCHBS. Before the second survey administration, each student who participated in the first test received th e envelope with his or her name written across the seal. After removing and completing survey B, the students answer sheets were collected and the empty envelopes were deposited in a wastebasket. Envelopes belonging to students who were absent on th e second survey administration were also disposed of. Throughout all the methodologi cal steps, considerable attention and emphasis was placed on ensuring anonymity, privacy, and confidentiality among all student respondents. Of the 188 students present on the first testing day, 13 were absent on the second testing day, thus providing 175 response set pair s. Upon visual insp ection of the response pairs, 18 sets were deemed unusable when r easonable exclusion criteria were applied, therefore yielding 157 completed sets. The prespecified criteria included: completion of less than one-half of the 19 re levant items; indication of bl atant patterning (e.g., use of

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67 one response option only, Christmas-treeing, etc. ); and use of response options that were not viable. Although the simple reliability coefficient can be used as an estimate of test-retest reliability, I chose to compute the Kappa ( ) coefficients for items of interest upon completion of the pilot study. The simple reliab ility coefficient is easy to calculate and understand. However, a disadvantage to that approach is that it does not take chance agreement into consideration (Di lo rio, 2005). Many rese archers cite the statistic as being the most appropriate method of analysis between raters or ev ents when the data are categorical (Tooth & Ottenbacher, 2004, p. 1372). Kappa is an extension of simple percent agreement in that it is ...the proportion of the to tal amount of agreement not explained by chance for which the observers accounted (Cohen, 1960, p. 38). Thus, the most acceptable reliability statistic, adjusti ng for the agreement based on chance, is the coefficient, calculated using the formula: = (Po-Pc)/(1-Pc), where = Kappa, Po = the proportion of the total number of observations in agreement, and Pc = the proportion of observations in the agreement cells expected by chance given the marginal distributions (Di lorio, 2005). Although Kappa values can range from 1.0 to 1.0, values greater than zero are most typical and indicate agreement better than chance. For example, a of 0.60 means that the student responses to the instrume nt account for 60% of the agreement over and

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68 above what would be expected by chance alone. Kappa values less than zero therefore, indicate agreement worse than chance. Various interpretations of the Kappa stat istic have been proposed and differ only slightly (Tooth & Ottenbacher, 2004). Res earchers however, often cite the benchmarks offered by Landis and Koch (1977) as the rough cut points for interpretation (Table 1). Scholars therefore agree that values between 0.21 and 0.40 are fair, those between .41 and .60 are moderate, those be tween .61 and .80 are substantial, and those above .80 are near perfect (Landis & Koch, 1977; Tooth & Ottenbacher, 2004). Table 1. Benchmarks for Interpretation of the Kappa Statistic (Landis & Koch, 1977) Kappa Statistic Strength of Agreement <0.00 Poor 0.00-0.20 Slight 0.21-0.40 Fair 0.41-0.60 Moderate 0.61-0.80 Substantial 0.81-1.00 Almost Perfect Item Reliability As estimates of test-retest reliability, simple Kappa values were calculated for all items and weighted values for Kappa were calculated for selected items (Table 2). Simple and weighted Kappas have separate but related indications for use. The simple considers all disagreements in ratings as equa l in weight, as for truly nominal data. When there is an implied ranking in the data as for a likert-type ordinal scale however, weighted may become more useful (Tooth & Ottenbacher, 2004). A weighted treats

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69 disagreement in response options that are si milar as less significant than disagreements that are dissimilar. For example, when referring to prevalence-related items on the SRCHBS one student may have responded they used prescription pain relievers or 2 times during test one and or 4 times dur ing test two. For the same item, a second student may have answered or 2 times during test one and to 99 times during test two. These two students would have influenced simple in exactly the same way. However, the first student, whose responses were not the same but still similar, would have influenced weighted less than the second student who provided divergent answers during the two tests. Therefore, both simple and weighted Kappa values may be useful when assessing the reliability of ordinal re sponse options, specifically those regarding prevalence and age at first use on the SRCHBS. According to the pilot test analysis, si mple Kappa values for 14 of the 19 items fell within the range of good or excelle nt. Two items had a Kappa within the moderate range and three items fell within in the poor range. Due to the nominal nature of some of the response options, we ighted Kappa was only computed for items with ordinal response options. Of the 10 items for which weighted was calculated, four items fell within the excellent range. Tw o fell within the good and two fell within the moderate ranges. One item perfor med in the poor range. Weighted for three applicable items could not be computed due to the fact that too few response options were indicated in the student data set. B ecause an overwhelming majority of the items fell within the moderate, good, or excellent range of test-retest reliability, the items were deemed appropriate for further use in the main study.

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70 Table 2. The Santa Rosa County Health Be havior Survey Pilot Study: Measures of Test-Retest Reliability for Prescrip tion Drug Abuse Items Using Kappa ( ) Statistics Item n Simple (95% CI) Weighted (95% CI) 1. Pain Relievers: Lifeti me use 1570.77 (0.66, 0.89) 0.90 (0.84, 0.96) 2. Pain Relievers: Age at first use 1570.79 (0.68, 0.90) 0.89 (0.81, 0.97) 3. Pain Relievers: 30 day use 1570.54 (0.33, 0.76) 0.56 (0.36, 0.77) 4. Pain Relievers: 30 day use at school 1570.66 (0.05, 1.28) ---5. Pain Relievers: Where obtained 1570.85 (0.75, 0.95) N/A 6. Pain Relievers: Why use 1570.63 (0.51, 0.74) N/A 7. Depressants: Lifetime use 1570.66 (0.49, 0.84) 0.79 (0.65, 0.92) 8. Depressants: Age at fi rst use 1570.75 (0.58, 0.92) 0.83 (0.68, 0.99) 9. Depressants: 30 day use 1570.32 (0.04, 0.59) 0.46 (0.16, 0.76) 10. Depressants: 30 day use at sc hool 157-0.00 (-0.01, 0.00) -0.00 (-0.01, 0.00) 11. Depressants: Where obtained 1570.79 (0.62, 0.95) N/A 12. Depressants: Why use 1570.75 (0.58, 0.92) N/A 13. Stimulants: Lifetime use 1570.75 (0.58, 0.92) 0.73 (0.53, 0.92) 14. Stimulants: Age at first use 1570.74 (0.55, 0.93) 0.85 (0.69, 1.00) 15. Stimulants: 30 day use 1570.42 (0.03, 0.81) 0.49 (0.14, 0.84) 16. Stimulants: 30 day use at school 1570.66 (0.05, 1.28) ---17. Stimulants: Where obtained 1570.71 (0.51, 0.91) N/A 18. Stimulants: Why use 1570.80 (0.64, 0.96) N/A 19. Prescription drug for wei ght loss 1550.00 (-0.00, 0.00) ---

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71 Data Collection I served as the coordinator and princi pal data collector for this study. Data collection for the main study proceeded in a manne r similar to that for the pilot test with some minor changes due to logistical c onstraints placed upon the researcher by school administrators. In my original proposal to the Santa Rosa C ounty school district, I planned to administer the survey among all 9-12 grade students en rolled in a single high school. Of the prospective high schools cont acted, none would agree to administration of the survey among the entire student body. However, two high schools did agree to conduct the study among only seni or students and in a group setting. Thus, although the pilot study was conducted among 9 through 12 grad ers in a classroom setting, the final study population was comprised only of 12th grade students and took place in the cafeterias of the two participating high sc hools during group assemblies. Finally, rather than teachers, I served as the moderator for the larger study at both schools while teachers served as co-data collectors by assisting in the distributi on and collection of survey materials. Unlike the pilot study for which no students were denied parental permission, a total of four students returned forms denying permission to participate. Teachers were instructed not to distribute surveys to these students. In order to maximize student participa tion, the self-administered SRCHBS in the form of a survey booklet and Scantron answer sheet was provided to all participants during a senior assembly. Sharpened pencil s were also distributed to students who neglected to bring their own. On May 1, 2006, the survey was administered to 12th grade students at the first high school during a first period student assembly. While the first survey was underway, I left th e head guidance counselor in charge of collecting the finished surveys while I traveled to the s econd school to administer the survey during a

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72 second period student assembly. The use of a single class period at both high schools allowed for administration of the survey to al l students at the same time and in a similar setting, and helped control for the disrup tion of other school procedures. Survey administration during class periods earlier in the day also helped control for students who had afternoon jobs or who were dualenrolled in college courses. Data Analysis Student data from electronic answer sh eets were scanned by an optical scanner, saved in ASCII format, transferred to a data file, and then analyzed using the Statistical Analysis System (SAS), version 9.1. Descri ptive statistics were first generated to develop a demographic profile of the samp le. Unadjusted prevalence rates were calculated by dividing the number of students reporting the behavior by the total number of students in the sample Inferential statistics were then used to (1) examine the relationships between demographic characteristics with prescription drug abuse risk behaviors and (2) answer the research questions. Chi square statistics ( 2) were first used to determine whether individual demographic characte ristics (i.e., gender, grade, race) were associated with prescription drug abuse. Chi-s quare analysis is a test of significance appropriate when data are in the form of frequency counts o ccurring in two or more mutual mutually exclusive categories (Gay, 1996) The dependent variable, pr escription drug abuse, was represented as a dichotomous (y es/no) variable taken from the composite of prevalence items that correspond to the three major clas ses of abusable prescription drugs: opioid pain relievers, central nervous system stimulan ts, and central nervous system depressants. Demographics were treated as nominal vari ables with the follow ing corresponding levels: Gender (male, female), grade (9th, 10t h, 11th, 12th, ungraded), and race (American

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73 Indian/Alaska Native, Asian, Black/African American, Hispanic/L atino, Native Hawaiian or Other Pacific Islander, White, Other). Chi square test compares proportions actually observed in a study with proportions expected thus, providing a measure of how much the frequencies differ from one another. Five assumptions underlie the us e of the chi-square statistic: 1. Each observation must fall in one and only one category. 2. The observations in the sample are independent of one another. 3. The observations are measured as frequencies. 4. The expected frequency for each category is not less than 5 for df 2 and not less than 10 for df = 1. 5. The observed values of 2 with 1 degree of freedom must be corrected for continuity in order to use the table of values of 2 critical. (Shavelson, 1995) Multiple logistic regression was then used to predict the student level outcomes of prescription drug abuse when controlling for demographic characteristics that were significantly associated with prescription drug abuse according to the chi-square results. Logistic regression is a type of regression analysis where the dependent variable is a dummy variable (coded 0 or 1) and the independent variable can be of any type (Agresti, 1996). The logistic regression model is simp ly a non-linear transfor mation of the linear regression, using a logit dist ribution rather than a normal distribution, which constrains the estimated probabilities to lie be tween 0 and 1 (Agresti, 1996). Multiple logistic regression also allowed for me to examine the relationship between non-medical prescription drug use and the likelihood of engaging in the other risk behaviors addressed by the research questions. Specifically, those behaviors

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74 included driving under the influence of alc ohol or riding with a drunk driver; tobacco, alcohol, and marijuana use; other illicit drug use includi ng cocaine/crack, inhalants, heroin, methamphetamines, ecstasy, or hero in; violence-related behaviors; sadness, suicide ideation and attempts ; sexual risk taking, and the use of problem weight loss tactics. When conducting logistic regression, significance is most conveniently understood using the odds ratio. The odds ratio is commonly interpreted as the probability of the event (or yes outcome) di vided by the probability of the nonevent (or no outcome) (Agresti, 1996). In Chapter 4, results for the logi stic regression are therefore reported in the form of odds ratios and 95% confid ence intervals in addition to regression coefficient and test statistic. Summary Chapter 3 described the methods and ma terials used to conduct this study. It provided a description of the studys purpose, research design, re search variables, participants, instrumentation, data collection, and data analysis. Chapter 4 reports the results associated with the nine research questions in addition to providing an overview of the demographics and prevalence estim ates of prescription drug abuse among study participants.

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75 CHAPTER 4 RESULTS This study explored the re lationships between non-medi cal prescription drug use and other related adolescent ri sk behaviors using the Santa Rosa County Health Behavior Survey (SRCHBS)a version of the 2005 Yout h Risk Behavior Survey modified to include measures of prescription drug abuse. In this chapter, I present the data I co llected by the methods described in Chapter 3 using the SRCHBS. Specifically, I describe the demographic characteristics for the study participants; illustrate the profile for prescription dr ug misuse among respondents; and report the findings of data analyses relating to my research questions. Participant Demographics This section describes the demographi c characteristics of the participants surveyed in this study. The defined populati on was all 12th grade students enrolled at two Northwest Florida senior high school s during the last qu arter of the 2005-2006 academic year (N=727). Three-hundred and eigh ty-seven senior students were enrolled at the first school and 340 were enrolled at the second. An attempt was made to reach every member of the senior class at these two schools. This is co mmonly referred to as a census survey. Of the population of 727 seniors from the two schools combined, 229 and 256 students returned the questionna ires from the first and sec ond school, respec tively. As a result of sight-editing the an swer sheets, I disregarded 24 cases from the first and 26 cases from the second school from the data analysis, yielding a total of 435 useable

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76 questionnaires. As in the pilot study, rejection of a co llected survey was based on completeness of sections, and on the identifi cation of patterned, hos tile, or superfluous responses. In addition, students indicating a grade level other than 12 were also removed from the sample. A summary of participa tion rates by schools is provided in Table 3. Table 3. Santa Rosa County Health Beha vior Survey: Participation Rate by High School High School Enrollment Returned Useable Participation Rate (%) School 1 387 229 205 54.0 School 2 340 256 230 67.6 Total 727 485 435 59.8 Of the total useable re sponses, 48.2% were from males (n=208) and 51.8% (n=224) were from females, with a mean age of 17.6 (SD=0.49) years old. Over threequarters of the participants were White (n =338, 78.6%). Hispanics, Blacks and Asians represented 5.4% (n=23), 4.4% (n=19), and 3.5% (n=15) of the sample, respectively. Other racial/ethnic minority groups (includi ng American Indians/Alaska Natives and Pacific Islanders) made up approximately 8.1% (n=35) of the sample. Table 4 provides the demographic characteristics of the participants from th e two schools and of the total study sample. Because the study population has been de fined as the composite of 12th grade students from both schools, results will hereafte r be reported for the total sample and not for each school individually.

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77 Table 4. Santa Rosa County Health Behavior Survey: Demographic Characteristics of Participants (expresse d in percentages)* Demographic School 1 School 2 Total Gender (n= 432) Male Female 42.9 57.1 52.9 47.1 48.2 51.8 Race (n= 430) American Indian/Alaska Native Asian Black Hispanic Pacific Islander White Other 2.5 2.9 1.5 3.4 0.5 86.3 2.9 1.8 4.0 7.1 7.1 1.3 71.7 7.1 2.1 3.5 4.4 5.4 0.9 78.6 5.1 Age (n= 426) 16 17 18 0.5 31.8 67.7 0.0 40.0 60.0 0.2 36.2 63.6 Columns may not sum to 100% based on rounding error. Profile of Non-Medical Prescription Drug Use Behaviors This section describes re spondents self-reported nonmedical prescription drug use behaviors. Table 5 illustrates the compos ite prevalence estimates of lifetime, past 30 day use, and past 30 day non-medi cal use on school property of any prescription drug. Close to one third (n=138, 31.9%) of responde nts indicated lifetime prevalence of any class of prescription drug that is one or more instances of non-medical use of either prescription pain relievers, depressants, or st imulants; or any combination of the three. Sixteen percent (n=69) of the subjects indi cated past 30 day misuse, and 8.8% (n=38) indicated past 30 day misuse on school property of any prescription drug.

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78 Table 5. Santa Rosa County Health Behavior Survey: Lifetime, Past 30 Day, and Past 30 Day Non-Medical Use on School Property of any Prescription Drug Prevalence Estimate n % Lifetime use Ever Never 138 295 31.9 68.1 Past 30 day use Yes No 69 361 16.0 84.0 Past 30 day use on school property Yes No 38 392 8.8 91.2 Table 6 illustrates specific participan t responses to the items assessing lifetime use, past 30 day use, and past 30 day us e on school property found on the SRCHBS as classified by drug class. Results are reported as frequenc ies and percentages. The highest rates of non-medical prescription drug use are for pain relievers, followed by depressants and stimulants. Close to 30 pe rcent (n=124, 28.6%) of respondents indicated lifetime non-medical use of pain relievers, se veral of which tried the drugs only one or two times (n=47, 10.8%). However, as many as 13 students indicated extremely heavy lifetime use in the range of 100 or more ti mes. Regarding depressant drugs, 12.7% (n=55) of the students indicated lifetime use. Less than one-tenth of the respondents (n=39, 9%) reported lifetime stimulant non-medical use.

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79 Table 6. Santa Rosa County Health Beha vior Survey: Measures of Non-Medical Prescription Drug Use Prevalence by Dr ug Class (expressed in frequencies and percentages) Item Pain Relievers Depressants Stimulants Response Option n % n % n % Lifetime use 0 times 1 or 2 times 3 to 9 times 10 to 19 times 20 to 30 times 40 to 99 times 100 or more times 310 47 29 13 10 12 13 71.4 10.8 6.7 3.0 2.3 2.8 3.0 379 25 14 7 2 4 3 87.3 5.8 3.2 1.6 0.5 0.9 0.7 394 13 3 13 5 4 1 91.0 3.0 0.7 3.0 1.2 0.9 0.2 Past 30 day use 0 times 1 or 2 times 3 to 9 times 10 to 19 times 20 to 30 times 40 to 99 times 100 or more times 377 28 12 8 3 3 2 87.0 6.5 2.8 1.9 0.7 0.7 0.5 402 15 7 5 2 1 0 93.1 3.5 1.6 1.2 0.5 0.2 0.0 408 4 9 2 5 1 2 94.7 0.9 2.1 0.5 1.2 0.2 0.5 Past 30 day use on school property 0 times 1 or 2 times 3 to 9 times 10 to 19 times 20 to 30 times 40 to 99 times 100 or more times 403 14 6 6 3 1 0 93.1 3.2 1.4 1.4 0.7 0.2 0.0 423 2 7 1 0 0 0 97.7 0.5 1.6 0.2 0.0 0.0 0.0 415 5 4 1 4 1 1 96.3 1.2 0.9 0.2 0.9 0.2 0.2 Far fewer students reported past 30 da y non-medical use of prescription drugs, often used as a proxy measure of current use. Thirteen percent (n=56) of respondents indicated past 30 day use of pain relievers, while 6.9% (n= 30) and 5.3% (n=23) reported past 30 day use of depressant and stimula nt drugs, respectivel y. Less than 10% of respondents indicated past 30 day use while on school property for any of the three drug classes. Prevalence rates of past 30 day us e while on school property rates of were 6.9%

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80 (n=30), 2.3% (n=10), and 3.7 (n =16) for pain relievers, depressants, and stimulants, respectively. According to estimates obtained from the survey, prevalence of non-medical prescription drug use ranks high, second only to marijuana. As seen in Table 7, the nonmedical use of both prescripti on pain relievers and depre ssants is more common than ecstasy use. Non-medical use of prescription stimulants is more prevalent than the use of methamphetamine, steroids, hero in, or injected drugs. Table 7. Lifetime, 30-Day, and 30-Day on School Property Prevalence of Selected Drug Classes (expressed in percentages) Drug Class Lifetime 30-day* 30-day on school property* Marijuana 39.9 18.9 4.2 Any Prescription Drug Prescription Pain Relievers 31.9 28.6 16.0 12.9 8.8 6.9 Prescription Depressants 12.7 6.9 2.3 Ecstasy 11.7 ----Huffing (i.e., Paint, Glue, Sprays) 11.6 ----Cocaine 10.7 5.8 --Prescription Stimulants 9.0 5.3 3.7 Methamphetamine 6.3 ----Steroids 4.2 ----Heroin 3.0 ----Injected Drugs 2.1 ----* Selected drug classes were not assessed for 30-day and 30-day on school property prevalence. Table 8 illustrates participant responses to the items assessing age at first use, source of the specific drug, and motivations for non-medical use. These results are also categorized by drug class and feature the fr equencies and percentages of specific response options found on the SRCHBS. For each of the three drug classes, the majority of students reported non-medical prescription drug use initiation at the age of 15 years old or older. When asked where they obt ained the prescription drugs for non-medical

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81 use, the most frequent response across all three drug classes was a friend. However other common responses for pain relievers were took my own (n=20, 4.6%), and from a parent or guardian with their permission (n =19, 4.4%). For depressants, the next most common responses were from a parent or gua rdian with their permission (n=7, 1.6%), and from a parent or guardian without thei r permission (n=7, 1.6%). For stimulants, the next most common responses regarding sources of prescription drugs were took own (n=6, 1.4%) and other (n=3, 0.7%). Regarding motivations for prescripti on drug misuse, to get buzzed, high, or stoned was the response option chosen most frequently for each of the three drug classes. The next most common responses were to relieve physical pain (n=29, 6.7%) and to experiment (n=17, 3.9%) for pain re lievers; to experiment (n=12, 2.8%) and to relax or relieve stress (n=8, 1.9%) for de pressants; and to experiment (n=9, 2.1%) and to concentrate or study (n =4, 0.9%) for stimulants.

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82 Table 8. Santa Rosa County Health Behavior Survey: Self-Reported Age at First Use, Drug Source, and Motivations for NonMedical Use by Drug Class (expressed in frequencies and percentages) Item Pain Relievers Depressants Stimulants Response Option n % n % n % Age at first use Never used 8 years or younger 9 or 10 years old 11 or 12 years old 13 or 14 years old 15 or 16 years old 17 years old or older 307 7 4 7 29 45 35 70.7 1.6 0.9 1.6 6.7 10.4 8.1 372 3 5 2 11 19 21 85.9 0.7 1.2 0.5 2.5 4.4 4.9 395 1 2 5 10 11 8 91.4 0.2 0.5 1.2 2.3 2.6 1.9 Where obtained the Rx drug Never used Took own From a parent w/ permission From a parent w/o permission From brother or sister From another relative From a friend Stolen from a pharmacy/doctors From the Internet Other 314 20 19 5 3 1 54 3 1 13 72.5 4.6 4.4 1.2 0.7 0.2 12.5 0.7 0.2 3.0 378 2 7 7 1 0 35 0 0 3 87.3 0.5 1.6 1.6 0.2 0.0 8.1 0.0 0.0 0.7 392 6 1 2 2 0 24 1 0 3 91.0 1.4 0.2 0.5 0.5 0.0 5.6 0.2 0.0 0.7 Why used the Rx drug Never used To experiment To get buzzed, high, or stoned To fit in To get away from problems To alter the effects of other drugs To relax or relieve stress To help sleep To relieve physical pain To get more energy or stay awake To concentrate or study To lose weight Other 309 17 42 2 8 3 13 2 29 N/A N/A N/A 7 71.5 3.9 9.7 0.5 1.9 0.7 3.0 0.5 6.7 N/A N/A N/A 1.6 380 12 21 0 5 4 8 0 0 N/A N/A N/A 3 87.8 2.8 4.9 0.0 1.2 0.9 1.9 0.0 0.0 N/A N/A N/A 0.7 392 9 15 1 3 1 N/A N/A N/A 3 4 1 1 91.2 2.1 3.5 0.2 0.7 0.2 N/A N/A N/A 0.7 0.9 0.2 0.2

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83 Chi-Square Analysis Before answering the research questi ons, it was first necessary to conduct chisquare analyses to determine whether indivi dual demographic charac teristics (i.e., gender and race/ethnicity) were associated with nonmedical prescription drug use. In order to compute a valid chi-square test, it is importa nt to ensure that a minimal number of categories has an expected frequency greater th an or equal to 5. Because some levels of the race/ethnicity variable were rarely c ited, six levels African American, American Indian, Asian, Hispanic, Pacific Islander, a nd Otherwere collapsed to form the NonWhite category. According to the chi-square analyses (T able 9), no significant difference exists between prevalence rates and releva nt demographic variables at the = 0.05 significance level. Chi-square analyses were also conduct ed among the individual prescription drug classes pain relievers depressants, and stimulants. Th e values presented in tables 10, 11, and 12 illustrate that no si gnificant difference (at the =0.05 level) exists among prevalence rates for individual drug class based upon demographic classification.

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84 Table 9. Chi-square Analysis of Demogra phic Variables by Lifetime, Past 30 Day, and Past 30 Day on School Property Prevalence of any Non-Medical Prescription Drug Use Lifetime Past 30 Day Past 30 Day on School Property Demographic Variable n % 2 pvalue n % 2 pvalue n % 2 pvalue Gendera Male Female 70 68 33.0 31.3 0.7035 34 17.1 15.3 0.62 21 17 10.2 7.7 0.35 Race/Ethnicityb White Non-White 110 28 33.0 31.1 0.8056 8 16.6 14.8 0.68 28 10 8.3 11.3 0.38 aThe sample size for the Lifetime 2 analysis was 430 cases whereas the sample size for the other two 2 analyses was 427 cases. bThe sample size for the Lifetime 2 analysis was 428 cases whereas the sample size for the other two 2 analyses was 425 cases. Table 10. Chi-square Analysis of Demogra phic Variables by Lifetime, Past 30 Day, and Past 30 Day on School Property Preval ence of Non-Medical Prescription Pain Reliever Use Lifetime Use Past 30 Day Use Past 30 Day Use on school Property Demographic Variable n % 2 pvalue n % 2 pvalue n % 2 pvalue Gendera Male Female 63 61 30.4 27.2 0.4628 28 13.5 12.6 0.76 18 12 8.7 5.4 0.18 Race/Ethnicityb White Non-White 97 27 28.7 29.7 0.8644 12 13.0 13.3 0.93 21 9 6.2 10.0 0.21 aThe sample size for the Lifetime 2 analysis was 431 cases whereas the sample size for the other two 2 analyses was 430 cases. bThe sample size for the Lifetime 2 analysis was 429 cases whereas the sample size for the other two 2 analyses was 428 cases.

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85 Table 11. Chi-square Analysis of Demogra phic Variables by Lifetime, Past 30 Day, and Past 30 Day on School Property Preval ence of Non-Medical Prescription Depressant Use Lifetime Use Past 30 Day Use Past 30 Day Use on school Property Demographic Variable n % 2 pvalue n % 2 pvalue n % 2 pvalue Gender Male Female 25 30 12.1 13.4 0.7011 19 5.3 8.5 0.17 5 5 2.4 2.2 0.89 Race/Ethnicity White Non-White 44 11 13.0 12.1 0.8125 5 7.4 5.6 0.56 8 2 2.4 2.2 0.94 aThe sample size for the Lifetime 2 analysis was 431 cases; sample sizes for the Past 30 Day and Past 30 Day on School Property 2 were 427 and 430 cas es, respectively. bThe sample size for the Lifetime 2 analysis was 429 cases; sample sizes for the Past 30 Day and Past 30 Day on School Property 2 were 427 and 428 cases respectively. Table 12. Chi-square Analysis of Demogra phic Variables by Lifetime, Past 30 Day, and Past 30 Day on School Property Preval ence of Non-Medical Prescription Stimulant Use Lifetime Use Past 30 Day Use Past 30 Day Use on school Property Demographic Variable n % 2 pvalue n % 2 pvalue n % 2 pvalue Gender Male Female 20 19 9.7 8.5 0.6611 12 5.4 5.4 0.99 10 6 4.9 2.7 0.23 Race/Ethnicity White Non-White 32 7 9.5 7.8 0.6217 6 5.0 6.7 0.53 10 6 3.0 6.7 0.10 aThe sample size for the Lifetime 2 analysis was 430 cases ; sample sizes for the Past 30 Day and Past 30 Day on School Property 2 were 427 and 428 cas es, respectively. bThe sample size for the Lifetime 2 analysis was 428 cases whereas the sample size for the other two 2 analyses was 426 cases. As illustrated in Table 13, logistic regression analysis confirmed the results of the chi square analyses. Odds of prescription dr ug misuse were not signi ficantly influenced by the relevant demographic variables at the =0.05 level. Although no significant

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86 difference exists for demographic variables by prevalence rates, previous research has demonstrated contrary results among high sc hool students (McCabe & Boyd et al., 2005; McCabe, Teter, & Boyd, 2004; McCabe, Teter, Boyd, & Guthrie, 2004). The absence of an effect may be due in part to low sample size. In addition, the variables of race and gender may interact with the st udys independent vari ables in important ways that did not surface in these chi square and odds ratio analyses. Due to these circumstances, it was determined that it would be prudent to c ontrol for race and gender in the multivariate models when addressing th e research questions. Table 13. Odds of Lifetime, Past 30 Day, and Past 30 Day on School Property Prevalence of Prescription Drug Abus e by Demographic Characteristics Lifetime Past 30 Day Past 30 Day on School Property Demographic Variable ORa 95% CI ORa 95% CI ORa 95% CI Gender Male Female 0.92 (0.62, 1.39) 0.88 (0.52, 1.47) 0.74 (0.38, 1.44) Race/Ethnicity White Non-White 0.94 (0.57, 1.55) 0.87 (0.45, 1.68) 1.41 (0.65, 3.02) Reference category. The sample size for the Lifetime model was 425 cases whereas the sample size for the other two models was 422 cases. aOdds ratios are adjusted for the predictor variables, gender and race/ ethnicity, in all three models. Research Question One Is there a relationship between illicit drug use (i.e., marijuana, cocaine, inhalants, heroin, methamphetamine, ecstasy, and ster oids) and non-medical prescription drug use among a sample of 12th grade students? Logi stic regression analyses were used to determine whether the use of several types of illicit drugs were associated with nonmedical use of prescription drugs after adju sting for gender and r ace/ethnicity. Both

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87 lifetime and 30-day prevalence rates of non-medical prescription drug use were examined. Tables 14 through 17 report the odds ratios obtained from the constructed logistic regression models. As illust rated in Table 14, non-medical use of any prescription drug was highly correlated to illicit substance use. Table 14. Illicit Substance Use Behaviors Associated with Lifetime and Past 30-Day Non-Medical Use of Prescrip tion any Prescription Drug Lifetime non-medical prescription drug use Past 30 day non-medical prescription drug use Illicit substance use behaviors Use % No use % Adjusted OR a,b (95% CI) Use % No Use % Adjusted OR a,b (95% CI) Marijuana Lifetime use Past 30 day use Past 30 day on school property 71.1 42.0 9.4 25.1 7.8 1.4 7.1 (4.5, 11.1)*** 8.3 (4.8, 14.4)*** 7.4 (2.3, 23.0)** 75.4 60.9 18.9 33.2 10.8 1.1 5.9 (3.3, 10.7)*** 12.6 (7.0, 22.7)*** 20.4 (6.4, 65.2)*** Cocaine Lifetime use Past 30 day use 25.6 13.9 3.8 2.1 8.7 (4.3, 17.8)*** 7.6 (2.9, 19.7)*** 32.4 22.1 6.7 2.8 6.7 (3.4, 12.9)*** 9.9 (4.2, 23.7)*** Inhalants Lifetime use 26.3 4.8 6.9 (3.6, 13.4)*** 30.9 8.0 5.0 (2.6, 9.5)*** Heroin Lifetime use 7.4 1.0 7.6 (2.0, 28.4)** 11.8 1.1 12.0 (3.5, 41.9)*** Methamphetamine Lifetime use 17.7 1.0 20.5 (6.0, 69.5)*** 26.5 2.2 15.8 (6.5, 38.6)*** Ecstasy Lifetime use 31.6 2.4 19.0 (8.2, 43.9)*** 45.6 5.0 16.4 (8.2, 32.5)*** Steroids Lifetime use 7.4 2.7 2.8 (1.1, 7.4)* 12.0 2.5 5.4 (2.0, 14.8)** *P<0.05, **P<0.01, ***P<0.001. aThe reference group for each logistic regression model was students who did not engage in the health risk behavior at hand. bOdds ratios are adjusted for gender and race/ethnicity.

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88 For example, students who had used any prescription drug non-medically in their lifetime were approximately three times more likely to report lifetim e steroid use; about seven times more likely to report lifetime ma rijuana and inhalant use; approximately eight times more likely to report heroin use; and close to nine times more likely to report lifetime cocaine use. In addition, they were about 20 times more likely to report lifetime ecstasy or methamphetamine use. Odds rati os illustrated that illicit substance use behaviors were similarly associated with pa st 30 day non-medical use of any prescription drug. These odds ratio values ranged from 5.0 for lifetime inhalant use to 20.4 for past 30 day marijuana use on school property. Logistic regression analys es pertaining to illicit drug use were also conducted among the individual pres cription drug classes pain relievers depressants and stimulants Results of these analyses are pres ented in Tables 15 through 17. Similar significant relationships between illicit substance use behavi ors and each of the specific drug classes were also found. One exception was a non-significant relationship between the non-medical use of pain relievers and illic it steroid use at both the lifetime and 30-day prevalence intervals.

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89 Table 15. Illicit Substance Use Behaviors Associated with Lifetime and Past 30-Day Non-Medical Use of Prescription Pain Relievers Lifetime non-medical prescription pain reliever use Past 30 day non-medical prescription pain reliever use Illicit substance use behaviors Use % No use % Adjusted OR a,b (95% CI) Use % No Use % Adjusted OR a,b (95% CI) Marijuana Lifetime use Past 30 day use Past 30 day on school property 72.6 45.2 10.5 26.8 8.4 1.6 7.0 (4.4, 11.1)*** 8.7 (5.1, 14.9)*** 6.8 (2.4, 19.7)*** 75.0 62.5 17.9 34.8 12.5 2.1 5.4 (2.9, 10.3)*** 11.5 (6.1, 21.4)*** 9.9 (3.7, 26.6)*** Cocaine Lifetime use Past 30 day use 22.9 14.8 3.9 2.3 9.3 (4.6, 18.8)*** 7.3 (2.9, 18.1)*** 31.5 18.5 7.7 4.0 5.5 (2.7, 11.0)*** 5.6 (2.3, 13.4)*** Inhalants Lifetime use 26.2 5.8 5.6 (3.0, 10.4)*** 31.5 8.8 4.7 (2.4, 9.3)*** Heroin Lifetime use 7.4 1.3 5.9 (1.8, 10.4)** 9.3 1.9 5.4 (1.6, 17.9)** Methamphetamine Lifetime use 18.2 1.6 13.1 (4.8, 35.7)*** 24.1 3.5 8.8 (3.8, 20.3)*** Ecstasy Lifetime use 33.1 3.3 14.6 (6.9, 30.5)*** 44.4 6.7 11.2 (5.7, 22.2)*** Steroids Lifetime use 6.7 3.3 2.0 (0.8, 5.3) 7.4 3.5 2.2 (0.7, 7.1) *P<0.05, **P<0.01, ***P<0.001. aThe reference group for each logistic regression model was students who did not engage in the health risk behavior at hand. bOdds ratios are adjusted for gender and race/ethnicity.

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90 Table 16. Illicit Substance Use Behaviors Associated with Lifetime and Past 30-Day Non-Medical Use of Prescription Depressants Lifetime non-medical prescription depressant use Past 30 day non-medical prescription depressant use Illicit substance use behaviors Use % No use % Adjusted OR a,b (95% CI) Use % No Use % Adjusted OR a,b (95% CI) Marijuana Lifetime use Past 30 day use Past 30 day on school property 87.3 63.6 20.0 33.0 12.4 1.9 13.7 (6.0, 31.1)*** 12.4 (6.6, 23.5)*** 14.1 (5.1, 38.9)*** 80.0 80.0 33.3 36.8 14.2 1.7 6.8 (2.7, 17.2)*** 26.2 (10.1,68.1)*** 37.3 (11.7, 118.9)*** Cocaine Lifetime use Past 30 day use 52.8 26.4 4.8 2.9 26.2 (12.2,56.4)*** 13.8 (5.6, 33.8)*** 58.6 41.4 7.3 3.3 23.6 (9.6, 58.3)*** 36.9 (12.0, 114.0)*** Inhalants Lifetime use 39.6 7.7 8.2 (4.1, 16.1)*** 34.5 10.0 5.1 (2.2, 12.0)*** Heroin Lifetime use 15.1 1.3 15.1 (4.6, 50.1)*** 27.6 1.0 70.5 (15.9, 312.3)*** Methamphetamine Lifetime use 40.0 2.4 22.8 (9.3, 55.9)*** 51.7 2.8 54.1 (18.4, 159.4)*** Ecstasy Lifetime use 59.3 4.8 32.8 (15.3,70.4)** 66.7 7.3 29.7 (12.2, 72.6)*** Steroids Lifetime use 11.5 3.1 4.4 (1.5, 12.7)*** 17.9 3.0 10.1 (3.0, 34.3)*** *P<0.05, **P<0.01, ***P<0.001. aThe reference group for each logistic regression model was students who did not engage in the health risk behavior at hand. bOdds ratios are adjusted for gender and race/ethnicity.

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91 Table 17. Illicit Substance Use Behaviors Associated with Lifetime and Past 30-Day Non-Medical Use of Prescription Stimulants Lifetime non-medical prescription stimulant use Past 30 day non-medical prescription stimulant use Illicit substance use behaviors Use % No use % Adjusted OR a,b (95% CI) Use % No Use % Adjusted OR a,b (95% CI) Marijuana Lifetime use Past 30 day use Past 30 day on school property 89.7 59.0 18.0 34.8 14.7 2.5 15.9 (5.5, 45.7)*** 8.2 (4.1, 16.4)*** 8.2 (2.9, 23.3)*** 95.7 73.9 21.7 36.8 15.7 2.9 36.9 (4.9, 276.4)*** 14.9 (5.7, 39.4)*** 9.2 (2.9, 29.4)*** Cocaine Lifetime use Past 30 day use 47.4 26.3 7.1 3.8 11.9 (5.6, 25.3)*** 9.1 (3.7, 22.7)*** 45.5 31.8 8.8 4.4 8.9 (3.5, 22.4)*** 11.8 (4.0, 34.3)*** Inhalants Lifetime use 42.1 8.7 7.5 (3.6, 15.8)*** 45.5 9.8 7.9 (3.2, 19.6)*** Heroin Lifetime use 18.4 1.5 15.6 (4.8, 51.1)*** 22.7 2.0 15.1 (4.3, 53.0)*** Methamphetamine Lifetime use 42.1 2.8 26.5 (10.7, 65.4)*** 42.1 2.8 41.3 (14.8,115.2)*** Ecstasy Lifetime use 59.0 6.9 20.6 (9.5, 44.6)*** 65.2 8.6 19.5 (7.7, 49.5)*** Steroids Lifetime use 16.2 3.1 6.5 (2.2, 19.0)*** 27.3 3.0 13.1 (4.1, 41.7)*** *P<0.05, **P<0.01, ***P<0.001. aThe reference group for each logistic regression model was students who did not engage in the health risk behavior at hand. bOdds ratios are adjusted for gender and race/ethnicity. Research Question Two Is there a relationship between tobacco use and non-medical prescription drug use among a sample of 12th grade students? Logist ic regression was used to determine if the use of various tobacco products was si gnificantly associated with non-medical prescription drug use. Both lifetime and past 30-day non-medical prescription drug use

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92 were, for the most part, highly associated with the use of cigarettes chewing tobacco, and cigars or cigarillos. As seen in Tabl e 18, students who smoked cigarettes on school property had the highest odds of lifetime non-me dical prescription drug use. They were almost 14 times more likely to have ever used prescription drugs non-medically and just over 15 times more likely to have done so in the past 30 days than their student counterparts who did not smoke at school Significant relationships at the = 0.001 level were also seen between non-medical use of any prescription drug and lifetime cigarette use, past 30 day cigarette use, having ever been a regular smoker, past 30 day chewing tobacco use, and past 30 day cigar/cigari llo use. Having chewed tobacco on school property in the past 30 days was not associ ated with lifetime prescription drug use, however it was associated with past 30 day non-medical use of any prescription drug at the = 0.05 level. As indicated in Tables 19 through 21, t obacco use behaviors were associated with lifetime and past 30 day non-medical prescrip tion drug use among each of the three drug classes. According to the logistic regression analyses, a strong relationship exists between the use of cigarettes a nd prescription drugs in all th ree classes pain relievers, depressants, and stimulants. For example, students who reported past 30 day cigarette use were approximately five times more likely to report past 30 da y pain reliever use; they were 11 times more likely to report pa st 30 day stimulant use, and 13 times more likely to report past 30 day depressant use. The use of cigars or cigarillos was also significantly associated with lifetime and past 30 day prescription drug use for all three drug classes. Past 30 day use of chewing tobacco was however, not consistently associat ed with all three drug classes. Although

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93 the use of chewing tobacco was associated with both lifetime and past 30 day nonmedical use of depressants, it was not consis tently associated w ith the use of pain relievers and stimulants. Specifically, chewi ng tobacco was not associated with lifetime stimulant use nor was past 30 day chewing tobacco use on school property associated with lifetime pain reliever use. Chewing tobacco was only moderately associated with past 30 day stimulant and pain reliever use. Table 18. Tobacco Use Behaviors associat ed with Lifetime and Past 30-Day NonMedical Use of any Prescription Drug Lifetime non-medical prescription drug use Past 30 day non-medical prescription drug use Tobacco use behaviors Use % No use % Adjusted OR a,b (95% CI) Use % No Use % Adjusted OR a,b (95% CI) Cigarettes Lifetime use Past 30 day use Past 30 day on school property Ever smoked regularly 80.7 50.7 12.3 39.1 39.0 16.3 1.0 10.2 6.4 (3.9, 10.4)*** 5.3 (3.4, 8.4)*** 13.6 (3.9, 47.2)*** 5.6 (8.3, 9.3)*** 80.6 58.0 20.3 44.9 46.7 21.3 1.7 14.4 4.6 (2.4, 8.8)*** 5.1 (3.0, 8.8)*** 15.4 (5.6, 42.2)*** 4.8 (2.7, 8.3)*** Chewing tobacco Past 30 day use Past 30 day on school property 15.2 6.5 4.4 3.1 4.0 (1.9, 8.5)*** 2.1 (0.8, 5.6) 18.8 10.1 5.8 3.1 3.8 (1.7, 8.5)*** 3.5 (1.3, 9.6)* Cigars or cigarillos Past 30 day use 36.9 13.2 3.9 (2.4, 6.4)*** 42.0 16.9 3.6 (2.0, 6.3)*** *P<0.05, **P<0.01, ***P<0.001. aThe reference group for each logistic regression model was students who did not engage in th e health risk behavior at hand. bOdds ratios are adjusted for gender and race/ethnicity.

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94 Table 19. Tobacco Use Behaviors Associat ed with Lifetime and Past 30-Day NonMedical Use of Prescription Pain Relievers Lifetime non-medical prescription drug use Past 30 day non-medical prescription drug use Tobacco use behaviors Use % No use % Adjusted OR a,b (95% CI) Use % No Use % Adjusted OR a,b (95% CI) Cigarettes Lifetime use Past 30 day use Past 30 day on school property Ever smoked regularly 80.3 54.0 12.1 38.2 41.1 16.8 1.9 11.9 5.7 (3.4, 9.4)*** 5.9 (3.7, 9.4)*** 6.8 (2.6, 18.1)*** 4.5 (2.7, 7.5)*** 78.2 58.9 21.4 43.6 48.3 22.6 2.4 15.7 3.7 (1.9, 7.2)*** 4.8 (2.7, 8.8)*** 11.1 (4.4, 28.1)*** 4.1 (2.2, 7.5)*** Chewing tobacco Past 30 day use Past 30 day on school property 15.3 7.3 4.8 3.2 3.5 (1.7, 7.4)*** 2.2 (0.9, 5.7) 16.1 10.7 6.6 3.5 2.8 (1.1, 6.6)* 3.4 (1.2, 9.5)* Cigars or cigarillos Past 30 day use 38.7 13.9 3.9 (2.4, 6.4)*** 42.9 17.8 3.5 (1.9, 6.5)*** *P<0.05, **P<0.01, ***P<0.001. aThe reference group for each logistic regression model was students who did not engage in th e health risk behavior at hand. bOdds ratios are adjusted for gender and race/ethnicity.

