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Risk assessment of preventable drug-related morbidity in older persons

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Risk assessment of preventable drug-related morbidity in older persons
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MacKinnon, Neil John, 1971-
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Morbidity ( jstor )
Older adults ( jstor )
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RISK ASSESSMENT OF PREVENTABLE DRUG-RELATED MORBIDITY
IN OLDER PERSONS














By


NEIL JOHN MACKINNON


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


1999




























Copyright 1999

by

NEIL JOHN MACKINNON


















This dissertation is dedicated to my parents, James Elliott and Shirley MacKinnon. Thank you

for your love, support, and examples of faith.
















ACKNOWLEDGMENTS


Professional maturity has much in common with maturity as a person. One attribute
common to both is a world view, an expectation that one thrives best by using one's
power to serve something bigger than oneself. -Hepler and Strand (1990)



Completing a dissertation is one sure way to mature and grow as a person, as most

people who have obtained a Doctor of Philosophy degree would agree. It is a long process, from

the genesis of an initial idea for a research project, to the time the study comes to completion.

The process is filled with times of intense struggle, frustration and difficulty, but also with times

of excitement, learning, and finally, fulfillment. Perhaps the Apostle Paul had this in mind,

when he said in the Book of Romans that "tribulation produces perseverance; and perseverance,

character; and character, hope" (Romans 5:3b-4).

In completing a dissertation, there are many individuals who play key parts throughout

the process. Obviously one group of individuals that deserves considerable acknowledgment is

my dissertation committee. We worked together for countless hours shaping a rough research

idea into a real research project. Charles D. (Doug) Hepler, my committee chair, should take a

lot of credit. He proved to be an excellent mentor and hopefully I can have even a small portion

of the success he has had as a researcher. He also helped me balance the dual roles of graduate

student and research fellow over the past three and a half years.

Earlene Lipowski, a fellow Badger, gave invaluable advice, including encouraging me to

keep a day-by-day project journal, which I have done. Richard Segal helped me initially to

enroll in this graduate program and he always seems to be in a good mood, which is a welcomed









sight on many days. Finally, GeoffVining helped mold my perspective of statistics from a

necessary evil to a useful tool for the researcher.

There are also several other individuals who made considerable contributions to this

research project. They include several people at the Florida Hospital in Orlando, from which my

patient population came: Paul Garrett, Tim Regan, Lisa Hutchison, and Scott Neel. Two

individuals at MEDai, a medical artificial intelligence company, helped to assemble the study

database: Kathleen Costello and David Katz (without pay, I might add).

Several other individuals in the Pharmacy Health Care Administration department also

deserve recognition. The ladies in the office provided not only secretarial support, but also some

great conversation: Delayne Redding, Debbie Kemp and Jennifer Ryder. The other graduate

students also provided valuable input on my dissertation and welcomed diversions away from

my dissertation too, such as racquetball. The other faculty in the department helped to provide a

great learning environment for my three and a half years of graduate school. Finally, the faculty

also helped to directly support my dissertation through the Liberty Funds.

Finally, those individuals who have provided love and support definitely deserve special

recognition. This includes my family (thanks mom and dad!) and my fiance, Leanne Moore, as

well as my Lord and Savior Jesus Christ. Philippians 4:6-7 is one Bible passage from which I

have drawn strength many times: "Be anxious for nothing, but in everything by prayer and

supplication, with thanksgiving, let your requests be made known to God; and the peace of God,

which surpasses all understanding, will guard your hearts and minds through Christ Jesus."














TABLE OF CONTENTS
page

ACKN OW LEDGM ENTS............................................................................................................... iv

LIST OF TABLES ...........................................................................................................................x

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

ABSTRA CT ................................................................................................................................. xiii

CHAPTERS

1 IN TRODUCTION ....................................................................................................................... I

The N eed for the Study.............................................................................................................. I
Older Persons and Healthcare............................................................................................. 1
Drug-Related M orbidity and M ortality............................................................................... 2
Preventable Drug-Related M orbidity and M ortality........................................................... 3
Problem Statem ent.....................................................................................................................5
Study Objectives........................................................................................................................5
Rationale and Theoretical Introduction ..................................................................................... 5
Research Questions.................................................................................................................... 8
Research Question 1 ............................................................................................................ 8
Research Question 2............................................................................................................ 8
Research Question 3............................................................................................................ 8
Research Question 4............................................................................................................ 8

2 CON CEPTUAL FRAM EW ORK ................................................................................................ 9

System s Theory and the M education Use System ..................................................................... 9
The System Matrix Applied to the Medication Use System............................................. 12
Feedback and Communication Loops in the Medication Use System ............................. 14
Change and the M education Use System ........................................................................... 19
Biopsychosocial Model of Disease Etiology and Therapy...................................................... 20
Risk Factors for PDRM ........................................................................................................... 24
Summary Of The Conceptual Models To Be Used In This Study .......................................... 27

3 REVIEW OF THE LITERATURE ........................................................................................... 28

Drug-Related M orbidity and M ortality in Older Persons........................................................ 28
Identification of Patients at High Risk of Medical Problems in Older Persons ...................... 31
Prediction M odels for Drug Use in O lder Persons .................................................................. 33
Research Assum options and Hypotheses .................................................................................. 34
Research A ssum ptions...................................................................................................... 35









Research Hypotheses .......................................................................................................36

4 M ETHODS................................................................................................................................ 48

Phase I: Operational Definitions of PDRM ............................................................................. 48
Operationalization of the Study Construct PDRM ........................................................... 49
Review of Literature to Identify Specific Operational Definitions of PDRM in
O lder Persons.............................................................................................................. 52
Survey Developm ent......................................................................................................... 52
Geriatric M medicine Expert Panel....................................................................................... 53
Identification of Consensus-Approved Operational Definitions of PDRM in
Database......................................................................................................................55
Validation of Operational Definitions of Preventable Drug-Related Morbidity ..............55
Phase II: Identification of Risk Factors for PDRM ................................................................. 58
Selection of Possible Risk Factors for PDRM .................................................................. 58
Semantic Hierarchy of Risk Factors for Preventable Drug-Related Morbidity................ 60
Study Population............................................................................................................... 62
Data Collection and Formation of the Study Database..................................................... 62
Statistical Analysis............................................................................................................ 64
Logistic Regression Model with the 18 Hypothesized Risk Factors.......................... 64
Factor Analysis........................................................................................................... 69
Logistic Regression Models with Additional Variables............................................. 70
PDRM and Healthcare Resource Utilization.............................................................. 70

5 RESULTS.................................................................................................................................. 71

Delphi Technique The Geriatric M medicine Expert Panel...................................................... 71
Identification of Consensus-Approved Operational Definitions of PDRM in
Database......................................................................................................................73
Validation of Operational Definitions of Preventable Drug-Related Morbidity ..............77
Phase II: Identification of Risk Factors for PDRM ................................................................. 85
Logistic Regression with all 18 Hypothesized Risk Factors ............................................ 85
Factor Analysis.................................................................................................................. 89
Bivariate Analysis ............................................................................................................. 92
Logistic Regression Models with Additional Demographic Variables ............................ 95
Additional Analyses Related to Risk Factor Identification............................................ 101
Risk Stratification System ............................................................................................... 105
Testing the Hypotheses.......................................................................................................... 105
First Set of Hypotheses ................................................................................................... 105
Hypothesis H 1A ........................................................................................................ 107
Hypothesis H 1B ........................................................................................................ 107
Hypothesis H 1C ........................................................................................................ 107
Hypothesis H ID ........................................................................................................ 107
Hypothesis H IE ........................................................................................................ 107
Hypothesis H 1F ........................................................................................................ 108
Hypothesis H 1G ........................................................................................................ 108
Hypothesis H 1H ........................................................................................................ 108
Hypothesis H II ......................................................................................................... 108
Hypothesis H I J......................................................................................................... 108









Hypothesis H1K........................................................................................................ 109
Hypothesis H1L ........................................................................................................ 109
Hypothesis H 1M ....................................................................................................... 109
Hypothesis H1N........................................................................................................ 109
Hypothesis H10 ........................................................................................................ 109
Hypothesis HIP ........................................................................................................ 110
Hypothesis H1Q........................................................................................................ 110
Hypothesis H1R........................................................................................................ 110
Second Hypothesis .......................................................................................................... 110
Third Hypothesis............................................................................................................. 111

6 DISCUSSION ......................................................................................................................... 113

Use of the Delphi Technique with the Geriatric Medicine Expert Panel.............................. 113
Consensus-Approved Operational Definitions of PDRM Observed in the Study
Population ....................................................................................................................... 116
Validation of Operational Definitions of Preventable Drug-Related Morbidity................... 119
Risk Factors for PDRM ......................................................................................................... 121
Risk Factor 1: Four or M ore Recorded Diagnoses......................................................... 122
Risk Factor 2: Antihypertensive Drug Use ..................................................................... 122
Risk Factor 3: M ale Gender............................................................................................ 123
Risk Factor 4: Four or M ore Prescribers....................................................................... 123
Risk Factor 5: Six or M ore Prescription M edications.................................................... 124
General Discussion on the Final Prediction M odel for PDRM ............................................. 124
General and Specific Risk Factors......................................................................................... 126
PDRM and Healthcare Resource Utilization......................................................................... 127
Potential Limitations.............................................................................................................. 127
Significance ........................................................................................................................... 129
Contribution to the Profession of Pharmacy ......................................................................... 131
Contribution to Healthcare .................................................................................................... 131
Theoretical Contribution........................................................................................................ 132
Conclusions ............................................................................................................................ 133

APPENDICES

A GERIATRIC MEDICINE EXPERT PANEL MEMBERS.................................................... 135

B EXAMPLE SURVEY FOR GERIATRIC MEDICINE EXPERT PANEL MEMBERS
ROUND 1 OF DELPHI TECHNIQUE.............................................................................. 136

C INSTRUCTIONS FOR PATIENT CHART ABSTRACTER, PATIENT CHART
ABSTRACT FORM AND SAM PLE PATIENTS............................................................... 151

D MEMBERS OF THE CHART ABSTRACT REVIEWER PANEL...................................... 158

E INSTRUCTIONS FOR CHART ABSTRACT REVIEWER PANEL................................... 159

F PERSONAL WELLNESS PROFILE SENIOR ASSESSMENT........................................... 161

G ROUND 3 OF THE GERIATRIC MEDICINE EXPERT PANEL SURVEY...................... 174


viii









LIST OF REFEREN CES ............................................................................................................. 189

BIO GRAPHICAL SKETCH ........................................................................................................ 199














LIST OF TABLES


Table page
2.1 Eight Fundamentals (Necessities) for Safe and Effective Drug Therapy............................ 15