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95 Table 20. Tobacco Use Behaviors Associat ed with Lifetime and Past 30-Day NonMedical Use of Prescription Depressants Lifetime non-medical prescription drug use Past 30 day non-medical prescription drug use Tobacco use behaviors Use % No use % Adjusted OR a,b (95% CI) Use % No Use % Adjusted OR a,b (95% CI) Cigarettes Lifetime use Past 30 day use Past 30 day on school property Ever smoked regularly 98.2 72.7 21.8 57.4 45.6 20.8 2.4 14.0 61.5 (8.4, 449.8)*** 10.0 (5.2, 19.1)*** 11.7 (4.6, 29.6)*** 8.1 (4.4, 14.9)*** 100.0 80.0 33.3 70.0 48.6 23.1 2.5 15.4 N/A 13.0 (5.1, 32.7)*** 20.7 (7.5, 57.2)*** 12.5 (5.4, 28.8)*** Chewing tobacco Past 30 day use Past 30 day on school property 20.0 12.7 6.1 3.2 4.6 (2.0, 10.8)*** 5.0 (1.8, 14.0)** 16.7 16.7 7.2 3.2 3.6 (1.2, 11.2)* 9.0 (2.7, 30.6)*** Cigars or cigarillos Past 30 day use 43.6 17.7 3.9 (2.1, 7.3)*** 46.7 18.9 4.5 (2.0, 10.0)*** *P<0.05, **P<0.01, ***P<0.001. aThe reference group for each logistic regression model was students who did not engage in th e health risk behavior at hand. bOdds ratios are adjusted for gender and race/ethnicity. Unable to obtain odds ratio due to 100% lifetime prevalence value.

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96 Table 21. Tobacco Use Behaviors Associat ed with Lifetime and Past 30-Day NonMedical Use of Prescription Stimulants Lifetime non-medical prescription drug use Past 30 day non-medical prescription drug use Tobacco use behaviors Use % No use % Adjusted OR a,b (95% CI) Use % No Use % Adjusted OR a,b (95% CI) Cigarettes Lifetime use Past 30 day use Past 30 day on school property Ever smoked regularly 89.2 59.0 23.1 64.1 48.6 24.1 2.8 15.0 8.5 (3.0, 24.5)*** 4.5 (2.3, 9.0)*** 10.8 (4.0, 28.5)*** 10.0 (4.9, 20.5)*** 95.4 78.3 34.8 73.9 49.8 24.5 2.9 16.4 20.5 (2.7, 154.2)** 11.0 (4.0, 30.5)*** 17.0 (6.0, 48.0)*** 15.0 (5.6, 40.1)*** Chewing tobacco Past 30 day use Past 30 day on school property 15.4 10.3 7.1 3.6 2.3 (0.8, 6.2 ) 3.0 (0.9, 9.9) 21.7 13.0 7.1 3.7 4.2 (1.3, 13.4)* 4.3 (1.1, 17.0)* Cigars or cigarillos Past 30 day use 38.5 19.0 2.6 (1.3, 5.3)** 47.8 19.4 4.1 (1.7, 10.0)** *P<0.05, **P<0.01, ***P<0.001. aThe reference group for each logistic regression model was students who did not engage in th e health risk behavior at hand. bOdds ratios are adjusted for gender and race/ethnicity.

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97 Research Question Three Is there a relationship between alcohol use and non-medical prescription drug use among a sample of 12th grade students? As illustrated in Table 22, non-medical use of any prescription drug was highly related to alcohol use behaviors including those involving underage drinking, binge drinking, driving a car under the influence of alcohol, and riding in a car with a driv er who had been drinking. Table 22. Alcohol Use Behavi ors Associated with Lifetime and Past 30-Day NonMedical Use of any Prescription Drug Lifetime non-medical prescription drug use Past 30 day non-medical prescription drug use Alcohol use behaviors Use % No use % Adjusted OR a,b (95% CI) Use % No Use % Adjusted OR a,b (95% CI) Alcohol Lifetime use Past 30 day use Past 30 day binge Past 30 day use on school property 93.5 74.6 63.0 9.4 67.5 44.1 23.4 1.4 7.2 (3.5, 14.8)*** 4.0 (2.5, 6.5)*** 5.8 (3.7, 9.1)*** 7.4 (2.4, 23.4)*** 91.3 82.6 71.0 15.9 73.1 48.5 29.4 1.7 3.9 (1.6, 9.4)** 5.4 (2.8, 10.4)*** 6.0 (3.4, 10.5)*** 11.3 (4.0, 32.1)*** Alcohol and driving Riding in a car after alcohol use Driving a car after alcohol use 51.1 43.8 19.1 15.3 4.6 (2.9, 7.3)*** 4.3 (2.6, 6.9)*** 59.4 49.3 23.4 19.7 4.9 (2.9, 8.5)*** 3.9 (2.3, 6.7)*** *P<0.05, **P<0.01, ***P<0.001. aThe reference group for each logistic regression model was students who did not engage the health risk behavior at hand. bOdds ratios are adjusted for gender and race/ethnicity.

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98 For example, students who reported lifetime alcohol use were 7.2 times more likely to report lifetime use of any prescr iption drug. Students who reported past 30 day alcohol use were six times more likely to re port past 30 day use of any prescription drug; those reporting alcohol use on school property in the past month were more than 11 times more likely to use prescription drugs. The acts of riding in or drivi ng a car after alcohol use were also significantly associated with non-medical prescription drug use, increasing a persons odds about 4 to 5 times. Tables 23-25 provide a summary of alcohol use behaviors associated with lifetime and past 30 day use of prescription drugs by drug class. According to the analyses, every alcohol related behavior was significantly associated with each class of prescription drug, for both lifetime and past 30 day prevalence estimates.

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99 Table 23. Alcohol Use Behavi ors Associated with Lifetime and Past 30-Day NonMedical Use of Prescription Pain Relievers Lifetime non-medical prescription drug use Past 30 day non-medical prescription drug use Alcohol use behaviors Use % No use % Adjusted OR a,b (95% CI) Use % No Use % Adjusted OR a,b (95% CI) Alcohol Lifetime use Past 30 day use Past 30 day binge Past 30 day use on school property 96.0 75.0 62.3 10.5 67.7 45.5 25.8 1.6 12.0 (4.7, 30.5)*** 3.9 (2.4, 6.3) *** 4.7 (3.0, 7.4)*** 6.9 (2.3, 19.8)*** 94.6 87.5 67.9 16.1 72.9 48.8 31.0 2.4 6.8 (2.1, 22.2)** 7.9 (3.4, 17.9)*** 4.8 (2.6, 8.8)*** 7.8 (2.9, 21.1)*** Alcohol and driving Riding in a car after alcohol use Driving a car after alcohol use 52.9 45.5 20.1 16.1 4.7 (3.0, 7.5)*** 4.3 (2.7, 6.9)*** 60.7 51.8 24.5 20.5 4.9 (2.7, 8.9)*** 4.1 (2.3, 7.4)*** *P<0.05, **P<0.01, ***P<0.001. aThe reference group for each logistic regression model was students who did not engage the health risk behavior at hand. bOdds ratios are adjusted for gender and race/ethnicity.

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100 Table 24. Alcohol Use Behavi ors Associated with Lifetime and Past 30-Day NonMedical Use of Prescription Depressants Lifetime non-medical prescription drug use Past 30 day non-medical prescription drug use Alcohol use behaviors Use % No use % Adjusted OR a,b (95% CI) Use % No Use % Adjusted OR a,b (95% CI) Alcohol Lifetime use Past 30 day use Past 30 day binge Past 30 day use on school property 98.2 83.6 63.6 20.0 72.6 49.6 31.9 1.9 20.5 (2.8, 150.7)** 5.3 (2.5, 11.3)*** 3.8 (2.1, 6.9)*** 14.9 (5.3, 41.7)*** 93.3 86.7 70.0 20.0 74.4 51.2 33.3 2.7 4.6 (1.1, 19.8)* 6.0 (2.1, 17.7)** 4.8 (2.1, 10.8)*** 11.2 (3.6, 34.8)*** Alcohol and driving Riding in a car after alcohol use Driving a car after alcohol use 69.1 54.5 23.6 20.1 7.3 (3.9, 13.6)*** 4.8 (2.7, 8.7)*** 83.3 56.7 25.0 22.0 14.6 (5.4, 39.4)*** 4.9 (2.3, 10.5)*** *P<0.05, **P<0.01, ***P<0.001. aThe reference group for each logistic regression model was students who did not engage the health risk behavior at hand. bOdds ratios are adjusted for gender and race/ethnicity.

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101 Table 25. Alcohol Use Behavi ors Associated with Lifetime and Past 30-Day NonMedical Use Prescription Stimulants Lifetime non-medical prescription drug use Past 30 day non-medical prescription drug use Alcohol use behaviors Use % No use % Adjusted OR a,b (95% CI) Use % No Use % Adjusted OR a,b (95% CI) Alcohol Lifetime use Past 30 day use Past 30 day binge Past 30 day use on school property 92.3 87.2 82.1 20.5 74.1 50.5 31.5 2.3 4.2 (1.3, 14.1)* 7.0 (2.7, 18.5)*** 10.0 (4.3, 23.5)*** 11.3 (4.0, 31.9)*** 95.7 87.0 82.6 17.4 75.0 52.2 33.6 3.2 7.7 (1.0, 58.1)* 6.5 (1.9, 22.5)** 9.7 (3.2, 29.2)*** 6.4 (1.9, 21.9)** Alcohol and driving Riding in a car after alcohol use Driving a car after alcohol use 66.7 56.4 25.5 21.2 6.0 (2.9, 12.2)*** 4.8 (2.4, 9.4)*** 73.9 56.5 26.9 22.6 8.1 (3.1, 21.3)*** 4.4 (1.9, 10.5)*** *P<0.05, **P<0.01, ***P<0.001. aThe reference group for each logistic regression model was students who did not engage the health risk behavior at hand. bOdds ratios are adjusted for gender and race/ethnicity.

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102 Research Question Four Is there a relationship between sexual ri sk behaviors and non-medical prescription drug use among a sample of 12th grade students? Logistic regression was also used to determine the relationship between sexual risk behaviors and non-medical use of prescription drugs. The sexual ri sk behaviors that were invest igated included ever having sexual intercourse; and for those students who had engaged in intercourse, behaviors relating to number of sexual partners, subs tance use during sex, and condom use during sex were also assessed. As illustrated in Table 26, selected sexual risk behaviors were significantly associated with use of any prescription dr ug. Students who had ever engaged in sexual intercourse for example, were about 4 times more likely to report lifetime prescription drug use and approximately 5 times more likely to report past 30 day non-medical prescription drug use. Drinking alcohol or using drugs at last sexual intercourse was also significantly associated with both lifetime a nd past 30 day prescription drug use. The behavior of more than 3 lifetime partners was significantly associated with lifetime prescription drug use; having more than 3 part ners in the past 30 da ys was significantly associated with past 30 day non-medical use of prescription drugs. The reference group of three or more sexual partners was chosen fo r the analyses because this has shown to be a significant correlate of multiple risk behavi ors in previous research (Valois, Oeltman, Waller, & Hussey, 1999)

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103 Table 26. Sexual Risk Behaviors Associat ed with Lifetime and Past 30-Day NonMedical Use of any Prescription Drug Lifetime non-medical prescription drug use Past 30 day non-medical prescription drug use Sexual risk behaviors Use % No use % Adjusted OR a,b (95% CI) Use % No Use % Adjusted OR a,b (95% CI) Sexual intercourse Ever had sexual intercourse More than 3 lifetime partners# Sexual intercourse in past 3 months# More than 3 partners in past 3 months# 78.2 60.0 77.4 19.1 49.7 42.7 75.9 11.0 3.7 (2.3, 6.0)*** 2.0 (1.2, 3.3)* 1.2 (0.6, 2.2) 2.0 (0.9, 4.1) 84.9 57.9 83.9 25.0 53.9 47.8 74.2 11.3 4.9 (2.4, 9.9)*** 1.5 (0.8, 2.7) 2.1 (0.9, 4.8) 2.7 (1.3, 5.8)* Substance use Drank alcohol or used drugs before last sexual intercourse# 45.6 26.2 2.4 (1.4, 4.1)** 54.7 28.7 3.0 (1.6, 5.6)*** Condom use No condom use at last sexual intercourse# 48.5 46.4 1.1 (0.6, 1.8) 44.2 48.2 0.9 (0.5, 1.6) *P<0.05, **P<0.01, ***P<0.001. aThe reference group for each logistic regression model was students who did not engage in the health risk behavior at hand. bOdds ratios are adjusted for gender and race/ethnicity. #Includes only students who engaged in sexual intercourse. Tables 27-29 illustrate the result of regression analyses of sexual risk behaviors by specific prescription drug class. For pain relievers, the strongest relationship was found between students who had ever had interc ourse and past 30 day non-medical use of the drugs, followed by lifetime use. Substan ce use, before last sexual intercourse was also associated with both prevalence estimates of pain reliever use. Other significant

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104 relationships for pain reliever s included: having more than three lifetime sexual partners and lifetime prevalence; having sexual interc ourse in the past three months and past month use; and having more than three sexual partners in the past three months and both lifetime and past 30 day pain reliever prevalen ce. Somewhat similar relationships were also found between sexual risk behaviors a nd the non-medical use of depressants. One additional significant relationshi p was that between past 30 day use of depressants and having more than three lifetime sexual partners. The use of prescription stimulants was less likely to be associated with sexual risk behaviors. Like pain reliev ers and depressants, significant relationships existed between having ever had sexual intercourse and both lif etime and past 30 day use of the drugs. Having more than three sexual partners was al so a significant correlate of past month prescription stimulant use.

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105 Table 27. Sexual Risk Behaviors Associat ed with Lifetime and Past 30-Day NonMedical Use of Prescription Pain Relievers Lifetime non-medical prescription drug use Past 30 day non-medical prescription drug use Sexual risk behaviors Use % No use % Adjusted OR a,b (95% CI) Use % No Use % Adjusted OR a,b (95% CI) Sexual intercourse Ever had sexual intercourse More than 3 lifetime partners# Sexual intercourse in past 3 months# More than 3 partners in past 3 months# 79.2 61.7 77.7 21.3 50.5 42.9 75.7 10.1 3.8 (2.3, 6.3)*** 2.1 (1.2, 3.6)** 1.2 (0.7 2.3) 2.5 (1.2, 5.2)* 87.3 61.7 87.0 23.9 54.5 47.4 74.0 12.2 5.8 (2.6, 13.2)*** 1.8 (0.9, 3.4) 2.6 (1.0, 6.7)* 2.3 (1.0, 5.2)* Substance use Drank alcohol or used drugs before last sexual intercourse# 47.3 26.5 2.5 (1.5, 4.4)*** 56.8 29.4 3.2 (1.6, 6.2)*** Condom use No condom use at last sexual intercourse# 45.7 48.3 0.9 (0.5, 1.5) 42.2 48.5 0.8 (0.4, 1.5) *P<0.05, **P<0.01, ***P<0.001. aThe reference group for each logistic regression model was students who did not engage in the health risk behavior at hand. bOdds ratios are adjusted for gender and race/ethnicity. #Includes only students who engaged in sexual intercourse.

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106 Table 28. Sexual Risk Behaviors Associat ed with Lifetime and Past 30-Day NonMedical Use of Prescription Depressants Lifetime non-medical prescription drug use Past 30 day non-medical prescription drug use Sexual risk behaviors Use % No use % Adjusted OR a,b (95% CI) Use % No Use % Adjusted OR a,b (95% CI) Sexual intercourse Ever had sexual intercourse More than 3 lifetime partners# Sexual intercourse in past 3 months# More than 3 partners in past 3 months# 81.1 78.1 85.4 36.6 55.4 44.5 74.6 10.0 3.5 (1.7, 7.3)*** 4.4 (2.0, 9.8)*** 2.0 (0.8, 5.2) 5.5 (2.5, 12.2)*** 82.1 78.3 95.5 40.9 57.1 47.3 74.6 11.8 3.6 (1.3, 9.6)* 3.9 (1.4, 11.0)** 6.8 (0.9, 52.6) 5.5 (2.1, 14.2)*** Substance use Drank alcohol or used drugs before last sexual intercourse# 61.9 28.6 4.1 (2.1, 8.3)*** 60.0 32.0 3.5 (1.4, 9.0)* Condom use No condom use at last sexual intercourse# 46.2 47.5 0.9 (0.5, 1.8) 42.9 47.7 0.7 (0.3, 1.9) *P<0.05, **P<0.01, ***P<0.001. aThe reference group for each logistic regression model was students who did not engage in the health risk behavior at hand. bOdds ratios are adjusted for gender and race/ethnicity. #Includes only students who engaged in sexual intercourse.

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107 Table 29. Sexual Risk Behaviors Associat ed with Lifetime and Past 30-Day NonMedical Use of Prescription Stimulants Lifetime non-medical prescription drug use Past 30 day non-medical prescription drug use Sexual risk behaviors Use % No use % Adjusted OR a,b (95% CI) Use % No Use % Adjusted OR a,b (95% CI) Sexual intercourse Ever had sexual intercourse More than 3 lifetime partners# Sexual intercourse in past 3 months# More than 3 partners in past 3 months# 91.7 63.6 87.9 24.2 55.6 48.1 74.6 12.9 9.0 (2.7, 29.8)*** 1.8 (0.9, 4.0) 2.6 (0.9, 7.9) 2.2 (0.9, 5.4) 90.9 70.0 89.5 31.6 56.9 48.4 75.3 13.0 7.7 (1.8, 33.5)** 2.4 (0.9, 6.5) 3.0 (0.7, 13.6) 3.1 (1.1, 8.9)* Substance use Drank alcohol or used drugs before last sexual intercourse# 43.8 32.9 1.6 (0.8, 3.4) 42.1 33.6 1.4 (0.6, 3.7) Condom use No condom use at last sexual intercourse# 64.5 44.8 2.2 (1.0, 4.9)* 46.4 57.8 1.6 (0.6, 4.4) *P<0.05, **P<0.01, ***P<0.001. aThe reference group for each logistic regression model was students who did not engage in the health risk behavior at hand. bOdds ratios are adjusted for gender and race/ethnicity. #Includes only students who engaged in sexual intercourse.

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108 Research Question Five Is there a relationship between viol ence-related behaviors and non-medical prescription drug use among a sample of 12t h grade students? Table 30 provides the odds ratios, confidence intervals and p-values as a result of th e logistic regression analyses of various violence rela ted behaviors. These behavior s were classified into three categories: weapon carrying, fee ling threatened or unsafe, a nd assault. Weapon carrying was assessed using three items: carrying any weapon during the past 30 days; carrying a gun in the past 30 days; and carrying any w eapon to school during the past 30 days. Feeling threatened or unsafe was addressed by three items as well: missing school in the past 30 days attributed to feeling unsafe; bei ng threatened or injured with a weapon in the past year at school; and having property stolen or damaged in the past year at school. Assault was assessed using five items: being in volved in one or more fighting incident during the past year; being injured during a fight in the past year; fighting on school property in the past year; being hit, slapped, or hurt by a boyfriend/girlfriend in the past year; and ever being forced to have sexua l intercourse. All of the weapon carrying behaviors were significantly a ssociated with lifetime and past 30 day non-medical use of any prescription drug. Of the three behavi ors related to feeling unsafe or being threatened, missing school was significantly associated with past 30 day use of any prescription drug, being threaten ed/injured with a weapon at school was associated with lifetime prescription drug use, and havi ng ones property stolen or damaged was significantly associated with both lifetim e and past 30 day non-medical use of prescription drugs. Of the assault-related be haviors, fighting, bei ng injured fighting, and being hurt by a boyfriend/girlfriend were all as sociated with both lifetime and past 30 day

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109 use. Ever being forced to have sex was si gnificantly associated with past 30 day nonmedical prescription drug us e but not lifetime use. Tables 31-33 present the results of the logistic regression analyses when differentiated by drug class. For the most part, weapon carrying behaviors significantly increased odds of non-medical pain reliever, depressant, and stimulant use. Behaviors related to feeling unsafe or being threatened at school wh ere most associated with depressant use however. For example, students who reported missing school because they felt unsafe were 16 times more likely to use depressants non-medically than students who did not miss school. Most behaviors rela ted to assault were also significantly associated with both lifetime and past 30 day use of each of the three classes of drugs. All of the assault-related behaviors were asso ciated with lifetime and past 30 day use of both pain relievers and depressants with the exception of fighting on school property which was significantly correlated with past 30 day use in the two drug classes. Among the assault behaviors, those significantly associated with lifetime and past 30 day stimulant use were fighting and being injured fighting. Forced sexual activity was also a correlate of past 30 day non-medical stimulant use.

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110 Table 30. Violence-Related Behaviors Asso ciated with Lifetime and Past 30-Day NonMedical Use of any Prescription Drug Lifetime non-medical prescription drug use Past 30 day non-medical prescription drug use Violence related behaviors Use % No use % Adjusted OR a,b (95% CI) Use % No Use % Adjusted OR a,b (95% CI) Weapon carrying Any weapon in past 30 days Gun in past 30 days Any weapon on school property in past 30 days 25.4 11.6 14.5 11.9 5.1 3.1 2.7 (1.5, 4.5)*** 2.4 (1.1, 5.2)* 5.6 (2.4, 12.9)*** 33.3 17.4 18.8 13.0 5.3 4.2 3.7 (1.9, 7.1)*** 4.0 (1.7, 9.0)** 5.7 (2.5, 13.1)*** Felt threatened or unsafe Missed school because felt unsafe in past 30 days Threatened or injured with a weapon in past year at school Stolen/damaged property in past year at school 5.8 10.1 30.4 2.0 4.4 20.0 2.9 (1.0, 8.5) 2.4 (1.1, 2.8)* 1.8 (1.1, 2.8)* 10.1 10.1 37.7 1.9 5.3 20.8 5.6 (1.9, 16.7)** 2.0 (0.8, 5.0) 2.3 (1.3, 4.1)** Assault Fought in past year Injured fighting in past year Fought on school property in past year Boyfriend or girlfriend hit, slapped, or hurt you in past year Ever forced to have intercourse 39.1 9.5 10.9 32.6 17.5 13.9 1.7 5.8 18.0 11.9 4.3 (2.6, 7.0)*** 5.9 (2.1, 17.1)** 1.9 (0.9, 4.0) 2.2 (1.4, 3.5)** 1.6 (0.9, 2.8) 47.8 11.8 13.0 43.5 25.0 16.9 2.5 6.4 18.6 11.6 4.9 (2.7, 8.7)*** 5.1 (1.9, 14.0)** 2.2 (0.9, 5.0) 3.3 (1.9, 5.8)*** 2.6 (1.4, 4.9)** *P<0.05, **P<0.01, ***P<0.001. aThe reference group for each logistic regression model was students who did not engage in the health risk behavior at hand. bOdds ratios are adjusted for gender and race/ethnicity.

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111 Table 31. Violence-Related Behaviors Asso ciated with Lifetime and Past 30-Day NonMedical Use of Prescription Pain Relievers Lifetime non-medical prescription drug use Past 30 day non-medical prescription drug use Violence related behaviors Use % No use % Adjusted OR a,b (95% CI) Use % No Use % Adjusted OR a,b (95% CI) Weapon carrying Any weapon in past 30 days Gun in past 30 days Any weapon on school property in past 30 days 25.8 11.3 14.5 12.6 5.8 3.9 2.4 (1.4, 4.2)** 2.0 (0.9, 4.2) 4.2 (1.9, 9.1)*** 32.1 17.9 19.6 14.1 5.8 4.8 3.2 (1.6, 6.4)** 3.7 (1.6, 8.8)** 5.2 (2.2, 12.2)*** Felt threatened or unsafe Missed school because felt unsafe in past 30 days Threatened or injured with a weapon in past year at school Stolen/damaged property in past year at school 5.7 10.5 29.8 2.3 4.8 21.0 2.4 (0.8, 7.1) 2.2 (1.0, 4.8) 1.5 (1.0, 2.6) 7.1 12.5 39.3 2.7 5.3 21.2 2.7 (0.8, 9.1) 2.5 (1.0, 6.5) 2.4 (1.3, 4.4)** Assault Fought in past year Injured fighting in past year Fought on school property in past year Boyfriend or girlfriend hit, slapped, or hurt you in past year Ever forced to have intercourse 40.3 8.9 10.5 33.9 19.4 14.8 2.3 6.1 18.5 11.7 4.2 (2.4, 6.6)*** 4.1 (1.5, 10.8)** 1.7 (0.8, 3.6) 2.2 (1.4, 3.6)*** 1.8 (1.0, 3.2)* 48.2 10.9 14.3 44.6 26.8 18.0 2.9 6.4 19.4 12.0 4.6 (2.5, 8.5)*** 4.1 (1.4, 11.7)** 2.4 (1.0, 5.7)* 3.3 (1.8, 6.0)*** 2.7 (1.4, 5.3)** *P<0.05, **P<0.01, ***P<0.001. aThe reference group for each logistic regression model was students who did not engage in the health risk behavior at hand. bOdds ratios are adjusted for gender and race/ethnicity.

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112 Table 32. Violence-Related Behaviors Asso ciated with Lifetime and Past 30-Day NonMedical Use of Prescription Depressants Lifetime non-medical prescription drug use Past 30 day non-medical prescription drug use Violence related behaviors Use % No use % Adjusted OR a,b (95% CI) Use % No Use % Adjusted OR a,b (95% CI) Weapon carrying Any weapon in past 30 days Gun in past 30 days Any weapon on school property in past 30 days 36.4 16.4 18.2 13.5 6.1 5.3 4.9 (2.4, 10.1)*** 3.6 (1.5, 8.7)** 4.6 (1.9, 11.1)*** 40.0 20.0 20.0 14.4 6.2 5.5 7.3 (2.8, 18.9)*** 6.2 (2.0, 19.2)** 6.4 (2.1, 19.1)*** Felt threatened or unsafe Missed school because felt unsafe in past 30 days Threatened or injured with a weapon in past year Stolen/damaged property in past year at school 7.3 14.6 32.7 2.6 5.3 22.2 3.0 (0.9, 9.9) 3.4 (1.4, 8.5)** 1.8 (0.9, 3.3) 20.0 13.3 43.3 2.0 5.5 21.9 16.0 (4.8, 53.3)*** 3.6 (1.1, 12.1)* 3.0 (1.4, 6.5)** Physical fighting Fought in past year Injured fighting in past year Fought on school property in past year Boyfriend or girlfriend hit, slapped, or hurt you in past year Forced to have intercourse 49.1 10.9 9.1 43.6 23.6 18.2 3.2 7.1 19.8 12.4 5.1 (2.7, 9.6)*** 3.9 (1.4, 10.9)* 1.3 (0.5, 3.6) 3.1 (1.7, 5.7)*** 2.2 (1.1, 4.4)* 56.7 20.0 20.0 63.3 34.5 19.2 2.7 6.5 19.5 12.2 7.9 (3.4, 18.3)*** 11.5 (3.6, 36.5)*** 4.4 (1.6, 12.2)** 7.4 (3.3, 16.2)*** 3.9 (1.7, 9.0)** *P<0.05, **P<0.01, ***P<0.001. aThe reference group for each logistic regression model was students who did not engage in the health risk behavior at hand. bOdds ratios are adjusted for gender and race/ethnicity.

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113 Table 33. Violence-Related Behaviors Asso ciated with Lifetime and Past 30-Day NonMedical Use of Prescription Stimulants Lifetime non-medical prescription drug use Past 30 day non-medical prescription drug use Violence related behaviors Use % No use % Adjusted OR a,b (95% CI) Use % No Use % Adjusted OR a,b (95% CI) Weapon carrying Any weapon in past 30 days Gun in past 30 days Any weapon on school property in past 30 days 35.9 23.1 18.0 14.2 5.6 5.6 3.8 (1.7, 8.4)** 5.6 (2.2, 14.4)*** 3.9 (1.4, 10.3)** 39.1 30.4 26.1 15.0 5.9 5.6 4.4 (1.6, 11.9)** 9.1 (2.9, 28.3)*** 6.4 (2.1, 19.0)*** Felt threatened or unsafe Missed school because felt unsafe in past 30 days Threatened or injured with a weapon in past year at school Stolen/damaged property in past year at school 10.3 7.7 35.9 2.5 6.1 22.1 4.3 (1.3, 14.6)* 1.2 (0.3, 4.4) 2.0 (1.0, 4.0) 8.7 13.0 39.1 2.9 5.9 22.6 3.1 (0.6, 14.8) 2.4 (0.6, 8.9) 2.2 (0.9, 5.3) Physical fighting Fought in past year Injured fighting in past year Fought on school property in past year Boyfriend or girlfriend hit, slapped, or hurt you in past year Ever forced to have intercourse 46.2 12.8 15.4 28.2 21.1 19.5 3.3 6.6 22.1 12.9 3.7 (1.8, 7.6) *** 4.2 (1.4, 12.5)* 2.6 (1.0, 6.8) 1.4 (0.7, 2.9) 1.8 (0.8, 4.3) 47.8 17.4 8.7 34.8 30.4 20.6 3.4 7.4 22.1 12.8 3.7 (1.5, 9.1)** 6.0 (1.8, 20.6)** 1.1 (0.3, 5.2) 1.9 (0.8, 4.6) 2.9 (1.1, 7.4)* *P<0.05, **P<0.01, ***P<0.001. aThe reference group for each logistic regression model was students who did not engage in the health risk behavior at hand. bOdds ratios are adjusted for gender and race/ethnicity.

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114 Research Question Six Is there a relationship between depre ssion and suicide idea tion and non-medical prescription drug use among a sample of 12th grade students? Logistic regression was also used to assess the asso ciation between non-medical use of any prescription drug and selected behaviors that were indicators of depression and suicide ideation including: feeling of depression in the past year; a planne d or attempted suicide in the past year, or sustaining an injury resulting from an attempt in the past year. As Table 34 illustrates, adjusted odds ra tios for the regression models show a relationship between selected be haviors related to depression and suicide and use of any prescription drug. Those behaviors associ ated with lifetime non-medical use of prescription drugs were having feelings of depression in th e past 30 days, and having planned an attempt in the past year. T hose behaviors associated with past 30 day prevalence were having feelings of depression in the past 30 days; and having considered, planned, or attempted suicid e in the past year. Tables 35-37 represent the results of l ogistic regression anal yses of depression and suicide ideation by drug class. For pain re lievers, significant relationships existed at the = 0.05 or 0.01 level and mirrored many of the same associations found for the composite value of use of any prescription drug. One significant re lationship was unique to pain relievers; the association between f eelings of depression in the past year and lifetime use of pain relievers. Relationships were demonstrated between feelings of depres sion and both lifetime and past 30 day prevalence of prescription depressant use. All four reported suicide behaviors were associated with past 30 da y non-medical use of depressants; a planned attempt in the past year was additionally asso ciated with lifetime depressant use. For

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115 stimulants on the other hand, feelings of depr ession or consideration of a suicide attempt were not significant correlates of prescrip tion drug use from that class. A planned attempt, or injury sustained during a pl anned attempt however, were significantly associated with lifetime and past 30 day nonmedical use of stimulants. For example, students who were injured in a suicide attemp t during the previous year were almost 16 times more likely to use prescription stim ulant than those who had not reported an attempt-related injury. Table 34. Feelings of Depression and Suic ide Ideation Associated with Lifetime and Past 30-Day Non-Medical Use of any Prescription Drug Lifetime non-medical prescription drug use Past 30 day non-medical prescription drug use Depression and suicide ideation Use % No use % Adjusted OR a,b (95% CI) Use % No Use % Adjusted OR a,b (95% CI) Depression Feelings during the past 12 months 32.9 21.0 1.8 (1.1, 2.8)* 43.3 21.4 2.8 (1.6, 4.8)*** Suicide Considered an attempt in past 12 months Planned to attempt in past 12 months Attempted in past 12 months Injured in an attempt in past 12 months 17.4 16.1 16.0 8.0 11.5 7.8 12.5 3.0 1.6 (0.9, 2.9) 2.2 (1.2, 4.2)* 1.4 (0.8, 2.5) 2.2 (0.7, 6.6) 21.7 22.1 23.2 10.1 11.9 8.3 11.7 3.3 2.1 (1.1, 4.1)* 3.2 (1.6, 6.3)** 2.4 (1.2, 4.6)** 1.6 (0.5, 5.4) *P<0.05, **P<0.01, ***P<0.001. aThe reference group for each logistic regression model was students who did not engage in the health risk behavior at hand. bOdds ratios are adjusted for gender and race/ethnicity.

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116 Table 35. Feelings of Depression and Suic ide Ideation Associated with Lifetime and Past 30-Day Non-Medical Use of Prescription Pain Relievers Lifetime non-medical prescription drug use Past 30 day non-medical prescription drug use Depression and suicide ideation Use % No use % Adjusted OR a,b (95% CI) Use % No Use % Adjusted OR a,b (95% CI) Depression Feelings during the past 12 months 32.5 21.6 1.7 (1.1, 2.7)* 42.9 22.1 2.6 (1.4, 4.6)** Suicide Considered an attempt in past 12 months Planned to attempt in past 12 months Attempted in past 12 months Injured in an attempt in past 12 months 18.6 17.1 16.3 7.3 11.3 8.1 12.6 3.6 1.8 (1.0, 3.2)* 2.3 (1.2, 4.3)** 1.4 (0.8, 2.5) 1.4 (0.5, 4.1) 23.2 21.8 23.2 8.9 11.9 9.0 12.0 3.7 2.3 (1.1, 4.6)* 2.8 (1.3, 5.9)** 2.3 (1.1, 4.6)* 1.1 (0.3, 1.7) *P<0.05, **P<0.01, ***P<0.001. aThe reference group for each logistic regression model was students who did not engage in the health risk behavior at hand. bOdds ratios are adjusted for gender and race/ethnicity.

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117 Table 36. Feelings of Depression and Suic ide Ideation Associated with Lifetime and Past 30-Day Non-Medical Use of Prescription Depressants Lifetime non-medical prescription drug use Past 30 day non-medical prescription drug use Depression and suicide ideation Use % No use % Adjusted OR a,b (95% CI) Use % No Use % Adjusted OR a,b (95% CI) Depression Feelings during the past 12 months 38.2 22.8 2.1 (1.1, 3.7)* 60.0 22.2 5.3 (2.5, 11.6)*** Suicide Considered an attempt in past 12 months Planned to attempt in past 12 months Attempted in past 12 months Injured in an attempt in past 12 months 20.0 21.8 18.2 10.9 12.4 9.0 13.0 3.7 1.8 (0.9, 3.7) 2.7 (1.3, 5.8)** 1.5 (0.7, 3.2) 2.0 (0.6, 7.1) 26.7 36.7 30.0 20.0 12.4 8.5 12.2 3.2 2.7 (1.1, 6.4)* 6.3 (2.8, 14.6)*** 3.1 (1.3, 7.3)*** 3.1 (0.8, 12.2)** *P<0.05, **P<0.01, ***P<0.001. aThe reference group for each logistic regression model was students who did not engage in the health risk behavior at hand. bOdds ratios are adjusted for gender and race/ethnicity.

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118 Table 37. Feelings of Depression and Suic ide Ideation Associated with Lifetime and Past 30-Day Non-Medical Us e of any Prescription Stimulants Lifetime non-medical prescription drug use Past 30 day non-medical prescription drug use Depression and suicide ideation Use % No use % Adjusted OR a,b (95% CI) Use % No Use % Adjusted OR a,b (95% CI) Depression Feelings during the past 12 months 35.6 23.7 1.8 (0.9, 3.6) 34.8 24.3 1.5 (0.7, 3.9) Suicide Considered an attempt in past 12 months Planned to attempt in past 12 months Attempted in past 12 months Injured in an attempt in past 12 months 23.1 25.6 20.5 15.4 12.4 8.9 13.0 3.6 2.2 (1.0, 5.0) 3.6 (1.6, 8.0)** 1.8 (0.8, 4.2) 5.3 (1.1, 24.1)** 26.1 34.8 30.4 26.1 12.8 9.1 12.8 3.4 2.3 (0.9, 6.3) 5.1 (2.0, 13.0)*** 3.0 (1.2, 7.7)* 15.9 (1.7, 143.9)*** *P<0.05, **P<0.01, ***P<0.001. aThe reference group for each logistic regression model was students who did not engage in the health risk behavior at hand. bOdds ratios are adjusted for gender and race/ethnicity.

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119 Research Question Seven Is there a relationship between decr eased physical activity and non-medical prescription drug use among a sample of 12th grad e students? According to the results of multiple logistic regression analyses found in Table 38, indicators of decreased physical activity were generally not associated with lifetime or past 30 day prevalence of any prescription drug. The decrease d physical activity indicators included: vigorous exercise on less than 3 days, moderate exercise on le ss than 5 days, no participation in team sports, not taking P.E. classes at school, a nd watching television for more than one hour on a school night. Referent valu es for physical activity were selected based on whether the student met the American College of on recomm endations for physical activity (Neiman, 2003). Participation in one or more sports teams was the only significant correlate prescription drug use based on th e composite measure, and was limited to lifetime use only. When categorized by drug class (see Tabl es 39-41), few significant correlates of prescription drug use surfaced. Participat ion on one or more sports teams was significantly associated with the use of pain relievers at the = 0.05 level; low levels of vigorous physical activity was associated with both lifetime and past 30 day prescription stimulant use at the = 0.01 level. No indicators of decreased physical activity were associated with prescription depressant use.

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120 Table 38 Indicators of Phys ical Inactivity Associated with Lifetime and Past 30-Day Non-Medical Use of any Prescription Drug Lifetime non-medical prescription drug use Past 30 day non-medical prescription drug use Indicators of physical inactivity Use % No use % Adjusted OR a,b (95% CI) Use % No Use % Adjusted OR a,b (95% CI) Exercise Vigorous activity on less than 3 days Moderate activity on less than 5 days No team sports No P.E. 57.3 86.2 58.9 64.4 51.9 81.7 46.4 65.2 1.3 (0.8, 1.9) 1.4 (0.8, 2.5) 1.6 (1.0, 2.6)* 0.9 (0.6, 1.5) 63.3 87.0 52.1 58.2 51.5 82.3 49.5 66.3 1.6 (0.9, 2.8) 1.4 (0.7, 3.1) 1.1 (0.6, 2.0) 0.7 (0.4, 1.2) TV watching More than 1 hour per school day 36.2 37.0 0.9 (0.6, 1.5) 33.3 37.4 0.8 (0.5, 1.4) *P<0.05, **P<0.01, ***P<0.001. aThe reference group for each logistic regression model was students who did not engage in the health risk behavior at hand. bOdds ratios are adjusted for gender and race/ethnicity.

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121 Table 39. Indicators of Phys ical Inactivity Associated with Lifetime and Past 30-Day Non-Medical Use of Prescription Pain Relievers Lifetime non-medical prescription drug use Past 30 day non-medical prescription drug use Indicators of physical inactivity Use % No use % Adjusted OR a,b (95% CI) Use % No Use % Adjusted OR a,b (95% CI) Exercise Vigorous activity on less than 3 days Moderate activity on less than 5 days No team sports No P.E. 55.7 84.7 60.6 64.4 52.9 82.6 46.4 65.2 1.2 (0.7, 1.8) 1.2 (0.7, 2.1) 1.8 (1.1, 2.8)* 0.9 (0.6, 1.5) 58.9 87.5 51.4 60.5 52.8 82.5 49.7 65.7 1.3 (0.7, 2.3) 1.5 (0.6, 3.4) 1.0 (0.5, 2.1) 0.7 (0.4, 1.4) TV watching More than 1 hour per school day 37.9 36.1 1.0 (0.7, 1.6) 35.7 36.6 0.9 (0.5, 1.7) *P<0.05, **P<0.01, ***P<0.001. aThe reference group for each logistic regression model was students who did not engage in the health risk behavior at hand. bOdds ratios are adjusted for gender and race/ethnicity.

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122 Table 40. Indicators of Phys ical Inactivity Associated with Lifetime and Past 30-Day Non-Medical Use of Prescription Depressants Lifetime non-medical prescription drug use Past 30 day non-medical prescription drug use Indicators of physical inactivity Use % No use % Adjusted OR a,b (95% CI) Use % No Use % Adjusted OR a,b (95% CI) Exercise Vigorous activity on less than 3 days Moderate activity on less than 5 days No team sports No P.E. 65.5 85.5 65.0 75.0 52.0 82.9 48.2 63.7 1.7 (1.0, 3.2) 1.2 (0.5, 2.6) 2.0 (1.0, 3.9) 1.6 (0.8, 3.3) 76.7 83.3 66.7 76.2 51.7 83.1 49.0 64.5 2.9 (1.2, 7.0) 1.0 (0.4, 2.6) 2.0 (0.7, 5.6) 1.4 (0.5, 4.0) TV watching More than 1 hour per school day 29.1 37.7 0.7 (0.4, 1.3) 33.3 36.8 0.9 (0.4, 2.0) *P<0.05, **P<0.01, ***P<0.001. aThe reference group for each logistic regression model was students who did not engage in the health risk behavior at hand. bOdds ratios are adjusted for gender and race/ethnicity.

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123 Table 41. Indicators of Phys ical Inactivity Associated with Lifetime and Past 30-Day Non-Medical Use of Prescription Stimulants Lifetime non-medical prescription drug use Past 30 day non-medical prescription drug use Indicators of physical inactivity Use % No use % Adjusted OR a,b (95% CI) Use % No Use % Adjusted OR a,b (95% CI) Exercise Vigorous activity on less than 3 days Moderate activity on less than 5 days No team sports No P.E. 74.4 87.2 62.1 71.9 51.5 82.7 49.0 64.4 2.9 (1.4, 6.3)** 1.4 (0.5, 3.8) 1.7 (0.8, 3.6) 1.4 (0.6, 3.1) 82.6 78.3 61.1 61.1 51.7 83.3 49.4 65.2 4.6 (1.5, 13.9)** 0.7 (0.3, 2.0) 1.6 (0.6, 4.1) 0.8 (0.3, 2.1) TV watching More than 1 hour per school day 43.6 36.0 1.4 (0.7, 2.7) 52.2 36.0 1.9 (0.8, 4.5) *P<0.05, **P<0.01, ***P<0.001. aThe reference group for each logistic regression model was students who did not engage in the health risk behavior at hand. bOdds ratios are adjusted for gender and race/ethnicity.

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124 Research Question Eight Is there a relationship between the use of problem weight lo ss strategies and nonmedical prescription drug use among a sample of 12th grade students? As indicated by the results of logistic regression analyses in Table 42, the use of problem weight loss tactics was largely associated with both lif etime and past 30 day non-medical use of any prescription drug. Problem weight loss tac tics included fasting (the substantially restriction of calories); use of diet pills, pow ders, or liquids; vomiti ng or taking laxatives; and taking a prescription drug for weight lo ss without a doctors advice. Depending on the weight loss method, students who used th ese various tactics over the past 30 days were between three and six times more likel y to report lifetime non-medical prescription drug use; they were between 3.4 and 9.1 tim es more likely to report past 30 day prescription use. Table 42. Problem Weight Loss Strategies Associated with Lifetime and Past 30-Day Non-Medical Use of any Prescription Drug Lifetime non-medical prescription drug use Past 30 day non-medical prescription drug use Problem weight loss behaviors Use % No use % Adjusted OR a,b (95% CI) Use % No Use % Adjusted OR a,b (95% CI) Past 30 day tactics for weight loss Fasted Use of diet pills, powders or liquids Vomited or took laxatives Took Rx drugs without a doctors advice 18.4 15.2 11.1 5.7 6.9 3.8 2.1 1.4 3.0 (1.6, 5.7)*** 4.9 (2.2, 10.9)*** 5.7 (2.1, 15.8)*** 4.1 (1.2, 14.4)* 23.0 16.1 12.9 10.0 8.0 5.4 3.2 1.2 3.4 (1.7, 7.0)*** 3.5 (1.5, 8.0)** 4.4 (1.7, 11.5)** 9.1 (2.5, 33.4)*** *P<0.05, **P<0.01, ***P<0.001. aThe reference group for each logistic regression model was students who did not engage in the health risk behavior at hand. bOdds ratios are adjusted for gender and race/ethnicity.

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125 Tables 43-45 illustrate the results of analyses performed on the specific drug classes as opposed to a composite measure of any non-medical pres cription drug use. Use of each of weight loss methods was signifi cantly associated with non-medical use of prescription stimulants at either the = 0.001 or 0.01 levels. Associations for these behaviors ranged from 3.7 times greater odds of lifetime stimulant use when fasting was present in the past 30 days, to a 22.4 greater odds of past 30 day stimulant use when the student took prescription drugs without a doctors advice. Associations between problem weight loss tactic and non-medical prescription pain reliever or depressant us e were generally not as strong as those for stimulants. In addition, no relationship existed between vomiti ng or taking laxatives and depressant use or pain reliever use in the past 30 days Likewise, no association between taking prescription drugs without a doc tors advice and lifetime pain reliever use was found. Table 43. Problem Weight Loss Strategies Associated with Lifetime and Past 30-Day Non-Medical Use of Prescription Pain Relievers Lifetime non-medical prescription drug use Past 30 day non-medical prescription drug use Problem weight loss behaviors Use % No use % Adjusted OR a,b (95% CI) Use % No Use % Adjusted OR a,b (95% CI) Past 30 day tactics for weight loss Fasted Use of diet pills, powders or liquids Vomited or took laxatives Took Rx drugs without a doctors advice 17.9 16.2 10.6 5.5 7.6 3.4 2.7 1.7 2.6 (1.3, 5.0)** 5.1 (2.3, 11.2)*** 4.3 (1.7, 10.8)** 3.3 (1.0, 11.0) 20.0 18.0 9.8 10.2 8.9 5.6 3.9 1.4 2.5 (1.2, 5.6)* 3.8 (1.6, 9.1)* 2.6 (0.9, 1.6) 7.7 (2.1, 27.6)** *P<0.05, **P<0.01, ***P<0.001. aThe reference group for each logistic regression model was students who did not engage in the health risk behavior at hand. bOdds ratios are adjusted for gender and race/ethnicity.

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126 Table 44. Problem Weight Loss Strategies Associated with Lifetime and Past 30-Day Non-Medical Use of Prescription Depressants Lifetime non-medical prescription drug use Past 30 day non-medical prescription drug use Problem weight loss behaviors Use % No use % Adjusted OR a,b (95% CI) Use % No Use % Adjusted OR a,b (95% CI) Past 30 day tactics for weight loss Fasted Use of diet pills, powders or liquids Vomited or took laxatives Took Rx drugs without a doctors advice 22.5 16.3 10.0 10.4 8.8 6.1 4.1 1.7 3.0 (1.4, 6.4)** 3.0 (1.3, 7.3)* 2.5 (0.9, 7.1) 6.7 (2.0, 23.0)** 28.0 20.0 12.0 13.0 9.0 6.2 4.2 1.8 4.0 (1.5, 10.3)** 3.7 (1.2, 10.7)* 3.0 (0.8, 11.1) 8.3 (1.9, 36.1)** *P<0.05, **P<0.01, ***P<0.001. aThe reference group for each logistic regression model was students who did not engage in the health risk behavior at hand. bOdds ratios are adjusted for gender and race/ethnicity.

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127 Table 45. Problem Weight Loss Strategies Associated with Lifetime and Past 30-Day Non-Medical Use of Prescription Stimulants Lifetime non-medical prescription drug use Past 30 day non-medical prescription drug use Problem weight loss behaviors Use % No use % Adjusted OR a,b (95% CI) Use % No Use % Adjusted OR a,b (95% CI) Past 30 day tactics for weight loss Fasted Use of diet pills, powders or liquids Vomited or took laxatives Took Rx drugs without a doctors advice 26.5 20.0 17.1 12.1 9.0 6.1 3.7 1.9 3.7 (1.6, 8.7)** 3.9 (1.5, 10.1)** 5.2 (1.9, 14.7)** 7.1 (1.9, 25.6)** 36.8 25.0 23.8 26.3 9.1 6.4 3.8 1.5 5.7 (2.1, 15.5)*** 4.9 (1.6, 14.7)** 7.6 (2.5, 23.6)*** 22.4 (6.1, 82.5)*** *P<0.05, **P<0.01, ***P<0.001. aThe reference group for each logistic regression model was students who did not engage in the health risk behavior at hand. bOdds ratios are adjusted for gender and race/ethnicity. Summary This chapter reports the results associated with the eight research questions. First, an overview of participant demographics a nd profile of non-medical prescription drug use behaviors were presented. Table 3 provides a breakdown of the demographic characteristics of the student participants. Tables 5-8, provide a su mmary of self-reported behaviors related to non-medical prescription drug use. Second, analyses determining the re lationship between two demographic characteristics, gender and race/ethnicity, and prescription drug use were reported in Tables 9-12. The purpose of these chi-s quare and preliminary logistic regression analyses was to determine if the demographic ch aracteristics needed to be controlled for. Although no significant relationshi p existed between the demographic variables and nonmedical prescription drug use, it was decided th at they should still be incorporated into

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128 the logistic regression models in case they demonstrated a significant relationship with one more or the other independent variables. The remaining tables (13-45) illustrate the results of multiple logistic regression analyses conducted to assess the relati onship among non-medical prescription drug use and multiple adolescent risk behaviors. Behaviors pertaining to illicit substance use, tobacco use, alcohol use, sexual risk, violen ce, depression and suicide ideation, decreased physical activity, and use of problem weight loss tactics were built into research questions one through eight. A significant relati onship exists between nearly all of the specific behaviors related illic it drug, tobacco, and alcohol use and lifetime and past 30 day non-medical use of prescription drugs. A lthough to a lesser extent than substance use behaviors, behaviors related sexual ri sk, violence, depression and suicide ideation, and problem weight loss practices were also associated with both lifetime and past 30 day non-medical use of prescription drugs. Overa ll, behaviors related to decreased physical activity were not associated with non -medical prescription drug use. Chapter 5 presents a summary of th e purposes, methodologies and results; discussion; implications for future re search and practice; and conclusions.

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129 CHAPTER 5 SUMMARY, DISCUSSION, IMPLICATIONS, AND CONCLUSION Summary In this chapter, a summary of the purposes, methodologies, results; discussion; implications; and conclusions are presented. The implications include recommendations for future research, practice, and strengt hened surveillance re garding non-medical prescription drug use. This investigation examined the sc ope of non-medical prescription drug use among a population of high school seniors usi ng a modified version of the YRBS, the Santa Rosa County Health Behavior Survey (SRCHBS). The main purposes of the study were to design reliable and valid prescripti on drug items for the YRBS, and to identify correlates non-medical prescription drug use an d other adolescent risk behaviors. The investigation was also conducted with future expectations of im proving monitoring and tracking systems; raising awareness among pub lic health professionals about risks of non-medical prescription drug use among adol escents; informing policy, prevention, and treatment measures related to non-medica l prescription drug use and abuse; and encouraging inclusion of prescription drug items on surveys that monitor drug use and youth risk behaviors. For this study, data were collected usi ng a cross-sectional census survey research design. The study took place in three stages: th e design and expert panel review of items addressing non-medical prescrip tion drug use; pilot testing of these new items within the

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130 context of the SRCHBS; and administration of the revised instrument to a group of 12th graders during the spring of 2006. Upon the development of items based on a review of the literature, I enlisted a panel of experts who used guidelines to establ ish content validity, a nd appropriateness of the survey items for the target population. The experts chosen to conduct the review included professionals with expertise in th e area of study, measurem ent specialists, and researchers familiar with the target audien ce. Based upon the experts comments, items were modified and prepared for the pilot study. The pilot study served two functions : to test the procedures used for administration of SRCHBS; and to assess test-re test reliability of the newly constructed items. The survey, which included a to tal of 102 items, was administered to a convenient sample of 175 9th through 12th grade students from a Northwest Florida public high school on two occasions approximate ly two weeks apart. Teachers serving as proctors received a brief instructional session describing the study and the protocol designed for collecting data. Upon completion of the pilot study, reliability of the new items was evaluated by kappa coefficient dete rmination. Most items were assessed as having either good or excellent reliability. For the main study, the survey was admini stered to the 12th grade student body of two Northwest Florida public high-schools (differe nt from the pilot) in a format similar to that for the pilot study. Unlik e in the pilot study however, survey administration for the main study was conducted in a group setting due to logistical constraints imposed by the high school principals. Data collection took place on Ma y 1, 2006 during two student assemblies. Nonetheless, considerable attention was placed on ensuring anonymity,

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131 privacy, and confidentiality among all student respondents. A total of 485 questionnaires were collected from the student population of the two schools enlis ted for the study. Of those collected questionnai res, 50 were disregarded upon sight editing yielding 435 usable questionnaires for a 59.8% response rate. A profile of non-medical prescription drug use behaviors illustra ted that 31.9% of respondents used a prescription drug non-medica lly during their lifetime on one or more occasion. In reference to specific drug cla sses, 28.6% of the students reported lifetime non-medical use of prescription pain reli evers; 12.7% reported non-medical use of prescription depressants, and 9% indicated non-medical use of prescription stimulants during their lifetime. Far fewe r students reported past 30 da y use of prescription drugs. Most students who reported non-medical pres cription drug use initiation at age 15 or older. Most students who us e prescription drugs reported they obtain the drugs for nonmedical use from a friend and used the drug to get buzzed, high, or stoned. In addition to providing measures of prevalence of non-medical prescription drug use, the study was designed to answer eight research questions: 1. Is there a relationship between illicit drug use (i.e., marijuana, cocaine, inhalants, heroin, methamphetamine, ecstasy, and steroids) and non-medical prescription drug use? 2. Is there a relationship between tobacc o use and non-medical prescription drug use? 3. Is there a relationship between alcoho l use and non-medical prescription drug use? 4. Is there a relationship between sexual ri sk behaviors and non-medical prescription drug use? 5. Is there a relationship between viol ence-related behaviors and non-medical prescription drug use?

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132 6. Is there a relationship between depre ssion and suicide idea tion and non-medical prescription drug use? 7. Is there a relationship between decr eased physical activity and non-medical prescription drug use? 8. Is there a relationship between the use of problem weight loss tactics and nonmedical prescription drug use? To address the research questions, inferen tial statistics were used to examine the relationship among non-medical pr escription drug use and othe r adolescent health risk behaviors of interest. The dependent vari able, non-medical pres cription drug use, was reported two ways: using dichotomized (y es/no) measures of prevalence items corresponding to the three majo r classes of abusable prescr iption drugs pain relievers, depressants, and stimulants; and using a dic hotomized (yes/no) composite score of those prevalence items to denote any non-medical prescription drug use. The independent variables included the use of illicit drugs, tobacco, and alcohol; sexual risk behaviors; violence-related behaviors; depression, suicide ideation, a nd suicide attempts; decreased physical activity; and the use of problem wei ght tactics. Multipl e logistic regression analyses were used to investigate whethe r significant relationships exist between the independent and dependent variable s among the study participants. According to the multiple logistic regression analyses, non-medical use of prescription drugs was significantl y related to almost all heal th risk behaviors accounted for by this study when adjusting for race and ge nder. This included risk behaviors related to illicit drug, tobacco, and alcohol use; se x; violence; depressi on and suicide; and problem weight loss. Most of the risk beha viors pertaining to decr eased physical activity however, were not significantly associated wi th non-medical prescription drug use.

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133 Discussion Based on the findings from the pilot study, the SRCHBS seems to be a valid and reliable instrument. The YRBS, the su rvey upon which the SRCHBS was based, had already been deemed a valid and reliable in strument (Brener et. al, 2003, 1995). By first basing my newly designed items on questions found in valid nati onally administered surveys, and second, addressing the revisions pr ovided by the expert review panel, I was able to have a reasonable amount of confid ence that items were representative of the concepts they were intended to reflect. In addition to those measures, I also built a comment box into the pilot-tested survey to assess whether or not students had any confusion pertaining to th e questionnaire items. By computing simple and weighted Kappa coefficients for the items of interest, I was able to estimate that approximately two-th irds of my items fell within the range of good or excellent reliability. Two items fell within the range of moderate and one item fell within the poor range. It is important to note that the items which demonstrated lower levels of reliability d ealt with past 30 day measures. Because the methods called for a test and re-t est that were two weeks apart, it is wholly possible that students were accurately repor ting behaviors on the two occasions. However, their 30day window of drug use behaviors was sh ifted two weeks forward on the second occasion. This would perhaps cause them to report different levels of use on the two occasions. Because most of the items fell within the moderate, good, or excellent ranges, I was able to conclude the items were reli able and appropriate for the main study. Moreover, as a result the fee dback received during the expe rt panel review and the reliability coefficents obtained during the pi lot study, I was confident that adding the

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134 prescription drug use items would not threat en the integrity (i.e., the validity and reliability) of the YRBS. In interpreting the results of the larger study, it is impor tant to recognize that as the data were cross sectional, inferences about causality are limited. Therefore, it is not possible to assess whether the non-medical us e of prescription drugs was preceded by the risk behavior correlates or vice-versa. Longitudinal data would be necessary to examine the directionality of these asso ciations. Nonetheless, the pr esent study does contribute to the understanding of the scope of non-medical prescription drug use among a selected group of adolescents. Furthermore, the study is subject to th e limitations of the self-report methods used. Health risk behavior survey research has been widely accepted as a valid method for obtaining such information especially when measures are taken to maintain confidentiality (Brener et al., 2003). However, some students may be inclined to over-or under-report behaviors, especially when sensitive items are presented. In addition, non-response may have introduced potential bias into the study. As reported earlier, 40.2% of the eligible st udy population did not participate. The two student assemblies used to collect data were conducted on May 1, three weeks before the end of the school year. Although I was ensure d that the assemblies would be mandatory for all seniors, it was apparent that many did not attend, likely due to proximity to the end of the school year. Furthermore, advanced placement (AP) testi ng was being conducted on the day of survey administration in both sc hools. It is not possible however, to estimate the influence these nonparticipators may have on the final study results.

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135 Descriptive statistics generated fo r the main study found a relatively equal distribution of male (48.2%) and female ( 51.8%) participants who were mostly White (78.6%). Hispanics, Blacks, and other et hnic groups comprise d 5.4%, 4.4%, and 11.6% of the studys population, respectively. Racial /ethnic distribution fo r the present study differed slightly from the distribution among high school student in the greater Santa Rosa County school district. Whites, Hispan ics, Blacks, and othe r racial ethnic groups comprised 89.4%, 2.0%, 4.8%, and 3.8% respec tively, of the school district in 2004 (Florida Department of Education, 2005). Descriptive analyses also provided a pr ofile of non-medical prescription drug use prevalence and associated responses among the survey respondents. Close to one-third (31.9%) of participants repor ted lifetime use of a pres cription drug for non-medical reasons; 16% of the study subjects reported past 30 day non-medical use and 8.8% reported past 30 day non-medical use on school property. According to national prevalence estimates, 13.7% of U.S. youth ages 12-17 have used prescription drugs nonmedically in their lifetime; and 4.0% are current users (S AMHSA 2004a). These figures may not be directly comparable due to the fact that NSDUH measur es include 12-17 year olds and the present study only includes hi gh school seniors, a subset of the youth segment. As a simple function of age, high school seniors are likely to have higher rates of drug use as compared to the general youth population. When classified by specific drug class, descriptive analyses revealed approximately 30% of the survey respondent s reported lifetime non-medical use of pain relievers, whereas approximately 13% and 9% of students reported lifetime depressant

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136 and stimulant use, respectively. Estimates of past 30-day prevalence were 12.9%, 6.9%, and 5.3%, for pain relievers, depressa nts, and stimulants, respectively. In the scheme of illicit drug use in the U.S., national estimates rank non-medical prescription drugs second only to marijuan a as the drugs of choice among American Youth (Johnston et al., 2005b). Data from the present study confirm this assertion. As seen in Table 7, the use of pain reliever s and depressants follow marijuana in both lifetime and past 30-day prevalence estimates; stimulants trail behind cocaine. Curiously, when placed into the context of use on school property, prescription pain relievers are used more readily than marijuana. One e xplanation for this might be the ease of concealment of pain relievers which often come in tablet form and that can be easily ingested as opposed to marijuana which is mo st often smoked and emits a characteristic, conspicuous odor. Most study participants who had used pr escription drugs non-medically indicated they first used the drugs at the age of 15 years or older. Specifically, students using pain relievers or stimulants reporte d initiation at age 15 or 16 mo st often. However, a large proportion of pain reliever (36.9%) and stimulan t users (48.6%) also repo rted initiation at age 14 or younger. Of the students who repor ted the use of depressants, most initiated use at age 17 or older. Slightly fewer user s of depressants (34.4%) initiated use at age 14 or younger. These results suggest that some students may begin their experimentation with prescription pain relievers or stimulant s before moving on to depressants. These data pertaining to age of first use give cause for concern because they suggest that over one-third of all initiation takes place during the middle school years or younger.

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137 When asked where they obtain the pr escription drugs they were using nonmedically, the most common response was from a friend, although respondents also cited their parents (both with and without permission), and thei r personal supply as sources. These findings are consistent with st udies assessing social (i.e., friends, family members) versus commercial (i.e., bought or stol en from stores, Internet, dealers) sources of drugs (Harrison, Fulkerson, & Park, 2000). In prior research, friends have been established as the most common source for any substance, including tobacco, alcohol, illicit drugs, and prescription drugs for il licit use (Harrison, Fu lkerson, & Park, 2000; McCabe & Boyd, 2005). Second to friends, fa mily members (with out without their consent) are also an impo rtant source of drugs (McC abe & Boyd, 2005). Reliance on friends and family is especially strong when drug use is infrequent (Harrison, Fulkerson, & Park, 2000). The most striking about these data is th e relative rarity for which participants cited the Internet or stealing from a pharmacy /doctors office as a source of the drugs. The Internet has been highlighted as an emerging source of prescription drug diversion (NCASA 2006, 2005). Many Web sites, though vi olating federal law, sell controlled prescription drugs without a prescription. Estimates of the number of Internet pharmacies dispensing prescription drugs are as high as 1,400 (NCASA, 2006). Though little or no data is available on the percentage of prescription drug abusers who get their drugs online, the Internet serves as an obvious source by which technologically savvy youth might be able to obtain the drugs. Theft from retailers or medical providers has likewise been cited as an important source of prescription drug di version (NCASA, 2005). In th e case of the present study,

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138 however, they did not play a si gnificant role as s ources of the drugs for the students. Based on the results, the importance of soci al networks as sources of the drugs is dramatically clear. Although policy contro l and curbing teen access to controlled substances is important, the findings suggest that the widespread accessibility from friends and family merits further discussion. With the exception of prescription stimula nts, research on motives for non-medial use of controlled prescription drugs has been relatively sparse (Teter, McCabe, Cranford, Boyd, & Guthrie, 2005). The present study stro ngly suggests that across all three drug classes, students who use the drugs, primarily do so to get buzzed, high, or stoned or in other words, for the euphoric effects of the drugs. Re spondents also readily cited experimentation as a primary motive across all three drug classes. Some students also claimed to use the drugs to alter the aff ects of other drugs or get away from problems. These results further confirm the va lidity of the items in that they assess nonmedical drug use that is, using a drug was not prescribed for the respondent and was carried out only for the feeling it caused. Other prevailing responses suggest that students no t only used the drugs for general euphoric effects but also took them to self-medicate. For example, a large portion of respondents indicated they took a pain reliever to relieve physical pain, a valid use for the medications but nonetheless cons idered non-medical use if taken without a doctors prescription. A moderate percentage of respondents reporte d prescription pain reliever and depressant use to relax or relieve stress, also a valid use for the medications which serve as central nervous sy stem depressants. Stimulants were also cited as being used for medically accepted purposes: to concentr ate or study, to get

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139 more energy or stay awake, and to lose weight. Despite these known valid uses for the drugs, the use of any controlled drug can be dangerous without the permission and supervision of a medi cal professional. For the current study, chi-square anal ysis found no gender or race/ethnicity differences for non-medical prescription drug us e. This contradicts pervious findings from national data sets or similar studies conducted at state le vels which have found significant differences in use patterns among the genders and races. Previous studies have in fact reported a number of conflic ting results including adolescents females greater propensity for opioid pain reliever use (Boyd et al., 2006; Sung et al., 2005); although others studies have reported males as being significantly more likely to use opioid pain medications such Vicodin and OxyContin non-medically (McCabe & Boyd et al., 2005). Some studies indicate that males report high er stimulant use (McCabe, Teter, & Boyd, 2004) while others report no gende r differences for the non-medical use of stimulants such as Ritalin (McCabe, Tete r, Boyd, & Guthrie, 2004). Although no studies have reported on the use of depressants in hi gh school students, college males more likely to use benzodiazepines (depressants) than females (McCabe, 2005). On the other hand, previous studies ha ve expressed a consistently stable relationship between race/ethnicity and nonmedical prescription drug use. With the exception of one small-scale study (Boyd et al, 2006) White adolescents report significantly higher prevalence for non-medial prescription drug use across all three drug classes including opioids pain relievers (McCabe & Boyd et al. 2005; Sung, 2005), benzodiazepine depressants (M cCabe, 2005), and stimulants (McCabe, Teter, & Boyd, 2004; McCabe & Knight, et al., 2005; McCa be, Teter, Boyd, & Guthrie, 2004).

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140 In the present study, an absence of an e ffect due to gender or race/ethnicity may have been due in part to low sample si ze and/or having conducted the study among a relatively homogeneous group in relation to race. In additi on, because there were so few ethnic minority students among the particip ants, it was necessary to combine the race/ethnicity levels to form simply tw o levels White and non-White. Although this gave me the ability to conduct the chi-square analysis, it also lowered specificity of the analyses when it came to race. Due to thes e circumstances, I decide d it would be sensible to control for gender and race/ethnicity wh en addressing the research questions. Of the risk behaviors examined us ing inferential statistics, non-medical prescription drug use was most significantl y related to the use of various illicit substances. Those students who reported the non-medical use of any prescription drug in their lifetime were 20 times more likely to use highly controlled drugs such as methamphetamine and ecstasy, both classifi ed as schedule I drug based on their high tendency for abuse and lack of accepted medical use (U.S. DEA, n.d.). Use of marijuana, cocaine, inhalants, and heroin were also high (around 6 to 8 times as high), among students who used prescription drugs non-medica lly in their lifetime. Students reporting past 30-day non-medical prescription drug use (a proxy for current use), currently used illicit drugs at a rate high er than students who had simply used prescription drug nonmedically sometime during their life. This was true for both marijuana and cocaine for which past 30-day measures were present. Furthermore, past 30 day non-medical use of prescription drugs, exacerbated the odds that a student used marijuana on school property. These students were 20 times more likely to smoke marijuana at school than their counterparts who had not used prescription drugs non-medically in the past month.

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141 When the inferential results pertaini ng to illicit drug use were presented by specific prescription drug class, they painte d an interesting pict ure of the scope of prescription drug use in relation to illicit drug use. Prevalence rates for lifetime and past 30-day non-medical prescription pain reliever use (28.6% and 12.9%) were far greater than those for depressants (12.7% and 6.9%) or stimulants (9.0% and 5.3%). This would denote that the non-medical use of prescrip tion pain relievers is a more commonplace occurrence among adolescents. With the exceptio n of steroids, odds of co-occurrence of non-medical use of pain relievers and all othe r classes of drugs was high. However, the odds of prescription and illicit drug use co-occ urrence appeared to be higher when the prescription drug was a stimulant and s ubstantially higher wh en the drug was a depressant. Furthermore, the use of depre ssants highly exacerbate s the odds of using drugs that are generally viewed as harder or have more abuse and harm potential, including heroin, cocaine, heroin, and methamphetamine. These findings may suggest that not only is the non-medical use of pain relievers more common than depressant or stimulant use, but it is associated to a lesse r extent with deviant or problem behavior. Although not directly comparable these results appear to be consistent with previous findings which suggest the non-me dical use and abuse of pres cription drugs increases an adolescents odds for a host of other drug use behaviors (McCabe, 2005; McCabe, Boyd, & Teter, 2005; McCabe, Teter, Boyd, & Guthrie, 2004). Though to a lesser degree than illicit dr ug use, tobacco and alcohol use behaviors were also associated with nonmedical prescription drug use. Participants who had ever smoked a full cigarette were about six times more likely than their non-smoking peers to have engaged in lifetime non-medical use of any prescription drug. Students who

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142 smoked cigarettes, cigars, cigarillos; or chewed tobacco in the past 30 days were four to five times more likely to us e prescription drugs non-medica lly. These data are fairly consistent with those from previous studies which demonstrate the fourto seven-fold increase in odds of prescription drug use when students report past month cigarette smoking (Boyd, McCabe, & Teter, 2006; McCa be, 2005; McCabe, Boyd, & Teter, 2005; McCabe, Teter, Boyd, & Guthrie, 2004). Th e odds of any non-medical prescription drug use were greatly inflated if the student smoked cigarettes on school property. Smoking on school property, perhaps, exacerbates odds of non-medi cal prescription drug use because it is viewed as a particularly devious be havior. This is likely due to the fact that Florida schools are smoke-free facilities and state laws strictly prohibits smoking among minors within 1,000 square feet of a public or private elementary, middle, or high school (Florida Legislature, 2006). Particular attent ion should be paid to th e large covariance of lifetime cigarette and lifetime depressant us e (OR=61.5). As with the strong relationship to illicit drugs, the use of depressants seems to represent slightly riskier behavior than pain relievers or stimulants. Engaging in alcohol-drinking behaviors in cluding lifetime use; past 30-day use, past 30-day binge, and past 30-day use on sc hool property all signifi cantly increase the odds of non-medical use of any prescription drug. Additional alcohol-related behaviors including driving a car after al cohol use and riding in a car w ith a driver that has been drinking also significantl y increase a students odds of nonmedical prescription drug use. Consistent with the extant l iterature, odds of pr escription drug use are increased from fourto seven-fold when a student reports alcohol use and relate d behaviors, depending on the drug class analyzed (Boyd, McCabe & Teter, 2006; McCabe, 2005; McCabe,

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143 Boyd, & Teter, 2005; McCabe, Teter, Boyd, & Gu thrie, 2004). As with tobacco use on school property, alcohol use on school prope rty, representing a particularly deviant behavior, generally exacerbates the odds of lifetime and past 30 day use of prescription drugs (including on school property) from all three drug classes. Selected behaviors suggestive of sexual risk taking were associated with nonmedical prescription drug use. These incl uded ever having sexual intercourse, having more than three lifetime or past month partne rs, and having used alcohol or drugs before last sexual encounter. No relationship was f ound between contraceptive use (specifically condom use) and prescription drug use among any of the drug classes. When classified by drug class however, prescription depressants demonstrated the strongest relationships with the sexual risk taking behaviors, agai n suggesting that this type of drug use is slightly more deviant than prescription pain reliever or stimulant use. Although relationships between sexual ri sk taking and non-medical prescription drug use have never been reported in the lite rature, numerous studies have demonstrated a relationship between sexual risk behaviors and problem drug use using a version of the YRBS (Lowry et al., 1994; Sh rier et al., 1996; Shrier & Crosby, 2003; Valois et al., 1999). Some of the strongest correlates of beha viors that put adolesce nts at risk for HIV and other sexually transmitted infections have been alcohol, tobacco, marijuana, and cocaine use (Lowry et al., 1994; Shrier et al ., 1996; Shrier & Cros by, 2003; Valois et al., 1999). Considering that non-medical prescr iption drug use was to a large extent significantly related to alcohol, tobacco, and other drug use in this study, it is no surprise that it is likewise related to sexually ris ky behaviors. These findings suggest that programs designed to prevent sexually transmitted diseases would benefit students by

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144 addressing prescription drug use and abuse (a s well as other drug use) in addition to sexual behavior. Youth violence has been the focus of si gnificant attention in recent years due to violent acts such as school shootings and high rates of cr imes involving violence and theft among American adolescen ts (Valois, MacDonald, Bret ous, Fisher, & Drane, 2002; Valois et al., 1995). Numerous indicators of violence with the respondent either being the perpetrator or victim were signifi cantly correlated with non-medical use of prescription drugs. These included carry ing any weapon including a gun; feeling so unsafe they missed school; being threatened or injured with a w eapon; having property stolen or damaged; physical fighting and fighting-relate d injuries; being hurt by a romantic partner; or ever being forced to have sexual intercourse. These behaviors tended to most related to non-medical prescrip tion drug use when depressant or stimulant drugs were involved. Previous studies have confirmed the re lationship between adolescent violence and aggression and substance use behaviors. Subs tance use such as any alcohol use, binge drinking, and illegal drug use (i.e., marijuan a, cocaine, and intravenous drug use) have been strong predictors of fighting, wea pon carrying (Valois & McKeown, 1998; Valois, et al., 1995). Overall, thes e comparisons suggest that the relationships between nonmedical prescription drug use and violence perp etration are not unlike those of alcohol or illicit drug use and violence perpetration. Interestingly, violence victimization ha s not been addressed a great deal in the extant literature. However, when it was addr essed, it was only slightly (not significantly) inversely associated with adolescent drug use (Albus et al., 2004). That is, victims of

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145 violence were generally less likely to use drugs. In the present study, opposite effects were found in which students who felt unsafe at school, were threaten ed or had property stolen/damaged were more likely to us e prescription drugs non-medically. This inconsistency suggests that more research shou ld perhaps, be conducted on this area of study. Specifically among females, forced sexual intercourse has been associated with heavy smoking, alcohol and/or dr ug use before sex, and the use of hard drugs such as cocaine (Howard & Wang, 2005; Silverman et al., 2001). Although the current study does not account for prescription drug use befo re sex or the recency of the forced sex experience, it is possible that these young women (and their part ners) are in fact using the prescription drugs before sex which may impair their own abilities or alter their partners personalities so they become more aggressive. These result s suggest that clinicians who treat adolescent sexual assault victims should provide referrals for support services that address substance use and abuse, incl uding that of prescription drugs. Ideations and behaviors re lated to depression and suicide were to some extent related to non-medical use of any prescrip tion drug. Students who used prescription drug in the past 30 days were close to three times more likely to report f eelings of depression in the past year than stud ents who did not use the drugs Students who used any prescription drug in the past 30 days were also signi ficantly more likely to consider, plan, and attempt suicide. As with most of the other health risk beha viors, the indicators related to depression and suic ide had strongest correlations with prescription depressants and stimulants.

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146 Suicide, a leading cause of death for yout h aged 12 to 19 is often conceptualized as the endpoint of a continuum that begins with suicide ideation, followed by planning, attempting, and completing suicide (Barrios et al., 2000). Depressionthe persistence of a sad, anxious, or empty mood is widely rec ognized as a risk factor for suicide and is thus, strongly linked the outcomes of planning or effecting a suicide attempt (National Institute of Mental Health [NIMH], 2003) The use of both gateway (including cigarettes, alcohol and marijuana) and hard (including cocaine, othe r illicit stimulants, and inhalants) substances have been linked to depression and suicide ideation in past studies (Galaif, Chou, Sussman, & Dent, 1998) There are two main hypotheses regardi ng the relationship between adolescent depression and substance use. The first is that substance use has a negative impact on adolescent mental health. Re search indicates for example, that alterations in key brain neurotransmitters such as serotonin, dopa mine, norepinephrine, and acetylcholine (affected by various prescription dr ugs) have all been associated with an increased risk of suicide (NIMH, 2003; Brown & Gershon, 1993). The second, or the self-medication hypothesis, purports that harmful substance use behaviors develop and persist as an attempt to cope with feelings of depressi on and suicide ideation (Galaif et al., 1998). Ninety percent of people who succeed at suicide have depression or another diagnosable mental or substance abuse disorder (NIM H, 2003). Although dire ctionality of the relationship between non-medical prescrip tion drug use, depression, and thoughts or attempts of suicide cannot be established in this study, the fi ndings imply that there is indeed a relationship betw een these variables.

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147 Recently, dissatisfaction with weight and body size have been increasing among both female and male adolescents (Berg, 2001). During the same time, the American population has become increasingly overwei ght and obese (CDC, 2006). In 2002 for example, there were more than three times as many overweight adolescents as there were in 1980 (Hedley, Ogden, Johnson, Carroll, Curti n, & Flegal, 2004). Studies have shown that adolescents with body dissatisfaction even those within healthy body weight rangesoften engage in unhealthy weight cont rol behaviors, such as fasting and selfinduced vomiting which can lead to abnor mal physical and psychological development (Neumark-Sztainer et al., 2002; Ne umark-Sztainer & Hannan, 2000). Adolescents with body dissati sfaction issues are not only susceptible to unhealthy weight control behaviors, but they may also be at heightened ri sk for substance use (Garry, Morrissey, & Whetstone, 2002; Rafiroiu et al., 2003). Extreme adolescent dieters (who fast, use diet pills, or use self-induced vomiting for weight control) are more likely to use alcohol, cigarettes, and marijuana than moderate adolescent dieters (who use exercise or eating fewer calorie s to control their weight) or adolescents who do not diet at all (Rafiroiu et al., 2003). Although these unhealthy weight control behaviors assessed through the use of the YRBS cannot be charac terized as eating disorders, the extant literature is also beset with evidence on the co-occurrence of substance abuse (including both licit and illicit substan ces) and eating disorders, espe cially bulimia nervosa (Lock, Reisel, & Steiner, 2001; Neumark-Sztainer et al., 1998; Ross & Ivis, 1999; Von Ranson, Iacono, & McGue, 2002). Particularly relevant to the present investigation, recent studies have found relationships between binge-andpurge eating and many t ypes of prescription drug including stimulants, barbiturates, and tr anquilizers among both males and females.

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148 After alcohol, tobacco, and mar ijuana, barbiturates (prescri ption depressant drugs) were the most favored drugs by adolescent male bingers while stimulants were the most favored drugs by female bingers (Ross & Ivis, 1999). The present study supports ex isting literature in that the non-medical use of any prescription drug was significantly associated with all of the four unhealthy weight control behaviors included in the study: fasting; use of diet pills, powders or liquids; vomiting or taking laxatives; or taking a prescription drug wi thout a doctors advice. The non-medical use of prescription stimulant drugs was most highly asso ciated with the use of weight loss tactics. This phenomenon is not surprising considering a medical indication for some stimulant drugs is weight loss. Perhaps, these students were selfprescribing drugs they knew could potentially enhance the effects of their weight loss attempts. In addition, these studentswho we re likely dealing with issues associated with body imagemay have been self-medi cating for some of the same reasons depressed students self medicated using prescription drugs. Surprising results involved the relative lack of associati on between non-medical prescription drug use and lack of exercise. Pr evious research found inverse relationships between physical activity leve l and level of cigarette, sm okeless tobacco, or marijuana use (Pate et al., 1996; Winnail et al., 1995). These findings imply that low levels of physical activity may be associated with multip le substance use behaviors. However, the relationship between phy sical inactivity and substance use did not extend to prescription drugs in the present study. Th e only activity-related behavior a ssociated with any type of prescription drug use was non-par ticipation in team sports. When reported by drug class, use of pain relievers was only associated with non-participation in team sports; stimulant

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149 use was only associated with vigorous activity on less than 3 days of the week; and depressant drugs were not associated w ith any exercise-related behaviors. Implications The present study provides important im plications regarding the scope of nonmedical prescription drug use among a speci fic group of 12th grade students. The nonmedical prescription drug use behaviors exam ined in the present study correlate with other health risk behaviors (substance use, vi olence, sexual activity, et c.) fitting to some degree with the Jessors mode l of problem behavior (Je ssor & Donovan et al., 1991). The results suggest that non-medical prescrip tion drug use (which may lead to abuse) is indeed related to a syndrome, an organize d constellation of risk taking or problem behaviors among adolescents. This suggests th at institutional efforts aimed at the prevention of non-medical prescription drug use should not focus on prescription drug alone. Rather, efforts aimed at prevention and/or treatment of non-medical prescription drug use and abuse should focus on multiple risk behaviors and co-occurring disorders. Furthermore, the results have impli cations not only for the content of programs/interventions aimed at preven ting and managing the non-medical use of prescription drugs but also provide insight for the ch annels through which these prevention messages and strategies should be disseminated. The two leading sources of prescription drugs (and likely information a bout those drugs) were friends and family. As part of prevention efforts, students and family members need to understand the potential dangers with providing abusable prescription drugs to their peers and relatives. According to this study, prevalence of non-medical prescription drug use was high among adolescents and is inextricably linked to most other youth risk behaviors including tobacco, alcohol, and illicit drug us e; violence; sexual experience; depression

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150 suicide ideation; and problem weight loss stra tegies. Surveillance systems that monitor youth risk behaviors, namely the CDCs Yout h Risk Behavior Survey, would therefore benefit by including items that assess non-medi cal prescription drug use and abuse. Based upon the findings from the present study, I propose the following recommendations for future research and practice. Recommendations for Research 1. Replicate the study among a larger, more geographically and racially diverse population. This study was conducted among students in two of six schools in a mid-sized Northwest Florida county. These schools had a relatively small proportion of minority students and were so mewhat more affluent than schools in the larger Northwest Florida region. Br oader-based replica tion studies would increase generalizability of th e study to other populations. 2. Replicate the study among 9th-12th grader s. The original research design included surveying a convenient populati on of students in grades 9 through 12. However, logistical constr aints placed upon the researcher by the school district restricted the study to 12th graders. Replication studies involving multiple grade levels would allow for the monitoring of trends in non-medical prescription drug use across multiple age groups. 3. Conduct replication studies earlier in the sc hool year when students are not preoccupied with advanced placement tes ting, final examinations, or the end of the school year. This would likely re duce the non-response rate because both students and teachers would be more focused on the task of completing the survey.

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151 4. Replicate the study, as originally propose d, in the individual classroom setting rather than the large group setting of the school cafeteri a to enhance validity. The YRBS was initially designed to be admi nistered by teachers in the classroom, where students are instructed to spread out so they can not see each others responses. CDC methodology al so encourages students to cover their responses as they complete the ques tionnaire and includes provisi ons for students to seal completed answer booklets before turning them in (Brener et al., 2004). In the present study, seating in the cafeteria was close and may not have provided adequate opportunity for students to spr ead out and cover their answers so they could respond in an honest manner without being influenced by their peers. 5. Conduct qualitative research to refine th e definition of non-medical prescription drug use among adolescents. Focus group a nd one-on-one research methods have been used to enhance cont ent validity through consulta tion with members of the target population (Vogt, King, & King, 2004). The definition used in the current study may have captured students who used the drugs for therapeutic reasons although without a physicians prescrip tion. Although this is considered nonmedical use by many sources, it is considered misuse by others. Consultation with the target popul ation regarding their understa nding of the definition used may serve to enhance validity of the items. 6. Conduct further research to ga in insight into not only th e risk but also protective factors associated with nonmedical prescr iption drug use and abuse. Behavioral researchers have recently begun to focu s greater attention upon the assessment and promotion of youth developmental asse ts in the prevention of health risk-

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152 taking (Zweig, Phillips, & Lindberg, 2002; Hawkins, Catalano, & Miller, 1992). Prevention efforts would benefit from research identifying those assets specifically associated with prescription drug use and abuse. Relevant assets would likely be classified into the psychosocial (e.g., depression, self-worth, decision-making, positive outlook), indivi dual (e.g., physical ac tivities, hobbies, housework), school (e.g., school connect edness), and family (e.g., parental expectations for education, parental closen ess, overall relation ship with parents) domains. Recommendations for Practice 1. Educate students about the dangers associated with sharing their own prescriptions or prescriptions obtained fr om other sources with their peers. Considering that friends were cited most often as s ources of prescription drugs for non-medical use, it would be wise to pl ace heightened emphasis on the risks and consequences associated with sharing or accepting prescription from peers even if they are a trusted friends. Message s designed for this purpose could be disseminated through school-based drug e ducation curricula or during school orientation sessions when general school polic ies are introduced to the students. 2. Educate parents, and other family memb ers on dangers of providing prescription drugs to youth without a physicians prescr iption. Second only to friends, parents and were readily cited as sources of prescription drugs for non-medical use. Parents should be provided with strategies to safegua rd prescription medications at home from their children; refrain from conveying messages or actions that may condone the casual use of pr escription drugs in the household; monitor their childrens use of controlled prescripti on drugs; and refrain from administering

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153 prescription drugs to their children wi thout a doctors prescription. Parent education measures can be disseminated through a number of vehicles such as public service announcements or by pi ggy-backing on federally or locally sponsored campaigns that focus on youth alcohol, marijuana or illicit drug use. Parents can also be reached through school and community forums such as back to school night, town hall meetings, or PTSA meetings. 3. Train physicians, pharmacists, or other appropriate healthcare providers to monitor prescribing and di stribution practices to mi nimize diversion; identify patients who maybe diverti ng or abusing prescription drugs; and educate their clients about the dangers associated with improper use and sharing their prescription drugs. Although most student s did not cite doctors offices or pharmacies as a source prescription drugs for non-medical use, most drugs are likely originally diverted from these sources. 4. Assure access to appropriate treatment for teenagers for whom prescription drug abuse is part of a larger problem of illicit drug and often, polydrug use. In addition to recognizing prescr iption drug abuse as a larger problem of drug use, treatment programs/interventions should also address co-occurring disorders that compromise the health of adolescents su ch as social deviance, depression or eating disorders. 5. Refine measures on national surveys and re porting in the research literature so consistent terminology in referring to non-medical prescripti on drug use, misuse, and abuse is used. Throughout the course of this research study, I encountered varied definitions for the use of pres cription drug that was unapproved by a doctor

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154 or used for recreational purposes. Non-medical use misuse and abuse have distinct meanings though they are often us ed interchangeably in the literature. 6. Advocate for the expansion of the YRBS to include the non-medical prescription drug use items designed for this stud y. Although other national surveillance systems monitor non-medic al prescription drug use (e.g., National Survey on Drug Use and Health, Monitoring the Fu ture) the U.S., states and local communities largely rely on the YRBS for monitoring health-risk behaviors which contribute to the leading causes of morbidity and mortality among youth and young adults. Bearing in mind that the prevalence of non-medical prescription drug use ranks second, only afte r marijuana, it would be prudent for the U.S. Centers for Disease Control and Prevention to recognize non-medical prescription drug use along with the use of other licit and il licit drugs as a youth risk behavior. Conclusion Findings from the current study confirm th at valid and reliable measures of nonmedical prescription drug use can be feasibly integrated into surveys that assess multiple risk behaviors among youth, namely the Youth Risk Behavior Survey. While the U.S. has made great strides in cont rolling increases in tobacco, al cohol, and illicit drug use in recent years, the non-medical use and abuse of prescription pain relievers, depressants, and stimulants remain a problem. The reasonable first step to curb the recent increase in non-medical prescription drug use is to improve surveillance systems by introducing valid and reliable items assessing prescripti on drug use and abuse prevalence. By obtaining these estimates over time on federal surveys of multiple youth risk behaviors,

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155 we will be in a better position to inform policy, prevention and treatment methods related to prescription drug use and abuse.

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156 APPENDIX A SANTA ROSA COUNTY FINAL QUESTIONNAIRE Thanks for your help! The Santa Rosa County Health Behavior Survey General Instructions This survey is about health behavio r. It has been developed so you can tell us what you do that may affect your health. The information you give will be used to develop better health educ ation for young people like yourself. DO NOT write your name on this su rvey or the answer sheet provided. The answers you give will be kept private. No one will know what you write. Answer the questions based on what you really do. Completing this survey is voluntary. Whether or not you answer the questions will not affect your grade in this class. Th ere are no right or wrong answers. If you are not comfortable answe ring a question, just leave it blank. The questions that ask about your background will be used only to describe the types of students completing the survey. The information will not be used to find out your name. No names will ever be reported. Make sure you read every question. Fill in the ovals on the answer sheet completely. When you are finished, follow the instructions of the person giving you the survey.