2.2 The Fundamentals/Human Agents Cell of the System Matrix Applied to the
M education U se System ....................................................................................................... 16

4.1 Phase I M ethodology............................................................................................................ 50

4.2 Phase II M ethodology .......................................................................................................... 59

4.3 Semantic Hierarchy in Risk Factors for Preventable Drug-Related Morbidity................... 61

4.4 Hypothesized Risk Factors and Their Measurement........................................................... 65

4.5 Additional Demographic Variables and Their Measurement.............................................. 67

5.1 Geriatric Medicine Expert Panel Results............................................................................. 72

5.2 Round 2 of the Delphi Technique........................................................................................ 74

5.3 Clinical Scenarios That Were Rejected As Preventable Drug-Related Morbidities ........... 76

5.4 Patients with Outcomes and PDRM..................................................................................... 78

5.5 Number of PDRMs and Specific Outcomes by Individual Patients.................................... 79

5.6 Chart Abstract Reviewer Panel Results for Hyperglycemia with no................................... 80
Regular HgA I c M onitoring................................................................................................. 80

5.7 Chart Abstract Reviewer Panel Results: Secondary Myocardial Infarction........................ 81
in Patients Without ASA and/or Beta-Blocker Use............................................................. 81

5.8 Sensitivity and Specificity for Both of the Operational Definitions of PDRM................... 82

5.9 Sensitivity and Specificity of the Operational Definition of PDRM (Hyperglycemia
O utcom e) ............................................................................................................................. 83

5.10 Sensitivity and Specificity of the Operational Definition of PDRM (Secondary
M yocardial Infarction Outcome)......................................................................................... 84

5.11 Logistic Regression Model With All 18 Hypothesized Variables...................................... 86









5.12 Correlation Matrix for Significant Variables in Regression Model With All 18
Hypothesized V ariables....................................................................................................... 88

5.13 Classification Table for the Regression Model with the 18 Hypothesized Variables........ 90

5.14 Rotated Factor Matrix Varimax........................................................................................ 91

5.15 Rotated Factor Matrix Harris-Kaiser................................................................................ 93

5.16 Rotated Factor Matrix Varimax Validation Group........................................................... 94

5.17 Bivariate Analysis of Patients With, and Without, PDRM Categorized by
H ypothesized V ariables ...................................................................................................... 96

5.18 Bivariate Analysis of Patients With, and Without, PDRM, Categorized by
Additional Demographic Variables..................................................................................... 97

5.19 Logistic Regression Model Including Additional Demographic Variables........................ 98

5.20 Correlation Matrix for Significant Variables in Regression Model with Additional
V ariables............................................................................................................................ 100

5.21 Rotated (Varimax) Factor Matrix of Five Original Risk Factors and Three New Risk
Factors .............................................................................................................................. 102

5.22 Logistic Regression Model Excluding Antihypertensive Drug Use................................. 104

5.23 Bivariate Analysis of Patients With and Without PDRM, Categorized by Healthcare
R source U tilization ........................................................................................................ 112















LIST OF FIGURES

Figure page
2.1 The M education Use System ................................................................................................ 10

2.2 High Leverage Points W within the M education Use System.................................................. 18

5.1 Risk Stratification System.................................................................................................. 106















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



RISK ASSESSMENT OF PREVENTABLE DRUG-RELATED MORBIDITY
IN OLDER PERSONS

By

Neil John MacKinnon

May, 1999

Chairman: Charles D. Hepler, Ph.D.
Major Department: Pharmacy Health Care Administration


This study had three primary objectives: 1) to create operational definitions of

preventable drug-related morbidity (PDRM), 2) to identify patients who are at particular risk of

PDRM and who may therefore benefit from comprehensive pharmaceutical care, and 3) to create

a risk stratification system for PDRM based on the number of risk factors present in an

individual patient. The study was conducted in two phases. In the first phase, the Delphi

technique was used with a geriatric medicine expert panel to create 52 operational definitions of

PDRM in older persons. Ninety-seven patients who matched these definitions were found in

3365 older persons in a Medicare managed care health plan. This was a health plan offered by

the Florida Hospital Healthcare System, a provider-sponsored network with a Medicare contract,

available to all Medicare beneficiaries in three counties of Central Florida. Chart abstracts from a

sample of these patients were given to a panel of five pharmacists to validate the operational

definitions. Overall, the two operational definitions of PDRM that were validated were found to

have a sensitivity of 87.5 percent and a specificity of 73.5 percent as compared to the panel of

pharmacists.


xiii









In the second phase, a prediction model was created to identify risk factors for PDRM.

The dependent variable was the occurrence of a PDRM as operationally defined, and the

independent variables were the hypothesized risk factors. Forward inclusion logistic regression

models and factor analysis were used to identify risk factors for PDRM. Five risk factors for

PDRM were identified in the final prediction model: four or more recorded diagnoses,four or

more prescribers, six or more prescription medications, antihypertensive drug use, and male

gender. A risk stratification system was developed for PDRM based on the number of risk

factors present in an individual patient. Finally, patients with PDRM were shown to use

significantly more healthcare resources then patients who did not experience a PDRM.














CHAPTER 1
INTRODUCTION


The Need for the Study



Older Persons and Healthcare


The special health care concerns of older persons are important factors in health care

policy and research. This problem is predicted to worsen as the baby boom generation becomes

older and the percentage of the total population that is elderly becomes greater. Currently, the

fastest growing segment of the United States population comprises people age 85 and older. By

the year 2030, 70 million people may be enrolled in Medicare, up from the current 33 million

(Rhinehart, 1996).

Health care resource utilization patterns are different in older persons. This is, in part,

because older persons typically have at least one chronic condition and may have multiple

disease states, experience more morbidity, and have more functional limitations than younger

people. Older persons consume a disproportionate share of health care resources. Forty percent

of all health care expenditures are related to older persons (Fincham, 1996) and older persons

account for a large percentage of all hospital stays. Thirty-eight percent of all medications are

prescribed for older persons, even though older persons only make up twelve percent of the total

population (Dalziel, Byszewski and Ross, 1996). Older persons, then, clearly make a significant

economic impact on the healthcare system.

While the utilization of healthcare services is higher for the geriatric population, a small

subset of older persons is responsible for the majority of the utilization. A study which examined

the utilization patterns of continuously enrolled Medicare beneficiaries over a four year period

concluded that ten percent of the population was responsible for 88 percent of the costs (McCall









and Wai, 1983). High users in the first year tended to be high users in the following years

(McCall and Wai, 1983). Anderson and Knickman (1984) studied the temporal patterns of

Medicare beneficiaries' medical expenditures and concluded that unusually high expenditures

for a specific person in one year allow a good prediction of high expenditures in following years.

Therefore, this subset of older persons consistently uses more health care resources year after

year.


Drug-Related Morbidity and Mortality


While medications are prescribed to millions of older persons in an attempt to improve

their health-related quality of life, often an optimum outcome from these medications will not be

achieved. A drug-related problem (DRP) is any patient and time-specific event or situation

involving the medication regimen that interferes with the achievement of an optimum outcome

(Hepler and Strand, 1990). Eight types of DRPs have been described (Hepler and Strand, 1990;

Strand et al., 1990):

1. Untreated indication: The patient has a medical problem that required drug therapy

(an indication for drug use), but is not receiving a drug for that indication.

2. Improper drug selection: The patient has a drug indication but is taking the wrong

drug.

3. Sub-therapeutic dosage: The patient has a medical problem that is being treated with

too little of the correct drug.

4. Failure to receive drugs: The patient has a medical problem that is the result of his or

her not receiving a drug (e.g.; for pharmaceutical, psychological, sociological, or

economic reasons).

5. Over-dosage: The patient has a medical problem that is being treated with too much

of the correct drug (toxicity).









6. Adverse drug reactions: The patient has a medical problem that is the result of an

adverse drug reaction or adverse effect.

7. Drug interactions: The patient has a medical problem that is the result of a drug-

drug, drug-food, or drug-laboratory interaction.

8. Drug use without indication: The patient is taking a drug for no medically valid

indication.

DRPs may lead to drug-related morbidity, which is the failure of a therapeutic agent to

produce the intended therapeutic outcome (therapeutic malfunction or miscarriage) (Hepler and

Strand, 1990). The drug-related morbidity results from either (a) the production of an adverse or

toxic effect or (b) the failure to produce the desired effect within a reasonable time.

If the DRP is unrecognized or unresolved, then drug-related mortality can occur. Recent

literature reports that the clinical, economic, and humanistic outcomes of drug-related morbidity

and mortality are substantial. Lazarou, Pomeranz and Corey (1998) conducted a meta-analysis of

prospective studies involving drug-related morbidity and mortality and concluded that the

overall incidence of serious drug-related morbidity is 6.7 percent and the incidence of drug-

related mortality is 0.32 percent. This estimate places drug-related morbidity and mortality

between the fourth and sixth top cause of death (Lazarou, Pomeranz and Corey 1998). The real

incidence may actually be higher, as their meta-analysis did not include drug-related morbidity

due to noncompliance, drug administration errors, drug abuse, poisonings or therapeutic failures.

Phillips, Christfeld and Glynn (1998) reported that this problem is growing, as between 1983 and

1993, drug-related mortality increased by 257 percent.


Preventable Drug-Related Morbidity and Mortality


Some drug-related morbidities are not preventable, including those resulting from patient

idiosyncracy, while others are preventable. As described by Hepler and Strand (1990),

preventable drug-related morbidity (PRDM) has four unique elements. Given an adverse clinical









outcome, a pre-existing DRP must have been recognizable and the adverse outcome or treatment

failure must have been foreseeable. In addition, the causes of the DRP and the outcome must

have been both identifiable and controllable. However, there are no published criteria to help

determine what drug-related morbidities are, and are not, preventable. Schumock and Thorton

(1992) have attempted to develop such criteria for adverse drug reactions (one type of DRP).

Although their criteria are primarily drug-oriented, a similar patient-oriented approach for drug-

related morbidity may be possible. Others have used these criteria successfully to determine

whether adverse drug reactions were preventable (Pearson et al., 1994).

The extent of the problem of PDRM was not known until recently. A recent estimate of

the total annual cost of drug-related morbidity and mortality in the ambulatory setting in the

United States by Johnson and Bootman (1995) is $76 billion, with a range from $30.1 to $136.8

billion, in their cost-of-illness model. Although they used the opinions of experts and not actual

utilization data to obtain this estimate, this figure is quite comparable with the costs of other

major diseases, such as asthma and diabetes. Schneider et al. (1995) concluded that the annual

cost to one academic medical center was approximately $1.5 million. Nelson and Talbert (1996)

reported that 16.2 percent of admissions in 452 consecutive patients were due to drug-related

morbidity, and 49.3 percent of these admissions were definitely preventable. Fifty-six percent of

all drug-related hospital admissions in older persons in a study in Denmark were judged to have

been "definitely" or "probably" avoidable (Hallas et al., 1991). Therefore, it appears that PDRM

is a serious problem, especially in older persons.

Often the problem of drug-related morbidity and mortality is under-reported and

underestimated. Nelson and Talbert (1996) concluded that the discharge summary of almost 20

percent of patients with a drug-related admission made no mention of this fact. They and others

have lamented that there have been no rigorous methods for identifying and evaluating drug-

related morbidity and mortality. Underestimation of drug-related morbidity is especially

prevalent among community-living older adults, where (1) they may fail to recognize the





5



symptoms of drug-related morbidity or (2) their clinicians may attribute these symptoms to

aging, rather than to the drugs (French, 1996).


Problem Statement


As indicated by the previous discussion, the problem of PDRM in older persons has been

long recognized. The literature continues to grow with new studies of problem drugs and new

estimates of the extent of the problem. Despite this, little quantitative information is known

about the factors associated with increased risk that an older person would experience PDRM,

especially in the ambulatory setting. A better understanding of this relationship may help to

create more effective intervention strategies and more efficient use of scarce healthcare

resources.


Study Obiectives


There are three primary research objectives of this proposed study. These objectives are

1. To create operational definitions of PDRM,

2. To identify patients who are at particular risk of PDRM (as defined) and who may

therefore benefit from comprehensive pharmaceutical care, and

3. To create a risk stratification system for PDRM based on the number of risk factors

present in an individual patient.


Rationale and Theoretical Introduction


The rationale for this study will be two related models that are based on systems theory.

These two models are the medication use (pharmaceutical care) system and the biopsychosocial

model of disease etiology and therapy. The use of risk factors is the final important aspect of this

study.









The first of these two models the medication use system. Pharmaceutical care has been

defined by Hepler and Strand (1990) as being the cooperative, responsible provision of drug

therapy to achieve definite outcomes intended to improve a patient's quality of life.

Pharmaceutical care is quite different from our current approach to medications use.


Pharmaceutical care differs from traditional drug treatment because ... it is an explicitly
outcome-oriented, cooperative, systematic approach to providing drug therapy, directed
not only at clinical outcomes but also activities of daily life and other dimensions of
health-related quality of life. Preventing, detecting and resolving pharmacotherapeutic
problems before they become adverse outcomes increases the effectiveness of drug
therapy. (Hepler and Grainger-Rousseau, 1995, p.8)

The development of a predictive model for PDRM in older persons may support

prospective prevention of adverse outcomes related to drug therapy.

The usual medication use process is far from the ideal pharmaceutical care system. This

process has been previously described (Hepler and Grainger-Rousseau, 1995). Communication

and cooperation between the patient, physician, and pharmacists may be unsystematic and

ineffective. The patient may develop a drug-related problem, which, if left unresolved, may

develop into drug-related morbidity or even mortality. However, no one may recognize the

problem or attribute it to drug therapy. This flow of care is described as a process, and not a

system, because there is no feedback loop after the patient receives the prescription.

Compared to a drug therapy process, a pharmaceutical care system emphasizes the

prevention, detection, and resolution of drug-related problems before they can become drug-

related morbidities. To do this, a pharmaceutical care system emphasizes monitoring of patients-

in-therapy to detect problems. Monitoring, in turn, increases communication among the patient,

physician, and pharmacist. All three parties need more information (more often) from each other,

not only to detect problems but also to prevent and resolve them.

A second dimension of the conceptual framework of this research is the biopsychosocial

model. According to this model, which is also based on systems theory, many clinical outcomes,

including those of drug therapy, result in part from complex interactions of psychological and









sociological factors. These factors are in addition to the physiological and chemical explanations

provided in the biomedical view. Therefore, it may be possible to identify risk factors for PDRM

from among a wider variety of variables than would be predicted by reasoning from the

biomedical model. Furthermore, this model views illness as being more continuous and less

episodic than the biomedical model and therefore it may be possibly more descriptive of the

medical problems of older persons.

The third dimension of the conceptual framework of this study is the use of risk factors.

Risk factors in this study are those variables that are statistically associated with PDRM (the

outcome event) in older persons. While general risk factors for PDRM will be identified for an

entire geriatric population, the constructed model should be applied on a patient-specific basis.

This approach follows the similar use of probability theories and the rationale behind screening

tests, although this study is not a classical epidemiological study. These tests use population-

based information to identify the risk factors, but then the information is applied on an individual

basis to identify those patients who might be at particular risk for problems. Risk factors may be

used to help allocate scarce healthcare resources and as potential indicators to identify patients

who need interventions. Careful consideration of these risk factors should be incorporated into

the therapeutic and monitoring plans of health care professionals in order to proactively prevent

PDRM in older persons. A goal of identifying older persons at risk for PDRM is to intervene in

their medical care before they experience a PDRM. Risk factors are patient characteristics and

therefore they may or may not be able to be changed (e.g.; it may be hard to change a patient's

gender or drug therapy if it is essential). If a risk factor can not be eliminated in an individual,

then at least prospective management of that risk factor should occur. This study describes an

approach to use population-based information to construct a model for PDRM in older persons

that will be applied on a patient-specific basis.









Research Questions


To achieve the purpose of this study, four research questions will be investigated. These

questions will explore different aspects of risk factor identification of PDRM in older persons.


Research Question 1


What are the issues in developing and using operational definitions of PDRM?


Research Question 2


What are major risk factors for PDRM in older persons?


Research Question 3


Are there general, and disease (or drug), specific risk factors for PDRM in older

persons?


Research Question 4


What is the relationship between PDRM and the utilization ofhealthcare resources?















CHAPTER 2
CONCEPTUAL FRAMEWORK



The conceptual framework for this study consists of two models that incorporate systems

theory. These two models are the medication use (pharmaceutical care) system and the

biopsychosocial model of disease etiology and therapy. The concept of risk factors is a third

fundamental component of this study. Brief descriptions and empirical findings of these models

will be discussed.


Systems Theory and the Medication Use System


Medication use can be described as a process. In general, model patients enter the health

care system when they recognize a health problem and see a physician. The physician then

diagnoses the patient's problem and constructs a therapeutic plan, which is often accompanied

by a prescription. The therapeutic plan is implemented when the patient goes to a pharmacy and

a pharmacist dispenses the prescription and provides advice about the use of the medication. The

typical medication use process is completed when the patient consumes the medication.

Unintended outcomes of this process include drug-related problems, which, left unresolved or

undetected, may lead to drug-related morbidity. As discussed in chapter one, there are many such

unintended adverse outcomes from the current medication use process and several authors have

argued that this current process must be changed (Shane, 1992; Smith and Benderev, 1991).

In contrast, a medication use system emphasizes systematic monitoring and

communication. This model of medication use has been described as being a philosophy of

practice for pharmacists and its knowledge base is formed from systems theory (Hepler, 1996).

The structure of this model is seen in Figure 2.1. Patient "progress" is monitored to prevent,











Prescribing
Evaluation


Prescribing /
Influences / \
(Formulary, /
Education) /

t Recognize Assess Therapeuti,
Patient __ Patient __ Plan,
s Problem Problem Prescriptio





Drug-Related Resolve
Morbidity Patient roduct(s)pense
Products)
Problem and Provid
(if any) /Advice

Monitor o-*
Outcomes
According
to Plan Consume
Administer
Products)


Drug-Related Problem


Figure 2.1 The Medication Use System


(Adopted from Hepler and Grainger-Rousseau, 1995)









detect, and resolve DRPs before they develop into drug-related morbidities. Communication

between the physician, patient, pharmacist, and other health care professionals is critical to

proper functioning of the system. Prevention is important, especially in older persons, since

DRPs occur frequently and affect clinical, psychosocial, and economic outcomes. The

medication use system has a goal of patient-care outcomes, rather than performance of tasks, as

in the medication use process (Smith and Benderev, 1991).

Systems theory is the foundation upon which the medication use system is built. The

phrase "systems theory" may be deceptive, as according to some definitions, it is really a model

and not a theory (Babbie, 1983). Laszlo (1973) refers to systems theory as a philosophy.

Regardless, at the fundamental level, systems theory, or systems thinking, is a body of

knowledge that has gradually developed over the past fifty years into a model, which helps one

to see and influence things from a different, larger perspective (Senge, 1994). Key to systems

theory is the word systems itself. It is frequently used but infrequently understood. A system can

be defined as follows:

Basically, a system is (1) a group of related entities that (2) does something receives
inputs, affects them in some way, and produces outputs to achieve some purpose. Almost
anything in the world can be called a system. A sheep can be considered a system. It
takes in fodder and produces wool and lamb chops. (Nadler and Hibino, 1994, pp. 198-
199)

It is clear from this definition that one of the tenets central to systems theory is seeing the "big

picture" and "whole structures". Senge (1994) states that systems theory is a method to (1) see

wholes, (2) interrelationships rather than things, and (3) patterns of change rather than single

elements. Nadler and Hibino (1994) call this the "systems principle": each problem is just a

piece of a larger system. Many authors argue that these patterns, or structures, underlie complex

situations (Senge, 1994; Nadler and Hibino, 1994; Laszlo, 1973). By learning to recognize and

see them, the way by which one approaches problems will be modified. Senge (1994) elaborates

on these "structures":

one of the most important, and potentially most empowering, insights to come from the
young field of systems thinking is that certain patterns of structure recur again and again.








These 'systems archetypes' or 'generic structures' embody the key to learning to see
structures in our personal and organizational lives. The systems archetypes of which
there are only a relatively small number- suggest that not all... problems are unique.
(Senge, 1994, p.94)

These structures, then, are an important concept in systems theory and allow one to view

problems with a larger perspective.

The medication use system has built upon many of the central principles of systems

theory. These include the interrelationship of the various elements and dimensions of the

medication use system, the importance of communication and feedback loops, identification of

key areas of change, and the individualization of drug therapy goals and monitoring for specific

patients. Systems theory also emphasizes that the medication use system can be viewed as being

a subset of a larger system the health care system or subsets of the medication use system can

be thought of systems in their own right. For example, the act of a physician writing a

prescription for a patient is a complex system that has been extensively studied. The physician

uses multiple inputs when deciding which drug to prescribe, such as the price of the prescription,

the condition of the patient, the information given to him by a pharmaceutical representative,

whether the drug is in his evoked set, and other factors. Similarly, there are many outputs and

feedback mechanisms after he/she has written the prescription, such as whether the patient has

improved, drug use evaluation, and any adverse effects experienced by the patient. One way of

viewing these aspects of the medication use system is through the use of the system matrix.