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157 DIRECTIONS: Choose only one answer for each question. Use a #2 pencil only. Make dark marks. Fill in the oval on your answer sheet that matches the letter of your answer. Erase completely to change your answer. 1. How old are you? A. 12 years old or younger B. 13 years old C. 14 years old D. 15 years old E. 16 years old F. 17 years old G. 18 years old 2. What is your sex? A. Female B. Male 3. In what grade are you? A. 9th grade B. 10th grade C. 11th grade D. 12th grade E. Ungraded or other grade 4. How do you describe yourself? (Select only one response) A. American Indian or Alaska Native B. Asian C. Black or African-American D. Hispanic or Latino E. Native Hawaiian or Other Pacific Islander F. White G. Other 5. Which of the following schools do you attend? A. High School 1 B. High School 2 C. High School 3 6. How do you describe your health in general? A. Excellent B. Very good C. Good D. Fair E. Poor The next 4 questions ask about personal safety. 7. When you rode a bicycle during the past 12 months, how often did you wear helmet? A. I did not ride a bicycle during the past 12 months B. Never wore a helmet C. Rarely wore a helmet D. Sometimes wore a helmet E. Most of the time wore a helmet F. Always wore a helmet 8. How often do you wear a seat belt when riding in a car driven by someone else? A. Never B. Rarely C. Sometimes D. Most of the time E. Always 9. During the past 30 days, how many times did you ride in a car or other vehicle driven by someone who had been drinking alcohol ? A. 0 times B. 1 time C. 2 or 3 times D. 4 or 5 times E. 6 or more times 10. During the past 30 days, how many times did you drive a car or other vehicle when you had been drinking alcohol ? A. 0 times B. 1 time C. 2 or 3 times D. 4 or 5 times E. 6 or more times The next 11 questions ask about violence-related behaviors. 11. During the past 30 days, on how many days did you carry a weapon such as a gun, knife, or club? A. 0 days B. 1 day C. 2 or 3 days D. 4 or 5 days E. 6 or more days

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158 12. During the past 30 days, on how many days did you carry a gun ? A. 0 days B. 1 day C. 2 or 3 days D. 4 or 5 days E. 6 or more days 13. During the past 30 days, on how many days did you carry a weapon such as a gun, knife, or club on school property ? A. 0 days B. 1 day C. 2 or 3 days D. 4 or 5 days E. 6 or more days 14. During the past 30 days, on how many days did you not go to school because you felt you would be unsafe at school or on your way to or from school? A. 0 days B. 1 day C. 2 or 3 days D. 4 or 5 days E. 6 or more days 15. During the past 12 months, how many times has someone threatened or injured you with a weapon such as a gun, knife, or club on school property ? A. 0 times B. 1 time C. 2 or 3 times D. 4 or 5 times E. 6 or 7 times F. 8 or 9 times G. 10 or 11 times H. 12 or more times 16. During the past 12 months, how many times has someone stolen or deliberately damaged your property such as a car, clothing, or books on school property ? A. 0 times B. 1 times C. 2 or 3 times D. 4 or 5 times E. 6 or 7 times F. 8 or 9 times G. 10 or 11 times H. 12 or more times 17. During the past 12 months, how many times were you in a physical fight? A. 0 times B. 1 time C. 2 or 3 times D. 4 or 5 times E. 6 or 7 times F. 8 or 9 times G. 10 or 11 times H. 12 or more times 18. During the past 12 months, how many times were you in a physical fight in which you were injured and had to be treated by a doctor or nurse? A. 0 times B. 1 time C. 2 or 3 times D. 4 or 5 times E. 6 or more times 19. During the past 12 months, how many times were you in a physical fight on school property ? A. 0 times B. 1 time C. 2 or 3 times D. 4 or 5 times E. 6 or 7 times F. 8 or 9 times G. 10 or 11 times H. 12 or more times 20. During the past 12 months, did your boyfriend or girlfriend ever hit, slap, or physically hurt you on purpose? A. Yes B. No 21. Have you ever been forced to have sexual intercourse when you did not want to? A. Yes B. No The next 5 questions ask about sad feelings and attempted suicide. Sometimes people feel so depressed about the future that they may consider attempting suicide, that is, taking some action to end their own life.

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159 22. During the past 12 months, did you ever feel so sad or hopeless almost every day for two weeks or more in a row that you stopped doing some usual activities? A. Yes B. No 23. During the past 12 months, did you ever seriously consider attempting suicide? A. Yes B. No 24. During the past 12 months, did you make a plan about how you would attempt suicide? A. Yes B. No 25. During the past 12 months, how many times did you actually attempt suicide? A. 0 times B. 1 time C. 2 or 3 times D. 4 or 5 times E. 6 or more times 26. If you attempted suicide during the past 12 months, did any attempt result in an injury, poisoning, or overdose that had to be treated by a doctor or nurse? A. I did not attempt suicide during the past 12 months. B. Yes C. No The next 12 questions ask about tobacco use. 27. Have you ever tried cigarette smoking, even one or two puffs? A. Yes B. No 28. How old were you when you smoked a whole cigarette for the first time? A. I have never smoked a whole cigarette B. 8 years old or younger C. 9 or 10 years old D. 11 or 12 years old E. 13 or 14 years old F. 15 or 16 years old G. 17 years old or older 29. During the past 30 days, on how many days did you smoke cigarettes? A. 0 days B. 1 or 2 days C. 3 to 5 days D. 6 to 9 days E. 10 or 19 days F. 20 to 29 days G. All 30 days 30. During the past 30 days, on the days you smoked, how many cigarettes did you smoke per day ? A. I did not smoke cigarettes during the past 30 days B. Less than 1 cigarette per day C. 1 cigarette per day D. 2 to 5 cigarettes per day E. 6 to 10 cigarettes per day F. 11 to 20 cigarettes per day G. More than 20 cigarettes per day 31. During the past 30 days, how did you usually get your own cigarettes? (Select only one response). A. I did not smoke cigarettes during the past 30 days B. I bought them in a store such as a convenience store, supermarket or gas station C. I bought them from a vending machine D. I gave someone else money to buy them for me E. I borrowed (or bummed) them from someone else F. A person 18 years old or older gave them to me G. I took them from a store or family member H. I got them some other way

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160 32. During the past 30 days, on how many days did you smoke cigarettes on school property ? A. 0 days B. 1 or 2 days C. 3 to 5 days D. 6 to 9 days E. 10 or 19 days F. 20 to 29 days G. All 30 days 33. Have you ever smoked cigarettes regularly, that is, at least one cigarette every day for 30 days? A. Yes B. No 34. During the past 12 months, did you ever try to quit smoking cigarettes? A. I did not smoke during the past 12 months B. Yes C. No 35. During the past 30 days, on how many days did you use chewing tobacco, snuff, or dip such as Redman, Levi Garret, Beechnut, Skoal, Skoal Bandits, or Copenhagen? A. 0 days B. 1 or 2 days C. 3 to 5 days D. 6 to 9 days E. 10 or 19 days F. 20 to 29 days G. All 30 days 36. During the past 30 days, on how many days did you use chewing tobacco, snuff, or dip on school property ? A. 0 days B. 1 or 2 days C. 3 to 5 days D. 6 to 9 days E. 10 or 19 days F. 20 to 29 days G. All 30 days 37. During the past 30 days, on how many days did you smoke cigars, cigarillos, or little cigars ? A. 0 days B. 1 or 2 days C. 3 to 5 days D. 6 to 9 days E. 10 or 19 days F. 20 to 29 days G. All 30 days The next 5 questions ask about drinking alcohol. This includes beer, wine, wine coolers, and liquor such as rum, gin, vodka, or whiskey. For these questions, drinking alcohol does not include drinking a few sips of wine for religious purposes. 38. During your life, on how many days have you had at least one drink of alcohol? A. 0 days B. 1 or 2 days C. 3 to 9 days D. 10 to 19 days E. 20 to 39 days F. 40 to 99 days G. 100 or more days 39. How old were you when you had your first drink of alcohol other than a few sips? A. I have never had a drink of alcohol other than a few sips B. 8 years old or younger C. 9 or 10 years old D. 11 or 12 years old E. 13 or 14 years old F. 15 or 16 years old G. 17 years old or older 40. During the past 30 days, on how many days did you have at least one drink of alcohol? A. 0 days B. 1 or 2 days C. 3 to 5 days D. 6 to 9 days E. 10 to 19 days F. 20 to 29 days G. All 30 days

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161 41. During the past 30 days, on how many days did you have 5 or more drinks of alcohol in a row, that is, within a couple of hours? A. 0 days B. 1 or 2 days C. 3 to 5 days D. 6 to 9 days E. 10 to 19 days F. 20 to 29 days G. All 30 days 42. During the past 30 days, on how many days did you have at least one drink of alcohol on school property ? A. 0 days B. 1 or 2 days C. 3 to 5 days D. 6 to 9 days E. 10 to 19 days F. 20 to 29 days G. All 30 days The next 4 questions ask about marijuana use. Marijuana also is called grass or pot. 43. During your life, how many times have you used marijuana? A. 0 times B. 1 or 2 times C. 3 to 9 times D. 10 to 19 times E. 20 to 39 times F. 40 to 99 times G. 100 or more times 44. How old were you when you tried marijuana for the first time? A. I have never tried marijuana B. 8 years old or younger C. 9 or 10 years old D. 11 or 12 years old E. 13 or 14 years old F. 15 or 16 years old G. 17 years old or older 45. During the past 30 days, how many times did you use marijuana? A. 0 times B. 1 or 2 times C. 3 to 9 times D. 10 to 19 times E. 20 to 39 times F. 40 or more times 46. During the past 30 days, how many times did you use marijuana on school property ? A. 0 times B. 1 or 2 times C. 3 to 9 times D. 10 to 19 times E. 20 to 39 times F. 40 or more times The next 6 questions ask about prescription pain relievers which doctors sometimes prescribe after surgery or when someone is in a lot of pain. Prescription pain relievers include drugs such as: Vicodin, OxyContin, Percocet, Percodan, Codeine (Tylenol 3 & 4), Fentanyl, Darvon, Demerol, Dilaudid, Morphine and Methadone. They are sometimes called Painkillers Pain pills, Oxies, Percs, Perc-a-pops, Miss Emma, Hillbilly Heroin or Demmies. 47. During your life, how many times have you used a prescription pain reliever that was NOT prescribed to you or you took only for the feeling it caused? A. 0 times B. 1 or 2 times C. 3 to 9 times D. 10 to 19 times E. 20 to 39 times F. 40 to 99 times G. 100 or more times 48. How old were you when you first tried a prescription pain reliever that was NOT prescribed to you or you took only for the feeling it caused? A. I have never used a prescription pain reliever in a way not prescribed for me B. 8 years old or younger C. 9 or 10 years old D. 11 or 12 years old E. 13 or 14 years old F. 15 or 16 years old G. 17 years old or older

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162 49. During the past 30 days, how many times did you use a prescription pain reliever that was NOT prescribed to you or you took only for the feeling it caused? A. 0 times B. 1 or 2 times C. 3 to 9 times D. 10 to 19 times E. 20 to 39 times F. 40 to 99 times G. 100 or more times 50. During the past 30 days, how many times did you use a prescription pain reliever that was NOT prescribed to you or you took only for the feeling it caused on school property ? A. 0 times B. 1 or 2 times C. 3 to 9 times D. 10 to 19 times E. 20 to 39 times F. 40 to 99 times G. 100 or more times 51. Where did you usually get the prescription pain reliever you used in a way NOT prescribed for you? (Please select only one ) A. I have never used a prescription pain reliever in a way not prescribed for me B. I took my own prescription pain reliever; but in a way not prescribed for me C. From a parent or guardian with their permission D. From a parent or guardian without their permission E. From a brother or sister F. From another relative (grandparent, aunt, uncle, cousin, etc.) G. From a friend H. From a pharmacy or doctors office (stolen or forged prescription) I. From the Internet J. Other 52. Why did you use the prescription pain reliever in a way NOT prescribed for you? (Please select the one most important reason) A. I have never used a prescription pain reliever in a way not prescribed for me B. To experiment C. To get buzzed, high, stoned, or wasted D. To fit in E. To get away from my problems F. To alter the effects of other drugs (including alcohol) G. To relax or relieve stress H. To help me sleep I. To relieve physical pain J. Other The next 6 questions are about prescription depressant drugs which doctors sometimes prescribe to calm people down, relax their muscles, or help people sleep. Prescription depressants include drugs such as: Valium, Xanax, Halcion, Librium, Ativan, Ambien, ProSom, Seconal, Nembutal and Lunesta. They are sometimes called Nerve pills, Downers, Downs, Sleepers, Barbs, Benos, Stumblers or Zanibars 53. During your life, how many times have you used a prescription depressant that was NOT prescribed to you or you took only for the feeling it caused? A. 0 times B. 1 or 2 times C. 3 to 9 times D. 10 to 19 times E. 20 to 39 times F. 40 to 99 times G. 100 or more times

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163 54. How old were you when you first tried a prescription depressant that was NOT prescribed to you or you took only for the feeling it caused? A. I have never used a prescription depressant in a way not prescribed for me B. 8 years old or younger C. 9 or 10 years old D. 11 or 12 years old E. 13 or 14 years old F. 15 or 16 years old G. 17 years old or older 55. During the past 30 days, how many times did you use a prescription depressant that was NOT prescribed to you or you took only for the feeling it caused? A. 0 times B. 1 or 2 times C. 3 to 9 times D. 10 to 19 times E. 20 to 39 times F. 40 to 99 times G. 100 or more times 56. During the past 30 days, how many times did you use a prescription depressant that was NOT prescribed to you or you took only for the feeling it caused on school property ? A. 0 times B. 1 or 2 times C. 3 to 9 times D. 10 to 19 times E. 20 to 39 times F. 40 to 99 times G. 100 or more times 57 Where did you usually get the prescription depressant you used in a way NOT prescribed for you? (Please select only one ) A. I have never used a prescription depressant in a way not prescribed for me B. I took my own prescription depressant; but in a way not prescribed for me C. From a parent or guardian with their permission D. From a parent or guardian without their permission E. From a brother or sister F. From another relative (grandparent, aunt, uncle, cousin, etc.) G. From a friend H. From a pharmacy or doctors office (stolen or forged prescription) I. From the Internet J. Other 58. Why did you use the prescription depressant in a way NOT prescribed for you? (Please select the one most important reason) A. I have never used a prescription depressant in a way not prescribed for me B. To experiment C. To get buzzed, high, stoned, or wasted D. To fit in E. To get away from my problems F. To alter the effects of other drugs (including alcohol) G. To relax or relieve stress H. To help me sleep I. To relieve physical pain J. Other The next 6 questions are about prescription stimulant drugs which doctors sometimes prescribe to treat attention-deficit hyperactivity disorder (ADHD) or depression, help people stay awake, or help people lose weight. Prescription stimulants include drugs such as: Ritalin, Concerta, Adderall, Dexedrine, Meridia, and Benzedrine. They are sometimes also called Uppers, Speed or Pep Pills

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164 59. During your life, how many times have you used a prescription stimulant that was NOT prescribed to you or you took only for the feeling it caused? A. 0 times B. 1 or 2 times C. 3 to 9 times D. 10 to 19 times E. 20 to 39 times F. 40 to 99 times G. 100 or more times 60. How old were you when you first tried a prescription stimulant that was NOT prescribed to you or you took only for the feeling it caused? A. I have never used a prescription stimulant in a way not prescribed for me B. 8 years old or younger C. 9 or 10 years old D. 11 or 12 years old E. 13 or 14 years old F. 15 or 16 years old G. 17 years old or older 61. During the past 30 days, how many times did you use a prescription stimulant that was NOT prescribed to you or you took only for the feeling it caused? A. 0 times B. 1 or 2 times C. 3 to 9 times D. 10 to 19 times E. 20 to 39 times F. 40 to 99 times G. 100 or more times 62. During the past 30 days, how many times did you use a prescription stimulant that was NOT prescribed to you or you took only for the feeling it caused on school property ? A. 0 times B. 1 or 2 times C. 3 to 9 times D. 10 to 19 times E. 20 to 39 times F. 40 to 99 times G. 100 or more times 63. Where did you usually get the prescription stimulant you used in a way NOT prescribed for you? (Please select only one ) A. I have never used a prescription stimulant in a way not prescribed for me B. I took my own prescription stimulant; but in a way not prescribed for me C. From a parent or guardian with their permission D. From a parent or guardian without their permission E. From a brother or sister F. From another relative (grandparent, aunt, uncle, cousin, etc.) G. From a friend H. From a pharmacy or doctors office (stolen or forged prescription) I. From the Internet J. Other 64. Why did you use the prescription stimulant in a way NOT prescribed for you? (Please select the one most important reason) A. I have never used a prescription stimulant in a way not prescribed for me B. To experiment C. To get buzzed, high, stoned or wasted D. To fit in E. To get away from my problems F. To alter the effects of other drugs (including alcohol) G. To get more energy or stay awake longer H. To help me concentrate or study I. To help me lose weight J. Other The next 9 questions ask about other drugs.

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165 65. During your life, how many times have you used any form of cocaine, including powder, crack, or freebase? A. 0 times B. 1 or 2 times C. 3 to 9 times D. 10 to 19 times E. 20 to 39 times F. 40 or more times 66. During the past 30 days, how many times have you used any form of cocaine, including powder, crack, or freebase? A. 0 times B. 1 or 2 times C. 3 to 9 times D. 10 to 19 times E. 20 to 39 times F. 40 or more times 67. During your life, how many times have you sniffed glue, or breathed the contents of aerosol spray cans, or inhaled any paints or sprays to get high? A. 0 times B. 1 or 2 times C. 3 to 9 times D. 10 to 19 times E. 20 to 39 times F. 40 or more times 68. During your life, how many times have you used heroin (also called smack, junk, or China White)? A. 0 times B. 1 or 2 times C. 3 to 9 times D. 10 to 19 times E. 20 to 39 times F. 40 or more times 69. During your life, how many times have you used methamphetamines (also called speed, crystal, crank, or ice)? A. 0 times B. 1 or 2 times C. 3 to 9 times D. 10 to 19 times E. 20 to 39 times F. 40 or more times 70. During your life, how many times have you used ecstasy (also called MDMA)? A. 0 times B. 1 or 2 times C. 3 to 9 times D. 10 to 19 times E. 20 to 39 times F. 40 or more times 71. During your life, how many times have you taken steroid pills or shots without a doctors prescription? A. 0 times B. 1 or 2 times C. 3 to 9 times D. 10 to 19 times E. 20 to 39 times F. 40 or more times 72. During your life, how many times have you used a needle to inject any illegal drug into your body? A. 0 times B. 1 time C. 2 or more times 73. During the past 12 months, has anyone offered, sold, or given you an illegal drug on school property ? A. Yes B. No The next 7 questions ask about sexual behavior. 74. Have you ever had sexual intercourse? A. Yes B. No 75. How old were you when you had sexual intercourse for the first time? A. I have never had sexual intercourse B. 11 years old or younger C. 12 years old D. 13 years old E. 14 years old F. 15 years old G. 16 years old H. 17 years old or older

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166 76. During your life, with how many people have you had sexual intercourse? A. I have never had sexual intercourse B. 1 person C. 2 people D. 3 people E. 4 people F. 5 people G. 6 or more people 77. During the past 3 months, with how many people did you have sexual intercourse? A. I have never had sexual intercourse B. I have had sexual intercourse, but not during the past 3 months C. 1 person D. 2 people E. 3 people F. 4 people G. 5 people H. 6 or more people 78. Did you drink alcohol or use drugs before you had sexual intercourse the last time ? A. I have never had sexual intercourse B. Yes C. No 79. The last time you had sexual intercourse, did you or your partner use a condom? A. I have never had sexual intercourse B. Yes C. No 80. The last time you had sexual intercourse, what one method did you or your partner use to prevent pregnancy ? (Select only one response). A. I have never had sexual intercourse B. No method was used to prevent pregnancy C. Birth control pills D. Condoms E. Depo-Provera (injectable birth control) F. Withdrawal G. Some other method H. Not sure The next 7 questions ask about body weight. 81. How do you describe your weight? A. Very underweight B. Slightly underweight C. About the right weight D. Slightly overweight E. Very overweight 82. Which of the following are you trying to do about your weight? A. Lose weight B. Gain weight C. Stay the same weight D. I am not trying to do anything about my weight 83. During the past 30 days, did you exercise to lose weight or to keep from gaining weight? A. Yes B. No 84. During the past 30 days, did you eat less food, fewer calories, or foods low in fat to lose weight or to keep from gaining weight? A. Yes B. No 85. During the past 30 days, did you go without eating for 24 hours or more (also called fasting) to lose weight or to keep from gaining weight? A. Yes B. No

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167 86. During the past 30 days, did you take diet pills, powders, or liquids without a doctors advice to lose weight or keep from gaining weight? (Do not include meal replacement products such as Slim fast.) A. Yes B. No 87. During the past 30 days, did you vomit or take laxatives to lose weight or to keep from gaining weight? A. Yes B. No 88. During the past 30 days, did you take prescription drugs without a doctors advice to lose weight or to keep from gaining weight? A. Yes B. No The next 7 questions ask about food you ate or drank during the past 7 days. Think about all meals and snacks you had from the time you got up until you went to bed. Be sure to include food you ate at home, at school, at restaurants, or anywhere else. 89. During the past 7 days, how many times did you drink 100% fruit juices such as orange juice, apple juice, or grape juice? (Do not count punch, Kool-Aid, sports drinks, or other fruitflavored drinks.) A. I did not drink 100% fruit juice during the past 7 days B. 1 to 3 times during the past 7 days C. 4 to 6 times during the past 7 days D. 1 time per day E. 2 times per day F. 3 times per day G. 4 or more times per day 90. During the past 7 days, how many times did you eat fruit ? (Do not count fruit juice.) A. I did not eat fruit during the past 7 days B. 1 to 3 times during the past 7 days C. 4 to 6 times during the past 7 days D. 1 time per day E. 2 times per day F. 3 times per day G. 4 or more times per day 91. During the past 7 days, how many times did you eat green salad ? A. I did not eat green salad during the past 7 days B. 1 to 3 times during the past 7 days C. 4 to 6 times during the past 7 days D. 1 time per day E. 2 times per day F. 3 times per day G. 4 or more times per day 92. During the past 7 days, how many times did you eat potatoes ? (Do not count french fries, fried potatoes, or potato chips.) A. I did not eat potatoes during the past 7 days B. 1 to 3 times during the past 7 days C. 4 to 6 times during the past 7 days D. 1 time per day E. 2 times per day F. 3 times per day G. 4 or more times per day 93. During the past 7 days, how many times did you eat carrots? A. I did not eat carrots during the past 7 days B. 1 to 3 times during the past 7 days C. 4 to 6 times during the past 7 days D. 1 time per day E. 2 times per day F. 3 times per day G. 4 or more times per day

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168 94. During the past 7 days, how many times did you eat other vegetables ? (Do not count green salad, potatoes, or carrots.) A. I did not eat other vegetables during the past 7 days B. 1 to 3 times during the past 7 days C. 4 to 6 times during the past 7 days D. 1 time per day E. 2 times per day F. 3 times per day G. 4 or more times per day 95. During the past 7 days, how many glasses of milk did you drink? (Include the milk you drank in a glass or cup, from a carton, or with cereal. Count the half pint of milk served at school as equal to one glass.) A. I did not drink milk during the past 7 days B. 1 to 3 times during the past 7 days C. 4 to 6 times during the past 7 days D. 1 time per day E. 2 times per day F. 3 times per day G. 4 or more times per day The next 7 questions ask about physical activity. 96. On how many of the past 7 days did you exercise or participate in physical activity for at least 20 minutes that made you sweat and breathe hard such as basketball, soccer, running, swimming laps, fast bicycling, fast dancing, or similar aerobic activities? A. 0 days B. 1 day C. 2 days D. 3 days E. 4 days F. 5 days G. 6 days H. 7 days 97. On how many of the past 7 days did you participate in physical activity for at least 30 minutes that did not make you sweat or breathe hard, such as fast walking, slow bicycling, skating, pushing a lawn mower, or mopping floors? A. 0 days B. 1 day C. 2 days D. 3 days E. 4 days F. 5 days G. 6 days H. 7 days 98. During the past 7 days, on how many days were you physically active for a total of at least 60 minutes per day ? (Add up all the time you spend in any kind of physical activity that increases your heart rate and makes you breathe hard some of the time.) A. 0 days B. 1 day C. 2 days D. 3 days E. 4 days F. 5 days G. 6 days H. 7 days 99. On an average school day, how many hours do you watch TV? A. I do not watch TV on an average school day B. Less than 1 hour per day C. 1 hour per day D. 2 hours per day E. 3 hours per day F. 4 hours per day G. 5 or more hours per day 100. In an average week when you are in school, on how many days do you go to physical education (PE) classes? A. 0 days B. 1 day C. 2 days D. 3 days E. 4 days F. 5 days

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169 101. During an average physical education (PE) class, how many minutes do you spend actually exercising or playing sports? A. I do not take PE B. Less than 10 minutes C. 10 to 20 minutes D. 21 to 30 minutes E. 31 to 40 minutes F. 41 to 50 minutes G. 51 to 60 minutes H. More than 60 minutes 102. During the past 12 months, on how many sports teams did you play? (Include any teams run by your school or community groups). A. 0 teams B. 1 team C. 2 teams D. 3 or more teams Were any questions on this survey confusing to you or hard to answer? Please tell us which questions were confusing and why in the orange comment box on the back of your answer sheet. This is the end of the survey. Thanks for your help!

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170 APPENDIX B LIST OF EXPERT PANEL REVIEW ERS FOR INSTRUMENT ASSESSMENT April 2006 1. Paul L. Doering, M.S. Distinguished Service Professor University of Florida School of Pharmacy 101 South Newell Drive HPNP Bldg. 212 Rm. 3307 Gainesville, Fl 32611 doering@shands.ufl.edu 2. J. David Haddox, D.D.S., M.D. Vice President, Risk Management & Health Policy Purdue Pharma L.P. One Stamford Forum Stamford, CT 06901-3431 Dr.J.David.Haddox@pharma.com 3. Jack Henningfield, Ph.D. Adjunct Professor Department of Psychiatry and Behavioral Sciences Johns Hopkins University School of Medicine, and Vice President for Research and Health Policy Pinney Associates 3 Bethesda Metro Center, Suite 1400 Bethesda, MD 20814 jhenning@pinneyassociates.com 4. Linda Simoni-Wastila, Ph.D. Associate Professor University of Maryland School of Pharmacy 515 W. Lombard Street, Room 162 Baltimore, MD 21201 lsimoni@rx.umaryland.edu

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171 5. Laura Kann, Ph.D. National Center for Chronic Diseas e Prevention and Health Promotion Division of Adolescent and School Health U.S. Centers for Disease Control and Prevention 1600 Clifton Rd., NE (K-33) Atlanta, GA 30333 lkk1@cdc.gov 6. Robert J. McDermott, Ph.D. Professor of Health Education Department of Community and Family Health University of South Florida College of Public Health 13201 Bruce B. Downs Blvd., (MDC 56) Tampa, FL 33612-3805 rmcdermo@hsc.usf.edu 7. Randall D. Penfield, Ph.D. Assistant Professor, Educatio nal and Psychological Studies School of Education Room 311A Merrick Building 5202 University Drive Coral Gables, FL 33146 penfield@miami.edu 8. Mohammad Torabi, Ph.D. Chancellors Professor & Chair Department of Applied Health Science HPER 116 1025 E. 7th Street Bloomington, IN 47405 torabi@indiana.edu

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172 APPENDIX C EXPERT PANEL EMAIL COVER LETTER

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173 Dear Dr. Haddox: Thanks for agreeing to serve as a member of my expert panel for my dissertation research. You were selected because of your professional expertis e in areas directly related to my study. Your task is to review 12 in itial items designed to measur e the prevalence of non-medical prescription drug use, which wi ll be embedded in the 2005 Y outh Risk Behavior Survey as part of a larger study. Attached you will find an abstract of my dissertation topic. (See Morris below.) This abstract describes the purpos es, significance, procedures, an d anticipated outcomes of my overall study. The abstract might provide you a context for reviewing the 12 proposed items. You will also find a copy of the 12 items. (See Items below.) Please use this document as a reference during your assessment. To conduct your review, please go the Rx Drug Expert Review Panel website by clicking on http://express.perseus.com/pe rseus/surveys/1734848031/2e385e0c.htm This website contains the instructions and procedures for conducting your review, and an on-line form for recording your ratings and providing feedback. I hope you can complete your online assessm ent by Wednesday, February 22, 2006. If you have any questions about your task or have any pr oblems opening the attached documents or executing web-based assessm ent, please contact me by email or by telephone at 850.944.8693 (home), or 305.726.6673 (cell). Again, I thank for your time and effort in serving as a panel re view member. I look forward to receiving your evaluation and comments! Sincerely yours, Melissa C. Morris, M.P.H., C.H.E.S. Ph.D. Candidate Attachments : Morris M. C. (2006). ABSTRACT: "Measurin g Non-Medical Prescription Drug Use and Associated Risk-Taking Behaviors Items Non-Medical Prescription Drug Use.

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174 APPENDIX D ABSTRACT OF THE PROPOSED STUDY MEASURING NON-MEDICAL PRESCRIP TION DRUG USE AND ASSOCIATED RISK-TAKING BEHAVIORS STATEMENT OF THE PROBLEM : While rates of illicit dr ug, alcohol, and tobacco use have declined among adolescents in recent years, non-medical prescription drug use has risen. Non-medical prescription drug use has be en defined as the use of drug that was not prescribed for you or that was taken onl y for the experience or feeling it caused (Substance Abuse and Mental Health Se rvices Administration (SAMHSA, 2004). Reports indicate that close to 14% of youth ages 12-17 have used prescription drugs nonmedically in their lifetime, a nd each year close to half of non-medical prescription drug use initiates are under th e age of 18. When placed into pe rspective relative to other types of youth substance use, the non-medical use of prescription drugs us e ranks fourth, after alcohol, cigarettes, and ma rijuana, respectively. Both theory and research suggest that drug taking behavior s are not isolated events in the lives of adolescents. Findings from empirical studies suggest that teens who use prescription drugs for non-medical purposes are more likely to engage in multiple substance use behaviors. Few studies however, have i nvestigated th e relationship between non-medical prescription drug use a nd other health risk behaviors including those related to accidental in jury and violence, sexual expe rience, suicide and depression, decreased physical activit y, and use of problem we ight loss practices. PURPOSES OF THE STUDY: This investigation will exam ine the nature of non-medical prescription drug use using a modified vers ion of the CDCs Youth Risk Behavior Survey (YRBS). The purposes of the study are to: (1) design reliable and valid measures of non-medical prescription drug use to be inco rporated into the YRBS; (2) describe the relationships between non-medical prescrip tion drug use and other adolescent risk behaviors; (3) raise awareness among public h ealth professionals a nd key stakeholders about risks of non-medical prescription drug use among adolescents; (4) improve monitoring and tracking systems by encouragin g the inclusion of pr escription drug items in surveys that monitor drug use and youth risk behaviors; and (5) inform policy, prevention, and treatment measures related to non-medical prescription drug use. SIGNIFICANCE OF THE STUDY : Although there is cons iderable research on adolescent substance use, limited data exist on the non-medical use of prescription drugs. To better inform policy, prevention, and treatm ent initiatives, it has been suggested that national surveys of drug use should include specific items that measure non-medical prescription drug use. One such survey is the CDCs YRBS. Only after fully understanding the scope, severi ty, and correlates of non-me dical prescription drug use

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175 among adolescents, will health education and prevention specialists be able to tailor interventions that address prescr iption drugs as part of the larger problem of drug abuse. SUMMARY OF PROCEDURES : This study will take place in three stages and will include: (1) an expert panel review (2) pilot testing of new instrument items and (3) administration of the revised instrument. I wi ll first enlist a panel of experts who will use guidelines to establish content validity, and appropriateness of the survey items for the target population. The experts chosen to c onduct the review will be professionals with expertise in the area of study, measurement specialists, or th ose familiar with the target audience. If necessary, the newly developed items will be revised based on expert panel feedback. The pilot study will serve two functions : to test the procedures used for administration of the modified YRBS; and to assess test-retest reliability of the newly constructed items. For the pilot, I will coll ect data using a version of the 2005 YRBS expanded to include 12 items assessing non-medi cal use of the thr ee major classes of abusable prescription drugs: opioid pain relievers, stimulan ts, and depressants. The modified survey, which will include a total of 97 items, will be administered to a convenience sample of 200 students from a Florida public high school on two occasions, approximately two weeks apart. Teachers serving as proctors will receive a brief instructional session describing the study and the protocol designed for collecting data. Upon completion of the pilot study, reliability will be assessed upon kappa coefficient determination. If necessary, modification to the procedures and instrument will be made based on findings from the pilot study. For the main study, the survey will be ad ministered to the entire population of a single Florida public high-school (d ifferent from the pilot) in a format similar to that for the pilot study. As in the pilot study, teacher s, who will serve as proctors, will receive a brief instructional session de scribing the study and the prot ocol designed for collecting data. These steps will be taken to ensu re uniformity in conducting the survey. Considerable attention and emphasis will be placed on ensuring anonymity, privacy, and confidentiality among all st udent respondents. Upon completion of the final administration, inferential statistics will be used to examine the relationship (if any) among non-medical prescription drug use and othe r adolescent health risk behaviors. The dependent variable, non-medical prescrip tion drug use, will be measured using a dichotomized composite score of prevalen ce items that correspond to the three major classes of abusable prescription drugs. Th e independent variable s will include tobacco, alcohol, and marijuana use; other illicit drug use includi ng cocaine/crack, inhalants, heroin, methamphetamine, and ecstasy; violen ce-related behaviors; sadness ideation and suicide attempts; sexual risk taking; and the use of problem weight tactics. ANTICIPATED OUTCOMES: As a result of the st udy, I anticipate the following outcomes: (1) there will be a signifi cant difference in adolescent non-medical prescription drug use based on selected de mographics, (2) adolescent students who engage in non-medical prescription drug use ar e more likely to take part in drug-taking behaviors than those students who do not e ngage in non-medical prescription drug use, (3) students who participate in non-medical prescription drug use are more likely to take part in other health risk behaviors (i.e., acci dental injury and violence, sexual experience,

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176 suicide and depression, decreased physical activity, and use of problem weight loss practices) than those students who do not enga ge in non-medical prescription drug use. Melissa C. Morris, M.P.H., C.H.E.S. Ph.D. Candidate Department of Health Education & Behavior University of Florida Gainesville, FL

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177 APPENDIX E PRELIMINARY ITEMS FOR EXPERT PANEL REVIEW

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178 Section A: Prescription Pain Relievers The next 4 questions are about pain relievers, wh ich doctors sometimes prescribe after surgery or when someone is in a lot of pain. People are only supposed to take prescription pain relievers if they have a prescription from a doctor for t hem. Prescription pain relievers are sometimes called Pain pills, Oxies, Percs, H illbilly heroin, or Demmies. They include drugs such as: Vicodin, OxyContin, Percocet, Percodan, Codeine (Tylenol 3 & 4), Darvon, Demerol, Dilaudid, Morphine, Methadone. In the answers you provide, we are only interested in your use of prescription pain relievers if the drug was NOT prescribed to you or you took the drug only for the experience or feeling it caused. We are not interested in your use of over-the-counter pain relievers such as aspirin, Tylenol, or Advil that can be bought in drug stores or grocery stores without a doctors prescription. 1. During your life, how many times have you used a prescription pain reliever that was not prescribed to you or you took only for the feeling it caused? A. 0 times B. 1 or 2 times C. 3 to 9 times D. 10 to 19 times E. 20 to 39 times F. 40 to 99 times G. 100 or more times 2. How old were you when you first tried a prescription pain reliever not prescribed to you or you took only for the feeling it caused? A. I have never used a prescription pain reliever in a way not prescribed for me by a doctor B. 8 years old or younger C. 9 or 10 years old D. 11 or 12 years old E. 13 or 14 years old F. 15 or 16 years old G. 17 years old or older 3. During the past 30 days, how many times did you use a prescription pain reliever that was not prescribed to you or you took only for the feeling it caused? A. 0 times B. 1 or 2 times C. 3 to 9 times D. 10 to 19 times E. 20 to 39 times F. 40 or more times 4. During the past 30 days, how many times did you use a prescription pain reliever not prescribed to you or you took only for the feeling it caused on school property? A. 0 times B. 1 or 2 times C. 3 to 9 times D. 10 to 19 times E. 20 to 39 times F. 40 or more times

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179 Section B: Prescription Depressants The next 4 questions are about use of depressant drugs, which doctors sometimes prescribe to calm people down, relax their muscles, or help pe ople sleep. People are only supposed to take prescription depressants if they have a pre scription from a doctor for them. Prescription depressants are sometimes called Nerve pills, Downs, Downers, Sleepers, Barbs, Benzos, or Stumblers They include drug such as: Valium, Xanax, Halcion, Librium, Ativan, Ambien, ProSom, Mebaral, Seconal, Nembutal. In the answe rs you provide, we are only interested in your use of prescription depressants if the drug was NO T prescribed to you or you took the drug only for the experience or feeling it caused. We are not interested in your use of over-the-counter depressants such as Sominex, Unisom, Nytol, or Benadryl that can be bought in drug stores or grocery stores without a doctors prescription. 5. During your life, how many times have you used a prescription depressant not prescribed to you or you took only for the feeling it caused? A. 0 times B. 1 or 2 times C. 3 to 9 times D. 10 to 19 times E. 20 to 39 times F. 40 to 99 times G. 100 or more times 6. How old were you when you tried a prescription depressant that was not prescribed to you or you took only for the feeling it caused? A. I have never used a prescription depressant in a way not prescribed for me by a doctor B. 8 years old or younger C. 9 or 10 years old D. 11 or 12 years old E. 13 or 14 years old F. 15 or 16 years old G. 17 years old or older 7. During the past 30 days, how many times did you use a prescription depressant that was not prescribed to you or you took only for the feeling it caused? A. 0 times B. 1 or 2 times C. 3 to 9 times D. 10 to 19 times E. 20 to 39 times F. 40 or more times 8. During the past 30 days, how many times did you use a prescription depressant that was not prescribed to you or you took only for the feeling it caused? A. 0 times B. 1 or 2 times C. 3 to 9 times D. 10 to 19 times E. 20 to 39 times F. 40 or more times

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180 Section C: Prescription Stimulants The next 4 questions are about use of stimulant drugs, which doctors sometime prescribe to treat attention-deficit hyperactivity disorder (ADH D), stay awake, or help people lose weight. Prescription stimulants are sometimes called Uppers, Speed, or Pep Pills They include drugs such as: Ritalin, Concerta, Adderall, Dexedrine, Meridia, Methedrine, Benzedrine. In the answers you provide, we are only interested in your use of prescription stimulants if the drug was NOT prescribed to you or you took the drug only for the experience or feeling it caused. We are not interested in your use of over-t he-counter stimulants such as Dexatrim or No-Doz that can be bought in drug stores or grocery stor es without a doctors prescription. 9. During your life, how many times have you used a prescription stimulant that was not prescribed to you or you took only for the feeling it caused? A. 0 times B. 1 or 2 times C. 3 to 9 times D. 10 to 19 times E. 20 to 39 times F. 40 to 99 times G. 100 or more times 10. How old were you when you first tried a prescription stimulant that was not prescribed to you or you took only for the feeling it caused? A. I have never used a prescription stimulant in a way not prescribed for me by a doctor B. 8 years old or younger C. 9 or 10 years old D. 11 or 12 years old E. 13 or 14 years old F. 15 or 16 years old G. 17 years old or older 11. During the past 30 days, how many times did you use a prescription stimulant that was not prescribed to you or you took only for the feeling it caused? A. 0 times B. 1 or 2 times C. 3 to 9 times D. 10 to 19 times E. 20 to 39 times F. 40 or more times 12. During the past 30 days, how many times did you use a prescription stimulant that was not prescribed to you or you took only for the feeling it caused? A. 0 times B. 1 or 2 times C. 3 to 9 times D. 10 to 19 times E. 20 to 39 times F. 40 or more times

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181 APPENDIX F EXPERT PANEL REVIEW ITEM ASSESSMENT SURVEY

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182 Expert Panel Review Dear Reviewer: For the expert panel review, you will be as ked to assess instructions, questions, and response options for 12 items pertaining to nonmedical use of prescription drugs. The six criteria to keep in mind are listed below, each with examples of questions to consider. a. Focus : Are the instructions, questions, etc., fo cused directly on the issue or topic to be measured? b. Brevity : Are the instructions, questions, etc ., stated as briefly as possible? c. Clarity : Are the instructions, questions, etc., ex pressed as clearly and as simply as possible, concerning the type and scope of information wanted, the location for the responses, and the form in which answers are to be given? d. Readability (vocabulary) : Are the words used familiar to the respondents? Are items worded with terminology that is appropriate for respondents? e. Assurance : Are instructions worded to allay fears, suspicion, embarrassment, or hostility on the part of the respondent? Are instructions worded in an interesting and inviting manner so as to encourage the respondent to participate fully and to respond in an honest manner? f. Adequacy of Options : Are response options all inclus ive, mutually inclusive, and meaningful? Are ample numbers of response options provided to measure the behavior under inves tigation adequately? You are not asked to answer the questions as though you were in the role of a student. Rather, please review inst ructions and items according to the six criteria and your professional expertise. Although I have provide d you with a text version of the items for review, please note any comments you might have in the boxes provided on this webbased survey only. Please click the Next button below to continue with the assessment. Next Powered by SurveySolutions: Conduct your own employee satisfaction survey

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183 Expert Panel Review Note to Reviewers: Please use th is page to comment on instructions for Section A: Prescription Pain Relievers. ________________________________________________________________________ Section A: Prescription Pain Relievers ________________________________________________________________________ The next 4 questions are about pain relievers, which doctors sometimes prescribe after surgery or when someone is in a lot of pai n. People are only supposed to take prescription pain relievers if they have a prescription from a doctor for them. Prescription pain relievers are sometimes called Pain pills, Oxie s, Percs, Hillbilly heroin, or Demmies. They include drugs such as: Vicodin, OxyC ontin, Percocet, Percodan, Codeine (Tylenol 3 & 4), Darvon, Demerol, Dilaudid, Morphine Methadone. In the answers you provide, we are only interested in your use of prescription pain re lievers if the drug was NOT prescribed to you or you took the drug only for the experience or feeling it caused We are not interested in your use of over-the -counter pain relievers such as aspirin, Tylenol, or Advil that can be bought in drug st ores or grocery stores without a doctors prescription. A1. Overall, would you say that instructions for Section A: Prescription Pain Relievers are: Yes No a. Focused b. Brief c. Clear d. Readable e. Assuring A2. If you checked "No" to any of the above criteria, please wr ite your comments and suggestions for revision below.

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184 A3. Do you believe the slang (e.g., Pain pills, Oxies, Percs, etc.) and trade names (e.g., Vicodin, OxyContin, Percocet, etc.) in this section reasonably represent the universe of names used to describe prescription pain relievers? Yes No Don't Know A4. If you checked "No" to th e above question, please indica te how you would revise the slang and/or trade names below. A5. Are there any other compone nts of the instructions for Section A: Prescription Pain Relievers that should be omitted, modified, or ad ded? If so, please comment below. Please click the Next button below to continue with the assessment. Next Powered by SurveySolutions: Conduct your own employee satisfaction survey

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185 Expert Panel Review Note to Reviewers: Please use this page to comment on items and response options for Section A: Prescription Pain Relievers. 1. During your life, how many times 3. During the past 30 days, how many have you used a prescription pain times did you use a prescription reliever that was not prescr ibed to pa in reliever that was not prescribed you or you took only for the feeling to you or you took only for the it caused? feeling it caused? A. 0 times A. 0 times B. 1 or 2 times B. 1 or 2 times C. 3 to 9 times C. 3 to 9 times D. 10 to 19 times D. 10 to 19 times E. 20 to 39 times E. 20 to 39 times F. 40 to 99 times F. 40 or more times G. 100 or more times 2. How old were you when you first 4. During the past 30 days, how many tried a prescription pain reliever not times did you use a prescription prescribed to you or you took only pain reliever not prescribed to you for the feeling it caused? or you took only for the feeling it A. I have never used a prescription caused on school property? pain reliever in a way not A. 0 times prescribed for me by a doctor B. 1 or 2 times B. 8 years old or younger C. 3 to 9 times C. 9 or 10 years old D. 10 to 19 times D. 11 or 12 years old E. 20 to 39 times E. 13 or 14 years old F. 40 or more times F. 15 or 16 years old G. 17 years old or older A6. Overall, would you say the questions (1-4) in this section are: Yes No a. Focused b. Brief c. Clear d. Readable

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186 A7. If you checked "No" to any of the above criteria, please wr ite your comments and suggestions for revisions below. A8. Overall, would you say the response options (for questions 1-4) in this section are: Yes No a. Clear b Adequate A9. If you checked "No" to either of the a bove criteria, please write your comments and suggestions for revision below. A10. Please use the space below if you have additional comments related to Section A: Prescription Pain Relievers. Please click the Next button below to continue with the assessment. Next Powered by SurveySolutions: Conduct your own employee satisfaction survey

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187 Expert Panel Review Note to Reviewers: Please use th is page to comment on instructions for Section B: Prescription Depressants. ________________________________________________________________________ Section B: Prescription Depressants ________________________________________________________________________ The next 4 questions are about use of de pressant drugs, which doctors sometimes prescribe to calm people down, re lax their muscles, or help people sleep. People are only supposed to take prescription depressants if they have a prescription from a doctor for them. Prescription depressants are sometimes called Nerve pills, Downs, Downers, Sleepers, Barbs, Benzos, or Stumblers. They include drug such as: Valium, Xanax, Halcion, Librium, Ativan, Ambien, ProSom, Mebaral, Seconal, Nembutal. In the answers you provide, we are only interested in your use of prescription depre ssants if the drug was NOT prescribed to you or you took the drug only for the experience or feeling it caused. We are not interested in your use of over-the-counter de pressants such as Sominex, Unisom, Nytol, or Benadryl th at can be bought in drug stores or grocery stores without a doctors prescription. B1. Overall, would you say that instructions for Section B: Prescription Depressants are: Yes No a. Focused b. Brief c. Clear d. Readable e. Assuring B2. If you checked "No" to any of the above criteria, please wr ite your comments and suggestions for revision below.