The System Matrix Applied to the Medication Use System


While it is useful to recognize that structures exist, the size of most systems could

quickly cause one to become overwhelmed by the amount of information in these structures. One

way of organizing this information is to use a method called the system matrix (Nadler and

Hibino, 1994). According to the system matrix, a system consists of elements and dimensions.

The elements of a system are (1) purpose (the mission, aim, need, primary concern, or results

sought from a system), (2) inputs (physical items, information or human beings on which work,








conversion, or processing takes place), (3) outputs (desired and undesired physical items,

information, humans and services that result from processing inputs), (4) sequence (the

conversion, work, process, or transformation by which the inputs become the outputs), (5)

environment (the physical and sociological factors within which the other factors operate), (6)

human agents (those who aid in the steps of the sequence without becoming part of the outputs),

(7) physical catalysts (resources that aid in the steps of the sequence without becoming part of

the outputs), and (8) information aids (include knowledge and data resources that help in the

steps of the sequence without becoming part of the outputs) (Nadler and Hibino, 1994).

Dimensions help clarify the conditions for each element in a specific situation (Nadler and

Hibino, 1994). The dimensions of a system are (1) fundamentals (tangible, overt, observable,

physical, or basic structural characteristics), (2) values and goals (motivating beliefs, human

expectations, global desires, ethics, equity, and moral concerns that can be ascribed in some form

to each element, (3) measures (translate the fundamentals and values dimensions into particular

performance factors and operational objectives), (4) control (comprises methods for ensuring

that the fundamentals, measures, and even value specifications, are maintained as desired during

the operation of the system), (6) interface (the relationships of the fundamentals, values,

measures, and control specifications to other elements and to external systems), and (7) future

(changes in each specification of the other dimensions in the future) (Nadler and Hibino, 1994).

A system matrix clearly delineates the relationships of elements and their interdependences and,

most importantly according to Nadler and Hibino (1994), it prevents the omission of critical

components of the system.

The system matrix can be used to describe the medication use system. As previously

discussed, a system matrix helps to view a system by listing the elements and dimensions of that

system. Chiefly, this allows one to see the structure of a system; in this case, the medication use

system by specifying each element and dimension cell in the system matrix. For example, the








fundamentals (one of the dimensions of a system) have been previously described by Grainger-

Rousseau et al. (1997). As seen in Table 2.1, these eight fundamentals, or necessities, must be

present to ensure that drug therapy will be safe, effective, and humane (Grainger-Rousseau et al.,

1997). The human agents (one of the elements of a system) can be described for these eight

fundamentals for the medication use system. In the medication use system, the main human

agents are the prescriber, the pharmacist, and the patient (and caregiver). Table 2.2 shows how

each of the eight fundamentals of a safe and effective medication use system relates to these

three human agents. The boxes with checkmarks indicate an important relationship between an

element and a dimension. For example, fundamental one (timely recognition of signs and

symptoms) often depends on the action of the prescriber, pharmacist, or patient/caregiver. This

example shows how the system matrix can be applied to one cell for the medication use system.



Feedback and Communication Loops in the Medication Use System


An important concept in systems theory is feedback, also referred as communication

loops. Feedback is defined by Senge (1994) as being a broad concept meaning any reciprocal

flow of influence, and every influence acts as both a cause and an effect. Senge (1994) continues,

The practice of systems thinking starts with understanding a simple concept called
'feedback' that shows how actions can reinforce or counteract (balance) each other. It
builds to learning to recognize types of 'structures' that recur again and again: the arms
race is a generic or archetypal pattern of escalation, at its heart no different from turf
warfare between two street gangs, the demise of a marriage, or the advertising battles of
two consumer goods companies fighting for market share. (Senge, 1994, p.73)

Two types of feedback have been described. The first type is reinforcing (also called amplifying

or positive) feedback which causes growth. The second type is balancing (also called stabilizing

or negative) feedback which counteracts the reinforcing feedback. These feedback cycles or

communication loops often may have "delays", in which the flow of influence is interrupted,

resulting in a slowing down of events (Senge, 1994).








Table 2.1 Eight Fundamentals (Necessities) for Safe and Effective Drug Therapy
(Adopted from Grainger-Rousseau et al., 1997)

1. Timely recognition of drug indications and other signs and symptoms relevant to drug
use with accurate identification of underlying disease. "Correct" therapy for a late or
incorrect diagnosis cannot improve a patient's quality of life.
2. Safe, accessible, and cost-effective medicines. Safe and cost-effective (efficient) drug
products must be legally and financially available.
3. Appropriate prescribing for explicit (clear, measurable, and communicable) objectives.
Explicit therapeutic objectives simplify the assessment of prescribing appropriateness and
are necessary for assessing (monitoring) therapeutic outcomes.
4. Drug product distribution, dispensing, and administration with appropriate patient
advice. Including: (a) ensuring that a patient actually obtained the medicine, (b) negotiating
a regimen that the patient can tolerate and afford, (c) ensuring that a patient (or caregiver)
can correctly use the medicine and administration devices, (d) advising to empower the
patient or caregiver to cooperate in his or her own care as much as possible.
5. Patient participation in care (intelligent adherence). The ambulatory patient or caregiver
should consent to therapeutic objectives and know the signs of therapeutic success, side
effects and toxicities; when to expect them; and what to do if they appear.
6. Monitoring (problem detection and resolution). Many failures can be detected while they
are still problems and before they become adverse outcomes or treatment failures.
7. Documentation and communication of information and decisions. Communication and
documentation are necessary for cooperation in a system.
8. Product and system performance evaluation and improvement. Practice guidelines,
performance indicators, and databases are a useful approach to achieving and maintaining
improved system performance (outcomes).









Table 2.2 The Fundamentals/Human Agents Cell of the System Matrix Applied to the
Medication Use System

Fundamental for safe and effective Human Agents of the Medication Use System

drug therapy Prescriber Pharmacist Patient/ Caregiver

Timely recognition of signs and

symptoms 4 "

Safe, accessible, and cost-effective

medicines

Appropriate prescribing V

Distribution, dispensing,

administration, and patient advice V '

Patient participation V

Monitoring I 1

Documentation and communication V/ / I

System evaluation









The acknowledgement of the importance of feedback in systems theory allows one to

accurately discern the role of individuals in a system. In systems theory, individuals are seen as

simply part of the feedback process, and not as a separate component of a system. Individuals

can act as either reinforcing or balancing feedback. This is different from the common perception

that individuals are somehow special and are different from the other elements of a system.

Senge (1994) notes that considering individuals as a form of feedback implies that an individual

is usually not solely responsible for an action, but that everyone shares in the responsibility for

the event occurring. Individuals may have different levels of influence but most systems

problems are solved by looking at all the types of feedback.

Several feedback loops occur in the medication use system. As seen in Figure 2.2, there

is a feedback loop for aggregated prescribing that includes prescribing data, prescribing (drug

use) evaluation, and prescribing influences such as a formulary and education. A second

feedback loop exists for a single patient's prescription or drug regimen. This is when information

obtained by a pharmacist or another health care professional from the patient can be used to alter

the therapeutic plan. A third feedback loop, which is not formalized in the medication use

process, involves monitoring the patient for drug-related problems, resolving any problems

which exist, and using this information to revise the therapeutic plan as appropriate.

The medication use system contains feedback loops that are reinforcing and it may also

contain delays. Patient monitoring by the pharmacist can be used to reinforce or strengthen the

therapeutic plan. Monitoring includes the determination of which information to collect for the

evaluation of the progress of therapy, the evaluation of achieving the therapeutic objectives, and

responding to evaluations (Grainger-Rousseau et al., 1997). This constant, critical re-evaluation

of the therapeutic plan is an important reinforcing feedback loop that helps facilitate the

provision of pharmaceutical care (Strand et al., 1991). Delays can occur at many places in the

medication use system, such as when pharmacists or patients do not provide timely data to the

physician, such that the therapeutic plan is not revised and poor patient care results.









Prescribing
Evaluation


Prescribing
Influences
(Formulary,
Education)


Drug-Related Resolve
Morbidity Patient
Problem
(if any)

High Monitor
Leverage Outcomes
Point According
to Plan




Dru2-Related Problem


Figure 2.2 High Leverage Points Within the Medication Use System
(Adopted from Hepler and Grainger-Rousseau, 1995)


Prescribing
Data


High
Leverage
Point










Change and the Medication Use System


The next logical step is to ask how one can influence and change the various types of

feedback that exist in a given system. This skill of knowing where to affect a system to produce

the greatest possible intended effect is known as leverage. Senge (1994) explains this concept,

The bottom line of systems thinking is leverage seeing where actions and changes in
structures can lead to significant, enduring improvements.., our nonsystemic ways of
thinking are so damaging specifically because they consistently lead us to focus on low-
leverage changes: we focus on symptoms where the stress is greatest. We repair or
ameliorate the symptoms. But such efforts only make matters better in the short run, at
best, and worse in the long run. (Senge, 1994, p. 14)

In order to change systems the most effectively, then, high-leverage changes should be sought.

By recognizing which feedback mechanisms result in the high-leverage changes, one can target

those feedback points and obtain the intended effect.

In order to most effectively change the medication use system such that PDRM is

minimized, high leverage change points must be identified and successful intervention strategies

must be implemented. Two such high leverage points for the prevention of PDRM within the

medication use system are identified in Figure 2.2: the therapeutic plan and patient monitoring.

The creation and re-evaluation of the therapeutic plan is a key point in the medication use

process. Without a therapeutic plan that includes explicit, realistic objectives, therapy

management is practically impossible (Hepler and Grainger-Rousseau, 1995). Also, the patient

risks for PDRM are generally unknown during the formation of the therapeutic plan (Strand et

al., 1991):

It seems evident that we have accumulated very little hard information that describes
pharmaceutical-care needs, patient risks related to pharmacotherapy, and the drug-
related problems present. Before the concept of... pharmaceutical care can be developed
any further, this information needs to be collected and evaluated. (Strand et al., 1991,
p.550)

Some medications, such as digoxin, have been identified in the literature as being commonly

prescribed inappropriately in the older population (Aronow, 1996). There is also some evidence









that those medications that are prescribed inappropriately lead to PDRM in older adults. French

(1996) argues that up to 25 percent of community-living older persons are at risk for drug-related

morbidity due to inappropriate prescribing, a key part of the therapeutic plan. Ideally, a method

that could prospectively identify these high-risk medications, and incorporate this information

into the therapeutic plans for them, would be of tremendous value.

The monitoring and management of patient outcomes according to the therapeutic plan is

a second key point in the medication use process. As early as 1981, (Campbell, 1981) an

advisory committee on pharmaceutical needs in older persons recommended that identification

of toxic drug effects and drug monitoring be priority functions of the pharmacist. In 1988,

Grymonpre et al. (1988) stated that their study on drug-related adverse patient events confirmed

the need for increased caution and monitoring of all consequences and outcomes of medication

use in older persons. More recently, Johnson and Bootman (1995) recommended that urgent

attention be given to this problem and monitoring should be emphasized to help lessen PDRM.

Patient monitoring is also required to ensure that the desired patient outcomes and goals are

attained (Strand et al., 1991; McDonough, 1996). Identification of medications for which

monitoring is extremely important may greatly assist the pharmacist during patient monitoring

and management to prevent PDRM in older persons.


Biopsychosocial Model of Disease Etiology and Therapy


The previous discussions on (1) the importance of feedback and communication within

the medication use system, and (2) the high leverage points (the therapeutic plan and patient

monitoring), show the importance of accounting for individual patient differences. As well, as

was seen in Table 2.2, many of the fundamentals of safe and effective drug therapy rely on the

cooperative actions of the prescriber, pharmacist, and patient. This holistic, individualized

approach to medication use is complimented by the biopsychosocial model of disease etiology

and therapy.









The biopsychosocial model was first proposed by Engel (1977), in response to the

biomedical model. The biomedical model of disease had molecular biology as its basic scientific

discipline (Engel, 1977) and tried to explain disease as being deviations from 'norms'-

measurable biological variables (DiMatteo, 1991). Engel (1977, 1981) argued that such a model

did not consider the person as a whole, and failed to incorporate psychological, social or

behavioral aspects of illness.

Engel turned to nature, and biology in particular, for an alternative. The knowledge base

from which the biopsychosocial model is built upon is the use of systems theory in biology

(Engel, 1981; Sadler and Hulgus, 1992). Engel (1981) describes this foundation,


systems theory, by providing a conceptual framework within which both organized
wholes and component parts can be studied, overcomes this centuries-old limitation and
broadens the range of the scientific method to the study of life and living systems,
including health and illness (Engel, 1981, p. 103)

and,

systems theory is best approached through the common sense observation that nature is a
hierarchically arranged continuum, with its more complex larger units being
superordinate to the less complex smaller units. (Engel, 1981, pp. 103-104)

As previously discussed, systems theory emphasizes this hierarchy and views each component of

the system, whether it be a person or the biosphere, as not existing in isolation, but being

influenced by its environment. A bee, for example, can not truly be studied without studying its

environment (flowers, other bees, honeycomb, etc.). Systems theory, then, was useful to help

explain how things are ordered in nature and how these different "systems" in nature interacted.

Engel advocated taking this knowledge base of systems theory in biology and applying it

to the medical care of individuals, as well as medical research and teaching. This would allow

for a holistic evaluation of patients considering cultural, social, psychological, and behavioral

dimensions of illness (DiMatteo, 1991), humane, empathic and rigorous medical care (Sadler and

Hulgus, 1992), and patient-centered and patient-specific medical care (Howell, 1992;








Zimmermann and Tansella, 1996). This is especially important in older persons, where medical

information and diagnosis are insufficient in predicting their health care needs and consideration

of these other dimensions are critical (Rock et al., 1996).

This distinction between the biomedical and biopsychosocial models can be seen in the

medication use system. Traditional drug treatment follows a population-based approach to care,

whereas a pharmaceutical care system follows a patient-based approach. In order to improve the

medications use process, the current approach utilizes population-based methods such as drug

formularies and drug use evaluation. These methods follow a biomedical model of disease,

whereby the disease becomes the emphasis and a mechanistic, reductionistic, and dualistic

perspective is employed. Patients who have illness must be deviating from objective somatic

norms and thus there is always a drug of choice that can be chosen for a whole population.

In contrast, a biopsychosocial model approach states that deviation from the somatic

norm is insufficient to explain disease, and biological relationships to illness are complex

interactions with the mind and environment. This model accounts for patient-specific differences

in illness because it holds a monistic perspective that states the mind and body are intimately

related. While a population basis can be employed to change a system, the biopsychosocial

requires that individual patient differences be considered. Variation between patients is expected

and is not necessarily negative. It even allows for patient disagreement with the health

professional's plan of care (Kasahara, Shemon and Holzschuh, 1994). This model incorporates

psychological aspects of illness (such as anxiety or depression), the individual's cultural

expectations about illness, and the present social context of the illness as well as the biological

parameters (DiMatteo, 1991).

The biopsychosocial model fits well with the approach to be used in this proposed

investigation of PDRM in older persons, which will consider these other dimensions of illness.

First, illness presentation in older persons is different from other age groups and this raises some








unique concerns. One of these unique factors is that many older persons have atypical disease

presentations that do not fit a classic disease model. Jarrett et al. (1995) concluded that atypical

disease presentation is associated with adverse hospital outcomes. This makes the issues of

disease diagnosis and treatment far more complex in older persons and further strengthens the

need to account for patient differences. Second, PDRM has been reported and analyzed on a

patient-specific basis. Third, drug-related morbidity often has atypical or paradoxical

manifestations in older persons (French, 1996; Harper, Newton and Walsh, 1989). This may be

because patient-specific differences play a large role in the development of drug-related

morbidity and mortality. Gurwitz and Avomrn (1991) state that patient-specific physiologic and

functional patient characteristics, such as pharmacokinetic and pharmacodynamic changes, are

often very important to predict drug-related morbidity in older persons due to the inter-individual

variability of the aging process. As Strand et al. (1991) explain,

each patient must receive individualized treatment not only because of information
derived from scientific knowledge but also because he or she must be consistently
respected as a unique individual with specific needs. (Strand et al., 1991, p.549)

The biopsychosocial model allows for the inclusion of social, psychological, and behavioral

factors in a prediction model of PDRM in older persons. This includes such things as trouble

paying for medications, difficulty taking medications, patient belief that he/she is on too many

medications, and the patient's own perception of their health. This is important, as, for example,

an older person's self-assessment of their health as being poor has been shown to be associated

with mortality in a study of recipients of community-based long-term care (Fried, Pollack, and

Tinetti, 1998). A prediction model that includes this and other social, psychological, and

behavioral factors may be the best way to identify PDRM in older persons.








Risk Factors for PDRM


The medication use system emphasizes an individualized approach to care. However,

population-based information can be useful in planning for a specific patient. One such type of

population-based information is risk factors. Risk factors are variables that are statistically

associated with an outcome event. Prediction models use risk factors identified from a

population and apply this information to the individual patient. The individual patient is then told

the probability, or risk, of having a certain disease or medical condition.

Probability theory and its applications play a central role in the development and

function of screening tests and risk factors. Medical tests results are rarely positive or negative,

instead they lie on a continuum; test results that are beyond a cutoff threshold are called positive.

This cutoff threshold separates the two separate, but usually overlapping Gaussian distributions

of patients with, and without, the disease (condition). This relates to the concepts of sensitivity

and specificity. Sensitivity is the percentage of patients with a disease (condition) who are

labeled "positive" by the test, whereas specificity is the percentage of patients without the

disease who are labeled "negative" by the test. A test or prediction model with a high specificity

and sensitivity will have a low rate of false negatives and false positives.

Risk factor identification is an important issue, but equally important is the actual use of

these risk factors in an assessment or screening program. Risk assessment generally involves a

prospective investigation of a person's health risks to facilitate interventions before the

occurrence of a preventable health crisis (Kerekes and Thornton, 1996). Such a risk assessment

program may utilize the risk factors previously identified to target certain patients proactively.

Nikolaus et al. (1995) were able to develop an instrument to assess nutritional risk in older

persons. They argue that risk assessment is a major component of the medical management of

older persons. Interventions are often designed based on the risk factors identified. For example,








an intervention program which targets older persons who are frail and who have chronic illness

has demonstrated a significant decrease in emergency room visits, admissions, and length of stay

in a risk assessment program in St. Joseph Medical Center (Swindle, Weyant and Mar, 1994).

Cargill (1992) developed criteria to help identify those patients at highest risk for

problems related to medication noncompliance. These criteria included: multiple medications

(more than three medications per day prescribed), medication regimen changes (a change in the

past six months), multiple prescribers, and memory, sensory, and cognitive deficits (unable to

verbalize name or purpose and frequency of medication, unable to read the label, unable to

calculate how many 10mg pills in a 20mg dose, and unable to judge appropriately administration

times for twice a day dosing regimensXCargill, 1992). Cargill (1992) reported that those older

persons with more risk factors were older and had more medications prescribed, but they did not

have different compliance patterns than those with fewer risk factors.

Identifying possible risk factors for drug-related morbidity based on theory has been a

difficult problem. Strand et al. (1991) used the following approach to identify possible risk

factors,

we identify three categories of risk factors that can affect the type and level of
pharmacotherapeutic risk: (1) risk factors associated with the patient's clinical
characteristics, (2) risk factors associated with the patient's disease, and (3) risk
factors associated with the patient's pharmacotherapy. The interaction of these three
types of risk factors ultimately determines the level of risk associated with a patient's
pharmacotherapy and therefore the level of pharmaceutical care required of the
pharmacist. (Strand et al., 1991, p.549)

The approach used in this study will be different. Possible risk factors for PDRM in older adults

will be identified by careful consideration of the high leverage points within the medication use

system and the biopsychosocial model of disease etiology and therapy. Based on this conceptual

basis and empirical evidence, possible risk factors for PDRM will be entered into logistic

regression models for PDRM. Therefore, risk factors in this study will be those variables that

help to explain some proportion of the variance of PDRM in older persons. Variables with odds








ratios greater than one will be positive risk factors for PDRM, while variables with odds ratios

less than one will be negative risk factors for PDRM. This approach will also allow estimation of

the proportion of variance in PDRM in older persons that can be accounted for by these risk

factors.

Risk factors for PDRM may be used in a variety of ways. First, if health care

professionals know that an individual has one or more risk factors for PDRM, a proactive change

in the medical care of that patient may be made to help prevent the occurrence of PDRM in the

future. This knowledge will hopefully allow health care professionals to anticipate the possibility

of PDRM occurring in individual patients. Boult et al. (1998) argue that high-risk older persons

should be identified so that a comprehensive assessment of their health-related needs may be

performed, and interventions planned to meet these needs. In the medication use system,

physicians, in particular, should consider these risk factors when creating a therapeutic plan for

the patient. Risk factors may be considered as indicators that help direct the health care

professional to patients with potential problems. Koecheler et al. (1989) used six prognostic

indicators (or risk factors without the empirical evidence) for patients who might warrant

pharmacist monitoring. Second, the risk factors identified can be related to the medication use

system for interpretability in order to determine the best possible manner of use. For example, if

a risk factor is known to relate to patient monitoring, then every attempt should be made to

ensure that the patient receives proper monitoring. This is important because often risk factors

can not be eliminated from the individual patient (e.g. gender), so proper management of the

patient becomes paramount. Finally, risk factors may be used to help in the allocation of scarce

healthcare resources.