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188 B3. Do you believe the slang (e.g., Nerve pills, Downs, Downers, etc.) and trade names (e.g., Valium, Xanax, Halcion, etc.) used in th is section reasonably represent the universe of names used to describe prescription depressants? Yes No Don't Know B4. If you checked "No" to the above quest ion, please indicate how you would revise the slang and/or trade names below. B5. Are there any other compone nts of the instructions for Section B: Prescription Depressants that should be omitted, modified, or added? If so, please comment below. Please click the Next button below to continue with the assessment. Next Powered by SurveySolutions: Conduct your own employee satisfaction survey

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189 Expert Panel Review Note to Reviewers: Please use this page to comment on items and response options for Section B: Prescription Depressants. 5. During your life, how many times have you used a prescription depressant not prescribed to you or you took only for the feeling it caused? A. 0 times B. 1 or 2 times C. 3 to 9 times D. 10 to 19 times E. 20 to 39 times F. 40 to 99 times G. 100 or more times 6. How old were you when you tried a prescription depressant that was not prescribed to you or you took only for the feeling it caused? A. I have never used a prescription depressant in a way not prescribed for me by a doctor B. 8 years old or younger C. 9 or 10 years old D. 11 or 12 years old E. 13 or 14 years old F. 15 or 16 years old G. 17 years old or older 7. During the past 30 days, how many times did you use a prescription depressant that wa s not prescribed to you or you took only for the feeling it caused? A. 0 times B. 1 or 2 times C. 3 to 9 times D. 10 to 19 times E. 20 to 39 times F. 40 or more times 8. During the past 30 days, how many times did you use a prescription depressant that wa s not prescribed to you or you took only for the feeling it caused on school property? A. 0 times B. 1 or 2 times C. 3 to 9 times D. 10 to 19 times E. 20 to 39 times F. 40 or more times B6. Overall, would you say the questi ons (5-8) in this section are: Yes No a. Focused b. Brief c. Clear d. Readable

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190 B7. If you checked "No" to any of the above criteria, please wr ite your comments and suggestions for revisions below. B8. Overall, would you say the response options (for questions 5-8) in this section are: Yes No a. Clear b. Adequate B9. If you checked "No" to either of the above criteria, please write your comments and suggestions for revision below. B10. Please use the space below if you have additional comments related to Section B: Prescription Depressants Please click the Next button below to continue with the assessment. Next Powered by SurveySolutions: Conduct your own online surveys

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191 Expert Panel Review Note to Reviewers: Please use th is page to comment on instructions for Section C: Prescription Stimulants. ________________________________________________________________________ Section C: Prescription Stimulants ________________________________________________________________________ The next 4 questions are about use of stimul ant drugs, which doctors sometimes prescribe to treat attention-deficit hypera ctivity disorder (ADHD), help people stay awake, or help people lose weight. Prescripti on stimulants are sometimes called Uppers, Speed, or Pep Pills. They include drugs such as: Ritalin, Concerta, Adderall, Dexedrine, Meridia, Methedrine, Benzedrine. In the answers you pr ovide, we are only interested in your use of prescription stimulants if the drug was NOT prescribed to you or you took the drug only for the experience or feeling it caused. We are not interested in your use of overthe-counter stimulants such as Dexatrim or No-Doz that can be bought in drug stores or grocery stores without a doctors prescription. C1. Overall, would you say that instructions for Section C: Prescription Stimulants are: Yes No a. Focused b. Brief c. Clear d. Readable e. Assuring C2. If you checked "No" to any of the above criteria, please wr ite your comments and suggestions for revision below.

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192 C3. Do you believe the slang (e.g., Uppers, Speed, Pep pills) and trade names (e.g., Ritalin, Concerta, Aderall, etc.) used in this section reasonably repr esent the universe of names used to describe prescription stimulants? Yes No Don't Know C4. If you checked "No" to the above quest ion, please indicate how you would revise the slang and/or trade names below. C5. Are there any other compone nts of the instructions for Section C: Prescription Stimulants that should be omitted, modified, or a dded? If so, please comment below. Please click the Next button below to continue with the assessment. Next Powered by SurveySolutions: Conduct your own online surveys

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193 Expert Panel Review Note to Reviewers: Please use this page to comment on items and response options for Section C: Prescription Stimulants. 9. During your life, how many times have you used a prescription stimulant that was not prescribed to you or you took only for the feeling it caused? A. 0 times B. 1 or 2 times C. 3 to 9 times D. 10 to 19 times E. 20 to 39 times F. 40 to 99 times G. 100 or more times 10. How old were you when you first tried a prescription stimulant that was not prescribed to you or you took only for the feeling it caused? A. I have never used a prescription stimulant in a way not prescribed for me by a doctor B. 8 years old or younger C. 9 or 10 years old D. 11 or 12 years old E. 13 or 14 years old F. 15 or 16 years old G. 17 years old or older 11. During the past 30 days, how many times did you use a prescription stimulant that was not prescribed to you or you took only for the feeling it caused? A. 0 times B. 1 or 2 times C. 3 to 9 times D. 10 to 19 times E. 20 to 39 times F. 40 or more times 12. During the past 30 days, how many times did you use a prescription stimulant that was not prescribed to you or you took only for the feeling it caused on school property? A. 0 times B. 1 or 2 times C. 3 to 9 times D. 10 to 19 times E. 20 to 39 times F. 40 or more times C6. Overall, would you say the questi ons (9-12) in this section are: Yes No a. Focused b. Brief c. Clear d. Readable

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194 C7. If you checked "No" to any of the above criteria, please wr ite your comments and suggestions for revisions below. C8. Overall, would you say the response options (for questions 9-12) in this section are: Yes No a. Clear b. Adequate C9. If you checked "No" to either of the above criteria, please write your comments and suggestions for revision below. C10. Please use the space below if you ha ve any additional comments related to Section C: Prescription Stimulants. Please click the Next button below to continue with the assessment. Next Powered by SurveySolutions: Conduct your own online surveys

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195 Expert Panel Review Please use the space below to provide any additional comments. Instrument Assesment Completed by: Name: Title: Institution: S ubmit Survey Powered by SurveySolutions: Conduct your own customer satisfaction surveys

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196 APPENDIX G EXPERT PANEL COMMENTS FOR REVISIONS Comments Regarding Instructions Even though your wording "you took the drug only for the experience or feeling it" corresponds to SAMHSA's I be lieve that this can be c onfusing and suggest that you consider the additional explanat ion (perhaps parenthetically) th at is often offered to lay and professional persons alike, e.g., (did you use it to alter your mood such as to get "high"?) Way too long I would have skipped it and just answered the questions. Have you checked the reading level for high school kids it should be about 7th grade. I would delete the entire 2nd sentence since it is not ne utral and may create socially desirable responses. In terms of readability, everything is 'scrunched up' I have always had concern about the opera ting definition of non-medical use employed by SAMHSA on the NHSDUH because, while the intent is really to get at "nontherapeutic use", the test is imprecise. As an example, this wording, if followed properly, would capture a person who took an an algesic offered to them by a parent for an appropriate reason, that is, the "experience or feeling it caused" was analgesia. Do you really want to capture that use? You have an opportunity to sort this out, yet still use the SAMHSA definition, a real contribution to the literature -after this item, ask them to either write in or choose from a list all the ex periences or feelings they sought from the NMU of the drug. This would provide very useful data. The reading level is high. Flesch-Kincaid and SMOG it. Aspirin should be mentioned as an over-the-counter drug. You appear to be using the same definition of non-medical use as that used in the NSDUH; is this for comparison across surveys? Please note that this definition has its critics.... I am not sure I like the term 'depressant s'; it seems somewhat nondescriptive for these classes of drugs and is not widely use d. Perhaps tranquilizers and sedatives. I am wondering if it is possible for a drug to be prescribed, but taken only for the feeling. For example, could someone not get a fraudul ent prescription (i.e., faking symptoms) just

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197 to get the meds. Or, could someone have been prescribed the meds legitimately at some point for something that they didn't really need it for (I know that I was prescribed way more pain killers than necessary when I had a root canal). Just a thought, because this may (or may not) be an important distinction. Lose the big introductions just ask the ques tions maybe with a few examples of what each type of drug is. It is way too wordy, complicated, and directive now. Comments Regarding Slang and Trade Names You may want to consider adding some generic names to the brand names (eg, oxycodone, hydrocodone). I would consider listing the drug names and possibly the slang names as well. The slang terms may not be remotely familiar to even people who are users/abusers. Also, I would use the trade names first, then the slang names. In the slang list, start with "Painkillers". Also missing from this list is fentanyl, in both transdermal and buccal lozenge formulations. Street names are "morphine patches" (even though they contain fentanyl) and "Perc-a-pops (even though they contain fentanyl). Also, street name for codeine is "C" or "Lean and dean" (Rx cough syrup); for hydrocodone cough syrup is "Tuss" and "syrup" ; for hydromorphone "D's"; for morphine "Miss Emma"; for pentazocine "T's". I just don't think everyone else will know them by these names. There are endless slang names that are used and they change with such frequency that one could never keep up with all of them The trade names are okay as written. I believe that the use of the words Oxies, Percs, Hillbilly heroin, or Demmies are pejorative and suggest that it is socially unacceptable to use thes e drugs. In this context, I am afraid that it might stifle honest responses. I would not use these slang terms for the sa me reason stated above: Nerve pills, Downs, Downers, Sleepers, Barbs, Benzos, or Stumblers These are pejorative names: Uppers, Speed, or Pep Pills ... you might want to include some of the ne w ones [depressants]Lunesta, for example, which is being promoted DTC on TV. The trade name Meberal is only found in museums these days.

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198 I would present trade names first. These drugs [stimulants] have other uses as well, including treatment of depression (generally refractory depression). Se e prior comments from other classes. General audience will not know these names universally. I would suggest getting rid of methedrine because it is too confusing with Crystal Meth that is not a prescription drug. Besides, methedrine is ra rely encountered today as a prescription drug. Comments Regarding Items "... on school property" in Question 4 shoul d be bold-faced to let readers easily distinguish the differences between Questions 3 & 4. Underline "on school property" so it stands out in Q4. You may wish to emphasize "on school prope rty" by using italics or bolding to set it apart from the previous question I think the statement should read : "...pain reliever that was NO T prescribed to you or that you took only for the feeling it caused" with th e NOT in all caps and bold. I would also bold the important words in each item that diffe rentiate it from the others, given the way most young folks read today, so in item 1(dur ing your life), item 2 (how old were you), item 3 (during the past 30 days), item 4 (during the past 30 days AND on school property). Items' use of "or" may confuse some people or cause them to stumble on the question or or skip it. Moreover, I thi nk it is important to know wh ether people took something for its supposed therapeutic effect vs. its "high."" Comments Regarding Response Options "When you can avoid categorical responses, do so. Strive for highest level of measurement possible. I would use the same frequency of use for lifetime, past year, and past-month. I would include option G on item 3 -you might get some takers, believe it or not. I would also include option G on item 4 as a test of credibility of the questionnaire, as someone who endorses abusing more than 100 times on school property is probably not being honest.

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199 Keep frequency of use consistent across lifet ime, past-year and past-month. Also, it just now occurs to me that you will not be getting at daily use in past year if you limit to your current options. Other Comments I think it would be a great c ontribution to the field if you could ask questions on where these folks obtained the drugs th at they are using non-medically Thanks for asking me to do this it was fun

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200 APPENDIX H APPLICATION TO THE UNIVERSITY OF FLORIDA INSTITUTIONAL REVIEW BOARD

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201 UNIVERSITY OF FLORIDA INSTITUTIONAL REVIEW BOARD 1. TITLE OF PROTOCOL: Secondary Analysis of a M odified Youth Risk Behavior Survey 2. PRINCIPAL INVESTIGATOR: Melissa C. Morris, M.P.H. Ph.D. Candidate Department of Hea lth Education and Behavior FLG-69 PO Box 118210 Gainesville, FL 32611-8210 Tel: (352) 392-0583, ext. 1254 Fax: (352) 392-3186 Eml: mcmorris@hhp.ufl.edu 3. SUPERVISOR (If PI is student): Robert M. Weiler, Ph.D., M.P.H. Associate Professor Department of Hea lth Education and Behavior FLG-5 PO Box 118210 Gainesville, FL 32611-8210 Tel: (352) 392-0583, ext. 1282 Fax: (352) 392-3186 Eml: rweiler@hhp.ufl.edu 4. DATES OF PROPOSED PROTOCOL: From February 21, 2006 To February 20, 2007 5. SOURCE OF FUNDING FOR THE PROTOCOL: None 6. SCIENTIFIC PURPOSE OF THE INVESTIGATION: According to the U.S. Department of Health and Human Services, 75 percent of adolescent mortality is due to preventable causes including motor vehicle crashes, homicide, and suicide. Modi fiable behaviors particularly relevant to adolescents involve unintentional injuri es, violence, substance use, mental health, reproductive health, physical activity levels, and dietary habits. Schools and communities can benefit when priority health risk behaviors that contribute to th e leading causes of death, disability and social problems ar e periodically monitored through survey research. Research conducted to determin e the prevalence of health risk behaviors and assess whether health risk behaviors in crease, decrease or stay the same over

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202 time, can be used to inform school-based curricula and health education programs so they may be better tailored for student populations. One survey used to monitor health be havior trends among American adolescents is the Youth Risk Behavior Survey (YRBS ), conducted by the federal Centers for Disease Control and Prevention (CDC). Th e CDC administers this survey on national and state representative samples of 9th through 12th grade students administered every two years. However, local agenci es frequently choose to modify and administer the survey to obtain needs assessment data for their communities. For this research, I have been commissi oned by the School District of Santa Rosa County, Florida to assist in the modificat ion of the YRBS to include items that monitor an emerging trend in youth risk behavior prescription drug abuse. Currently, the YRBS contai ns no items that measure drug abuse although a large body of evidence suggests prescription drug abuse is increasing in magnitude in the U.S., particularly among young people. I have additionally been asked to analyze data obtained from the school-based survey in order to prepare a district a nd school profile. This profile will help determine where health priorities for the sc hool district lie and will serve as a needs assessment. Thus, survey results may ultimately be used to create school health programs to help reduce these risk be haviors and promote health enhancing behaviors. The purpose of this study, therefore, is twofold: (1) to first assist Santa Rosa County in modifying the YRBS to monitor an emergent tre nd relevant to adolescent healthprescription drug abuse; and (2) to provide a comprehensive assessment of priority adolescent health behaviors to the Santa Rosa community. 7. DESCRIBE THE RESEARCH METHODOLOGY IN NON-TECHNICAL LANGUAGE : This research will consist of a secondary analysis of data collected by Santa Rosa County District Schools during the fourth gr ading period, 2006. This study will take place in two stages and will include: (1) analysis of the pilot test data and (2) analysis of the final instrument administration data. Stage 1: Pilot test data analysis. The pilot test data an alysis portion of this study will serve to assess test-retest reliability of newly constructed prescription-drug related items integrated into the YRBS. Test -retest reliability is concerned with the extent to which responses on survey item s are similar when administered at two points in time. Anonymous student data collected on two occasions at Pace High School will be analyzed using the Statistic al Analysis System (SAS), version 9.1. The kappa coefficient, or Cohens K, will be calculated as an estimate of test-retest reliability. If necessary, modification of the instrument will be made based on findings from the data analysis. Stage 2: Final questionnaire data analysis. After a final draft of the modified YRBS is administered to students at Gulf Breeze High School, anonymous student data from electronic answer sheets will be scanned by an optical scanner, transferred to a data file, and then analyzed using the Statistical Analysis Sy stem (SAS), version 9.1. Descriptive statistics will first be gene rated to develop a demographic profile of the sample. Inferential sta tistics (e.g., chi-squa re and logistic regression analyses)

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203 will then be used to examine the relations hip (if any) among non-medical prescription drug use and other adolescent health risk be haviors. A report based on study findings will be generated and disseminated to stak eholders including participating schools principals, district level school health a dvisors, and law enforcement officials. 8. POTENTIAL BENEFITS AND ANTICIPATED RISK: Potential benefits include the development of new items that monitor prescription drug abuse and a contribution to the sp arse body of knowledge on the nature of prescription drug abuse among adoles cents. In addition, the high-schools participating in the survey research will each receive a final report of study results which can then be compared to local, state, and national data. Anticipated risks are minimal, as access to the secondary data source will in no way allow for identification of any student who has participates in the survey. Students who complete the survey are in structed not to place any identifying information on the survey itself. Because the survey is anonymous, there will be no way to trace survey responses back to participants. 9. DESCRIBE HOW PARTICIPANT(S) WILL BE RECRUITED, THE NUMBER AND AGE OF THE PARTICIPANTS, AN D PROPOSED COMPENSATION (If any): The first portion of this study will invo lve secondary analysis of surveys completed by approximately 200 students in the 9th through 12th grades (ages 15 to 18) at Pace High School, a large public highschool in Santa Rosa County, Florida. The second portion of this study will i nvolve secondary analysis of surveys completed by approximately 1560 students in the 9th through 12th grades at Gulf Breeze High School, another large public high-school in Santa Rosa County. No compensation has been allotted for this study. 10. DESCRIBE THE INFORMED CONSENT PROCESS. INCLUD E A COPY OF THE INFORMED CONSENT DOCU MENT (If applicable): Because the present study involves a sec ondary analysis of existing data, an informed consent documen t is not applicable. ___________________________ ___________ Principal Investigator's Signature Date Melissa C. Morris, M.P.H. ___________________________ ___________ Supervisor's Signature Date Robert M. Weiler, M.P.H., Ph.D. I approve this protocol fo r submission to the UFIRB: ___________________________ ____________ Dept. Chairs Signature Date Robert M. Weiler, M.P.H., Ph.D.

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204 APPENDIX I SANTA ROSA COUNTY SCHOOL BOARD LETTER OF INITIATION

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205

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206 APPENDIX J UNIVERSITY OF FLORIDA INTIUT IONAL REVIEW BOARD PROJECT EXEMPTION NOTICE

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207

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208 APPENDIX K PARENTAL CONSENT FORM

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209

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210 APPENDIX L TEACHER PROTOCOL FOR SU RVEY ADMINISTRATION Pace High School Health Behavior Survey Pre-Survey: March 21-23 Post-Survey: April 3-7 Introduction: These instructions detail the procedure to be followed for the administration of the Pace High School Youth Risk Behavior Survey. Please do not deviate from the procedures outlined. Participation: Please do not, under any circumstances, give a student a survey whose parents or guardian refused their par ticipation, or to a student who chooses not to participate at the time of the survey. Procedures: Week of March 21-23 IMPORTANT: PROCEED WITH THE INSTRUCTIONS AS QUICKLY AS POSSIBLE. SOME STUDENTS WILL NEED THE ENTIRE PERIOD TO COMPLETE THE SURVEY. 1. Pass out manila envelopes to all particip ating students and ask students to put away all other materials. 2. Briefly introduce the study by saying the following: Over next couple of weeks, our school is conducting a student survey. I would like to emphasize that this is not a test. This survey is about health behavior. It has been developed so you can tell us wh at you do that may affect your health. 3. After the introduction say: I have passed out an envelope to ea ch of you. Please open the envelope and check that it contains a survey booklet la beled A, a survey booklet labeled B, and two (2) Scantron sheets. Please put the survey booklet labeled B and one of the Scantron sheets back into the envel ope. Seal the envel ope and print your name on the back across the tab. Please pass the envelope you have just signed up the front of the class. You will comp lete survey B in approximately two weeks.

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211 4. Collect envelopes a place in the box provi ded. Set aside for distribution during the week of April 3-7. Students should now only be left with a survey booklet labeled A and a Scantron sheet. 5. Ask students to look at page one and follow along as you READ VERBATIM the General Instructions. 6. Then say: Again, let me say that your participa tion is voluntary. If you choose not to participate, please return your survey to me and sit quietly at your desk. Remember, do not write your name anywhere on the survey. If you do not understand a question on th e survey, raise your hand and I will assist you.* If you are unsure about how you wish to respond, continue on and then come back to that question after you have completed the rest of the survey. Read the instructions for each section of the survey carefully, starting with Section A: Demographics. The Pace High School Youth Risk Behavior Survey General Instructions This survey is about health behavior. It has been developed so you can tell us what you do that may affect your hea lth. The information you give will be used to develop better health education for young people like yourself. DO NOT write your name on this survey or the answer sheet provided. The answers you give will be kept private. No one will know what you wr ite. Answer the questions based on what you really do. Completing this survey is voluntary. Whet her or not you answer the questions will not affect your grade in this class. Ther e are no right or wrong answers. If you are not comfortable answering a que stion, just leave it blank. The questions that ask about your background will be used only to describe the types of students completing the survey. The information will not be used to find out your name. No names will ever be reported. If you have any questions concerning the survey, please contact Mrs. Sharon MyersMongue in the Santa Rosa Dist rict Schools Office at 983-5581. Make sure you read every question. Fill in the ovals on the answer sheet completely. When you are finished, follow the instructi ons of the person giving you the survey.

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212 When you are finished, put the Scantron sh eet inside your booklet and sit quietly at your desk until everyone has finished. 7. Conclude by saying: You may begin. 8. Announce to the class when there are ten mi nutes left in the administration period. At the end of the class period, collect al l of surveys. Be sure that all are accounted for, including those who didnt participate. 9. Complete the form titled CLASS DATA FORM Pace High School Health Behavior Survey, Pre-Survey: March 21-23. 10. Place the survey booklets and answer sheet s in the large envelope provided and seal in full view of the students. Make su re that the completed Class Data Form is attached. 11. A representative will come to your classr oom to collect the completed survey packet on March 23 at the end of the day. *Monitoring and Answering Questions: While the students complete the surve y, stay in front of the classroom (except to answer questions). You may answer questions about the general natu re of the survey. However, do not encourage questions and answers in front of the whole class. Students with questions s hould raise their hands When you go to the students desk to answer a question, avoid looking at anyones survey, this may arouse suspicion among some students. Do not look at a completed survey. REMEMBER: No one at the school may examine a completed survey. Do not review the surveys in the sight of students. Do not walk around the room during the survey. If a student refuses to fill out a surve y, collect their survey and ask then to sit quietly at their desk. DO NOT allow them to review a survey. Note the number of students who refused to participate on the Class Data Form. Complete the Class Data Form. Only pass out one survey packet per student. Your class packet will be picked up on the day indicated in the Procedures section

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213 Week of April 3-7 1. Pass out manila envelopes to the corres ponding students who participated in the survey two weeks prior. If a student was absent on the day of the original survey, ask them to read quietly at their desk. 2. Re-introduce the study by saying the following: I have passed back the envelopes y ou signed two weeks ago. Please break the seal and remove the envelopes cont ents. You should have a survey booklet labeled B and a Scantron sheet. Now fo ld the envelope in half. I will pass around a waste basket so you can discard only the envelopes. Make sure you keep your survey booklet and Scantron. 3. Pass around a waste paper basket so student s may throw away the envelopes. Also discard any packets be longing to students who are ab sent or who choose not to participate on the re-test day. 4. Ask students to look at page one and follow along as you READ VERBATIM the General Instructions. The Pace High School Youth Risk Behavior Survey General Instructions This survey is about health behavior. It has been developed so you can tell us what you do that may affect your hea lth. The information you give will be used to develop better health education for young people like yourself. DO NOT write your name on this survey or the answer sheet provided. The answers you give will be kept private. No one will know what you wr ite. Answer the questions based on what you really do. Completing this survey is voluntary. Whet her or not you answer the questions will not affect your grade in this class. Ther e are no right or wrong answers. If you are not comfortable answering a que stion, just leave it blank. The questions that ask about your background will be used only to describe the types of students completing the survey. The information will not be used to find out your name. No names will ever be reported. If you have any questions concerning the survey, please contact Mrs. Sharon MyersMongue in the Santa Rosa Dist rict Schools Office at 983-5581. Make sure you read every question. Fill in the ovals on the answer sheet completely. When you are finished, follow the instructi ons of the person giving you the survey.

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214 5. Then say: Again, let me say that your participa tion is voluntary. If you choose not to participate, please return your survey to me and sit quietly at your desk. Remember, do not write your name anywhere on the survey. If you do not understand a question on th e survey, raise your hand and I will assist you. If you are unsure about how you wish to respond, continue on and then come back to that question after you have completed the rest of the survey. Read the instructions for each section of the survey carefully, starting with Section A: Demographics. When you are finished, put the Scantron sh eet inside your booklet and sit quietly at your desk until everyone has finished. 6. Conclude by saying: You may begin 7. Announce to the class when there are ten mi nutes left in the administration period. At the end of the class period, collect al l of surveys. Be sure that all are accounted for, including those who didnt participate. 8. Complete the form titled CLASS DATA FORM, Pace High School Health Behavior Survey, Pre-Survey: April 3-7 9. Place the survey booklets and answer sheet s in the large envelope provided and seal in full view of the students. Make su re that the completed Class Data Form is included. 10. A representative will come to your classr oom to collect the completed survey packet on April 7 at the end of the day.

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220 Everett, S. A., Kann, L., & McReynolds, L. (1997). The Youth Risk Behavior Surveillance System: Policy and program applications. Journal of School Health 67, 333-335. Farone, S. V., & Wilens, T. (2003). Does stimulant treatment lead to substance use disorders? Journal of Clinical Psychiatry 64(Supplement 11), 9-13 Florida Department of Education. (2005). Profiles of Florida sc hool districts 2003-2004: Student and staff data Tallahassee, FL: Florida Department of Education. Retrieved October 13, 2006 from http://www.firn.edu/doe/eias/ eiaspubs/pdf/ssdata04.pdf Florida Legislature. (2006). The Florida Clean Indoor Air Act: 386.212 Smoking prohibited near school property; penalty. Tallahassee, FL: Florida Legislature. Retrieved October 21, 2006 from http://www.leg.state.fl.us /statutes/index.cfm?App_m ode=Display_Statute&Searc h_String=&URL=Ch0386/SEC212.HTM&Title=->2006->Ch0386>Section%20212#0386.212 Galaif, E. R., Chou, C., Sussmann, S., & Dent C. W. (1998). Depression, suicidal ideation, and substance use among high school students. Journal of Youth and Adolescence 27(3), 275-299. Garrison, C. Z., McKeown, R. E., Valois, R. F., & Vincent, M. L. (1993). Aggression, substance use, and suicidal beha viors in high school students. American Journal of Public Health 83(2), 179-184.

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233 Teter, C.J., McCabe, S.E., Cranford, J.A ., Boyd, C.J., & Guthrie, S.K. (2005). Prevalence and motives for illicit use of prescription stimulants in an undergraduate student sample. Journal of American College Health 53(6), 253262. Tooth, L. R., & Ottenbacher, K. J. (2004). The statistic in rehabilitation research: An examination. Archives of Physical Medicine and Rehabilitation 85, 1371-1376. Trochim, W. K. (2001). The Research Methods Knowledge Base (2nd ed.). Cincinnati, OH: Atomic Dog Publishing. U.S. Department of Health and Human Servic es (2004). The health consequences of smoking: A report of the Surgeon General. Atlanta: GA, Centers for Disease Control and Prevention, Of fice on Smoking and Health. U.S. Department of Health and Human Servic es (1996). Physical ac tivity and health: A report of the Surgeon General. Atlant a: GA, Centers for Disease Control and Prevention. U.S. Drug Enforcement Administration. (n.d.). Controlled substances act. Washington, DC: U.S. Department of Justice. Retrieved November 8, 2005 from http://www.usdoj.gov/dea/agency/csa.htm U.S. Food and Drug Administration (2005). New drug approval reports. Rockville, MD: Center for Drug Evaluation and Resear ch. Retrieved September 10, 2005 from http://www.fda.gov/cder/rdmt/default.htm Valois, R. F, MacDonald, J. M., Bretous, L., Fisher, M. A., & Drane, J. W. (2002). Risk factors and behaviors associ ated with adolescent vi olence and aggression. American Journal of Health Behavior 26(6), 454-464.

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236 BIOGRAPHICAL SKETCH Melissa Christina Morris was born on December 23, 1979 in Miami, Florida. She grew up in Miami and graduated from Coral Gables Senior High Sc hool in 1998. As an undergraduate at Wellesley College (Ma ssachusetts), Melissa double-majored in Neuroscience and Spanish. She also studied abroad for a semester in Madrid, Spain at the Universidad Complutense de Madrid dur ing her undergraduate tenure. Melissa graduated with honors from Wellesley in 2002. Shortly after college graduation, Melissa began her graduate studies at the University of Florida. She received her masters degree in p ublic health with a specialization in community he alth education in the summer of 2003. In the fall of 2003, Melissa was accepted into the doctoral program in the Department of Health Education & Behavior to pursue a Ph.D. in health behavi or. She was the recipient of an Alumni Gradate Fellowship Award, the highest award offered by the University of Florida to incoming graduate students. Throughout her graduate studies, Melissa maintained an active academic agenda. She was involved in several research projects that culminated in published abstracts, presentati ons at national meetings, and manuscripts. She is active in several professional organizations and is a Ce rtified Health Education Specialist. In 2004, Melissa was awarded the Outstanding Gra duate Student Award by the Department of Health Education & Behavior. She wa s also the recipient of the 2006 Delbert Oberteuffer Scholarship presented by the Amer ican Association for Health Education.

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237 Melissa joined the faculty of the Depart ment of Health, Leisure, and Exercise Science at the University of West Florida (UWF) during the fall of 2006. Upon graduation she will continue to teach c ourses in health education at UWF.


Permanent Link: http://ufdc.ufl.edu/UFE0017268/00001

Material Information

Title: Measuring Non-Medical Prescription Drug Use and Associated Risk-Taking Behaviors among Adolescents
Physical Description: Mixed Material
Copyright Date: 2008

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Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
System ID: UFE0017268:00001

Permanent Link: http://ufdc.ufl.edu/UFE0017268/00001

Material Information

Title: Measuring Non-Medical Prescription Drug Use and Associated Risk-Taking Behaviors among Adolescents
Physical Description: Mixed Material
Copyright Date: 2008

Record Information

Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
System ID: UFE0017268:00001


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Table of Contents
    Title Page
        Page i
        Page ii
    Acknowledgement
        Page iii
        Page iv
    Table of Contents
        Page v
        Page vi
        Page vii
    List of Tables
        Page viii
        Page ix
        Page x
        Page xi
    List of Figures
        Page xii
    Abstract
        Page xiii
        Page xiv
    Introduction
        Page 1
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    Review of the literature
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    Materials and methods
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    Results
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    Summary, discussion, implications, and conclusion
        Page 129
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    Appendix A: Santa Rosa County final questionnaire
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    Appendix B: List of expert panel reviewers for instrument assessment
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    Appendix C: Expert panel cover letter
        Page 172
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    Appendix D: Abstract of the proposed study
        Page 174
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    Appendix E: Preliminary items for expert panel review
        Page 177
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    Appendix F: Expert panel review item assessment survey
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    Appendix G: Expert panel comments for revisions
        Page 196
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    Appendix H: Application to the University of Florida Institutional Review Board
        Page 200
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    Appendix I: Santa Rosa County School Board letter of initiation
        Page 204
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    Appendix J: University of Florida Institutional Review Board project exemption notice
        Page 206
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    Appendix K: Parental consent form
        Page 208
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    Appendix L: Teacher protocol for survey administration
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    References
        Page 215
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    Biographical sketch
        Page 236
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Full Text












MEASURING NON-MEDICAL PRESCRIPTION DRUG USE AND ASSOCIATED
RISK-TAKING BEHAVIORS AMONG ADOLESCENTS












By

MELISSA CHRISTINA MORRIS


A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE
UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE
REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA


2006




























Copyright 2006

By

Melissa Christina Morris









ACKNOWLEDGEMENTS


To be a philosopher is not merely to have subtle thoughts, nor even to found a school,
but so to love wisdom as to live according to its dictates, a life of simplicity,
independence, magnanimity, and trust.
Henry David Thoreau, Walden, 1854


I would like to express my sincere gratitude for all of the assistance and support I

have received from my family, friends and professors, throughout my tenure as a doctoral

student- especially during the time I was writing this dissertation.

I would first like to thank Dr. Robert M. Weiler for serving as my academic

advisor, dissertation chair, mentor, and friend. It was Dr. Weiler that helped instill in me

a sense of self-confidence in my research abilities and provided me with the guidance to

see this project through to the end. His encouragement and enthusiasm for research and

scholarship have undoubtedly made for a rewarding academic experience over the past

three years. I am deeply indebted not only to Dr. Weiler but to my entire dissertation

committee for creating a supportive learning environment throughout the dissertation

process- Dr. R. Morgan Pigg, Jr., Dr. Virginia J. Dodd, Dr. Dennis Thombs, and Dr.

David Miller. Their direction and thoughtful criticism throughout the inception, review,

and revision of my dissertation are greatly appreciated.

I am grateful to the members of my expert panel for taking the time to carefully

examine the items constructed for my research and evaluate them for content validity. In

particular, I would like to thank Dr. David Haddox-Vice President of Risk Management

and Health Policy at Purdue Pharma- for his extended conversation regarding

modification of the items.









I wish to acknowledge the individuals in Santa Rosa County who made it possible

for me to conduct my study. I would especially like to thank Mrs. Sharon Meyers-

Mongue, for helping me coordinate and execute my project. For her assistance, along

with the cooperation of the school principals, teachers, and students involved with the

study, I extend my heartfelt thanks.

Throughout my education, I have encountered numerous other individuals who

helped shape my experiences as a student, researcher, and professional. My graduate

studies would not have been the same without the social and academic challenges and

diversions provided by my student-colleagues, the staff, and professors who comprise the

Department of Health Education and Behavior. Special thanks go to my fellow doctoral

students and friends, Erin and Jeff Wight, for opening up their home to me on my jaunts

to Gainesville during my last semester as a doctoral student.

I want to especially thank my mother and father who have always showered me

with emotional guidance, love, and support to pursue my interests. Their unwavering

faith in my abilities has instilled in me a conviction that I can accomplish almost

anything. Thanks go to my big brother, Ed, who has also provided me with support and

understanding during the long years of my education.

Finally, I wish to thank my best friend and my voice of reason, Doug, for his

compassion, endless patience, and encouragement when it was needed the most.
















TABLE OF CONTENTS



A C K N O W L E D G E M E N T S ............................................................................................... iii

LIST OF TABLES .......................................... viii

LIST OF FIGURES ......................................... .................................. xii

A B S T R A C T .................................................................................................... ........ .. x iii

CHAPTER

1 IN TR OD U CTION TO TH E STU D Y ...................................................... ...............1...

Statem ent of the R research Problem ......................................................... ...............2...
Purpose of the Study .......................... .. ........... ...................................... .. 7
Significance of the Study ................................................................... .... ......... ..8
Research Questions ...................... ........... ................................ 9
D elim itatio n s.............................................................................................................. 10
L im itatio n s ............................................................................................................... .. 1 0
A ssu m p tio n s ............................................................................................................... 1 1
Definition of Terms ............. .. .............................. ........ ..... ............... 11

2 REV IEW OF THE LITERA TURE .......................................................... ............... 15

N onm medical Prescription Drug U se Defined ......................................... ............... 15
Classes of Prescription Drugs Used Nonmedically .............................................. 18
O p io id s ........................................................................................................... . 1 8
S tim u la n ts ............................................................................................................ 2 0
Depressants ................ ...... ... ..... ................................. 21
Epidemiology of Nonmedical Prescription Drug Use...........................................23
In itia tio n .............................................................................................................. 2 4
P re v a le n c e ............................................................................................................ 2 5
Subgroup T rends ............................................ .. ....................... ... .... ........... 27
Indicators of the Problem 's Seriousness......................................... ................ 28
The Youth Risk Behavior Survey................................................ ................... 30
Correlates of Adolescent Prescription Drug Abuse ..............................................35
T o b a c c o U se ........................................................................................................ 3 5
Alcohol U se and Binge Drinking .................................................. 37
Marijuana Use ...................... ............ ............................. 38


v









O their Illicit D rug U se ............................................................. .. .................. .. 38
Driving Under the Influence of Alcohol ........................................................41
V violence R elated B ehaviors ........................................................... ................ 42
Sexual Experience and Risk Taking.....................................................44
D expression and Suicide Ideation .................................................... ................ 45
D ecreased Physical A activity ........................................................... ................ 46
Use of Problem Weight Loss Practices ............... ....................................48

3 MATERIALS AND METHODS .......................................................................... 50

R e search D esig n ......................................................................................................... 5 0
Research Variables ............................................. ........ ...... ...............52
S tu dy P o p u latio n ......................................................................................................... 5 3
In stru m en tatio n ........................................................................................................... 54
Item D evelopm ent ............. .. ............... .............................................. 56
C content V validity .......................................................................................... 58
H um an Subjects ... ... ........................................... ....................... .... ......... 63
P ilo t S tu d y ........................................................................................................... 6 3
Item R liability ........................................................................................... 68
D ata C o llectio n ........................................................................................................... 7 1
D ata A n aly sis .............................................................................................................. 7 2
S u m m a ry ..................................................................................................................... 7 4

4 R E S U L T S ................................................................................................................. .. 7 5

P articipant D em ographics...................................................................... ................ 75
Profile of Non-Medical Prescription Drug Use Behaviors.................................... 77
Chi-Square Analysis ..... ............................. ....... ................... 83
R research Q question O ne.. ..................................................................... ................ 86
R research Q question Tw o ...................................................................... ................ 9 1
R research Q question T hree ........................................... ......................... ................ 97
R research Q question Four........................................ ......................... ............... 102
Research Question Five ................................................................................ 108
Research Question Six .................................................................................. 114
Research Question Seven ............................................................................. 119
R research Q question E ight....................................... ......................... ............... 124
S u m m a ry ............................................................................................................... ... 12 7

5 SUMMARY, DISCUSSION, IMPLICATIONS, AND CONCLUSION ..............129

S u m m a ry ................................................................................................................... 1 2 9
D isc u ssio n ............................................................................................................. .. 1 3 3
Im p licatio n s .............. .... ... ................................................................................ 14 9
Recommendations for Research...... .... ...... ..................... 150
Recommendations for Practice...... ........ ...... ..................... 152
C o n c lu sio n ............................................................................................................... 1 5 4









APPENDIX

A SANTA ROSA COUNTY FINAL QUESTIONNAIRE...................................156

B LIST OF EXPERT PANEL REVIEWERS FOR INSTRUMENT ASSESSMENT 170

C EXPERT PANEL EMAIL COVER LETTER .............................. ..................... 172

D ABSTRACT OF THE PROPOSED STUDY...... .... .................................... 174

E PRELIMINARY ITEMS FOR EXPERT PANEL REVIEW................................ 178

F EXPERT PANEL REVIEW ITEM ASSESSMENT SURVEY ..............................181

G EXPERT PANEL COMMENTS FOR REVISIONS .................... ..................... 196

H APPLICATION TO THE UNIVERSITY OF FLORIDA INSTITUTIONAL
R E V IE W B O A R D .................................................. ............................................ 200

I SANTA ROSA COUNTY SCHOOL BOARD LETTER OF INITIATION.........204

J UNIVERSITY OF FLORIDA INTIUTIONAL REVIEW BOARD PROJECT
EX EM PTIO N N O TICE ........................................ ......................... ............... 206

K PARENTAL CONSENT FORM ...... ............ .......... ...................... 208

L TEACHER PROTOCOL FOR SURVEY ADMINISTRATION..........................210

REFERENCES ................................ .. ........... .....................................215

BIOGRAPH ICAL SKETCH .................. .............................................................. 236















LIST OF TABLES


Table page_

1. Benchmarks for Interpretation of the Kappa Statistic (Landis & Koch, 1977)........68

2. The Santa Rosa County Health Behavior Survey Pilot Study: Measures of Test-
Retest Reliability for Prescription Drug Abuse Items Using Kappa (K) Statistics ..70

3. Santa Rosa County Health Behavior Survey: Participation Rate by High School...76

4. Santa Rosa County Health Behavior Survey: Demographic Characteristics of
Participants (expressed in percentages)*............................................. ................ 77

5. Santa Rosa County Health Behavior Survey: Lifetime, Past 30 Day, and Past 30
Day Non-Medical Use on School Property of any Prescription Drug ..................78

6. Santa Rosa County Health Behavior Survey: Measures of Non-Medical
Prescription Drug Use Prevalence by Drug Class (expressed in frequencies and
p erc en tag e s) .............................................................................................................. 7 9

7. Lifetime, 30-Day, and 30-Day on School Property Prevalence of Selected Drug
Classes (expressed in percentages) ..................................................... ................ 80

8. Santa Rosa County Health Behavior Survey: Self-Reported Age at First Use,
Drug Source, and Motivations for Non-Medical Use by Drug Class (expressed
in frequencies and percentages) .......................................................... ................ 82

9. Chi-square Analysis of Demographic Variables by Lifetime, Past 30 Day, and
Past 30 Day on School Property Prevalence of any Non-Medical Prescription
D ru g U se ................................................................................................................. 8 4

10. Chi-square Analysis of Demographic Variables by Lifetime, Past 30 Day, and
Past 30 Day on School Property Prevalence of Non-Medical Prescription Pain
R reliever U se ............................................................................................................. 8 4

11. Chi-square Analysis of Demographic Variables by Lifetime, Past 30 Day, and
Past 30 Day on School Property Prevalence of Non-Medical Prescription
D ep ressa n t U se ........................................................................................................ 8 5









12. Chi-square Analysis of Demographic Variables by Lifetime, Past 30 Day, and
Past 30 Day on School Property Prevalence of Non-Medical Prescription
S tim u la n t U se ........................................................................................................... 8 5

13. Odds of Lifetime, Past 30 Day, and Past 30 Day on School Property Prevalence
of Prescription Drug Abuse by Demographic Characteristics ...............................86

14. Illicit Substance Use Behaviors Associated with Lifetime and Past 30-Day Non-
M medical Use of Prescription any Prescription Drug............................ ................ 87

15. Illicit Substance Use Behaviors Associated with Lifetime and Past 30-Day Non-
M medical Use of Prescription Pain Relievers ....................................... ................ 89

16. Illicit Substance Use Behaviors Associated with Lifetime and Past 30-Day Non-
M medical U se of Prescription D epressants ........................................... ............... 90

17. Illicit Substance Use Behaviors Associated with Lifetime and Past 30-Day Non-
M medical U se of Prescription Stim ulants.............................................. ............... 91

18. Tobacco Use Behaviors associated with Lifetime and Past 30-Day Non-Medical
U se of any P description D rug .............................................................. ................ 93

19. Tobacco Use Behaviors Associated with Lifetime and Past 30-Day Non-Medical
U se of Prescription Pain Relievers...................................................... ............... 94

20. Tobacco Use Behaviors Associated with Lifetime and Past 30-Day Non-Medical
U se of Prescription D epressants ......................................................... ............... 95

21. Tobacco Use Behaviors Associated with Lifetime and Past 30-Day Non-Medical
U se of Prescription Stim ulants ............................................................ ................ 96

22. Alcohol Use Behaviors Associated with Lifetime and Past 30-Day Non-Medical
U se of any P description D rug .............................................................. ................ 97

23. Alcohol Use Behaviors Associated with Lifetime and Past 30-Day Non-Medical
U se of Prescription Pain Relievers...................................................... ............... 99

24. Alcohol Use Behaviors Associated with Lifetime and Past 30-Day Non-Medical
Use of Prescription Depressants ....... ....... ........ ..................... 100

25. Alcohol Use Behaviors Associated with Lifetime and Past 30-Day Non-Medical
U se Prescription Stim ulants ........................................................ 101

26. Sexual Risk Behaviors Associated with Lifetime and Past 30-Day Non-Medical
U se of any Prescription D rug ....... .......... ............ ..................... 103

27. Sexual Risk Behaviors Associated with Lifetime and Past 30-Day Non-Medical
Use of Prescription Pain Relievers...... .... ...... ..................... 105









28. Sexual Risk Behaviors Associated with Lifetime and Past 30-Day Non-Medical
Use of Prescription Depressants ....... ....... ........ ..................... 106

29. Sexual Risk Behaviors Associated with Lifetime and Past 30-Day Non-Medical
U se of Prescription Stimulants ....... ........... ............ ..................... 107

30. Violence-Related Behaviors Associated with Lifetime and Past 30-Day Non-
M medical Use of any Prescription Drug....... ... ......................................... 110

31. Violence-Related Behaviors Associated with Lifetime and Past 30-Day Non-
M medical U se of Prescription Pain Relievers ..................................... ...................111

32. Violence-Related Behaviors Associated with Lifetime and Past 30-Day Non-
M medical Use of Prescription Depressants ....... ... ................... .................. 112

33. Violence-Related Behaviors Associated with Lifetime and Past 30-Day Non-
M medical Use of Prescription Stimulants....... ... ........................................ 113

34. Feelings of Depression and Suicide Ideation Associated with Lifetime and Past
30-Day Non-Medical Use of any Prescription Drug................... .................. 115

35. Feelings of Depression and Suicide Ideation Associated with Lifetime and Past
30-Day Non-Medical Use of Prescription Pain Relievers ............................... 116

36. Feelings of Depression and Suicide Ideation Associated with Lifetime and Past
30-Day Non-Medical Use of Prescription Depressants.................................. 117

37. Feelings of Depression and Suicide Ideation Associated with Lifetime and Past
30-Day Non-Medical Use of any Prescription Stimulants ................................118

38. Indicators of Physical Inactivity Associated with Lifetime and Past 30-Day Non-
M medical Use of any Prescription Drug....... ... ......................................... 120

39. Indicators of Physical Inactivity Associated with Lifetime and Past 30-Day Non-
M medical Use of Prescription Pain Relievers ..................................... ............... 121

40. Indicators of Physical Inactivity Associated with Lifetime and Past 30-Day Non-
M medical Use of Prescription Depressants ....... ... ................... .................. 122

41. Indicators of Physical Inactivity Associated with Lifetime and Past 30-Day Non-
M medical Use of Prescription Stimulants ....... ... ........................................ 123

42. Problem Weight Loss Strategies Associated with Lifetime and Past 30-Day
Non-M medical Use of any Prescription Drug....... ... ..................................... 124

43. Problem Weight Loss Strategies Associated with Lifetime and Past 30-Day
Non-M medical Use of Prescription Pain Relievers ....................... .................. 125









44. Problem Weight Loss Strategies Associated with Lifetime and Past 30-Day
Non-M medical Use of Prescription Depressants....... ................... .................. 126

45. Problem Weight Loss Strategies Associated with Lifetime and Past 30-Day
Non-M medical Use of Prescription Stimulants....... ... .................................... 127
















LIST OF FIGURES


Figure


page


1. Instructions and Items Regarding Prescription Pain Relievers Used for the
E xpert P anel R eview .. ...................................................................... ................ 58

2. Screen Capture of the Web-based Expert Panel Review Form...............................60















Abstract of Dissertation Presented to the Graduate School of the University of Florida in
Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy.