Summary Of The Conceptual Models To Be Used In This Study


In summary, the conceptual framework that will be used for the study risk assessment of

preventable drug-related morbidity in older persons is based on the application of systems

theory in two models. The first model that incorporates systems theory is the medication use

system. This model applies many of the central themes of systems theory, including the

interdependency of related things, identification of recurring structures, feedback and

communication loops, system goals, and identification of main change (or high leverage) points

(Senge, 1994; Laszlo, 1973; Nadler and Hibino, 1994). In the medication use system itself, a

structure has been proposed and essential elements identified, which includes key

communication and feedback loops between the physician, patient, and pharmacist, a system

goal of improving patient outcomes, and high leverage points such as patient monitoring (Hepler

and Grainger-Rousseau, 1995; Hepler and Strand, 1990). The biopsychosocial model, also based

on systems theory, permits a holistic evaluation of patients that includes cultural, social,

psychological, and behavioral dimensions of illness. Risk factors for PDRM in older persons will

be identified based on these first two models.














CHAPTER 3
REVIEW OF THE LITERATURE





The following review of the literature will focus on drug-related morbidity and mortality

in older persons, identification of older persons at high risk of medical problems, and finally, a

review of prediction models for drug use in older persons.


Drug-Related Morbidity and Mortality in Older Persons

Drug-related morbidity and mortality is a problem of special consideration in older

persons. The incidence of drug-related morbidity seems to be higher in older persons, although it

declines in the last decades of life (80 plus years) and age does not appear to be an independent

risk factor for drug-related morbidity (Carbonin et al., 1991; Gurwitz and Avomrn, 1991). Still,

Campbell (1981) argues the increasing number of older persons and their prominent utilization

of healthcare services, particularly medications, naturally extend itself to concern about the high

risk drug-related morbidity and mortality in this population.

Fincham (1996) gives a succinct statement as to the extent of this problem in older

persons:

providing for consistent and appropriate use of drugs is exceedingly important for the
ambulatory elderly. Studies have shown that when it does not occur, hospitalizations
occur due to noncompliance and to the occurrence of avoidable adverse drug reactions.
Significant predictors of preventable hospital readmission for the elderly include the
occurrence of preventable adverse drug reactions, noncompliance, overdose, lack of a
necessary drug therapy, and underdose. Others have noted that 50% of drug-induced
illnesses that require hospitalization could have been avoided. Elsewhere, researchers
have estimated that 75% of medications are misprescribed for the elderly, with overuse
and underuse rampant. There must be increasing efforts to ensure continuity of care for
the ambulatory elderly to avoid these and other drug-related problems. Because drug use








in the elderly is dynamic and increases with proximity to death, pharmacists are key
players to help the elderly avoid these drug-related problems. (Fincham, 1996, p.525)

The literature is rich with examples to substantiate these concerns. Grymonpre et al. (1988)

determined that 19 percent of all hospital admissions (23 percent of all admissions that involved

prescription drugs) of patients aged 50 and older exhibited at least one type of drug-related

morbidity and mortality in a tertiary care hospital in Manitoba, Canada. The major types of drug-

related morbidity and mortality identified were adverse drug reactions (48 percent), intentional

noncompliance (27 percent), treatment failures (19 percent), alcohol-related problems (14

percent) and medication errors (10 percent) (Grymonpre et al., 1988).

Ostrom et al. (1985) studied medication use in 183 independently living seniors in

Seattle and reported the prevalence of many medication problems. Seventy-five percent of the

older persons had at least one potential medication problem, with a label discrepancy (37

percent), potential drug interaction (27 percent), underuse of medication (24 percent), inability to

read label (14 percent), and failure to open prescription vial (12 percent) being the most common

problems (Ostrom et al., 1985).

Some researchers have identified drugs that have a particularly high risk for drug-related

morbidity and mortality in older persons. Dalziel, Byszewski and Ross (1996) constructed a list

of the top ten problem drugs in the older persons (they failed to provide any empirical evidence

as to why these certain medications made the list, however): Non-steroidal anti-inflammatory

drugs (NSAIDS), benzodiazepines, amitriptyline, fluoxetine, anticholingerics/antihistamines,

over-the-counter drugs/alcohol, cimetidine, centrally-acting antihypertensives/beta-blockers,

digoxin, and irritant laxatives/colacel. The most commonly implicated drugs in 162 cases of

drug-related morbidity and mortality identified in another study were: systemic steroids, digoxin,

nonsteroidal anti-inflammatory agents, methyldopa, calcium channel blockers, beta-blockers,

theophylline, furosemide, sympathomimetics, thiazides, and benzodiazepines (Grymonpre et al.,








1988). In a study of drug-related hospital admissions, hypoglycemic and diuretic agents were the

two most implicated drugs (Nelson and Talbert, 1996).

Beers et al. (1991) used the Delphi technique to develop 30 factors defining

inappropriate medication use in the nursing home setting. Using modified versions of these

criteria, other authors determined that 14.0 to 23.5 percent of older adults living in the

community were using at least one inappropriate drug (Stuck et al., 1994; Wilcox, Himmelstein

and Woolhandler, 1994). Beers' criteria was limited by its failure to include specific reasons why

these "inappropriate" drugs should be avoided, which newer lists have attempted to include

(Buerger, 1998).

Several authors have studied specific types of PDRM in older persons, such as falls and

hip fractures. Ray, Griffin and Downey (1989) studied a population of older persons in

Saskatchewan, Canada, and determined that the risk relative risk of hip fracture was higher for

users of long half-life benzodiazepines (1.7) as compared to those who used short half-life drugs

(1.1). Prudham and Grimley-Evans (1981) concluded that older persons who reported falls in a

one-year period were taking statistically significantly more tranquilizers and diuretics than older

persons who did not report having at least one fall. A case-control study assessed the risk of hip

fractures associated with four classes of psychotropic drugs and determined that there was an

increased risk with concomitant use of long-half-life hypnotic-anxiolytics, tricyclic

antidepressants, and antipsychotics (Ray et al., 1987).

Drug-related morbidity and mortality in older persons has been documented to occur in

many different locations, such as the ambulatory, nursing home, emergency room, and inpatient

settings. In a study conducted in the early 1960's in 178 older persons ambulatory patients with

chronic illness, 59 percent were found to have at least one type of drug-related morbidity and

mortality, with 26 percent of the cases viewed as being serious (Schwartz et al., 1962). Aronow

(1996) looked at the use ofdigoxin in 500 consecutive nursing home admissions and concluded








that 47 percent of the patients had an inappropriate indication for use. In the nursing home

setting, it has been estimated that the total cost of drug-related morbidity and mortality without

the services of consultant pharmacists is $7.6 billion annually (Bootman, Harrison and Cox,

1997). Adams et al. (1987) found that a large percentage of older persons who had an

emergency room visit at a hospital in England had a DRP in the categories of drug interaction

and improper drug selection. Six percent of older persons had a serious drug-diagnosis or drug-

laboratory contraindication and 19.7 percent of patients had a drug-drug interaction, although not

all of these were deemed to be clinically significant (Adams et al., 1987). Ray, Federspeil and

Schaffner's (1980) study of antipsychotic drug use in Tennessee nursing homes suggested that

many older persons were using drugs without an indication. A study of the use of sedative-

hypnotics in hospitalized older persons revealed that 20 percent of the prescriptions exceeded

recognized dosing guidelines and this was associated with a greater severity of illness (Zisselman

et al., 1996).



Identification of Patients at High Risk of Medical Problems in Older Persons


A risk factor is a variable statistically associated with an outcome event. Many

researchers have attempted to identify specific risk factors for health care resource utilization or

morbidity in older persons. These researchers reason that because health care resources are

limited it may be best to find those patients who are at particular risk and concentrate on those

individuals. For example, Nikolaus et al. (1995) identified risk factors associated with

malnutrition in older persons. The main risk factors were a high number of prescription drugs,

social isolation, chronic and painful diseases, and high consumption of alcohol or cigarettes

(Nikolaus et al., 1995). Their study was limited to hospitalized older persons so the authors

admit that further research must be done in other settings. Fowles et al. (1996) compared self-








reported health status (ShortForm-36) and diagnosis (Ambulatory Care Groups) to demographic

information and found that the former two were much better predictors of health care

expenditures in older adults. Therefore, it appears that demographic information is not sufficient

to predict high utilizers of health care and other factors must be considered.

Kramer, Fox, and Morgenstern (1992) describe the approaches taken by seven health

maintenance organizations (HMOs) with Medicare-risk contracts to identify high risk patients.

One of these HMOs, Kaiser Permanente Southern California, identified approximately 35

percent of all inpatient admissions as being a high risk group through the use of the following

criteria: age 80 or above, cerebral vascular accident, new fracture, admitted from a nursing

home, a hospital readmission within ninety days that was unplanned, immobility, activities of

daily living impairment, malnutrition, incontinence, confusion, prolonged bed rest, history of

falls, depression, or existence of social problems (Kramer, Fox and Morgenstern, 1992). Kramer,

Fox and Morgenstern (1992) did not state whether these initiatives to identify high risk patients

were successful.

Stuck et al. (1994) studied patient factors associated with a risk of using an

"inappropriate medication" in older persons by performing a multivariate logistic regression

analysis. It was determined that a depression score was a risk factor, however, age, gender,

income, number of chronic diseases, and activities of daily living score were not predictors

(Stuck et al., 1994). Wilcox, Himmelstein, and Woolhandler (1994) also examined the risk of

using an inappropriate medication in older persons and determined that patient risk factors were

a high number of prescription medications, female gender, people living in the Southern United

States, poor self-rated health status, and Medicaid beneficiaries.

Risk factors associated with drug-related morbidity have also been identified for older

persons. Grymonpre et al. (1988) determined that the risk of a drug-related morbidity in patients

aged 50 and older was related to the number of diseases and number of drugs used, but not to








age, health score, or gender. Hurwitz (1969) reported that predisposing risk factors associated

with drug-related morbidity included an age of 60 or greater, female gender, previous adverse

drug reaction, and history of allergic disease. In a review of the English-language literature,

Gurwitz and Avomrn (1991) found that age is not an independent risk factor for drug-related

morbidity, but rather patient-specific and functional characteristics are more important.

Therefore, it appears that the literature is rich in examples of possible risk factors for PDRM in

older persons.