MEASURING NON-MEDICAL PRESCRIPTION DRUG USE AND ASSOCIATED
RISK-TAKING BEHVIORS AMONG ADOLESCENTS

By

Melissa Christina Morris

December 2006

Chair: Robert M. Weiler
Major: Health and Human Performance

Although there is considerable research on adolescent substance use, limited data

exist on the non-medical use and abuse of prescription drugs. The purposes of this

investigation were to design reliable and valid measures of non-medical prescription drug

use to be incorporated into a comprehensive health risk behavior assessment and to

describe the relationships between non-medical prescription drug use and additional

health risk behaviors. The study's methodology took place in three stages: 1) the design,

expert panel review, and revision of instrument items, 2) the pilot testing of these items

among a convenient sample of 209 students in grades 9-12 at a Northwest Florida Public

High School, and 3) the administration of the final questionnaire to 485 12th grade

students at two Northwest Florida High Schools (different from the pilot study). Results

of the study revealed that lifetime prevalence of non-medical use of any prescription drug

use was relatively high among participants at 31.9%. When reported by class of abusable

prescription drug, participants most readily reported having used pain relievers non-









medically in their lifetime (28.6%), followed by depressants (12.7%) and stimulants

(9.0%).

Multiple logistic regression analyses were used to assess the covariance of non-

medical prescription drug use with selected health risk behaviors relevant to adolescents.

Results indicated that non-medical prescription drug use was significantly associated with

multiple health risk behaviors including tobacco, alcohol, and illicit drug use; violence;

depression and suicide ideation; sexual risk-taking; and problem weight loss strategies.

These findings suggest that non-medical prescription drug use behaviors are not isolated

events in the lives of youth. Rather, the behaviors covary with other health risk behaviors

suggesting that non-medical prescription drug use is linked to a syndrome of problem

behavior. The study's findings may be applied to improve youth risk behavior

surveillance systems and to better inform policy, prevention, and treatment initiatives

related to the non-medical use and abuse of prescription drugs.














CHAPTER 1
INTRODUCTION TO THE STUDY

Throughout the history of modern medicine, prescription drugs have made

tremendous positive changes in the health of the United States and the world, and today

they play an increasingly important role in restoring, maintaining, and improving health

and well-being. Additionally, for the millions of Americans that rely on prescription

drugs for therapeutic purposes, they can lead to dramatic improvements in the quality of

life.

In 2003, 3.4 billion prescriptions were filled in the U.S., a 70% increase in

number of prescriptions filled just 10 years prior (2.0 billion). When compared to the

U.S. population growth of 13% during that same time period, the average number of

prescriptions per capital increased from 7.8 to 11.8 (Kaiser Family Foundation, 2004).

Clearly, the use of prescription medication has increased in the U.S. as a result of

demands by an emerging older population that relies on drug therapy to stay healthy, and

to advances in medical technology which have led to hundreds of new drug approvals by

the U.S. Food and Drug Administration (FDA) in the past 10 years (U.S. Food and Drug

Administration [U.S. FDA], 2005).

With an understanding that indicated prescription drug use generally improves

health, most people who take prescription drugs take them responsibly. National general

population surveys of prescription drug use consistently find that the vast majority of

prescribed use of psychotherapeutic drugs is conservative, therapeutically appropriate,

and generally limited to short periods of time. Furthermore, when taken properly for









medical purposes, the benefit-to-risk ratio of for these substances is generally positive

with little evidence that abusive use of prescribed drugs is common (Peterson, 1993).

Yet, when abused or used for non-medical purposes, many prescription drugs can

have effects that hinder, rather than enhance lives. The non-medical use of prescription

drugs is defined as using a prescription drug that was not prescribed for you or that was

taken only for the experience or feeling it caused (Substance Abuse and Mental Health

Services Administration [SAMHSA], 2004a). According to the National Institute on

Drug Abuse (NIDA), the non-medical use or abuse of prescription drugs is a "serious

public health concern" (2001b, p. 1) and several indicators suggest that it is increasing in

magnitude in the U.S., particularly among young people (Johnston, O'Malley, Bachman,

& Schulenburg, 2005b).

Just as with the abuse of alcohol or illicit drugs such as marijuana, cocaine, or

heroin, prescription drug abuse and non-medical use may lead to addiction, serious

financial, emotional, social, and legal problems, and in extreme cases may lead to

disability or death (Zacny et al., 2003). Although prescription drug abuse is not a new

phenomenon in this country, it is one that deserves renewed research attention. This

investigation will increase the understanding of non-medical prescription drug use among

adolescents and, enhance surveillance measures currently available to monitor this type

of drug use among youth.

Statement of the Research Problem

While the rates of illicit drug, alcohol, and tobacco use have declined among teens

in recent years, the non-medical use and abuse of controlled prescription drugs has been

steadily rising (Johnston et al., 2005b). When placed into perspective relative to other

types of youth drug abuse, the prevalence of non-medical prescription drug use ranks









fourth, after alcohol, cigarettes, and marijuana respectively (Johnston et al., 2005b).

While many types of prescription drugs can be abused, the three major classes of drugs

used by youth for their mood altering properties include opioid pain relievers, central

nervous system (CNS) stimulants, and CNS depressants. According the National Survey

of Drug Use and Health (SAMHSA, 2004a), between 1992 and 2003 the rate of non-

medial use of controlled prescription drugs among 12- to 17- year olds increased 212

percent. Additionally, new use of prescription opioids among teens is up 542 percent,

more than four times the rate of increase among adults (SAMHSA, 2004a).

Indicators of the problem's seriousness include drug-related emergency

department visits and mortality. The DA WNReport (SAMHSA, 2003b), a publication

monitoring drug-related emergency department hospital visits and deaths, noted that

benzodiazepines, analgesics, and antidepressants represented nearly 30% of drugs

referenced in emergency department reports in 2001. Yet well-known illicit drugs (e.g.,

cocaine, heroin, and marijuana) continue to receive most of the research attention in drug

abuse research and remain the primary focus of most prevention efforts.

Furthermore, according to the Florida Office of Drug Control, one-third of all drug-

related deaths in the state during 2002 were the result of prescription drug abuse (Chun,

2003).

Adolescence is a developmental period of rapid physical, psychological,

sociocultural, and cognitive changes (DiClemente, Hansen, & Ponton, 1996). While it

marks the transition from childhood to adulthood, it is characterized by efforts to confront

and surmount challenges and to establish a sense of identity and autonomy. It also

remains a period associated with high rates of substance abuse, including that involving









prescription drugs due to biological and environmental factors (e.g., psychosocial, family,

peer) (Kaminer & Bukstein, 1998).

Both theory and research suggest that drug taking behaviors are not isolated

events in the lives of adolescents. Problem Behavior Theory (Jessor & Jessor, 1977)

contends that adolescent problem behaviors (i.e., alcohol, marijuana abuse, delinquency,

precocious sexual activity) are associated with one another and are inversely related to

conventional behaviors (i.e., church attendance, school performance) (Donovan, Jessor,

& Costa 1988, 1991). The theory postulates that the covariation of problem behaviors is

related to an underlying common factor that reflects "unconventionality," and represents

a "syndrome" of problem behavior (Donovan & Jessor, 1985). While the present study

will not attempt to explain the underlying causes or antecedents of prescription drug

abuse, the idea that risk behaviors tend to cluster in vulnerable adolescents is the

grounding framework for this study.

Data from national population surveys of drug use suggest that teens who use

prescription drugs for recreational purposes are twice as likely to use alcohol, five times

more likely to use marijuana, 12 times more likely to use heroin, 15 times more likely to

use ecstasy, and 21 times more likely to use cocaine, compared to teens who do not use

such drugs (SAMHSA, 2004a). National data also report that high school seniors who

use prescription opioids for their psychoactive effects are more likely to be involved in

other deviant behaviors (i.e., school expulsion, skipping school, bringing drugs to school,

getting high at parties, having friends who use marijuana) (McCabe, Boyd, & Teter,

2005). Studies conducted with smaller samples confirm national reports that prescription

adolescents using prescription drugs non-medically report higher rates of cigarette









smoking, alcohol, marijuana, ecstasy, and cocaine use, and other problem behaviors that

lead to unintentional injury (i.e., drove after binge drinking, passenger with a drunk

driver) (Boyd, McCabe, & Teter, 2006; McCabe & Boyd et al., 2005).

Findings from empirical studies suggest that adolescent risk behaviors are

correlated; that is, engaging in one behavior may indicate an increased likelihood for

engaging in other behaviors or patterns of risk behavior (Dryfoos, 1990; Jessor, Donovan,

& Costa, 1991). Specifically, numerous studies confirm that substance abusing behaviors

in adolescence are not only related to each other (i.e., tobacco, alcohol, and illicit

substance abuse) (Abdel-Ghany & Wang, 2003; Everett, Giovino, Warren, Crossett, &

Kann, 1998) but have also been correlated with behaviors that put adolescents at risk for

unintentional injury (McCabe, 2005; McCabe, Teter, & Boyd, 2004), aggressive and

violent behaviors (Orpinas, Basen-Engquist, Grunbaum, & Parcel, 1995; Valois,

McKeown, Garrison, & Vincent, 1995; Silverman, Raj, Mucci, & Hathaway, 2001),

decreased physical activity (Pate, Heath, Dowda, & Trost, 1996; Winnail, Valois,

McKeown, Saunders, & Pate, 1995), problem weight loss tactics (Neumark-Sztainer,

Story, Dixon, & Murray, 1998; Rafiroiu, Sargent, Parra-Medina, Drane, & Valois, 2003),

sexual experience and risk taking (Shrier & Crosby, 2003; Shrier, Emans, Woods, &

DuRant, 1996; Valois, Oeltmann, Waller, & Hussey, 1999) and depression/suicide

ideation (Barrios, Everett, Simon, & Brenner, 2000; Garrison, McKeown, Valois, &

Vincent, 1993). Collectively, these facts logically suggest that prescription drug abuse, a

subset of substance abuse, may also be related to a "syndrome" of risk taking.

However, because items related to non-medical prescription drug use are not

present on surveys assessing multiple risk behaviors (i.e., CDC's Youth Risk Behavior









Survey [YRBS]) we know little about whether this type of drug use is also associated

with other risk behaviors leading to social and health problems in adolescents. Although

substance use correlates of recreational prescription drug use have been examined

(McCabe, Teter, & Boyd, 2005, 2004; McCabe, Teter, Boyd, & Guthrie, 2004), no

previous research has been conducted to investigate relationships-if any-prescription

drug abuse may have with additional adolescent risk behaviors such as involvement in

violence, risky sexual behavior, depression and suicide ideation, physical inactivity, or

problem weight loss practices.

Furthermore, when non-medical prescription drug use behaviors and their

correlates are reported in research studies, generally only one class of drugs is reported at

a time (i.e., opioids, stimulants, or depressants) ((McCabe, Teter, & Boyd, 2005, 2004;

McCabe, Teter, Boyd, & Guthrie, 2004). While the unauthorized use of each of these

classes of drugs can compromise health, they each have unique effects on the nervous

system (NIDA, 2001b) and differ with respect to peer norms regarding the drugs, beliefs

about the dangers of the drugs, and perceived availability by adolescents (Johnston et al.,

2005b). No previous studies have made an attempt to report the risk behavior correlates

of all three classes of abusable prescription drugs concurrently. The existing body of

knowledge would therefore be enhanced if a study were to report all three classes of

prescription drugs when assessing co-morbidity so that differential levels of risk can be

considered.

The YRBS is one of three federally funded surveys assessing adolescent drug use

prevalence; Monitoring the Future (MTF), and the National Survey of Household Drug

Use and Health (NSDUH) are the others. Although the design, implementation, and









documentation of all three surveys have been deemed of high quality (Cowan, 2001), the

YRBS is the only one that does not include measures of non-medical prescription drug

use (other than steroid use), although psychoactive prescription drug abuse is a major

contributor to drug-related morbidity in teens. Adding non-medical prescription drug use

items to the YRBS will only serve to enhance the federal surveillance methods used to

estimate prevalence and trends of substance use among youth.

This study will expand upon the CDC's YRBS- one of the most widely used

surveys monitoring behaviors which place youth at risk for injury and disease- by

adding items that measure non-medical prescription drug use. By assessing the

prevalence and correlates of this type of drug use among adolescents, the study may

provide policy-makers, health behavior researchers, and health educators with a better

understanding of the scope of non-medical prescription drug use.

Purpose of the Study

This investigation examined the relationship between non-medical prescription

drug use and other risk behaviors among adolescents using a modified version of the

YRBS, the Santa Rosa County Health Behavior Survey. The purposes of this study were

to (1) design reliable and valid measures of adolescent prescription drug use to be

incorporated into the YRBS; (2) describe the relationships between non-medical

prescription drug use and other adolescent risk behaviors; (3) improve monitoring and

tracking systems; (4) raise awareness among public health professionals about risks of

non-medical prescription drug use among adolescents; (5) inform policy, prevention, and

treatment measures related to non-medical prescription drug use and abuse; and (6)

encourage inclusion of prescription drug items on surveys that monitor drug use and

youth risk behaviors.









Significance of the Study

Although there is considerable research on adolescent substance use, limited data

exist on the non-medical use of prescription drugs. To better inform policy, prevention,

and treatment initiatives, it has been suggested that national surveys of drug use should

include specific items that measure prescription drug use and abuse behaviors (National

Center on Addiction and Substance Abuse [NCASA], 2005). One such national survey is

the CDC's YRBS (Centers for Disease Control and Prevention [CDC], 2005). By

adding items relating to non-medical prescription drug use to the YRBS, federal agencies

will be able to inform policy by improving monitoring and tracking systems.

Healthy People 2010 (Office of Disease Prevention and Health Promotion

[ODPHP], 2000) a report of the nation's health objectives for the coming decade,

dedicated a chapter to substance abuse as a leading health indicator, identifying the

nation's goal as to: "Reduce substance abuse to protect the health, safety, and quality of

life for all, especially children" (p. 26-3). However, in outlining the issues and trends, the

report highlights alcohol first, and then turns to "illicit drugs," identifying marijuana,

cocaine, heroin, acid, inhalants, and methamphetamine, in arguing the serious

consequences of substance use and abuse. Although Healthy People 2010 (ODPHP,

2000) includes objectives related to reducing adverse consequences of substance use

(e.g., drug-induced deaths, drug-related hospital emergency department visits) and

identifies "substance-free youth" (p. 26-11) as a goal, prescription drugs are never

explicitly identified as part of the problem. Considering prescription drugs are the third

most commonly used class of psychoactive drugs for recreational purposes among

adolescents, the findings from this investigation may alert public health agencies to the

importance of including prescription drug abuse measures on federally funded surveys.









As a precursor to making associations between variables of interest on a survey, it

is necessary to first establish validity and reliability of the items assessing those variables.

The primary importance of this study lies in the assertion that it will generate items

providing adequate estimates of prescription drug use behaviors among adolescents.

Furthermore, this study is informed by Problem Behavior Theory, which provides

evidence for a "syndrome" of problem behavior (Jessor et al., 1991; Jessor & Jessor,

1977). The present study will also help provide additional evidence for the concept that

risk taking behaviors do not occur in isolation and that prescription drug abuse may be

part of a constellation of risk taking among adolescents. Only after fully understanding

the scope and severity of non-medical prescription drug use among adolescents will

health education and prevention specialists be able to tailor interventions that address

prescription drugs as part of the larger problem of drug use and abuse.

Research Questions

The eight research questions for this study were formulated for a sample of 12th

grade students. They included:

1. Is there a relationship between illicit drug use (i.e., marijuana, cocaine, inhalants,
heroin, methamphetamine, ecstasy, and steroids) and non-medical prescription
drug use?

2. Is there a relationship between tobacco use and non-medical prescription drug
use?
3. Is there a relationship between alcohol use and non-medical prescription drug
use?

4. Is there a relationship between sexual risk behaviors and non-medical prescription
drug use?

5. Is there a relationship between violence-related behaviors and non-medical
prescription drug use?

6. Is there a relationship between depression and suicide ideation and non-medical
prescription drug use?










7. Is there a relationship between decreased physical activity and non-medical
prescription drug use?

8. Is there a relationship between the use of problem weight loss tactics and non-
medical prescription drug use?

Delimitations

The following delimitations should be considered when interpreting the results

from this investigation:

1. This study enlisted a cross-sectional census survey design using a group
administered paper-pencil questionnaire.

2. Participants in this study included 12th grade students enrolled in two senior high
schools during the spring of 2006.

3. Participants included 12th grade students attending class on the day the survey
was administered in the selected schools during the spring of 2006.

4. Participants included 12th grade students from the study population who
voluntarily agreed to participate in the study.

5. Participants included only 12th grade students who obtained passive parental
consent.

6. Participants were able to read and understand the directions, the questions, and the
respective response options necessary to complete the questionnaire.

7. Findings from this study were based on self-report data collected by using the
Santa Rosa County Health Behavior Survey.

Limitations

The following limitations should be considered when interpreting the results from

this investigation:

1. Data collected from cross-sectional survey designs only reflect responses from
participants at a specific point in time and therefore cannot establish causation.

2. Findings from this study cannot be generalized to other populations of
adolescents.









3. Students who were absent on the day of data collection were excluded from the
study.

4. Students who did not voluntarily agree to participate were excluded from the
study.

5. Students who do not obtain passive parental consent were excluded from the
study.

6. Variations in student reading ability and comprehension may have affected study
results.

7. The study design relied on self-reported data, limiting the ability to determine the
extent of over-reporting or under-reporting data.

Assumptions

For the purposes of this study, the following assumptions were made:

1. The students who participated in the study adequately represented the students
enrolled in the schools.

2. The students who participated in the study were able to read and understand the
directions, the questions, and provide the response options necessary to complete
the questionnaire.

3. The students who agreed to participate in the study completed the Santa Rosa
County Health Behavior Survey honestly.

4. The Santa Rosa County Health Behavior Survey was a valid and reliable
instrument for collecting health behavior data from adolescents.

5. The research design was considered appropriate for the purpose of the study.

Definition of Terms

Addiction: A chronic, relapsing disease, characterized by compulsive drug seeking and

use and by neurochemical and molecular changes in the brain. (NIDA, 2001b)

Adolescence: The period of transition from late childhood to early adulthood

corresponding roughly to the period between the ages of 10 and 19. (World Health

Organization [WHO], 1986)









Adolescent: In this study, twelfth grade students, with an approximate age range of 17-

18, were referred to as adolescents.

Barbiturates: A sub-class of CNS depressant drugs prescribed for anxiety, insomnia, and

convulsions, sometimes referred to as sedatives. They are older and less frequently

prescribed than barbiturates. (Julien, 2001)

Benzodiazepines: A sub-class of CNS depressant drugs with with hypnotic, anxiolytic,

anticonvilsant, amnestic, and muscle relaxant properties sometimes often referred to as

tranquilizers. (Julien, 2001)

Central nervous system (CNS): The brain and spinal cord. (NIDA, 2001b)

CNS depressants: A class of drugs that slow CNS function, some of which are used to

treat anxiety and sleeping disorders; includes barbiturates and benzodiazepines. (Julien,

2001)

CNS stimulants: Drugs that enhance the activity of the brain and lead to increased heart

rate, blood pressure, and respiration; medically used to treat disorders such as narcolepsy

and attention-deficit hyperactivity disorder. (Doweiko, 2002)

Controlled drug: A drug whose distribution and use is closely monitored by the Federal

Drug Enforcement Agency (DEA) because of potential for abuse or risk. Drugs with the

highest abuse potential are placed in Schedule I, and those with the lowest abuse potential

are in Schedule V. (U.S. Drug Enforcement Administration [U.S. DEA], n.d.)

Dependence: An adaptive physiological state that can occur with regular drug use and

results in withdrawal when drug use is discontinued. (NIDA, 2001b)

Dopamine: A neurotransmitter present in regions of the brain that regulate movement,

emotion, motivation, and feelings of pleasure. (NIDA, 2001b)









Health risk behaviors: The variety of behaviors that put adolescents at risk for injury

and disease. In this study, health risk behaviors include tobacco, alcohol, and other drug

use; behaviors that compromise personal safety including driving under the influence of

alcohol and violence; sexual behaviors; depression and suicide ideation; lack of physical

activity; and problem weight loss practices. (CDC, 2005)

Non-medical prescription drug use: The use of prescription therapeutic drugs without

a physician's permission or taken only for the experience or feeling it caused.

(SAMHSA, 2004a)

Norepinephrine: A neurotransmitter present in some areas of the brain and the adrenal

glands; decreases smooth muscle contraction and increases heart rate; often released in

response to low blood pressure or stress. (NIDA, 2001b)

Opioids: Controlled drugs or narcotics most often prescribed for the management of

pain; natural or synthetic chemicals based on opium's active component morphine that

work by mimicking the actions of pain-relieving chemicals produced in the body. (NIDA,

2005d)

Polydrug abuse: The abuse of two or more drugs at the same time, such as CNS

depressant abuse accompanied by abuse of alcohol. (Rosenthal & Levounis, 2005)

Prescription drug abuse: A maladaptive pattern of prescription drug use leading to

clinically significant impairment or distress as manifested by one or more behaviorally

based criteria. These criteria include failure to fulfill major role obligations at work,

school, or home; recurrent substance use in situations in which it is physically hazardous;

recurrent substance-related legal problems; and continued use despite persistent or

recurrent social problems caused by effects of the substance (American Psychiatric









Association [APA], 2000)

Psychotherapeutics: Drugs that have an effect on the function of the brain and that often

are used to treat psychiatric disorders; can include opioids, CNS depressants, and

stimulants. (NIDA, 200 ib)

Tolerance: A condition in which higher doses of a drug are required to produce the same

effect as experienced initially. (NIDA, 2001b)

Withdrawal: A variety of symptoms that occur after chronic use of some drugs is

reduced or stopped. (NIDA, 2001b)














CHAPTER 2
REVIEW OF THE LITERATURE

Nonmedical Prescription Drug Use Defined

To understand the nature of nonmedical prescription drug use among adolescents,

it is meaningful to first illustrate the various ways this type of drug use is referred to in

the literature. Three terms used widely to indicate the use of prescription drugs in ways

other than therapeutically intended include misuse, nonmedical use, and abuse. Though

these terms are commonly used interchangeably, they have distinct meanings and

attention should be given to the way in which they are defined.

Misuse generally refers to a patient's incorrect use of a medication. This includes

taking it for other than the intended use, taking other than the prescribed amount, and

taking it more frequently or for longer than prescribed. However, physician behavior

may also lead to misuse when a medication is prescribed for the wrong ailment, at

improper doses, or for longer than is necessary (Ling, Wesson, & Smith, 2005).

Abuse is perhaps, the term used most widely and with the widest range of

definitions with reference to prescription drugs. The American Psychiatric Association's

(APA) Diagnostic and Statistical Manual (2000) defines abuse as a maladaptive pattern

of substance use leading to clinically significant impairment or distress as manifested by

one or more behaviorally based criteria. These "criteria" include failure to fulfill major

role obligations at work, school, or home; recurrent substance use in situations in which it

is physically hazardous; recurrent substance-related legal problems; and continued use

despite persistent or recurrent social problems caused by effects of the substance (APA,









2000). The U.S. Drug Enforcement Administration (U.S. DEA) defines abuse as the

nonmedical use of drugs that are regulated under existing federal law into one of five

schedules. These five schedules are based upon the substance's medicinal value,

harmfulness, and potential for abuse or addiction (U.S. DEA, n.d.). It is apparent that the

term "drug abuse" holds different meanings for different organizations. However, these

differences are not trivial, as the definitions may be each tailored to address specific or

policies concerned with the organization. The medical profession, concerned primarily

with clinical issues, and law enforcement agencies, concerned with street diversion, have

clearly adopted definitions relevant to their own practices.

Research agencies also have their own definitions for abuse, which are often

broader in scope to reach a wider audience. The Institute of Medicine (1996) defines

abuse as "any harmful use, irrespective of whether the behavior constitutes a 'disorder' in

the DSM diagnostic nomenclature" (p. 3). The Substance Abuse and Mental Health

Services Administration defines abuse as the nonmedical use of a substance for psychic

effect, dependence, or suicide attempt or gesture (SAMHSA, 2003a). Although NIDA

does not have an official definition of drug abuse, the agency often uses the phrase

"nonmedical use of a substance" in their educational materials (Zacny et al., 2003, p.

217).

Nonmedical use has been defined as using a psychotherapeutic drug "even once,

that was not prescribed for you, or that you took only for the feeling it caused"

(SAMHSA, 2004b). Some researchers distinguish between nonmedical use and abuse.

For example, Zacny et al. (2003) refer to any problematic prescription drug use (that has









not shown to meet DSM-IV criteria for substance abuse disorder) as non-medical

whereas they define abuse as that which meets DSM-IV criteria.

However, as evidenced by the IOM and SAMHSA definitions above, nonmedical

use is also frequently used interchangeably with abuse. In fact, overlapping of all three

terms within the same institution, is not uncommon. In a single research report for

example, NIDA cited, "the nonmedical use or abuse of prescription drug remains a

serious public health concern" (p.1), and "the misuse of prescribed medications may be

the most common form of drug abuse among the elderly" (p. 6) (NIDA, 2001b). A

related document also equates misuse with any nonmedical use when citing the National

Household Survey on Drug Use and Health (NIDA, 2005d).

In an attempt to define the use of prescription drugs in ways other than

therapeutically intended, organizations have provided us with diverse yet, sometimes

convergent definitions. This presents a potentially confusing situation for the researcher

trying to select one definition that is "more correct" than another. Because there is no

universally agreed upon meaning for any of these terms, indicators of problem

prescription drug use will need to be classified in a systematic manner.

For the purpose of this review of the literature, misuse will be defined as the

improper use of a drug (with regards to dosage, indication, timing, etc.). In this case, it is

an inadvertent action that can sometimes be perpetuated by the provider. In their

definitions of nonmedical use and abuse, agencies often include any use of prescription

drugs without a doctor's orders (SAMHSA, 2004a). However, clinicians generally view

this as misuse if it is for the medically intended purpose (Ling et al., 2005).









Nonmedical use and abuse will not be used interchangeably as in some research

studies. Public health agencies often recognize both abuse and nonmedical use as any

potentially problematic use that is intentional and deliberate, and has no corresponding

medical benefit (SAMHSA, 2004a; NIDA, 2001b). Yet, the medical community

generally acknowledges abuse only when impairment or distress presents itself on one or

more forms (APA, 2000). Thus, the widely used definition provided by SAMHSA

(2004a)- taking of prescription drug without a doctor's orders or only for the feeling it

caused-will be used in characterizing nonmedical prescription drug use, but not abuse,

which tends to have much more serious implications. Among young people, nonmedical

use is most commonly associated with recreational or mood altering purposes but may

also be associated with a suicide attempt, appetite suppression, or increased attentiveness

and focus (Meadows, 2001; SAMHSA, 2002).

Classes of Prescription Drugs Used Nonmedically

While virtually all prescription drugs can be misused, three major classes of drugs

are most commonly used nonmedically for their psychotherapeutic properties. The

public health, prevention, and law enforcement communities express particular concern

for the use of opioids, central nervous system (CNS), stimulants, and CNS depressants

(National Drug Intelligence Center, 2002; NIDA, 2001b; SAMHSA, 2004a). Many

prescription drugs in these three classes are controlled drugs, that is, are regulated by the

federal government under the Controlled Substances Act of 1970, because of their abuse

potential, addictive properties, and high risk for physical dependence (U.S. DEA, n.d.).

Opioids

Also referred to as narcotics, analgesics, pain killers, or pain relievers, opioids are

most commonly prescribed in the clinical setting for their pain relieving properties but









may also be used to treat severe coughs and diarrhea (NIDA, 2001b). The family of

opioids (meaning "like opiates") includes true opiates like morphine, derived from the

opium poppy, as well as synthetic compounds whose chemical properties and biological

actions resemble those of true opiates (Julien, 2001).

According to archaeological evidence, much of the early history of opium took

place before the advent of written records. It is thought that ancient Man ingested the

dried resin of the opium poppy to control pain, and treat children suffering from colic

(Doweiko, 2002). It was not until the early 1800s however, that an active agent, a pure

alkaloid base, was chemically isolated from opium to produce morphine (Doweiko,

2002). After the invention of the hypodermic needle in 1857 and the widespread use of

morphine and opium on Civil War battlefields, it did not take long before a massive

outbreak of opiate addiction took place in the U.S. (Doweiko, 2002). Throughout the

history of modern medicine, the medical community has struggled to strike a balance

between risk management strategies to prevent abuse, and the need for patients to have

appropriate access to opioids (Zacny et al., 2002).

Opioids are prescribed for three types of pain: acute or short-lived pain, chronic

malignant (cancer) pain, and chronic nonmalignant pain (Portenoy, 1992). They act by

attaching to proteins called opioid receptors found in the brain, spinal cord, and

gastrointestinal tract. When these drugs attach to opioid receptors, they block the

transmission of pain messages in the brain (Julien, 2001). Opioids are thus, an important

component of pain management in the clinical setting. However, like illicit opioids (e.g.,

heroin) they additionally affect regions of the brain that mediate what we perceive as

pleasure, thus resulting in an initial sense of euphoria for the user (Julien, 2001).









Side effects of opioids include unclear thinking, dizziness, drowsiness,

constipation, itching, and depending on the amount taken, depressed breathing. A large

enough single dose could cause severe respiratory depression resulting in death (NIDA,

2001b). Some of the more commonly abused opioids include morphine, codeine (e.g.,

Tylenol 3) hydrocodone (e.g., Vicodin, Lortab), oxycodone (OxyContin, Percocet),

hydromorphone (e.g., Dilaudid), propoxyphene (e.g., Darvon, Darvocet), and meperidine

(e.g., Demerol) (NIDA, 2001b; Johnston et al., 2005b). Because of their moderate to

high addictive properties, opioids are usually classified as Schedule II or III drugs (U.S.

DEA, n.d.).

Stimulants

As with opioids, the use of central nervous system (CNS) stimulants, also known

as psychostimulants or simply stimulants, dates back several thousands of years. In

ancient Rome for example, Gladiators were thought to use stimulants to counter the

effects of fatigue, gain energy, and fight longer. It has been common practice in modem

combat for soldiers and aviators to use stimulants to induce wakefulness (Doweiko,

2002). Stimulants have chemical structures similar to key brain neurotransmitters called

monoamines, which include norepinephrine and dopamine. Increasing levels of

norepinephrine in the brain and body increases blood pressure, heart rate, and blood

glucose; constricts blood vessels; and opens up the pathways of the respiratory system.

An increase in dopamine is associated with a sense of euphoria that can accompany the

use of these drugs (Julien, 2001; NIDA, 2001b).

The modern therapeutic uses of stimulants focus on treating narcolespsy, a sleep

disorder, and attention deficit (hyperactivity) disorder (AD[H]D), although they were

widely prescribed as recently as the 1980s for weight loss, depression, and asthma









(Julien, 2001; Doweiko, 2002). Though several families of drugs are classified as

stimulants, prescription stimulants are generally grouped into the amphetamines or

amphetamine-like drugs. Some of the commonly prescribed stimulants include

methyphenidate (e.g., Ritalin, Concerta), amphetamine-dexatroamohetamine (e.g.,

Adderall), dextroamphetamine (Dexedrine), and sibutramine hydrochloride monohydrate

(e.g., Meridia) (NIDA, 2001b; SAMHSA, 2004a).

Consequences of stimulant drug abuse include feelings of hostility or paranoia for

the user. Also, taking high doses of a stimulant may result in dangerously high body

temperatures and irregular heartbeat. These physiological effects increase a person's

potential for cardiovascular failure and fatal seizures (NIDA, 2001b).

Prescription stimulants have high addictive potential resulting in a majority of

them being classified as Schedule II drugs (U.S. DEA, n.d.). In recent years, drug abuse

researchers have placed particular attention on the drugs used to treat AD[H]D (e.g.,

Ritalin, Aderall, Concerta). There has been some debate as to whether prescribed use of

the drugs leads to substance abuse later on in life. However, experts agree that this is not

the case (Farone & Wilens, 2003). Most young people who use these drugs

nonmedically, do so to party, stay awake, study, or suppress appetite and often times do

not attain them through their own prescriptions (Klein-Schwartz & McGrath, 2003).

Depressants

Central nervous system (CNS) depressants, also known as sedative-hypnotics, are

substances that slow normal brain function. Because of their calming effects, depressants

are often prescribed to treat epilepsy, anxiety, and sleep disorders (Julien, 2001).

Depressants work in the brain affecting levels of gamma-aminobutyric acid (GABA), a

neurotransmitter which facilitates communication between brain cells. When GABA is









in an excited state, it causes a person to feel sedation, relief from anxiety, anticonvulsant

effects, and dilation of the blood vessels (Julien, 2001; NIDA, 200 ib).

Two main types of drugs comprise the prescription depressants, barbiturates and

benzodiazepines. Barbiturates, sometimes referred to as sedatives, have been used

therapeutically to induce anesthesia, relieve anxiety, and treat seizures. Examples of

barbiturates include mephobarbital (e.g., Mebaral) and pentobarbital sodium (e.g.,

Nembutal), (NIDA, 200 Ib). Benzodiazepines, sometimes referred to as tranquilizers, are

newer and more widely used than barbiturates. They have largely replaced barbiturates

because of their greater efficacy, lower abuse potential, and lower risk of inducing coma

or fatal intoxication (Julien, 2001). Examples of benzodiazepines often prescribed to

treat anxiety and panic disorder include include diazepam (e.g., Valium), alprazolam

(e.g., Xanax), chlordiazepoxide HCI (e.g., Librium), clonazepam (e.g., Klonopin), and

lorazepam (e.g., Ativan). Benzodiazepines with more sedating effects including

triazolam (e.g., Halcion) and estrazolam (e.g., ProSom) are more commonly used to treat

sleeping disorders (Julien, 2001).

Because of their addictive potential, depressants are classified as Schedules II-IV

drugs. Short-acting, high potency benzodiazepines (e.g., Valium) are more likely to lead

to addiction than slower- and longer-acting benzodiazepines (e.g., Librium), thus placing

them at a higher schedule according to the DEA (U.S. DEA, n.d.). Young people who

use prescription depressants nonmedically, generally do so for their calming and sedating

psychoactive effects. Nonmedical users of depressants may also use them to counter the

stimulatory effects of drugs such as cocaine or enhance the sedative effects of drug such

as alcohol (Sheehan, Sheehan, Torres, Coppola, & Francis, 1991).









Long-term use of depressants leads to tolerance to the drug. If cessation occurs

abruptly rather than through medically supervised tapering-off, the individual may

experience serious withdrawal symptoms, including seizures, hallucinations, and in some

cases, death. Barbiturates have a particularly high risk of inducing severe and potentially

life-threatening withdrawal symptoms. In contrast, taking benzodiazepines rarely leads

to death except when the drugs are used in combination with other drugs including

alcohol.

Epidemiology of Nonmedical Prescription Drug Use

While rates of illicit drug use for the most part have been stable or declining in

recent years, the abuse of controlled prescription drugs has been steadily increasing,

particularly among young people (SAMHSA, 2004a; Johnson et al, 2005b). Evidence of

this increase comes from a number of epidemiological databases including the National

Household Survey on Drug Use and Health (NSDUH) (SAMHSA, 2004b), the

Monitoring the Future (MTF) project (Johnston et al., 2005b), the Drug Abuse Warning

Network (SAMHSA, 2003a), and the Treatment Episode Data Set (SAMHSA, 2004d).

When investigating an emerging trend in drug use, such as the nonmedical use of

prescription drugs, the most important epidemiological findings are likely to be those

concerning the changes that take place over time. Important questions surveillance

surveys ask likely are, "What drugs are rising or declining in popularity?" "How fast are

these changes occurring?" and "What subgroups are most affected?" The answers to

these questions have important implications for the efforts of health educators, prevention

specialists, law enforcement, and public policy makers and deserve further attention.

Reports indicate that 13.7% of youth ages 12-17 have abused prescription drugs

in their lifetime; 4.0% currently use prescription drugs, as indicated by use in the month









preceding a national survey (SAMHSA, 2004a). Each year close to half (44%) of new

prescription drug abusers are under the age of 18 (SAMHSA, 2004a). When placed into

perspective relative to other types of youth drug abuse, the prevalence of prescription

abuse use ranks fourth, after alcohol, cigarettes, and marijuana respectively (Johnston et

al., 2005b).

Controlled prescription drugs most likely to be used for their psychotherapeutic

properties by young people include opioid pain relievers (i.e., opioids), stimulants and

depressants. Anabolic-androgenic steroids such as Anabol or Equipose are also use

nonmedically by young people, however primarily for purposes of body sculpting and

athletic performance (NIDA, 2000). Although the medical community and public health

agencies express concern for steroid abuse's adverse effects on health, especially among

young people (NIDA, 2000), this study concentrates on the three classes of controlled

psychotherapeutic drugs- opioid pain relievers, stimulants, and depressants- because

they are the fastest growing categories of drugs used nonmedically for recreational

purposes and have received relatively little research attention.

Initiation

The National Household Survey of Drug Use and Health shows that the initiation

of nonmedical prescription drug use occurs primarily among persons age 12 to 25, and

that new use has been increasing among young people (SAMHSA, 2004a). The annual

number of new users of pain relievers has been rising since the mid-1980s, from about

400,000 initiates to 2.4 million in 2004. New users of stimulants increased from

approximately 200,000 in 1991 to 793,000 in 2004 (SAMHSA, 2004a, 2003c). New

users of tranquilizers have been increasing since the mid-1980s, but the largest increase

has been recently, from more than 700,000 new users in 1999 to almost 1.2 million users









in 2004 (SAMHSA, 2004a, 2003c). The number of new users of sedatives remained

around 100,000 per year between 1988 and 1994. Starting in 1995 however, the number

rose from 111,000 to 240,000 in 2004 (SAMHSA, 2004a, 2003c). Between 1998 and

2004, there were more new users both opioids and stimulants among 12 to 17 year olds

than among 18 to 25 year olds (SAMHSA, 2004a, 2003c).

Prevalence

Nonmedical use rates for each class of abusable prescription drugs have also

generally increased each year during the past decade (Johnston et al., 2005b). Opioids,

most commonly prescribed for pain relieving properties, have gained a great deal of

attention in recent years because they are the most widely abused prescription

medications; up to 70% of non-medical prescription drug users report abusing opioids.

(SAMHSA, 2004a). From 1992 to 2000 annual prevalence of opioid abuse more than

doubled among 12th graders (3.3% to 7.0%); non-medical opioid use continued to

steadily increase until reaching peak levels in 2004 (9.5%). Specific drugs in this class

including Vicodin, codeine, Percocet, and OxyContin are among the drugs most

commonly mentioned by high school seniors in recent years (Johnston et al., 2005b).

In 2002, free-standing questions were included for two of those drugs in the

Monitoring the Future Study-Vicodin and OxyContin- because of considerable public

comment on the diversion of these medications. That year, both Vicodin and OxyContin

attained surprisingly high prevalence rates, especially among 12th graders, suggesting

that they very likely help to account for the upturn in the use of the general class of

opioids. Annual prevalence in 2002 for Vicodin and OxyContin were 9.6% and 4.6%,

respectively among 12th graders. In 2004, those rates shifted slightly, to 9.3% for

Vicodin and 5.0% for OxyContin (Johnston et al., 2005b).









Stimulants (i.e., amphetamines) follow opioids with the next highest rates of

nonmedical use. Since 1992, annual prevalence has veered upward, rising from 7% in

1992 to 10% in 1997, before peaking in 2002 (11%) among high-school seniors

(Johnston et al., 2005b). Annual prevalence of amphetamines however, decreased to

10% in 2004. Ritalin has become the single most widely abused stimulant in recent years;

its use increased among high school seniors from an annual prevalence of 0.1% in 1992

to 2.8% in 1998, and declined only slightly to 2.2% in 2000. This statistic rose

significantly in 2004 to 3.9% (Johnston et al., 2005b). Because of increasing concern for

Ritalin abuse, a newer stand-alone question was integrated into Monitoring the Future in

2001. While the newer question yielded somewhat higher absolute prevalence levels

(believed to be a more accurate)-5.1% in 2001, 4.0% in 2002, and 2003, and 5.1% in

2004-researchers hold that the trend Johnston et al., 2005b).

Depressants are grouped into two subcategories: barbiturates (sedatives) and

benzodiazepines (tranquilizers). Although barbiturates such as Seconal (secobarbital)

and Nembutal pentobarbitall sodium) are still on the pharmaceutical market,

benzodiazepines such as Valium diazepamm) and Xanax (alprazolam) are more widely

prescribed than barbiturates and are therefore more available for abuse (Julien, 2001).

Though barbiturates were widely used in the mid 1970s to mid 1980s, prevalence for the

drug in this class declined dramatically in the early 1990s. By 1992, annual prevalence

of use for 12th graders (2.8%) was less than one-third of the 1975 level (10.7%).

Over the past decade, annual prevalence of tranquilizer use among 12th graders has been

increasing steadily; from 2.8% in 1992 to 7.7% in 2002- about where it remains in 2004

(7.3%) (Johnston et al., 2005b).









Subgroup Trends

Gender. Most of the gender differences for abuse of individual classes of

prescription drugs have remained fairly parallel in the past 30 years- that is, any trends

in prevalence have remained stable with regard to gender (Johnston et al., 2005a). In

general, adolescent males report higher rates of all types of drug use although there are

some exceptions when it comes to prescription drugs. Females report slightly higher

rates of amphetamine and tranquilizer abuse in the lower (8th and 10th) grades (Johnston

et al., 2005a).