Prediction Models for Drug Use in Older Persons


There is some evidence in the literature that prediction models can be created to identify

individuals who are at risk of DRPs and these models can facilitate the development of

interventions. Beers et al. (1992) developed an operational definition of inappropriate medication

use in older persons in the nursing home setting and subsequent studies were able to use this

definition to determine the degree of inappropriate use and develop intervention strategies to

help correct this problem. Koecheler et al. (1989) developed six prognostic indicators for

patients who might warrant pharmacist monitoring: (1) five or more medications in present drug

regimen, (2) 12 or more medication doses per day, (3) medication regimen changed four or more

times during the past 12 months, (4) more than three concurrent disease states present, (5)

history of noncompliance, and (6) presence of drugs that require therapeutic drug monitoring.

Evidence of adverse outcomes related to drug therapy was identified in 33.1 percent of charts,

based on the use of these indicators (Koecheler et al., 1989). McGhan, Wertheimer, and Rowland

(1982) used Medicaid data to develop multivariate predictive equations to identify patients with

drug therapy problems.

In a recent study, McElnay et al. (1997) developed a risk model for predicting drug-

related morbidity and mortality in older persons, similar to the approach used in this study. Their

model was able to predict drug-related mortality and morbidity in older persons with a









specificity of 69 percent, a sensitivity of 41 percent, and an overall accuracy of 63 percent

(McElnay et al., 1997). McElnay et al. (1997) identified seven variables which influenced the

risk of drug-related morbidity and mortality: digoxin, antidepressants, chronic obstructive

airways disease, angina, abnormal potassium level, and patient belief that their medication was

in some way responsible for their hospital admission (McElnay et al., 1997).

Wilcox, Himmelstein, and Woolhandler (1994) state that measuring preventable drug-

related morbidity and mortality in the community setting is difficult but it is extremely important

since only a small proportion of drug-related morbidity results in hospital admissions and many

problems may be unreported or unrecognized. Therefore, there appears to be a continued need

for better prediction models of drug-related morbidity and mortality in older persons. Also, the

McElnay study did not consider preventability. Furthermore, there are no operational definitions

of PDRM in the peer-reviewed medical literature. The development of such definitions would

contribute to the conceptual framework of a systems approach to the medication use system.


Research Assumptions and Hypotheses


The research assumptions and hypotheses that will serve as the basis for this study are

proposed within the context of systems theory applied to the medication use system, the

biopsychosocial model of disease etiology and therapy, and the ability to use conditional

probabilities to identify at-risk individuals. As described earlier, while PDRM has been

previously determined to be an important problem within the medication use process, operational

definitions have not been adequately developed and the method to best identify those individuals

at-risk is unknown. Four research questions that address these unresolved problems will be

investigated. Based on the literature reviewed earlier in this chapter, and the conceptual

framework discussed in chapter two, propositions were made for all four of these research

questions.








Research Assumptions


The first research question is directed at the creation of operational definitions of PDRM.

A research assumption related to this research question was proposed. This research assumption

had to be met before the other research questions could be investigated. This research

assumption is as follows:

A1A: Valid operational definitions of PDRM can be developed by a panel of geriatric

medicine experts.

This assumption proposes that operational definitions of PDRM, consisting of criteria for

specific types of PDRMs in older persons, can be developed and tested for validity. Previous

authors have succeeded in creating algorithms for the assessment of adverse drug reactions (one

type of DRP that can lead to PDRM). These algorithms have been tested for validity (Karch and

Lasagna, 1977; Hutchinson et al., 1979; Kramer et al., 1979; Leventhal et al., 1979; Naranjo et

al., 1981). Schumock and Thornton (1992) have created criteria to determine the preventability

of adverse drug reactions, which has been subsequently used by others (Pearson et al., 1994). As

explained in the next chapter, an attempt will be made to demonstrate the validity of the

operational definitions of PDRM, although the lack of an accepted "gold standard" for PDRM is

a limitation.

The consensus method to be used is the Delphi technique. Therefore, research question

one was refined to focus on the usefulness of developing operational definitions of PDRM with

the Delphi technique. The utilization of expert panels via the Delphi technique to generate

consensus on healthcare issues has been quite extensive (Roberts, Sek Khee and Philp, 1994;

Butterworth and Bishop, 1995; Megel, Barna Elrod, and Rausch, 1996). This includes its use to

determine criteria for medication use in older persons. Fouts et al. (1997) used the Delphi

technique to identify risk factors for DRPs in older persons. Beers et al. (1991) used the Delphi

technique with 13 experts to reach consensus on explicit criteria for determining inappropriate

medication use in nursing home residents. Therefore, it seems there is sufficient evidence that










the use of an expert panel in the creation of operational definitions for PDRM in older persons is

reasonable and not without precedent.


Research Hypotheses


The second research question deals with the identification of major risk factors for

PDRM in older persons. The specific hypotheses proposed to address this research question are

the following:

HIA: Digoxin use will be a risk factor for PDRM in older persons.

H1B: Antidepressant drug use will be a risk factor for PDRM in older persons.

H1C: Long-acting benzodiazepine use will be a risk factor for PDRM in older persons.

HID: Antihypertensive drug use will be a risk factor for PDRM in older persons.

HIE: Gastrointestinal disorders will be a risk factor for PDRM in older persons.

H1F: Lung conditions (lung disease, emphysema, bronchitis and asthma) will be a risk

factor for PDRM in older persons.

HI1G: Kidney disease will be a risk factor for PDRM in older persons.

HIHI: A history offalling will be a risk factor for PDRM in older persons.

H1I: Four or more prescribers will be a risk factor for PDRM in older persons.

H1J: Six or more prescription medications will be a risk factor for PDRM in older

persons.

H1K: Four or more recorded diagnoses will be a risk factor for PDRM in older persons.

HIL: A previous adverse drug reaction will be a risk factor for PDRM in older persons.

HIM: High alcohol consumption will be a risk factor for PDRM in older persons.

HiN: Self-assessment ofpoor health status will be a risk factor for PDRM in older

persons.

H10: Trouble paying for medications will be a risk factor for PDRM in older persons.

HIP: Difficulty taking medications will be a risk factor for PDRM in older persons.









H1Q: Patient belief that they are taking too many medications will be a risk factor for

PDRM in older persons.

H1R: Female gender will be a risk factor for PDRM in older persons.



This proposition is based on (1) reports of the risk factors associated with different kinds

of drug-related problems and drug-related morbidity that are found in the peer-reviewed medical

literature, and (2) identification of possible risk factors as they might relate to the medication use

system and the biopsychosocial model of disease etiology and therapy. The rationale for each

individual risk factor is as follows.

Several variables relate monitoring of drug therapy:

HIA: Digoxin use

There is considerable evidence in the medical literature that digoxin use is a risk factor

for adverse drug reactions and drug-related morbidity and mortality. Williamson and Chopin

(1980) determined that digoxin is one of the highest risk drugs for drug-related hospital

readmissions in older persons. Digoxin was the most implicated drug in an inpatient study

involving 193 adverse drug reactions, accounting for 21 percent of all adverse drug reactions

(Ogilvie and Ruedy, 1967b). Digoxin has been labeled one of the top ten problem drugs in older

persons (Dalziel, Byszewski and Ross, 1996), and it was also identified as a risk factor for drug-

related morbidity in older persons (McElnay et al., 1997). A multidisciplinary panel of health

professionals in long-term care listed it as a risk factor for drug-related problems in elderly

nursing home residents (Fouts et al., 1997).

There are several reasons why digoxin use may be a risk factor for PDRM in older

persons. It is a water-soluble drug and has a smaller volume of distribution in older persons,

therefore, it requires a lower dose (Harper, Newton and Walsh, 1989). Digoxin is also implicated

in many drug-interactions and elimination of the drug may be a problem in older persons since

there is an age-related loss of renal function. Digoxin can also cause failure to thrive in older

persons through a diminished appetite (Harper, Newton and Walsh, 1989). Finally, digoxin









toxicity manifestations are often subtle or atypical in older persons (Daiziel, Byszewski and

Ross, 1996).

Many of these problems with digoxin use in older persons may also relate to

inappropriate prescribing, lack of patient advice or poor patient monitoring. As discussed in

chapter two, all these factors can impact PDRM. This is especially true in older persons, as

inadequate patient education on prescribed drugs was a factor that increased the risk of drug-

related morbidity in older persons (French, 1996).

H1B: Antidepressant drug use

There is also considerable evidence in the medical literature for including antidepressant

drug use as a possible risk factor for PDRM in older persons. Certain antidepressants

amitriptylinee and fluoxetine) were identified as being two of the top ten problem drugs in older

persons (Dalziel, Byszewski and Ross, 1996), and McElnay et al. (1997) also determined that

antidepressant use was a risk factor for drug-related morbidity in older persons.

Like digoxin, patient monitoring is extremely important for antidepressants in older

persons. In particular, tricyclic antidepressants are highly bound and there are lower albumin

levels in older persons so the free fraction is greater, increasing the likelihood of drug toxicity

(Harper, Newton and Walsh, 1989). Tricyclic antidepressants have anticholingeric/

antihistaminic side effects that are more evident in older persons.

H1 C: Long-acting benzodiazepine use

There is considerable evidence in the medical literature that long-acting benzodiazepine

use is a risk factor for adverse drug reactions and drug-related morbidity and mortality.

Williamson and Chopin (1980) determined that long-acting benzodiazepines are one of the

highest risk drug classes for drug-related hospital readmissions in older persons, and they were

identified as one of the top ten problem drugs in older persons (Dalziel, Byszewski and Ross,

1996). They were also a risk factor for adverse drug reactions associated with global cognitive

impairment in older persons (Larson et al., 1987). A multidisciplinary panel of health









professionals in long-term care listed it as a risk factor for drug-related problems in elderly

nursing home residents (Fouts et al., 1997).

There are many physiological reasons why long-acting benzodiazepine use may be a risk

factor for PDRM in older persons. Long-acting benzodiazepines are fat-soluble and have a larger

volume of distribution in older persons, leading to increased storage and prolonged half-life

(Harper, Newton and Walsh, 1989). Also, older persons are more sensitive to the effects of

benzodiazepines (Harper, Newton and Walsh, 1989). The oxidative metabolism of long-half life

benzodiazepines is often impaired in older persons (Harper, Newton and Walsh, 1989). As a

result, they can cause depression, falls, confusion and withdrawal symptoms. French (1996)

argues that age-related physiological changes that alter drug kinetics and pharmacological

responses to the prescribed medication are factors that increase the risk of drug-related morbidity

in older persons. Therefore, for long-acting benzodiazepine use, it appears that dosing and drug

monitoring are two critical elements needed to diminish PDRM in older persons.