Geographic Region. The U.S. is frequently divided into four regions-

Northeast, South, North Central, and West- for the purpose of monitoring geographic

trends. In general, the "South", which spans as far west as Texas and as far north as

Maryland has the lowest rates of use of any illicit drug use. Rates very somewhat with

respect to the different classes of abusable prescription drugs. Prevalence is highest in the

"West" for opioids when it is presented as a collective drug class, much of which is likely

accounted for by the high rates of Vicodin use in that region. Amphetamine abuse is

highest in the "South" and lowest in the "West." Greatest abuse of both sedatives and

tranquilizers also takes place in the "South" (Johnston et al., 2005a)

Population Density. For the purpose of assessing differences in drug use

according to population density, communities are classified into one of three strata-

Large Metropolitan Statistical Areas (LMSAs) (i.e., the 16 largest population areas

according to the 1990 census), Other MSAs (i.e., cities with a population larger than

50,000 but not classified as Large MSAs), and Non-MSAs (i.e., towns that do not have a

population of at least 50,000). "Other MSAs" tend to have the highest and "Large

MSAs" tend to have the lowest levels of any type of illicit drug use. This tendency









generally holds true for prescription drug abuse as with one exception; amphetamine

abuse is highest among "Non-MSAs" (Johnston et al., 2005a).

Socioeconomic Status. When parental education is used as a proxy for

socioeconomic status (SES), little systematic relationship exists for prescription drug

abuse. However, it seems that a slight curvilinear relationship exists for opioids,

amphetamines, and sedatives, with the highest and lowest SES groups being least

susceptible to abuse. However, one exception is that in the lower grades, lowest SES

groups report abuse of tranquilizers more than any other group (Johnston et al., 2005a).

Race/Ethnicity. While the three major racial/ethnic frequently groups cited-

Whites, African Americans, and Hispanics- have different levels of use of some drugs,

they have similar trends in almost all prescription drug classes. Abuse of opioids has

consistently been highest among White students, considerable lower among Hispanics,

and lowest among African Americans. In recent years, the rates have particularly

increased among Whites. These data on general opioid use are reported only for 12th

graders. Hispanics consistently fall in the middle for use of amphetamines, sedatives,

tranquilizers as well with one exception. In the 8th grade, the Hispanics have the highest

prevalence of amphetamine use (Johnston et al., 2005a).

Indicators of the Problem's Seriousness

In spite of public misperceptions that nonmedical use of prescription drugs is

comparatively safer than illicit drug use, indicators of the problem's seriousness are

reflected in high numbers of young people requiring prescription drug-related treatment

and emergency health care (SAMHSA 2004b, 2003b). Drug-related morbidity and

mortality affects more young people than the abuse of hallucinogens, methamphetamine,

ecstasy, inhalants, and heroin combined (SAMHSA, 2003b). The Drug Abuse Warning









Network (DAWN), reports that anxiolytics and sedative hypnotics (i.e., depressants) are

the most frequent psychotherapeutic agents motioned in drug related emergency

department (ED) visits, representing 11% of total ED mentions (SAMHSA, 2003b).

Closely following are the narcotic analgesics (i.e., opioids), comprising 10% of drug

related mentions in ED visits. Prescription stimulants are mentioned in less than 2% of

drug-related ED visits. Alcohol, marijuana, and cocaine are the only primary drugs of

abuse mentioned in greater frequency than prescription drugs (SAMHSA, 2003b).

The dangers of prescription drug abuse are further exacerbated when more than

one drug is present and are illustrated by the fact that three-quarters of all ED visits

related to both prescription opioids and benzodiazepines (78%) involve more than one

drug (SAMHSA 2004b, 2004c). The most frequent substances found in combination

with both these drugs are alcohol, other opioid pain relievers, and cocaine (SAMHSA

2004b, 2004c). Approximately 30% of all DAWN emergency department cases involve

patients aged 25 and under (SAMHSA, 2002).

In addition to ED visits, substance abuse treatment admissions further shed light

on the problem. Prescription drugs were the primary substances of abuse for

approximately 4% of the 1.9 million treatment admissions reported to the Treatment

Episode Data Set (TEDS) in 2002 (SAMHSA, 2004d). More than half (55%) of those

admissions were attributed opioids. Stimulants, tranquilizers, and sedatives comprised

approximately 28%, 10%, and 6% of primary substances mentioned in treatment

admissions, respectively. It is important to note that polydrug abuse is more common

among treatment admissions than the abuse of a single substance. Prescription drugs are









also frequently mentioned as secondary or tertiary substances of abuse, thus further

increasing their presence among treatment admissions (SAMHSA, 2004d).

The Youth Risk Behavior Survey

The Youth Risk Behavior Survey (YRBS) is a component of the Centers for

Disease Control and Prevention's (CDC) Youth Risk Behavior Surveillance System

(YRBSS). Additional components of the System include the National College Health

Risk Behavior Survey (NCHRBS), the National Health Interview Survey (NHIS), a

middle school version of the YRBS, and various special population surveys that have

been conducted periodically (Brener et al., 2004).

The CDC initiated the design of the surveillance system in 1988 upon review of

the leading causes of mortality among youths and young adults. Over 70 percent of

adolescent mortality is due to preventable causes including motor vehicle crashes

(32.3%), other unintentional injuries (11.7%), homicide (15.1%), and suicide (11.7%).

Furthermore, substantial morbidity and social problems result from substance use,

unintended pregnancies and sexually transmitted diseases, and risk factors for

cardiovascular disease (Grunbaum et al., 2004). These behaviors are often established

during youth, and extend into adulthood. In 1989, CDC took further steps to develop the

instrument by convening a panel of experts to delineate priority behaviors and devise

questions to measure those behaviors (Brener et al., 2004). Subsequent revisions led to

the first and second nationwide administration of the YRBS in the spring of 1990 and

1991, respectively (Kann, Kolbe & Collins, 1993). However, it was soon determined that

because behavioral change in populations typically occurs gradually, administrations of

the survey every two years would be sufficient to assess health risk-behaviors among

adolescents (Brener et al., 2004).









The YRBS therefore, has been administered biennially, in every odd year, since

1991 to measure the prevalence of six priority health risk behavior categories: (a)

behaviors that contribute to unintentional injuries; (b) tobacco use; (c) alcohol and other

drug use; (d) sexual behaviors that contribute to unintended pregnancy and sexually

transmitted diseases; (e) unhealthy dietary behaviors; and (f) physical inactivity (Brener

et al., 2004; Kann, Kolbe & Collins, 1993b). In focusing on these specific behaviors,

relevant agencies are able to monitor the leading preventable causes of adolescent death,

disability, and social problems to assess whether these behaviors increase, decrease, or

remain the same over time, and examine the co-occurrence of health-risk behaviors. In

addition, the YRBS provides a means to monitor relevant national health objectives, first

for the year 2000 (Public Health Service, 1991), and now for the year 2010 (ODPHP,

2000).

The YRBS includes national, state, and local school-based surveys of high school

students (Brener et al., 2004). The sampling frame for the most recent national YRBS

consisted of all public and private schools with students in grades 9-12 in the 50 states

and the District of Columbia. A three-stage cluster design was used to produce a

nationally representative sample from public and private high schools (Grunbaum, et al.,

2004). State and local administrations of the YRBS also rely on cluster sample designs to

obtain representative samples of high school students in the participating jurisdictions

(Grunbaum et al., 2004).

All students complete the YRBS during a regular class period (approximately 50

minutes) by recording their responses directly on a computer scannable questionnaire

booklet or answer sheet. Student participation is voluntary and anonymous, yet









methodologies may vary slightly with respect to local parental consent procedures.

Although the use of passive parental consent yields higher participation rates than active

consent, type of parental consent generally does not affect prevalence estimates for risk

of self-report behaviors on the YRBS (Eaton, Lowry, Brener, Grunbaum, & Kann, 2004).

In addition to differing consent procedures, states and cities may modify the 87-item core

questionnaire to meet individual needs under the conditions that they conserve at least

two-thirds of the core questions from the standard YRBS; limit response options to eight

mutually exclusive categories, and; do not use skip patterns or grid formats (Grunbaum et

al., 2004; Brener et al., 2004). Refraining from the use of grids a skip patterns helps

safeguard privacy because comparable amounts of time are required to complete the

survey regardless of risk status (Brenner et al., 2002).

Because data from the YRBS are used to develop policies and prevent health risk

behaviors among youth (Everett, Kann, & McReynolds, 1997), it is important that

researchers have confidence that it contains reliable and valid measures for youth

behaviors. Two CDC sponsored test-retest reliability studies have been conducted

throughout the life of the surveillance system and in both cases, the survey was

subsequently modified as a result (Brener et al., 2002; Brener, Collins, Kann, Warren, &

Williams, 1995). The most recent test-retest reliability assessment of the survey was

conducted in 2000 on a convenience sample of 4,619 high school students.

Results for the test-retest were reported in the form of kappa statistics and 95%

confidence intervals. Kappa statistics, which provide a measure of agreement among

items in the two administrations correcting for chance, revealed that nearly half (47.2%)

of the items had at least "substantial" reliability (kappas > 61%) and almost all (93.1%)









had at least "moderate" reliability (kappas >41%). Furthermore, based on

nonoverlapping 95% confidence intervals, 22.2% of the items had significantly different

prevalence estimates at Time 1 vs. Time 2. Examining reliability of the measures by

behavior category revealed that items pertaining to certain behaviors elicited higher

reliability than others. For example, items related to tobacco use, alcohol and other drug

use, and sexual behavior demonstrated significantly higher reliability than items related

to dietary behaviors, physical activity, and other health-related topics (e.g., ever been

taught about HIV/AIDS in school, saw a dentist during past 12 months, rarely or never

use sun screen, etc.).

Based on the 2000 test-retest, study items have been revised or deleted from

subsequent versions of the YRBS. Specifically, 11 items have been modified or

removed. These include items related to pregnancy and pregnancy prevention; the use of

a helmet when riding a motorcycle; 30-day prevalence of heroin and inhalant use; most

recent "check-up" visit to a health care provider (i.e., nurse, doctor, dentist); strength

training; sources of cigarettes; providing proof of age when buying cigarettes; and

frequency of wearing sunscreen (CDC, 2005, 1999). Since the 2000 test-retest study,

seven items have also been added. These include items related to lifetime ecstasy use;

seatbelt use in a car; having property stolen or damaged at school; being diagnosed with

asthma and year prevalence of asthma attacks; a measure of total weekly physical

activity; and a personal assessment of overall health (CDC, 1999; CDC 2005).

The 2005 YRBS consequently, is comprised of 87 items divided into twelve

major sections. The first section contains standard demographic questions and the last

section asks students about "other health topics" including asthma diagnosis and









HIV/AIDS education. The remaining health topics are divided across 10 behavioral

areas: (1) personal safety; (2) violence; (3) sadness, suicide ideation, and attempts; (4)

alcohol use; (5) marijuana use; (6) cocaine and other drug use; (7) sexual behaviors; (8)

body weight; (9) dietary behaviors; and (10) physical activity (CDC, 2005).

Although reliability is a necessary characteristic of a valid measure,

demonstration of the YRBS' reliability does not ensure validity. The truthfulness and

accuracy of self-report questionnaires like the YRBS may be compromised because

health-risk behaviors are often difficult to recall and some are so sensitive that

respondents may not feel comfortable reporting them (Brener, Billy, & Grady, 2003).

Specifically, validity allows researchers to answer to pivotal question, "Can we trust the

data from this questionnaire?"

Despite the importance of establishing validity, no study has validated all self-

report behaviors included on the YRBS. CDC has discussed difficulties in executing that

task given the lack of objective measures for many behaviors of interest (Brener et al.,

2003). In fact, validation of specific YRBS items has been limited to two studies. A

small study by McDermott and colleagues verified that YRBS questions related to

alcohol are valid measures of youth drinking behavior (McDermott, Sarvela, Caravella, &

Gast, 1995). However another study conducted by CDC to assess the validity of height

and weight measures, demonstrated that students tend to over-report height and

underreport weight, leading to underestimations of overweight (Brener et al., 2003). In

spite of the relative absence of validity estimates of YRBS self-report measures, CDC

officials maintain YRBS items likely accurately assess many youth-risk behaviors,

especially those related to drug, alcohol, and tobacco use. This belief is based on studies









of measures similar to YRBS items that have demonstrated validity of self-reported

alcohol and other drug use, tobacco use, suicidal ideation, sexual behavior, dietary

behaviors, and physical activity (Brener et al., 2002).

Correlates of Adolescent Prescription Drug Abuse

A substantial body of evidence suggests that adolescent risk behaviors do not

occur in isolation, but rather, cluster in vulnerable individuals (Bell & Bell, 1993;

Donohew, Palmgreen, Zimmerman, Harrington, & Lane, 2003; Dryfoos, 1990; Jessor et

al., 1991). Also, over time, involvement in one type of risk behavior increases the

likelihood of becoming involved in other risk behaviors (Osgood, Johnston, O'Malley, &

Bachman, 1988). Singly, any risk behavior and its associated health outcomes can

represent a serious threat to an adolescent's health. However the tendency for risk

behaviors to cluster, or covary, further exacerbates the health threat.

Studies of nonmedical prescription drug use in adolescents suggest that

prevalence is not only associated with other substance abusing behaviors, but is also

correlated with an array of academic and personal problems, and selected problem

behaviors (Boyd et al., 2006; McCabe & Boyd et al., 2005). Although limited data exist

on the health-risk correlates of prescription drug abuse, the literature is beset with studies

indicating that substance abuse is associated with multiple risk-taking behaviors in

adolescents (Bell & Bell; 1993; DiClemente et al., 1996). Because prescription drug

abuse is a subset of drug abuse, many of these correlates may also have particular

relevance to prescription drug abuse.

Tobacco Use

The onset and development of cigarette smoking and tobacco use usually occurs

in adolescence (Kopstein, 2001). Tobacco use deserves special attention because it is









highly addictive and remains the chief preventable cause of death in the U.S. (U.S.

Department of Health and Human Services [U.S. DHHS], 2004). It can not only lead to

negative short term effects in youth but also has long-term implications for adults,

especially for those who initiate tobacco use at young ages (Perry & Stufacker, 1996).

Smoking during childhood and adolescence appears to increase the risk for developing

chronic obstructive pulmonary disease; acute respiratory illness; heart disease; and

stroke; and can lead to several types of cancers including those of the lung, larynx, oral

cavity, pharynx, pancreas, and cervix (U.S. DHHS, 2004).

Recent research on a nationally representative sample of adolescents found that

high school seniors who used abused prescription opioids were approximately seven

times more likely to have smoked cigarettes in their lifetime as compared to non users.

OxyContin users in particular, were almost nine times more likely to have smoked

cigarettes in the past 30 days (McCabe & Boyd et al., 2005). Smoking has also been

correlated with stimulant abuse. In a secondary analysis of national data, McCabe, Teter,

Boyd, & Guthrie (2004) found that illicit methylphenidate (Ritalin) users in grades 8, 10,

and 12, were significantly more likely than non-users to report cigarette smoking in the

past month (64% vs. 18%). McCabe, Teter, and Boyd (2004), found similar results from

a survey conducted on 1,536 students in a Midwestern school district where one in every

two illicit stimulant users reported cigarette smoking as opposed to less than 10% among

non-stimulant users or prescribed stimulant users. Although no studies report the

correlation between tobacco and prescription depression abuse in adolescents, a

secondary analysis of the Harvard School of Public Health College data suggests tobacco

use is a correlate of nonmedical prescription benzodiazepine (e.g., Xanax, Valium) use









among college undergraduates. It is important to note that a strong dose dependent

relationship between tobacco use and multiple substance use behaviors has also been

found among a national sample of adolescents (Everett et al., 1998).

Alcohol Use and Binge Drinking

Alcohol use remains extremely widespread among young people. The majority of

adolescents (77%) have consumed alcohol by the end of high school and more than half

(60%) of 12th graders report having been drunk at least once in their life (Johnston et al.,

2005b). High levels of adolescent alcohol use have been associated with the three most

frequent forms of mortality among adolescents, that is, accidental deaths (especially

automobile accidents), homicides, and suicides (Mokdad, Marks, Stroup, & Gerberding,

2004). Higher levels of adolescent alcohol use are also associated with increased risk of

contracting HIV and other sexually transmitted diseases, teenage pregnancy, poor school

performance, and school dropout (Windle, Shope, & Bukstein, 1996).

According to national surveillance data, teens who admit to abusing controlled

prescription drugs are twice as likely to have used alcohol (SAMHSA, 2004a). High

school seniors who nonmedically use opioids (specifically Vicodin and OxyContin), are

over six times more likely to report alcohol use in the past month and heavy episodic

drinking in the past two weeks (McCabe & Boyd et al., 2005). Among a national sample

of 8th, 10th, and 12th grade students, those who abused prescription stimulants (i.e.,

Ritalin) were significantly more likely to report heavy episodic drinking than non-

abusing students (61% vs. 20%). Although no study reports the relationship between

adolescent depressant and alcohol use, a nationwide study of undergraduate students

illustrated that nonmedical tranquilizer use (e.g., Valium, Librium, Xanax, Ativan,









Klonopin) significantly increased students' odds of frequent binge drinking by four times

(McCabe, 2005).

Marijuana Use

Among the illicit drugs, use of marijuana is the most prevalent (Johnston et al.,

2005b). Among adolescents who use drugs, approximately 60% use only marijuana

(SAMHSA, 2004a). Marijuana shares with tobacco, smoking as the usual route of

administration, and as a result, may lead to many of the same adverse affects to the

respiratory and cardiovascular systems (Sidney, Beck, Tekawa, Quesenberry, &

Friedman, 1997). Research has shown that cannabis can produce a subtle impairment of

attention, memory, and the organization and integration of complex information.

Prolonged use may even lead to changes in the brain permanently affecting learning and

mood (Block & Ghoneim, 1993).

Results from the National Household Survey on Drug Use and Health suggest that

teens who abuse prescription drugs are five times more likely to use marijuana

(SAMHSA, 2004a). When broken down into specific drug class, adolescents age 10-18

who abuse opioids are approximately six times more likely to engage in past months

marijuana use. High school seniors who abuse the specific opioids Vicodin and

OxyContin are 10 and 14 times more likely to engage in past year marijuana use,

respectively. Students using the prescription stimulant Ritalin report much higher rates

of marijuana use (80% vs. 25%) than those who do not abuse the stimulant drug.

(McCabe, Teter, Boyd, & Guthrie, 2004).

Other Illicit Drug Use

Illicit drug use is greater among youth in the U.S. than has been documented in

any other developed country in the world (Blanken, 1993). To name some of the more









prominent consequences, illicit drug use is associated with suicide, homicide, motor-

vehicle injury, transmission of sexually transmitted diseases including HIV infection,

pneumonia, unwanted pregnancy, delinquency, violence, mental illness, and hepatitis

(Mokdad et al., 2004; SAMHSA, 2004a). Although national trends suggest illicit drug

use is decreasing in the U.S., substance use is nonetheless a problem with serious health

and legal implications.

Each class of illicit drugs has unique health implications. Cocaine for example,

acts as a stimulant and short-term effects can include excitability; increased temperature

and blood pressure; erratic behaviors; paranoia; and in large doses, heart failure. Long-

term use of cocaine can lead to psychosis, mood swings, weight loss, and insomnia.

(NIDA, 1999). As an opiate, heroin, has sedative effects, thus slowing vital functions

such as higher brain activity, breathing and heart rate and may lead to death. Other

heroin-related health risks include infection, associated with syringe use (NIDA, 2005a).

Ecstasy, a hallucinogenic stimulant, emerged as a popular drug among youth during the

mid to late 1990s. Many of the side effects users encounter with ecstasy are similar to

those found with cocaine. However, a major risk to health from taking ecstasy is

hyperthermia, or heatstroke, leading to cardiac failure (NIDA, 2004a). Though not

necessarily illicit drugs, inhalants including solvents, aerosols, and nitrates, are

sometimes used illicitly to get high. This type of substance use is often seen among

younger students because inhalants are readily available and relatively inexpensive

(NIDA, 2005b). Nonetheless, inhalants can cause bronchitis, skin problems, memory

impairment vital organ damage, asphyxiation, and are potentially lethal (NIDA, 2005b).

Steroids represent another class of substances that are potentially abused by adolescents









and should be mentioned. Unlike the other illicit drugs, steroids are not used for their

psychoactive properties but rather, for their body building and sculpting potential.

Consequences of steroid use in adolescence include severe acne, mood disruptions, and

can interfere with growth through growth halted prematurely through premature skeletal

maturation and accelerated puberty changes (NIDA, 2000).

The literature frequently describes "polydrug abuse" or "polysubstance abuse"

when referring to populations who concurrently use multiple substances whether licit or

illicit (Rosenthal & Levounis, 2005). Polydrug use can be dangerous due to a whole host

of effects that can occur by combining drugs in ways that are not physician approved-

putting them at risk for adverse interactions and overdose (NIDA, 2001b). In addition,

successful treatment of polydrug abuse may be difficult, as treatment programs may not

be designed to effectively address multiple drugs (SAMHSA, 2004a) Adolescent users

are a subgroup who have been identified as high risk for polysubstance use and compared

to older groups, younger users in treatment settings are more likely to report multiple

drugs (SAMHSA, 2004a). Among teens ages 12 to 17, 79.8% of prescription drug

abusers are polysubstance abusers. National data also indicate that adolescents who

abuse prescription drugs are at risk for "hard" illicit drug use at a much higher rate than

they are for marijuana use when compared to adolescents who do not abuse such drugs.

They are 12 times more likely to use heroin, 15 times more likely to use ecstasy, and 21

times more likely to use cocaine (SAMHSA, 2004a).

Correlations between abuse of the three specific classes of prescription drugs and

selected illicit drug have been made among young people. For example, middle and high

school students who abuse methylphenidate, a prescription stimulant, are significantly









more likely than nonabusing students (43.2% vs. 1.5%) to use ecstasy, another potent yet

illicit stimulant drug (McCabe, Teter, & Boyd, 2004). Prescription pain medications

have also been correlated with illicit drug use other than marijuana among adolescents.

In a Detroit-area public school district, in fact, students who used opioids nonmedically

experienced a nine-fold increased risk of illicit drug use (i.e., cocaine, inhalants, ecstasy,

inhalants, and psychedelics including LSD) (Boyd et al., 2006). Young people who

abuse prescription depressants including Xanax, Librium, and Valium also use illicit

drugs at increased rates. Specifically, nonmedical use of depressants increases the

tendency for college students to also use cocaine and ecstasy by 14 and 16 times,

respectively (McCabe, 2005). Although the use of heroin, steroids, and inhalants has not

been reported in studies correlating prescription drug abuse with illicit drug abuse, the

literature suggests that one type of drug abuse increases an adolescent's risk for another

(Abdel-Ghany, & Wang, 2003; Bukstein, 1995); therefore, implying that use of these

types of drugs may also be exacerbated by prescription drug use.

Driving Under the Influence of Alcohol

Motor vehicle crashes are the leading cause of death for U.S. teenagers,

accounting for nearly 40% of fatalities (National Center for Injury Prevention and

Control [NCIPC], 2005). Traffic crashes are more likely to result in death or injury if

alcohol is involved (i.e., those in which the driver or pedestrian had a blood alcohol

concentration (BAC) above zero). According to the National Highway Traffic Safety

Administration (2003), 41% of people fatally injured in traffic crashes were in alcohol

related crashes and 35% were crashes involving someone with a BAC of 0.08% or

higher. Although 14% of drivers in alcohol-related fatal crashes are between the ages of









16 and 20, this age group represents only 7% of the population, thus suggesting that

young people are at particular risk (Hingson & Winter, 2003).

The nonmedical use of prescription drugs among adolescents and young adults

has been shown to significantly increase risk of driving under the influence and riding

with an intoxicated driver (McCabe, 2005; McCabe, Teter, & Boyd, 2004; McCabe,

Teter, Boyd, Knight, & Wechsler, 2005). For example, among high school students who

partake in the nonmedical use of prescription stimulants, 66.7% report riding with a

driver who has had at least one drink and 44.4% report riding with an intoxicated driver.

These rates are both significantly higher than rates in the non-abusing population

(McCabe, Teter, & Boyd, 2004). Although studies have not correlated the abuse of the

other two readily abused classes of prescription drugs-depressants and opioids-with

driving under the influence among adolescents, correlations have been made among

college undergraduate students. Young adults who abuse prescription benzodiazepines or

opioids are approximately four times more likely to report driving a car after binge

drinking and almost six times more likely to report being a passenger with a drunk driver

(McCabe, 2005; McCabe, Teter, Boyd & Knight et al., 2005).

Violence Related Behaviors

CDC's YRBS includes acts such as physical fighting, carrying weapons to school,

feeling unsafe or threatened at school, school thefts, and forced sexual intercourse in its

definition of violence-related behaviors (CDC, 2005). Although most violent behavior

produces relatively minor injuries, long-term disability and death are also frequent

consequences, especially when a weapon is present. Approximately 90% of homicide

victims in the U.S. are killed with a weapon, that is, a gun, knife, or club (Anderson et al.,

2001). Homicide is the second leading cause of death among all adolescents and it is the









leading cause of death among Black youth aged 15-19 (NCIPC, 2005). Forced sexual

intercourse is also an important problem in adolescence, as it has also been associated

with poorer physical and mental health among women (Brener, McMahon, Warren, &

Douglas, 1999).

The association between violence and alcohol and drug use is well established. A

large scale study in a Texas school district assessed the co-occurrence of youth's violent

behavior and engagement in other risk behaviors including substance use using the YRBS

(Orpinas et al., 1995). Although prevalence of fighting and weapon carrying vary greatly

by gender (males engage significantly more in both), associations were demonstrated

between fighting and carrying a weapon, and heavy alcohol, tobacco, and other drug use

among both boys and girls. Highest correlations however, were found between frequency

of weapon carrying and frequency of drinking alcohol and being inebriated (Orpinas et

al., 1995). In another regional study of correlates of adolescent violent and aggressive

behaviors, alcohol was significantly associated with fighting among White female, while

binge drinking with fighting among White males only (Valois et al., 1995). Significant

relationships were also found for illicit drug use (i.e., marijuana, cocaine, intravenous

drug use) among Whites but not among Blacks. For carrying a weapon, the strongest

predictors also included illegal drug use among Whites but not among Blacks (Valois et

al., 1995).

Youth exposure to violence and victimization through indirect means has also

been shown to relate to adolescent drug use. In addition to being the victim or

perpetrator of a violent act, students who have knowledge of violence (i.e., know victims

of violence) or witness violence have higher a tendency for substance abuse (Albus,









Weist, & Perez-Smith, 2004). Forced sexual intercourse, is a topic largely neglected in

the literature. However, Howard & Wang (2005) recently demonstrated that substance

use behaviors including smoking, binge drinking, cocaine and inhalant use, and drinking

or using drug before sex are related to being forced to have sex. Although more

pronounced for females, the relationships also exists among males (Howard & Wang,

2005).

Sexual Experience and Risk Taking

Younger age at first intercourse and greater numbers of sexual partners are

associated with increased risk for unwanted pregnancy and sexually transmitted

infections (STIs) including HIV (Abma & Sonenstein, 2001). Research shows that

pregnancy often limits the educational achievements and vocational opportunities among

adolescents, especially for females, thus leading to social and economic problems

(Stevens-Simon & McNarney, 1996). One of the most immediate and significant risks to

the health of adolescents is acquiring a sexually transmitted infection. From an economic

and social standpoint, these infections take a large toll on adolescents and ultimately

society. In terms of health outcomes, sexually transmitted infections can be potentially

devastating. The potential impact of STIs is incredibly significant for example, in the

case of HIV infection, which can ultimately result in fatal illness, AIDS.

Studies using the YRBS to assess the correlates of sexual risk taking have focused

on sexual experience (Shrier & Crosby, 2003); condom use (Richter, Valois, McKeown,

& Vincent, 1992; Shrier et al., 1996); early initiation of sexual intercourse, (Coker et al.,

1994); multiple HIV virus-related sexual behaviors (Lowry et al., 1994); number of

sexual intercourse partners (Valois et al.,1999; Shrier et al., 1996) or have employed

composite scores of the various behaviors.









In each of these studies, substance use was significantly associated with one or

more measures of sexual risk taking. Lowry et al. (1994) for example, found that

students who reported no substance use, were least likely to report having had sexual

intercourse, having had four or more sex partners, and not having used a condom at last

sexual intercourse. Furthermore, sexual risk behaviors were greatest among students who

had used marijuana, cocaine, or other illicit drugs. Students who only used alcohol or

cigarettes had smaller but still significant increases in the likelihood of HIV-related

behaviors (Lowry et al., 1994). Studies have further confirmed the significant association

between drug use (i.e., alcohol, tobacco, and marijuana use) and increased number of

sexual partners in assessing a cluster of risk behaviors placing adolescents at risk for

unintended pregnancy, HIV, and other sexually transmitted infections (Shrier et al., 1996;

Valois et al., 1999).

Among alternative high school students similar results have also been found.

Shrier & Crosby (2003) found that sexually experienced students in alternative schools

were close to 50% more likely to engage in cocaine, inhalant, and needle use, and almost

three times as likely to use alcohol and marijuana than their sexually abstinent

counterparts, though they did not show higher rates of tobacco use. This may be

attributed to the fact that smoking is twice as prevalent among youth in alternative

schools and it may have been difficult to detect small differences in smoking between

sexually experienced and abstinent youth in this study (Shrier & Crosby, 2003).

Depression and Suicide Ideation

The relationship between depression and sadness and suicide attempts is well

established (Wulsin, Vaillant, & Wells, 1999). Empirical studies have indicated that

major depression is most highly correlated with suicide for girls (increasing risk 20-fold)









followed by a previous suicide attempt. Among boys, a previous attempt is the most

highly correlated factor (increasing risk 30-fold) followed by depression, alcohol, or drug

use, and disruptive behavior (Lewinsohn, Rohde, & Seeley, 1996). Fortunately, only a

fraction of youth who have attempted suicide are successful; less than one in 50 suicide

attempts result in death (Cohen, Spirito, & Brown, 1996). Nonetheless, suicide remains

the third leading cause of death among adolescents (NCIPC, 2005) and studies

identifying risk factors warrant further attention.

Although a number of different correlates or predictors of suicidal thoughts,

plans, and attempts have been addressed in the literature, drug use has perhaps, received

the most attention (Crumley, 1990; Garrison et al., 1993; Hallfors et al., 2004). In a study

of South Carolina youth, Garrison et al. (1993) confirm that illicit drug use is a highly

influential factor for suicidal thoughts, plans, and attempts, second only to female gender.

When broken down into specific drugs, the largest effects for suicide attempts are seen

for "harder drugs", that is, cocaine use, other drug use (i.e., LSD, PCP, ecstasy,

mushrooms, speed, heroin) and intravenous drugs although alcohol use and marijuana

also increase risk (Garrison et al., 1993). Data from the National Longitudinal Study of

Adolescent Health (Add Health) further illustrate that clusters of students who engage in

illegal drug use (including marijuana, and other drug use) had highest odds of depression,

suicidal ideation, and suicide attempts (Hallfors et al., 2004).

Decreased Physical Activity

The benefits of physical activity have been well documented. Participation in

regular physical activity helps control weight, strengthen bones, build lean muscle, and

reduce body fat content. Exercise also reduces feelings of depression and anxiety, and

promoting psychological well-being (U.S. DHHS, 1996). Over time, regular physical









activity decreases the risk of premature death and disability from causes including heart

disease, high blood pressure, diabetes, and certain cancers (U.S. DHHS, 1996). Despite

the benefits of exercise, high school students, particularly girls, generally do not

participate in sufficient levels of vigorous physical activity (Grunbaum et al., 2004).

Though no empirical studies have examined the association between prescription

drug abuse and physical activity in adolescents, research has shown significant

relationships among physical activity levels and other drug abuse behaviors including

those involving tobacco and marijuana (Pate et al., 1996; Winnail et al., 1995). In

comparing physically inactive (reporting fewer tan 2 days a week of light exercise) with

highly active adolescents (reporting 6 or more days exercise) Pate et al., (1996) found

that physical activity level was inversely related to cigarette and marijuana use but not

alcohol use. In fact, students who consumed one or more alcoholic beverages in the past

month were more likely to be in the high activity group (Pate et al., 1996).

In a similar study, Winnail et al. (1995) grouped a physical activity variable on

the South Carolina YRBS into three levels-"lower," "moderate," and "higher" to find

that higher level of physical activity served as a protective factor for White males in the

use of cigarettes, smokeless tobacco, and marijuana. The relationship however, did not

hold for Blacks or females. Although the study suggested a relationship among White

males, it was not based on a nationally representative sample, and results should therefore

be interpreted with caution. Evidence suggests that as adolescents age, not only do their

levels of physical activity decline but their substance use tends to increase (Grunbaum et

al., 2004). However, given the relative absence of studies examining the covariation of









physical activity and drug use and the mixed results of the studies that have been

conducted, it is apparent that further research needs to be conducted on the topic.

Use of Problem Weight Loss Practices

Studies have shown high rates of body dissatisfaction and dieting among

adolescents. This is especially pronounced among females, many of which engage in

unhealthy weight control behaviors such as fasting and self-induced vomiting.

(Neumark-Sztainer & Hannan, 2000). It is estimated that up to 8% of females in the U.S.

suffer from anorexia nervosa and/or bulimia during their lifetime (APA, 2004). Both

these disorders are associated with high rates of comorbidity. Extreme dieting and

disordered eating behaviors among youth are of concern in that they have the potential to

adversely affect nutrient intake, mental health status, and long-term health outcomes such

as osteoporosis (Neumark-Sztainer & Hannan, 2000).

Although problem weight loss tactics items on the YRBS can not be used to define eating

disorders, they help assess behaviors that are potentially problematic.

Although prescription stimulants are now rarely prescribed to treat obesity and

overweight, it is widely known that these drugs prevent weight gain. Studies correlating

problem weight loss practices with drug use have confirmed that adolescents who are

willing to engage in unhealthy weight loss behaviors such as restricting and purging, are

also likely to use prescription stimulants to lose weight. In a study of 854 adolescent

girls and young women age 12 through 23, 9.5% reported using stimulant drugs to

control their weight. Many of those same females also reported dieting, fasting, and

vomiting for weight control (Moore, 1988). A recent study on 101 female adolescents

clinically diagnosed with an eating disorder (i.e., meeting DSM-IV criteria) found that

both anorexic and bulimic patients were significantly more likely to abuse stimulant









drugs than those young women in a comparison group (Stock, Goldberg, Corbett, &

Katzman, 2002). It is also important to note that several studies of high school

populations, including those using the YRBS, have demonstrated an association with

problem weight loss tactics and the use of cigarettes, alcohol, or marijuana, suggesting

that they may cluster with substance abuse behaviors (Neumark-Sztainer et al., 1998;

Rafroiu et al., 2003).

The relationship among adolescent drug use and various health risk behaviors

demonstrates the importance of developing a deeper understanding of prescription drug

abuse exposure and involvement within the broader context of risk. These associations

provide compelling support that the abuse of prescription drugs may represent a part of

the pattern of polydrug abuse and a larger cluster of problem behaviors.














CHAPTER 3
MATERIALS AND METHODS

In this study, I examined the relationship between non-medical prescription drug

use and other risk behaviors among adolescents using a modified version of the 2005

Youth Risk Behavior Survey referred to hereafter as the Santa Rosa County Health

Behavior Survey (SRCHBS). In this chapter, the methods that I used to conduct the

research project are described. A description of the research design, research variables,

population, instrumentation, data collection procedures, and data analyses are presented.

Research Design

According to Creswell, research designs "are the specific procedures involved in

the last three steps of the research process: data collection, data analysis, and report

writing" (p. 51). Specifically, research designs allow investigators to answer our research

questions (Cottrell & McKenzie, 2005). A study's design is therefore important in

determining whether one's findings are scientifically sound.

This study employed a correlational research design using specific survey

measures for data collection. Cottrell and McKenzie (2005) describe correlational

research as "non-experimental research that examines relationships between or among

variables" (p. 7). In utilizing a correlational design, the researcher must be careful to

refrain from concluding a cause and effect relationship between factors. Causal

relationships can only be established by using experimental design (Cottrell & McKenzie,

2005).









A correlational study assumes that the researcher can first describe (by measuring

or observing) each of the variables she is trying to relate (Trochim, 2001). The most

widely used method to collect descriptive and behavioral data in health education is the

survey research design. According to Alreck and Settle (2004), a survey is "a research

technique where information requirements are specified, a population is identified, a

sample is selected and systematically questioned, and the results analyzed, generalized to

the population, and reported to meet the information needs" (p. 449). A survey's value

depends on both the amount of resources devoted to it and the care and expertise that

goes into the work. Surveys frequently take the form of questionnaires (e.g., paper-and-

pencil, electronic) or interviews (e.g., one-on-one, focus group, telephone) (Alreck &

Settle, 2004).

The myriad strengths of survey research explain why this method is so popular

among social scientists. As cited earlier, surveys can be administered through several

media and can be tailored to measure a wide range of characteristics from a sample. In

addition, surveys employ a standardized method of data collection and can be designed to

collect a large amount of information in a relatively short period of time (McDermott &

Sarvela, 1999).

Despite the usefulness of this type of research, survey methods also have some

disadvantages. One limitation of surveys is the potential of a low response rate, as only

those respondents who are accessible and motivated to complete the survey can become

sources of data. This is especially true for self-administered surveys, where the

investigator is not present to motivate the respondent or clarify any sources of confusion

(McDermott & Sarvela, 1999). Surveys are also limited in that they depend on direct









responses from the study sample. If sensitive items are included in a survey, respondents

may skip these items because they feel embarrassed or threatened by them or may tend to

over- or under-report behaviors (Alreck & Settle, 2004). Other disadvantages of survey

research include lack of a comparison group and absence of a pre-test for assessing

change scores (Aday, 1997).

This study used a cross-sectional group-administered census survey. Using a

version of the 2005 Youth Risk Behavior Survey (YRBS) that was modified to include

items on prescription drug abuse, I assessed health behaviors of 12th grade students from

two senior high schools in Northwest Florida. Cross sectional denotes that data

collection took place at a single point in time (Cottrell & McKenzie, 2005). A census

attempts to acquire data from each member of a population and was appropriate for this

study because the known population of interest was "relatively small and readily

accessible" (Gay, 1996, p. 252). The survey was administered in the group setting of the

schools' respective cafeterias during an assembly for convenience by the researcher.

Research Variables

When conducting correlational studies, researchers sometimes refrain from using

the terms independent variable and dependent variable. They may instead refer to the

correlation of two variables, in keeping with the principle that correlations measure

relationships among variables rather than measure the extent to which the independent

variable influences the dependent variable (Creswell, 2005). For this study however, I

have defined dependent and independent variables. The dependent variable was non-

medical prescription drug use, measured by the prescription drug items in the SRCHBS.

The dependent variable was measured using a composite score of prevalence items that

correspond to the three major classes of abusable prescription drugs: opioid pain









relievers, central nervous system stimulants, and central nervous system depressants. The

composite score was then dichotomized to indicate whether a student has or has not used

prescription drugs non-medically.

The study's independent variables included tobacco, alcohol, and marijuana use;

other illicit drug use including cocaine/crack, inhalants, heroin, methamphetamines,

ecstasy, or heroin; violence-related behaviors; sadness, suicide ideation and attempts;

sexual risk taking, and the use of problem weight loss tactics. These independent

variables were measured by using the corresponding items found in the 2005 YRBS. The

independent variables were selected because they have each been correlated with

adolescent substance abuse behaviors in previous research (DiClemente et al., 1996).

Study Population

Sampling involves selecting individuals from a population in a way that

generalizes results to the population from which the units were chosen (Trochim, 2001).

Researchers often enlist sampling methods to determine how specific study participants

will be selected. The alternative to sampling is enumeration (also referred to as a

census), that is, counting the entire population (Alreck & Settle, 2004). A researcher's

ability to conduct a census depends upon the number of potential participants, researcher

accessibility to participants, and data collection resources (Cottrell & Mckenzie, 2005).

Because the numbers of 12th graders in the two public high schools selected for

the study were relatively small and readily accessible, a census was employed.

Therefore, the population for this study included all 12th grade students enrolled in two

high schools in Santa Rosa County in Northwest Florida during the spring of 2006. Santa

Rosa County is home to six public senior high schools ranging in approximate size from

295 to 1850 students. The first school had a student body of approximately 1650









students, 387 of which were seniors. The second school had approximately 1600

students, 340 of which were seniors. Thus, the study population included 727 senior high

12th graders. Students who attended the senior student assemblies at the two schools on

May 1, 2006, who received parental permission, and who volunteered to complete the

group-administered, paper-pencil questionnaire served as the study participants.

Instrumentation

I collected data using a modified version of the 2005 YRBS. The YRBS is part of

the Youth Risk Behavior Surveillance System administered by the CDC. The 2005

YRBS contains 87 items designed to monitor six categories of priority health behaviors

among youth- (1) behaviors that contribute to unintentional injuries and violence, (2)

tobacco use, (3) alcohol and other drug use, (4) sexual behaviors that contribute to

unintended pregnancy and sexually transmitted diseases, (5) unhealthy dietary behaviors,

and (6) physical inactivity (CDC, 2005). The YRBS has been conducted biennially (in

odd numbered years) since 1991, on representative samples of U.S. students in grades 9-

12 (Kann, Kolbe, & Collins, 1993; Brener & Kann, et al., 2004).

Reliability measures the "extent to which an instrument's measurement is

consistent, dependable, and stable" (McDermott & Sarvela, 1999, p. 346). In 2000, CDC

conducted a test-retest reliability study of the questionnaire (Brenner et al., 2002). In that

study, the 1999 version of the questionnaire was administered to a convenience sample of

high school students on two occasions, approximately two weeks apart. Approximately

one of five items had significantly difference prevalence estimates for the first and

second administrations. The CDC used Kappa coefficients, a widely used method for

testing whether agreement on the first and second administration exceeds chance levels.

Kappa coefficient determination is a "straightforward approach to calculate simple









agreement" on two observations (Streiner & Norman, 1995, p. 116). Ten items (14%)

had both kappas <61% and significantly different time-1 and time-2 prevalence estimates,

indicating that the reliability of these items is questionable. Based on the 2000 reliability

study, certain items have been revised or deleted from subsequent versions of the

questionnaire (Brenner et al., 2002).

Validity is the "degree to which an instrument measures what it purports to

measure" (Cottrell & McKenzie, p. 143). Although no study has been conducted to assess

the validity of all self-reported behaviors that are included in the YRBS, in 2003 CDC

conducted a review of existing empirical literature to assess cognitive and situational

factors that might affect the validity of adolescent self-reporting of behaviors measured

by the questionnaire. Brener et al. (2003) discussed difficulties in determining validity of

self-report data. However, CDC had determined that for the most part, behaviors are

reported accurately, especially when they measure substance use (Brener et al., 2003).

The 2005 YRBS was modified by first excluding three supplemental items

regarding HIV/AIDS prevention and asthma, and two items regarding height and weight.

These items did not concern variables of interest for the study. Items which were added

included one demographic item regarding school site and 19 items regarding prescription

drug abuse. Eighteen of the prescription drug items (six each representing the three drug

classes of interest) largely paralleled the behavior anchors and response options of the

items in the YRBS that are designed to monitor marijuana use. The other measured

prescription drug abuse as a weight loss tactic. Instructions for the completion of the

newly added items were also written. Thus, the final questionnaire contained 102 items

(see Appendix A).









Item Development

The 19 items assessing non-medical prescription drug use were designed by (a)

reviewing literature via database searches (e.g., PubMed, PsycInfo) regarding

prescription drug use and abuse in adolescents, (b) reviewing existing prescription drug

use and abuse-related items on questionnaires appropriate for adolescents, (c) reviewing

the general drug abuse-related items on the 2005 YRBS, and (e) asking an expert panel to

critique the new items using specified guidelines.

For my study, I modified an existing instrument and therefore had to first assess

validity of any new items I incorporated. The validity of a survey reflects the degree to

which items are free from both random error and systematic bias (Alreck & Settle, 2004).

Validity is differentiated into three types: content, construct, and criterion. Content

validity is generally considered a prerequisite for other forms of validity (e.g., construct,

criterion-related, etc.) and is especially important for measures of social behaviors

(McDermott & Sarvela, 1999). Content validity of survey items "relies on judgments

about whether the questions chosen are representative of the concepts they are intended

to reflect" (Aday, 1997, p. 57). The use of assessments that lack content validity can lead

to invalid conclusions as well as the misapplications of findings (Vogt, King, & King,

2004).

According to Aday (1997), a researcher can ensure a reasonable amount of

validity by first reviewing questions or variables on the same topic that have been used in

other studies. Specifically, I referred to items found in the 2004 Monitoring the Future

Study (Johnston et al., 2005b) and the 2004 National Survey on Household Drug Use and

Health (Research Triangle Institute, 2003) because they are both considered reliable and









valid measures of drug use, and are administered at the national level annually (Cowan,

2001).

Based upon my review of the literature and existing questionnaire items, I initially

constructed 12 items and corresponding instructions for completion in a format analogous

to those monitoring marijuana use on the 2005 YRBS. Each major class of abusable

prescription drugs was represented by four parallel items for the purpose of consistency

across the measures. Specifically, the questions addressed lifetime prevalence; age at

first use; 30 day prevalence; and 30 day prevalence on school property. Figure 1

provides the instructions and the four items regarding prescription pain relievers

presented to the expert panel in the form of a text document that accompanied the Web-

based review form. Parallel items were also constructed for CNS depressants and

stimulants.











Section A: Prescription Pain Relievers

The next 4 questions are about pain relievers, which doctors sometimes prescribe after surgery or
when someone is in a lot of pain. People are only supposed to take prescription pain relievers if
they have a prescription from a doctor for them. Prescription pain relievers are sometimes called
Pain pills, Oxies, Percs, Hillbilly heroin, or Demmies. They include drugs such as: Vicodin,
OxyContin, Percocet, Percodan, Codeine (Tylenol 3 & 4), Darvon, Demerol, Dilaudid, Morphine,
Methadone. In the answers you provide, we are only interested in your use of prescription pain
relievers if the drug was NOT prescribed to you or you took the drug only for the experience or
feeling if caused. We are not interested in your use of "over-the-counter" pain relievers such as
aspirin, Tylenol, or Advil that can be bought in drug stores or grocery stores without a doctor's
prescription.


1. During your life, how many times
have you used a prescription pain
reliever that was not prescribed to
you or you took only for the feeling
it caused?
A. 0 times
B. 1 or 2 times
C. 3 to 9 times
D. 10 to 19 times
E. 20 to 39 times
F. 40 to 99 times
G. 100 or more times

2. How old were you when you first
tried a prescription pain reliever
not prescribed to you or you took
only for the feeling it caused?
A. I have never used a
prescription pain reliever in a
way not prescribed for me by
a doctor.
B. 8 years old or younger
C. 9 or 10 years old
D. 11 or 12 years old
E. 13 or 14 years old


F. 15 or 16 years old
G. 17 years old or older
3. During the past 30 days, how many
times did you use a prescription pain
reliever that was not prescribed to
you or you took only for the feeling
it caused?
A. 0 times
B. 1 or 2 times
C. 3 to 9 times
D. 10 to 19 times
E. 20 to 39 times
F. 40 or more times

4. During the past 30 days, how many
times did you use a prescription pain
reliever not prescribed to you or you
took only for the feeling it caused on
school property?
A. 0 times
B. 1 or 2 times
C. 3 to 9 times
D. 10 to 19 times
E. 20 to 39 times
F. 40 or more times


Figure 1. Instructions and Items Regarding Prescription Pain Relievers Used for the
Expert Panel Review


Content Validity

The most important element related to content validity is representativeness. That

is, the instrument must examine all the content areas adequately (McDermott & Sarvela,









1999). Specifically, the question, "do the items adequately represent non-medical

prescription drug use prevalence?" needed to be answered. After developing the

prescription drug abuse items in a format based upon the review of existing items, I

enlisted a panel of national experts (see Appendix B) who used guidelines to further

establish content validity of the survey items. These "experts" asked to conduct the

review included professionals with expertise in the area of study, measurement

specialists, and those familiar with the target audience as suggested by McDermott &

Sarvela (1999). Specifically, the experts consisted of two professors of pharmacy, both

with research interests in prescription drug abuse surveillance and prevention; the vice-

president of risk management and health policy at a major pharmaceutical manufacturing

firm; an adjunct professor of behavioral biology at a school of medicine, and vice

president for research and health policy at a consulting firm specializing in public health

and addiction treatment policy; a behavioral scientist and expert in youth risk behaviors

from the Division of Adolescent and School Health at the Centers from Disease Control

and Prevention who has worked with the YRBS since its inception in 1991; a professor in

the department of community and family health at a college of public health with

research interests in health behavior, adolescent health, and measurement; an assistant

professor specializing in statistics, measurement, and psychometrics from a college of

education; and a professor in the department of applied health science at a college of

health, physical education and recreation with research and teaching interests in

measurement and evaluation of health education programs.

Once members agreed to serve on the panel through electronic mail (e-mail)

correspondence, they were sent a follow-up e-mail message consisting of a cover letter







60



and an embedded hyperlink directing them to the Web-based item review instrument on


February 2, 2006 (see Appendix C). An abstract of my proposed research study


(Appendix D) and a text document containing the items for review were also attached to


the e-mail (see Appendix E). When expert panelists were ready to conduct the review,


they simply selected the link which connected them to the questionnaire and contained


explicit directions for the assessment of the items, their instructions, and response options


(Appendix F). Perseus Survey Solutions Express, Web-based survey software, was


used to design the expert panel review instrument and subsequently manage responses as


they were submitted by the reviewers. A screen capture of the Web-based survey


instructions is provided in Figure 2.

*4 Rx Drug Items Expert Panel Review Microsoft Inrernet Explorer


Rx Drug Items Expert Panel Review

Dear Reviewer:
For the expert panel review, you will be asked to assess instructions, questions, and response options for 12 items pertaining to non-medical use of prescription
drugs. The six criteria to keep in mind are listed below, each with examples of questions to consider.
a. Focus Are the instructions, questions, etc., focused directly on the issue or topic to be measured'
b. Brevity: Are the instructions, questions, etc., stated as briefly as possible'
c. Clarity: Are the instructions, questions, etc., expressed as clearly and as simply as possible: concerning the type and scope of information wanted, the
location for the responses, and the form in which answers are to be given'

d Readability (vocabulary): Are the words used familiar to the respondents? Are items worded with terminology that is appropriate for respondents?
e- Assurance: Are instructions worded to allay fears, suspicion, embarrassment, or hostility on the part of the respondent' Are instructions worded in an
interesting and inviting manner so as to encourage the respondent to participate fully and to respond in an honest manner?
f. Adequacy of Options: Are response options all inclusive, mutually inclusive, and meaningful Are ample numbers of response options provided to
measure the behavior under investigation adequately"
You are not asked to answer the questions as though you were in the role of a student. Rather, please review instructions and items according to the six criteria
and your professional expertise. Although I have provided you with a text version of the items for review, please note any comments you might have in the boxes
provided on this web-based survey only.

Please click the Next button below to continue with the assessment-





Powered by SurveySolutions Conduct your own employee satisfacton survey


Figure 2. Screen Capture of the Web-based Expert Panel Review Form









The criteria for item assessment were based upon six important attributes of

survey instructions and questions (i.e., focus, brevity, clarity, readability, assurance, and

adequacy of response options) adapted from Weiler's (1997) expert panel review. The

panelists were also asked to provide their professional opinion regarding the currency of

"slang" or "street" terms for prescription drugs commonly used by illicit users. Because

some of the panelists chosen were not experts in prescription drug abuse, but rather,

experts in measurement or youth risk behaviors, all were not expected to respond to these

items. Finally, the panel members were encouraged to provide additional comments and

recommendations regarding any aspect of the survey.

Overall, the response to the expert panel review was positive. However, the

experts did offer numerous comments for revision (see Appendix G). Some of the more

substantive suggestions included the need to evaluate the usefulness of the operational

definition of prescription drug abuse used in the survey; condensing the instructions in

order to improve readability; the addition or deletion of selected drug (either trade or

slang) names based on current trends; the addition of a response option for the items

addressing 30 day prevalence; and the integration of two additional items- one assessing

where the students are obtaining the drugs and the other, asking the students to indicate

reasons for taking the drugs. In addition, a small number of formatting suggestions were

offered, such as boldfacing or italicizing selected terms.

Upon review of the responses, it became clear that further dialogue with one or

more of the panelists who had expertise in prescription drug use and abuse would be

helpful to come to an agreement as to which items should be modified and to what extent

they should be changed. I first arranged a phone conference with the vice-president of









risk management and health policy at a pharmaceutical firm. We discussed the validity

of the operational definition of non-medical prescription drug use as taking a prescription

drug "that was not prescribed to you or you took only for the feeling it caused,"

frequently used on federally-sponsored surveys (i.e., National Survey on Drug Use and

Health). This definition has been criticized as imprecise because it may not only capture

intentional illicit use but also therapeutic use (e.g., a pain reliever offered to them by their

parent for an appropriate reason- analgesia). We discussed strategies to validate the

definition by including additional items. These additional items would ask respondents

to choose from reasons for use (i.e., the experiences or feelings sought from using the

drugs non-medically). This would allow me to conserve the definition used on Federal

surveys for the sake of comparison. It would also allow me to determine the students

who may not truly be abusing the drugs, but rather, using for the therapeutic purpose, just

without a doctor's permission. During the phone conversation, we also discussed the

inclusion of items exploring where the students get the prescription drugs for non-

medical use.

Based on that conference and reviewer comments, I then modified the initial set

of items by: significantly minimizing the length of instructions pertaining to each drug

class-completely removing the second, fifth, and sixth sentences from each prompt;

adding a single item regarding prescription drug abuse as a weight loss tactic; adding

two items for each drug class- addressing sources of the drugs (see items 52, 57, and

63) and reasons for use (see items 52, 58, and 64); adding the response option "G. 100 or

more times" to the items pertaining to 30-day prevalence; and boldfacing the terms "on

school property." Upon revision, the new set of items was e-mailed to the four content









reviewers for assessment, none of which had further suggestions. Thus, as a result of the

expert panel review process, the 19 prescription drug abuse-related items were

subsequently modified for the pilot study.

Human Subjects

Before initiation of any study components involving human participants, an

application to the University of Florida Institutional Review Board (UFIRB) was

submitted (Appendix H). The UFIRB reviews all University of Florida research projects

involving human subjects. Because Santa Rosa County agreed to oversee the

administration of parental consent forms and survey materials and maintain anonymity of

the research participants, the researcher's role was classified as secondary data analyst

(Appendix I). Thus the study, project number 2006-U-0164, was deemed exempt from

further university review in accordance with the Federal Office for Human Research

Protections (OHRP) code number 45 CFR 46.101b(4) (see Appendix J).

Pilot Study

A pilot study can be thought of as a "dress rehearsal" for the larger research study

in which methods, materials, and procedures to be used are tested for feasibility and

appropriateness among the target population (McDermott & Sarvela, 1999). The pilot

study often involves the administration of a preliminary survey on a small, convenience

sample, permitting the researcher to obtain vital feedback regarding data collection

instruments, instructional material, data collection procedures, and data analysis

procedures (Alreck & Settle, 2004). In the case of a survey, the pilot study is crucial, in

that it allows the researcher to estimate the length of time needed to complete the survey.

Any problems detected in the pilot study can therefore be corrected before the project









details are finalized and the larger, formal survey conducted (McDermott & Sarvela,

1999).

The pilot study served not only to test the procedures used for administration of

the SRCHBS but also allowed me to assess test-retest reliability of the newly constructed

items. Test-retest reliability is concerned with the extent to which responses on survey

items are similar when administered at two points in time (McDermott & Sarvela, 1999).

This type of reliability estimates the stability of repeated measures and is often conducted

on items that assess surveillance data (McDermott & Sarvela, 1999; Brenner et al., 2002).

In this approach, respondents complete the instrument twice and a coefficient is

computed for the relationship between the scores on the first and second administrations

(Di lorio, 2005).

Four teachers from a moderately large sized high school in Northwest Florida

were recruited for the pilot study. They were selected in a conveniently sampled fashion,

by the school principal to facilitate the survey in two of their classes. Each of the four

teachers taught a sample of students representing one of four grade levels: 9, 10, 11, and

12. One of the teachers taught 9th grade health and the remaining three teachers taught

English (grades 10, 11, and 12.)

A total of 209 students were registered for the classes recruited for the pilot study.

Two weeks before study initiation, on February 21, 2006, students received parental

consent forms and were instructed to present them to their parents or guardians and return

them to their teachers no later than March 10, 2006 (see Appendix K). No student in the

sample returned a form in which a parent or guardian refused consent for their child's

participation in the study.









Because they served as "data collection assistants" for the pilot study, the four

teachers who agreed to participate received a brief instructional session on March 20,

2006, describing the study and the protocol designed for collecting data (Appendix L).

The teachers were instructed not to deviate from the protocol when administering the

survey. These steps were taken to ensure uniformity in conducting the survey. At the

instructional meeting, participating teachers were also presented with a small token of

appreciation in the form of a thank-you card and a $50.00 gift certificate to a local

restaurant.

Data collection procedures employed by Brener et al. (2002) were used for the

pilot study, as this test-retest technique has been used successfully in previous studies and

adequately safeguards students' privacy. Before the first survey administration, a unique

number was assigned to two Scantron answer sheets. Each set of identically numbered

answer sheets was placed inside a separate questionnaire booklet: one marked with the

letter "A" in the upper right-hand corner, and the other marked with the letter "B". These

two surveys each consisted of the same 102 core items and differed only in that survey

"A" prompted students to offer written comments about any items they felt were

confusing or unclear. This provision allowed students the opportunity to express any

concerns about the newly added items. Both pairs of Scantron sheet/booklet sets were

placed in a large envelope.

On March 21, 2006 the students were given a class period to complete the first

"test" of the SRCHBS. Students were instructed to remove and use only answer

sheet/booklet set "A" during the first administration. Answer sheet/booklet set "B" was

to remain in the envelope which was then sealed by the student. Students were









subsequently instructed to write their name across the seal. Survey start time; time the

first student finished; and time the last student finished the 102-item survey instrument

was recorded during the survey administration by the teachers. The mean completion

time (and standard deviation), for the fastest students was 21.25 (SD=5.39) minutes, and

was 38.00 (SD=9.62) minutes for the slowest students. Completed answer sheets were

then placed in a large envelope and sealed by the teacher in full view of the students. Of

the 209 students enrolled for the classes participating in the pilot study, 23 students were

absent and two students chose not to participate in the survey on the first testing day,

therefore providing 188 survey responses.

On April 4, 2006, 14 days from the first survey administration, students were

asked to re-take the SRCHBS. Before the second survey administration, each student

who participated in the first test received the envelope with his or her name written across

the seal. After removing and completing survey "B," the students' answer sheets were

collected and the empty envelopes were deposited in a wastebasket. Envelopes

belonging to students who were absent on the second survey administration were also

disposed of. Throughout all the methodological steps, considerable attention and

emphasis was placed on ensuring anonymity, privacy, and confidentiality among all

student respondents.

Of the 188 students present on the first testing day, 13 were absent on the second

testing day, thus providing 175 response set pairs. Upon visual inspection of the response

pairs, 18 sets were deemed unusable when reasonable exclusion criteria were applied,

therefore yielding 157 completed sets. The pre-specified criteria included: completion of

less than one-half of the 19 relevant items; indication of blatant patterning (e.g., use of









one response option only, Christmas-treeing, etc.); and use of response options that were

not viable.

Although the simple reliability coefficient can be used as an estimate of test-retest

reliability, I chose to compute the Kappa (K) coefficients for items of interest upon

completion of the pilot study. The simple reliability coefficient is easy to calculate and

understand. However, a disadvantage to that approach is that it does not take chance

agreement into consideration (Di lorio, 2005). Many researchers cite the K statistic as

being the "most appropriate method of analysis between raters or events when the data

are categorical" (Tooth & Ottenbacher, 2004, p. 1372). Kappa is an extension of simple

percent agreement in that it is "...the proportion of the total amount of agreement not

explained by chance for which the observers accounted" (Cohen, 1960, p. 38). Thus, the

most acceptable reliability statistic, adjusting for the agreement based on chance, is the K

coefficient, calculated using the formula:

K = (Po-Pc)/(1-Pc),

where

K = Kappa,

Po = the proportion of the total number of observations in agreement, and

Pc = the proportion of observations in the agreement cells expected by chance given the

marginal distributions (Di lorio, 2005).

Although Kappa values can range from -1.0 to 1.0, values greater than zero are

most typical and indicate agreement better than chance. For example, a K of 0.60 means

that the student responses to the instrument account for 60% of the agreement over and









above what would be expected by chance alone. Kappa values less than zero therefore,

indicate agreement worse than chance.

Various interpretations of the Kappa statistic have been proposed and differ only

slightly (Tooth & Ottenbacher, 2004). Researchers however, often cite the benchmarks

offered by Landis and Koch (1977) as the rough cut points for interpretation (Table 1).

Scholars therefore agree that K values between 0.21 and 0.40 are fair, those between .41

and .60 are moderate, those between .61 and .80 are substantial, and those above .80 are

near perfect (Landis & Koch, 1977; Tooth & Ottenbacher, 2004).

Table 1. Benchmarks for Interpretation of the Kappa Statistic (Landis & Koch, 1977)
Kappa Statistic Strength of Agreement

<0.00 Poor

0.00-0.20 Slight

0.21-0.40 Fair

0.41-0.60 Moderate

0.61-0.80 Substantial

0.81-1.00 Almost Perfect


Item Reliability

As estimates of test-retest reliability, simple Kappa values were calculated for all

items and weighted values for Kappa were calculated for selected items (Table 2).

Simple and weighted Kappas have separate but related indications for use. The simple K

considers all disagreements in ratings as equal in "weight," as for truly nominal data.

When there is an implied ranking in the data, as for a likert-type ordinal scale however,

weighted K may become more useful (Tooth & Ottenbacher, 2004). A weighted K treats









disagreement in response options that are similar as less significant than disagreements

that are dissimilar. For example, when referring to prevalence-related items on the

SRCHBS one student may have responded they used prescription pain relievers "1 or 2

times" during test one and "3 or 4 times" during test two. For the same item, a second

student may have answered "1 or 2 times" during test one and "44 to 99 times" during

test two. These two students would have influenced simple K in exactly the same way.

However, the first student, whose responses were not the same but still similar, would

have influenced weighted K less than the second student who provided divergent answers

during the two tests. Therefore, both simple and weighted Kappa values may be useful

when assessing the reliability of ordinal response options, specifically those regarding

prevalence and age at first use on the SRCHBS.

According to the pilot test analysis, simple Kappa values for 14 of the 19 items

fell within the range of "good" or "excellent." Two items had a Kappa within the

"moderate" range and three items fell within in the "poor" range. Due to the nominal

nature of some of the response options, weighted Kappa was only computed for items

with ordinal response options. Of the 10 items for which weighted K was calculated, four

items fell within the "excellent" range. Two fell within the "good" and two fell within

the "moderate" ranges. One item performed in the "poor" range. Weighted K for three

applicable items could not be computed due to the fact that too few response options

were indicated in the student data set. Because an overwhelming majority of the items

fell within the "moderate," "good," or "excellent" range of test-retest reliability, the items

were deemed appropriate for further use in the main study.









Table 2. The Santa Rosa County Health Behavior Survey Pilot Study: Measures of
Test-Retest Reliability for Prescription Drug Abuse Items Using Kappa (cK)
Statistics


Item

1. Pain Relievers: Lifetime use

2. Pain Relievers: Age at first use

3. Pain Relievers: 30 day use

4. Pain Relievers: 30 day use at school

5. Pain Relievers: Where obtained

6. Pain Relievers: Why use

7. Depressants: Lifetime use

8. Depressants: Age at first use

9. Depressants: 30 day use

10. Depressants: 30 day use at school

11. Depressants: Where obtained

12. Depressants: Why use

13. Stimulants: Lifetime use

14. Stimulants: Age at first use

15. Stimulants: 30 day use

16. Stimulants: 30 day use at school

17. Stimulants: Where obtained

18. Stimulants: Why use

19. Prescription drug for weight loss


n

157

157

157

157

157

157

157

157

157

157

157

157

157

157

157

157

157

157

155


Simple K (95% CI)

0.77 (0.66, 0.89)

0.79 (0.68, 0.90)

0.54 (0.33, 0.76)

0.66 (0.05, 1.28)

0.85 (0.75, 0.95)

0.63 (0.51, 0.74)

0.66 (0.49, 0.84)

0.75 (0.58, 0.92)

0.32 (0.04, 0.59)

-0.00 (-0.01, 0.00)

0.79 (0.62, 0.95)

0.75 (0.58, 0.92)

0.75 (0.58, 0.92)

0.74 (0.55, 0.93)

0.42 (0.03, 0.81)

0.66 (0.05, 1.28)

0.71 (0.51, 0.91)

0.80 (0.64, 0.96)

0.00 (-0.00, 0.00)


Weighted K (95% CI)

0.90 (0.84, 0.96)

0.89 (0.81, 0.97)

0.56 (0.36, 0.77)


0.79

0.83

0.46

-0.00






0.73

0.85

0.49


N/A

N/A

(0.65, 0.92)

(0.68, 0.99)

(0.16, 0.76)

(-0.01, 0.00)

N/A

N/A

(0.53, 0.92)

(0.69, 1.00)

(0.14, 0.84)



N/A

N/A









Data Collection

I served as the coordinator and principal data collector for this study. Data

collection for the main study proceeded in a manner similar to that for the pilot test with

some minor changes due to logistical constraints placed upon the researcher by school

administrators. In my original proposal to the Santa Rosa County school district, I

planned to administer the survey among all 9-12 grade students enrolled in a single high

school. Of the prospective high schools contacted, none would agree to administration of

the survey among the entire student body. However, two high schools did agree to

conduct the study among only senior students and in a group setting. Thus, although the

pilot study was conducted among 9 through 12 graders in a classroom setting, the final

study population was comprised only of 12th grade students and took place in the

cafeterias of the two participating high schools during group assemblies. Finally, rather

than teachers, I served as the moderator for the larger study at both schools while teachers

served as co-data collectors by assisting in the distribution and collection of survey

materials. Unlike the pilot study for which no students were denied parental permission,

a total of four students returned forms denying permission to participate. Teachers were

instructed not to distribute surveys to these students.

In order to maximize student participation, the self-administered SRCHBS in the

form of a survey booklet and Scantron answer sheet was provided to all participants

during a senior assembly. Sharpened pencils were also distributed to students who

neglected to bring their own. On May 1, 2006, the survey was administered to 12th grade

students at the first high school during a first period student assembly. While the first

survey was underway, I left the head guidance counselor in charge of collecting the

finished surveys while I traveled to the second school to administer the survey during a









second period student assembly. The use of a single class period at both high schools

allowed for administration of the survey to all students at the same time and in a similar

setting, and helped control for the disruption of other school procedures. Survey

administration during class periods earlier in the day also helped control for students who

had afternoon jobs or who were dual-enrolled in college courses.

Data Analysis

Student data from electronic answer sheets were scanned by an optical scanner,

saved in ASCII format, transferred to a data file, and then analyzed using the Statistical

Analysis System (SAS), version 9.1. Descriptive statistics were first generated to

develop a demographic profile of the sample. Unadjusted prevalence rates were

calculated by dividing the number of students reporting the behavior by the total number

of students in the sample

Inferential statistics were then used to (1) examine the relationships between

demographic characteristics with prescription drug abuse risk behaviors and (2) answer

the research questions. Chi square statistics (x2) were first used to determine whether

individual demographic characteristics (i.e., gender, grade, race) were associated with

prescription drug abuse. Chi-square analysis is a test of significance "appropriate when

data are in the form of frequency counts occurring in two or more mutual mutually

exclusive categories" (Gay, 1996). The dependent variable, prescription drug abuse, was

represented as a dichotomous (yes/no) variable taken from the composite of prevalence

items that correspond to the three major classes of abusable prescription drugs: opioid

pain relievers, central nervous system stimulants, and central nervous system depressants.

Demographics were treated as nominal variables with the following corresponding levels:

Gender (male, female), grade (9th, 10th, 11th, 12th, ungraded), and race (American









Indian/Alaska Native, Asian, Black/African American, Hispanic/Latino, Native Hawaiian

or Other Pacific Islander, White, Other). Chi square test compares proportions actually

observed in a study with proportions expected thus, providing a measure of how much

the frequencies differ from one another.

Five assumptions underlie the use of the chi-square statistic:

1. Each observation must fall in one and only one category.

2. The observations in the sample are independent of one another.

3. The observations are measured as frequencies.

4. The expected frequency for each category is not less than 5 for df>2 and

not less than 10 for df= 1.

5. The observed values of 2 with 1 degree of freedom must be corrected for

continuity in order to use the table of values of critical. (Shavelson, 1995)

Multiple logistic regression was then used to predict the student level outcomes of

prescription drug abuse when controlling for demographic characteristics that were

significantly associated with prescription drug abuse according to the chi-square results.

Logistic regression is a type of regression analysis where the dependent variable is a

dummy variable (coded 0 or 1) and the independent variable can be of any type (Agresti,

1996). The logistic regression model is simply a non-linear transformation of the linear

regression, using a "logit" distribution rather than a normal distribution, which constrains

the estimated probabilities to lie between 0 and 1 (Agresti, 1996).

Multiple logistic regression also allowed for me to examine the relationship

between non-medical prescription drug use and the likelihood of engaging in the other

risk behaviors addressed by the research questions. Specifically, those behaviors









included driving under the influence of alcohol or riding with a drunk driver; tobacco,

alcohol, and marijuana use; other illicit drug use including cocaine/crack, inhalants,

heroin, methamphetamines, ecstasy, or heroin; violence-related behaviors; sadness,

suicide ideation and attempts; sexual risk taking, and the use of problem weight loss

tactics.

When conducting logistic regression, significance is most conveniently

understood using the odds ratio. The odds ratio is commonly interpreted as the

probability of the event (or "yes" outcome) divided by the probability of the nonevent (or

"no" outcome) (Agresti, 1996). In Chapter 4, results for the logistic regression are

therefore reported in the form of odds ratios and 95% confidence intervals in addition to

regression coefficient and test statistic.

Summary

Chapter 3 described the methods and materials used to conduct this study. It

provided a description of the study's purpose, research design, research variables,

participants, instrumentation, data collection, and data analysis. Chapter 4 reports the

results associated with the nine research questions in addition to providing an overview

of the demographics and prevalence estimates of prescription drug abuse among study

participants.














CHAPTER 4
RESULTS

This study explored the relationships between non-medical prescription drug use

and other related adolescent risk behaviors using the Santa Rosa County Health Behavior

Survey (SRCHBS)-a version of the 2005 Youth Risk Behavior Survey modified to

include measures of prescription drug abuse.

In this chapter, I present the data I collected by the methods described in Chapter

3 using the SRCHBS. Specifically, I describe the demographic characteristics for the

study participants; illustrate the profile for prescription drug misuse among respondents;

and report the findings of data analyses relating to my research questions.

Participant Demographics

This section describes the demographic characteristics of the participants

surveyed in this study. The defined population was all 12th grade students enrolled at

two Northwest Florida senior high schools during the last quarter of the 2005-2006

academic year (N=727). Three-hundred and eighty-seven senior students were enrolled

at the first school and 340 were enrolled at the second. An attempt was made to reach

every member of the senior class at these two schools. This is commonly referred to as a

census survey.

Of the population of 727 seniors from the two schools combined, 229 and 256

students returned the questionnaires from the first and second school, respectively. As a

result of sight-editing the answer sheets, I disregarded 24 cases from the first and 26

cases from the second school from the data analysis, yielding a total of 435 useable









questionnaires. As in the pilot study, rejection of a collected survey was based on

completeness of sections, and on the identification of patterned, hostile, or superfluous

responses. In addition, students indicating a grade level other than 12 were also removed

from the sample. A summary of participation rates by schools is provided in Table 3.

Table 3. Santa Rosa County Health Behavior Survey: Participation Rate by High
School

High School Enrollment Returned Useable Participation Rate (%)

School 1 387 229 205 54.0

School 2 340 256 230 67.6

Total 727 485 435 59.8


Of the total useable responses, 48.2% were from males (n=208) and 51.8%

(n=224) were from females, with a mean age of 17.6 (SD=0.49) years old. Over three-

quarters of the participants were White (n=338, 78.6%). Hispanics, Blacks and Asians

represented 5.4% (n=23), 4.4% (n=19), and 3.5% (n=15) of the sample, respectively.

Other racial/ethnic minority groups (including American Indians/Alaska Natives and

Pacific Islanders) made up approximately 8.1% (n=35) of the sample. Table 4 provides

the demographic characteristics of the participants from the two schools and of the total

study sample.

Because the study population has been defined as the composite of 12th grade

students from both schools, results will hereafter be reported for the total sample and not

for each school individually.









Table 4. Santa Rosa County Health Behavior Survey: Demographic Characteristics of
Participants (expressed in percentages)*
Demographic School 1 School 2 Total

Gender (n= 432)
Male 42.9 52.9 48.2
Female 57.1 47.1 51.8

Race (n= 430) 2.5 1.8 2.1
American Indian/Alaska Native
Asian 2.9 4.0 3.5
Blacksian 1.5 7.1 4.4
BHispanic 3.4 7.1 5.4
Hispanic 0.5 1.3 0.9
Pacific Islander 0.5 1.3 0.9
White 86.3 71.7 78.6
Other 2.9 7.1 5.1
Other

Age (n= 426)
16 0.5 0.0 0.2
17 31.8 40.0 36.2
18 67.7 60.0 63.6
* Columns may not sum to 100% based on rounding error.

Profile of Non-Medical Prescription Drug Use Behaviors

This section describes respondents' self-reported non-medical prescription drug

use behaviors. Table 5 illustrates the composite prevalence estimates of lifetime, past 30

day use, and past 30 day non-medical use on school property of any prescription drug.

Close to one third (n= 138, 31.9%) of respondents indicated lifetime prevalence of any

class of prescription drug- that is one or more instances of non-medical use of either

prescription pain relievers, depressants, or stimulants; or any combination of the three.

Sixteen percent (n=69) of the subjects indicated past 30 day misuse, and 8.8% (n=38)

indicated past 30 day misuse on school property of any prescription drug.









Table 5. Santa Rosa County Health Behavior Survey: Lifetime, Past 30 Day, and Past
30 Day Non-Medical Use on School Property of any Prescription Drug
Prevalence Estimate n %
Lifetime use
Ever 138 31.9
Never 295 68.1

Past 30 day use
Yes 69 16.0
No 361 84.0

Past 30 day use on
school property
Yes 38 8.8
No 392 91.2

Table 6 illustrates specific participant responses to the items assessing lifetime

use, past 30 day use, and past 30 day use on school property found on the SRCHBS as

classified by drug class. Results are reported as frequencies and percentages. The

highest rates of non-medical prescription drug use are for pain relievers, followed by

depressants and stimulants. Close to 30 percent (n=124, 28.6%) of respondents indicated

lifetime non-medical use of pain relievers, several of which tried the drugs only one or

two times (n=47, 10.8%). However, as many as 13 students indicated extremely heavy

lifetime use in the range of 100 or more times. Regarding depressant drugs, 12.7%

(n=55) of the students indicated lifetime use. Less than one-tenth of the respondents

(n=39, 9%) reported lifetime stimulant non-medical use.









Table 6. Santa Rosa County Health Behavior Survey: Measures of Non-Medical
Prescription Drug Use Prevalence by Drug Class (expressed in frequencies
and percentages)
Item Pain Relievers Depressants Stimulants
Response Option n % n % n %
Lifetime use
0 times 310 71.4 379 87.3 394 91.0
1 or 2 times 47 10.8 25 5.8 13 3.0
3 to 9 times 29 6.7 14 3.2 3 0.7
10 to 19 times 13 3.0 7 1.6 13 3.0
20 to 30 times 10 2.3 2 0.5 5 1.2
40 to 99 times 12 2.8 4 0.9 4 0.9
100 or more times 13 3.0 3 0.7 1 0.2

Past 30 day use
0 times 377 87.0 402 93.1 408 94.7
1 or 2 times 28 6.5 15 3.5 4 0.9
3 to 9 times 12 2.8 7 1.6 9 2.1
10 to 19 times 8 1.9 5 1.2 2 0.5
20 to 30 times 3 0.7 2 0.5 5 1.2
40 to 99 times 3 0.7 1 0.2 1 0.2
100 or more times 2 0.5 0 0.0 2 0.5

Past 30 day use on
school property
0 times 403 93.1 423 97.7 415 96.3
1 or 2 times 14 3.2 2 0.5 5 1.2
3 to 9 times 6 1.4 7 1.6 4 0.9
10 to 19 times 6 1.4 1 0.2 1 0.2
20 to 30 times 3 0.7 0 0.0 4 0.9
40 to 99 times 1 0.2 0 0.0 1 0.2
100 or more times 0 0.0 0 0.0 1 0.2

Far fewer students reported past 30 day non-medical use of prescription drugs,

often used as a proxy measure of current use. Thirteen percent (n=56) of respondents

indicated past 30 day use of pain relievers, while 6.9% (n= 30) and 5.3% (n=23) reported

past 30 day use of depressant and stimulant drugs, respectively. Less than 10% of

respondents indicated past 30 day use while on school property for any of the three drug

classes. Prevalence rates of past 30 day use while on school property rates of were 6.9%









(n=30), 2.3% (n=10), and 3.7 (n=16) for pain relievers, depressants, and stimulants,

respectively.

According to estimates obtained from the survey, prevalence of non-medical

prescription drug use ranks high, second only to marijuana. As seen in Table 7, the non-

medical use of both prescription pain relievers and depressants is more common than

ecstasy use. Non-medical use of prescription stimulants is more prevalent than the use of

methamphetamine, steroids, heroin, or injected drugs.

Table 7. Lifetime, 30-Day, and 30-Day on School Property Prevalence of Selected
Drug Classes (expressed in percentages)
Drug Class Lifetime 30-day* 30-day on
school
property*
Marijuana 39.9 18.9 4.2
Any Prescription Drug 31.9 16.0 8.8
Prescription Pain Relievers 28.6 12.9 6.9
Prescription Depressants 12.7 6.9 2.3
Ecstasy 11.7 --- ---
Huffing (i.e., Paint, Glue, Sprays) 11.6 --- ---
Cocaine 10.7 5.8 ---
Prescription Stimulants 9.0 5.3 3.7
Methamphetamine 6.3 --- ---
Steroids 4.2 --- ---
Heroin 3.0 --- ---
Injected Drugs 2.1 --- ---
* Selected drug classes were not assessed for 30-day and 30-day on school property
prevalence.

Table 8 illustrates participant responses to the items assessing age at first use,

source of the specific drug, and motivations for non-medical use. These results are also

categorized by drug class and feature the frequencies and percentages of specific

response options found on the SRCHBS. For each of the three drug classes, the majority

of students reported non-medical prescription drug use initiation at the age of 15 years

old or older. When asked where they obtained the prescription drugs for non-medical









use, the most frequent response across all three drug classes was "a friend." However

other common responses for pain relievers were "took my own" (n=20, 4.6%), and "from

a parent or guardian with their permission" (n=19, 4.4%). For depressants, the next most

common responses were "from a parent or guardian with their permission" (n=7, 1.6%),

and "from a parent or guardian without their permission" (n=7, 1.6%). For stimulants,

the next most common responses regarding sources of prescription drugs were "took

own" (n=6, 1.4%) and "other" (n=3, 0.7%).

Regarding motivations for prescription drug misuse, "to get buzzed, high, or

stoned" was the response option chosen most frequently for each of the three drug

classes. The next most common responses were "to relieve physical pain" (n=29, 6.7%)

and "to experiment" (n=17, 3.9%) for pain relievers; "to experiment" (n=12, 2.8%) and

"to relax or relieve stress" (n=8, 1.9%) for depressants; and "to experiment" (n=9, 2.1%)

and "to concentrate or study" (n=4, 0.9%) for stimulants.









Table 8. Santa Rosa County Health Behavior Survey: Self-Reported Age at First Use,
Drug Source, and Motivations for Non-Medical Use by Drug Class (expressed
in frequencies and percentages)
Item Pain Relievers Depressants Stimulants
Response Option n % n % n %

Age at first use
Never used 307 70.7 372 85.9 395 91.4
8 years or younger 7 1.6 3 0.7 1 0.2
9 or 10 years old 4 0.9 5 1.2 2 0.5
11 or 12 years old 7 1.6 2 0.5 5 1.2
13 or 14 years old 29 6.7 11 2.5 10 2.3
15 or 16 years old 45 10.4 19 4.4 11 2.6
17 years old or older 35 8.1 21 4.9 8 1.9

Where obtained the Rx drug
Never used 314 72.5 378 87.3 392 91.0
Took own 20 4.6 2 0.5 6 1.4
From a parent w/ permission 19 4.4 7 1.6 1 0.2
From a parent w/o permission 5 1.2 7 1.6 2 0.5
From brother or sister 3 0.7 1 0.2 2 0.5
From another relative 1 0.2 0 0.0 0 0.0
From a friend 54 12.5 35 8.1 24 5.6
Stolen from a pharmacy/doctor's 3 0.7 0 0.0 1 0.2
From the Internet 1 0.2 0 0.0 0 0.0
Other 13 3.0 3 0.7 3 0.7

Why used the Rx drug
Never used 309 71.5 380 87.8 392 91.2
To experiment 17 3.9 12 2.8 9 2.1
To get buzzed, high, or stoned 42 9.7 21 4.9 15 3.5
To fit in 2 0.5 0 0.0 1 0.2
To get away from problems 8 1.9 5 1.2 3 0.7
To alter the effects of other drugs 3 0.7 4 0.9 1 0.2
To relax or relieve stress 13 3.0 8 1.9 N/A N/A
To help sleep 2 0.5 0 0.0 N/A N/A
To relieve physical pain 29 6.7 0 0.0 N/A N/A
To get more energy or stay awake N/A N/A N/A N/A 3 0.7
To concentrate or study N/A N/A N/A N/A 4 0.9
To lose weight N/A N/A N/A N/A 1 0.2
Other 7 1.6 3 0.7 1 0.2









Chi-Square Analysis

Before answering the research questions, it was first necessary to conduct chi-

square analyses to determine whether individual demographic characteristics (i.e., gender

and race/ethnicity) were associated with non-medical prescription drug use. In order to

compute a valid chi-square test, it is important to ensure that a minimal number of

categories has an expected frequency greater than or equal to 5. Because some levels of

the race/ethnicity variable were rarely cited, six levels- African American, American

Indian, Asian, Hispanic, Pacific Islander, and Other-were collapsed to form the "Non-

White" category.

According to the chi-square analyses (Table 9), no significant difference exists

between prevalence rates and relevant demographic variables at the a = 0.05 significance

level.

Chi-square analyses were also conducted among the individual prescription drug

classes- pain relievers, depressants, and stimulants. The values presented in tables 10,

11, and 12 illustrate that no significant difference (at the a=0.05 level) exists among

prevalence rates for individual drug class based upon demographic classification.









Table 9. Chi-square Analysis of Demographic Variables by Lifetime, Past 30 Day, and
Past 30 Day on School Property Prevalence of any Non-Medical Prescription
Drug Use
Lifetime Past 30 Day Past 30 Day on
School Property
Demographic n % 2p- n % p- n % 2p-
Variable value value value
Gender
Male 70 33.0 0.70 35 17.1 0.62 21 10.2 0.35
Female 68 31.3 34 15.3 17 7.7

Race/Ethnicityb
White 110 33.0 0.80 56 16.6 0.68 28 8.3 0.38
Non-White 28 31.1 8 14.8 10 11.3

aThe sample size for the "Lifetime" x2 analysis was 430 cases whereas the sample size for
the other two x2 analyses was 427 cases. bThe sample size for the "Lifetime" x analysis
was 428 cases whereas the sample size for the other two x2 analyses was 425 cases.


Table 10. Chi-square Analysis of Demographic Variables by Lifetime, Past 30 Day, and
Past 30 Day on School Property Prevalence of Non-Medical Prescription Pain
Reliever Use
Lifetime Use Past 30 Day Use Past 30 Day Use on
school Property
Demographic n % 2p- n % p- n % 2p-
Variable value value value
Gender
Male 63 30.4 0.46 28 13.5 0.76 18 8.7 0.18
Female 61 27.2 28 12.6 12 5.4

Race/Ethnicityb
White 97 28.7 0.86 44 13.0 0.93 21 6.2 0.21
Non-White 27 29.7 12 13.3 9 10.0

aThe sample size for the "Lifetime" x2 analysis was 431 cases whereas the sample size for
the other two x2 analyses was 430 cases. bThe sample size for the "Lifetime" x analysis
was 429 cases whereas the sample size for the other two x2 analyses was 428 cases.









Table 11. Chi-square Analysis of Demographic Variables by Lifetime, Past 30 Day, and
Past 30 Day on School Property Prevalence of Non-Medical Prescription
Depressant Use
Lifetime Use Past 30 Day Use Past 30 Day Use on
school Property
Demographic n % 2p- n % 2p- n % 2p-
Variable value value value
Gender
Male 25 12.1 0.70 11 5.3 0.17 5 2.4 0.89
Female 30 13.4 19 8.5 5 2.2

Race/Ethnicity
White 44 13.0 0.81 25 7.4 0.56 8 2.4 0.94
Non-White 11 12.1 5 5.6 2 2.2

aThe sample size for the "Lifetime" x2 analysis was 431 cases; sample sizes for the "Past
30 Day" and Past 30 Day on School Property" x2 were 427 and 430 cases, respectively.
bThe sample size for the "Lifetime" x2 analysis was 429 cases; sample sizes for the "Past
30 Day" and Past 30 Day on School Property" x2 were 427 and 428 cases, respectively.


Table 12. Chi-square Analysis of Demographic Variables by Lifetime, Past 30 Day, and
Past 30 Day on School Property Prevalence of Non-Medical Prescription
Stimulant Use
Lifetime Use Past 30 Day Use Past 30 Day Use on
school Property
Demographic n % 2p- a % 2p- n % 2p-
Variable value value value
Gender
Male 20 9.7 0.66 11 5.4 0.99 10 4.9 0.23
Female 19 8.5 12 5.4 6 2.7

Race/Ethnicity
White 32 9.5 0.62 17 5.0 0.53 10 3.0 0.10
Non-White 7 7.8 6 6.7 6 6.7

aThe sample size for the "Lifetime" x2 analysis was 430 cases ; sample sizes for the "Past
30 Day" and Past 30 Day on School Property" x2 were 427 and 428 cases, respectively.
bThe sample size for the "Lifetime" x analysis was 428 cases whereas the sample size for
the other two x2 analyses was 426 cases.

As illustrated in Table 13, logistic regression analysis confirmed the results of the

chi square analyses. Odds of prescription drug misuse were not significantly influenced

by the relevant demographic variables at the a =0.05 level. Although no significant









difference exists for demographic variables by prevalence rates, previous research has

demonstrated contrary results among high school students (McCabe & Boyd et al., 2005;

McCabe, Teter, & Boyd, 2004; McCabe, Teter, Boyd, & Guthrie, 2004). The absence of

an effect may be due in part to low sample size. In addition, the variables of race and

gender may interact with the study's independent variables in important ways that did not

surface in these chi square and odds ratio analyses. Due to these circumstances, it was

determined that it would be prudent to control for race and gender in the multivariate

models when addressing the research questions.

Table 13. Odds of Lifetime, Past 30 Day, and Past 30 Day on School Property
Prevalence of Prescription Drug Abuse by Demographic Characteristics
Lifetime Past 30 Day Past 30 Day on
School Property


Demographic ORa 95% CI ORa 95% CI ORa 95% CI
Variable
Gender
Male -
Female 0.92 (0.62, 1.39) 0.88 (0.52, 1.47) 0.74 (0.38, 1.44)

Race/Ethnicity
White -
Non-White 0.94 (0.57, 1.55) 0.87 (0.45, 1.68) 1.41 (0.65, 3.02)

-Reference category. The sample size for the "Lifetime" model was 425 cases whereas
the sample size for the other two models was 422 cases. aOdds ratios are adjusted for the
predictor variables, gender and race/ethnicity, in all three models.

Research Question One

Is there a relationship between illicit drug use (i.e., marijuana, cocaine, inhalants,

heroin, methamphetamine, ecstasy, and steroids) and non-medical prescription drug use

among a sample of 12th grade students? Logistic regression analyses were used to

determine whether the use of several types of illicit drugs were associated with non-

medical use of prescription drugs after adjusting for gender and race/ethnicity. Both