HID: Antihypertensive drug use

At least two studies have identified antihypertensive drug use as a possible risk factor for

drug-related morbidity. This drug class was a risk factor for adverse drug reactions associated

with global cognitive impairment in older persons in the Larson study (Larson et al., 1987), and

it was one of the highest risk drugs for drug-related hospital readmissions in older persons in

another study (Williamson and Chopin, 1980). Also, centrally-acting antihypertensives/ beta-

blockers was identified as being one of the top ten problem drugs in older persons (Dalziel,

Byszewski and Ross, 1996).

Antihypertensive drugs have been well documented to cause drug-related morbidity in

older persons. Many antihypertensives have central nervous system side effects and may cause

acute confusion, hallucinations, impairment of memory, and reduced ability to perform complex

psychomotor tasks (Harper, Newton and Walsh, 1989). Older persons are also particularly

susceptible to depression and postural hypotension from certain antihypertensives. Therefore, it









appears that monitoring is also an important element of care for older persons who are taking this

class of drugs.

HI E: Gastrointestinal disorders

Gastrointestinal disorders was previously identified in one study has being a risk factor

for drug-related morbidity in older persons (McElnay et al., 1997). This could be because

medications used for gastrointestinal disorders are often used improperly (Tamblyn et al., 1997;

Moride et al., 1997) and many of these medications have been associated with drug-related

morbidity in older persons (Harper, Newton and Walsh, 1989; Dalziel, Byszewski and Ross,

1996). Therefore, it appears that elements three (appropriate prescribing for explicit objectives)

and six (monitoring) of the eight necessities for safe and effective drug therapy may also be

potential problem areas for patients with gastrointestinal disorders.

H1F: Lung conditions (emphysema, bronchitis or asthma)

Chronic obstructive airways disease was previously identified in one study has being a

risk factor for drug-related morbidity in older persons (McElnay et al., 1997). Patients with lung

diseases such as asthma are often on medications that require special monitoring and it has been

argued that these diseases can not be adequately explained by the biomedical model. For

example, severity of asthma symptoms may depend on such things as cleanliness of living

conditions and activities of daily living, which are better explained by the biopsychosocial

model. Therefore including lung conditions as a risk factor appears to be compatible with the

biopsychosocial model of disease and with the importance of monitoring drug therapy.

H 1G: Kidney disease

The presence of kidney disease will be included as a possible risk factor for PDRM

based on both the medical literature and theoretical considerations. A multidisciplinary panel of

health professionals in long-term care listed decreased kidney (renal) function as a risk factor for

drug-related problems in elderly nursing home residents (Fouts et al., 1997). Renal failure was

found to be an associated factor to drug-related morbidity in a retrospective study in Chile

(Zilleruelo, Espinoza and Ruiz, 1987). Older persons may be at a special risk of this. Renal blood









flow and glomerular filtration rate decrease with age (Harper, Newton and Walsh, 1989). This

leads to an elevated drug level and prolonged half-life for drugs excreted by the kidney. This can

cause drugs to accumulate and toxicity to develop. Therefore, prescribing proper doses of many

medications and drug level monitoring for PDRM are very important. As well, patient-specific

differences in renal function are great in older persons, which the biopsychosocial model

considers.

H1H: A history offalling

A history offalling was found to be a risk factor for drug-related morbidity associated

with global cognitive impairment in older persons in one study (Larson et al., 1987). In contrast,

a history offalling was not found to be a risk factor for drug-related morbidity in a later study

(Carbonin et al., 1991).

A history offalling may be a risk factor for PDRM in older persons because many

different drug classes, such as long-acting benzodiazepines, antihypertensives, and others have

been documented to cause falls. Therefore, it seems that appropriate prescribing, patient advice,

patient participation in care, monitoring, and appropriate documentation of previous falls may be

key elements to help prevent PDRM in older persons.

Several variables relate to the importance of communication for optimal drug therapy:

HI 1: Four or more prescribers

Four or more prescribers will be included as a possible risk factor, mainly based on

theoretical considerations. French (1996) did document that several providers prescribing

therapy independently was a factor that increased the risk of drug-related morbidity in older

persons. Theoretically, if a patient has multiple prescribers, PDRM could develop from

competing prescribing objectives, and poor documentation and communication of information

and therapy decisions.

H 1J: Six or more prescription medications

There is considerable evidence in the medical literature that the risk of drug-related

morbidity increases with an increase in the number of medications in the drug regimen. As early









as 1969, Hurwitz observed that patients with drug reactions had significantly more drugs during

their hospital stay then those who did not develop drug reactions. Five or more medications was

found to be the primary risk factor in a study of potential drug-drug interactions (Braverman et

al., 1996), and taking more than four drugs was found to be a risk factor of drug-related

morbidity (Carbonin et al., 1991). A multidisciplinary panel of health professionals in long-term

care said elderly nursing home patients who take nine or more medications are at risk for drug-

related problems (Fouts et al., 1997). Larson et al. (1987) looked at the relationship between the

number of medications and cognitive impairment and determined that the relative odds for

adverse drug reactions related to cognitive impairment was 9.3 for patients taking four or five

drugs, and 13.7 for patients taking six or more drugs. Finally, five or more drugs in a regimen

was a prognostic indicator chosen to identify ambulatory patients who warranted special

pharmacist monitoring (Koecheler et al., 1989).

It appears that as the number of medications in a regimen increases, the opportunity for

inappropriate prescribing, dispensing/administration errors, inadequate patient advice, lack of

patient participation in care, inadequate monitoring, and poor documentation and communication

all increase.

H 1K: Four or more recorded diagnoses

Patients with several diseases appear to be at greater risk for drug-related mortality.

Carbonin et al. (1991) found that more than four active medical problems was a risk factor for

drug-related morbidity. "Patients having more than three diseases" was used as a prognostic

indicator chosen to identify ambulatory patients who warranted special pharmacist monitoring

(Koecheler et al., 1989). A multidisciplinary panel of health professionals in long-term care

listed "more than six active chronic medical diagnoses" as a risk factor for drug-related problems

in elderly nursing home residents (Fouts et al., 1997).

The biopsychosocial model may be important in understanding why patients with several

diseases may be at an increased risk of PDRM. Gurwitz and Avorn (1991), upon reviewing the

medical literature on drug-related morbidity, stated that patient-specific physiologic and









functional characteristics are important in predicting drug-related morbidity. The

biopsychosocial model allows for consideration of the patient-specific differences. As well,

timely recognition of signs and symptoms and appropriate documentation may be even more

important for patients with several concurrent diseases. Patients with several diseases are often in

the care of a general practitioner and one or more specialists, who may not always communicate

their therapeutic plans, and which may be in conflict.

H1 L: A previous adverse drug reaction (ADR)

It appears that patients who had a previous adverse drug reaction are at increased risk for

experiencing another event in the future. A previous adverse drug reaction was previously found

to be a predisposing factor in adverse reactions to drugs (Hurwitz, 1969). A previous adverse

drug reaction was found to be an associated factor with adverse drug reactions in a retrospective

study in Chile (Zilleruelo, Espinoza and Ruiz, 1987). A multidisciplinary panel of health

professionals in long-term care listed it as a risk factor for drug-related problems in elderly

nursing home residents (Fouts et al., 1997). Also, in one study involving 177 patients who had

suffered adverse reactions during hospitalization, 32 percent had a second reaction (Ogilvie and

Ruedy, 1967a).

There are several reasons why these individuals may be at particular risk of PDRM.

They may have physicians, prescribers or patient-specific factors that contribute to poor

prescribing, dispensing, administration, patient advice, patient participation in care, monitoring

or documentation. As well, the biopsychosocial model may be useful to help explain why

specific patients are at risk of experiencing a second drug-related morbidity. The most important

factor, however, may be poor documentation and communication of their previous adverse drug

reaction.

Several variables relate to the biopsychosocial model and patient-specific differences:

HIM: High Alcohol Consumption

There is some evidence in the medical literature that high alcohol consumption is

associated with drug-related morbidity. Alcohol consumption was found to be a risk factor of









drug-related morbidity (Carbonin et al., 1991) and alcohol was listed as one of the top ten

problem drugs in older persons (Dalziel, Byszewski and Ross, 1996). The side effects of alcohol

intake in older persons are potentiated because both metabolism and excretion of alcohol are

altered with aging. In older persons, recognition of alcoholism is often difficult and delayed

because the manifestations may be subtle or erroneously attributable to normal aging (Dalziel,

Byszewski and Ross, 1996). Alcohol, when combined with many medications, can be dangerous

and lead to such events as falls. When a patient consumes alcohol and is taking medications,

patient advice and monitoring are both critical elements. As well, high alcohol consumption is

associated with sociological, behavioral, and psychological factors that can be best explained by

the psychosocial model of disease.

H1N: Self-assessment ofpoor health status

Self-assessment ofpoor health status will be included as a possible risk factor, also

primarily based on theoretical considerations. There is some evidence in the literature that self-

assessment of health as poor is associated with mortality (Fried, Pollack and Tinetti, 1998). In

addition, patient-specific physiologic and functional characteristics are important in predicting

drug-related morbidity (Gurwitz and Avorn, 1991). The biopsychosocial model is important in

helping to explain this as a possible risk factor for PDRM. This model allows the inclusion of

psychological and behavioral elements in illness.

H10O: Trouble paying for medications

Trouble paying for medications will be included as a possible risk factor for PDRM

based on theoretical considerations. A patient who reports that they are having trouble paying for

medications may exhibit poor medication compliance with their medication regimen due to the

cost of the medications. The expense of the drug is a factor that contributes to poor compliance.

Schneider et al. (1991) showed that noncompliance is related to a belief that taking medications

will not result in a successful medical outcome. Thus, trouble paying for medications may also

relate to a reluctance to take medications because of a belief that they will not help to improve

the health of the patient. O'Neil and Poirer (1998) showed that patients with poor perceptions of









their drug regimen had more adverse drug therapy outcomes. One study did determine that older

persons who do not comply with prescribed medicines are at an increased risk of drug-related

morbidity (French, 1996). Again, the biopsychosocial model is important in order to consider

sociological variables in this model. As well, patient participation in care is important to prevent

PDRM, and this includes compliance with medications and attributing medications with an

improved health status.

HIP: Difficulty taking medications

Difficulty taking medications will be included as a possible risk factor for PDRM based

on theoretical considerations. A patient's self-report that they are having difficulty taking

medications may also relate to their attitude of taking medication. Grembowski et al. (1993)

discovered that older persons with a low self-efficacy in health behaviors had poorer health.

Therefore, it is conceivable that many older persons have low self-efficacy in taking medications

and thus develop PDRM. The biopsychosocial model is important in order to consider

behavioral variables in this model. As with the previous variable, patient participation in care is

important to prevent PDRM, and this includes self-efficacy with taking medications. In addition,

French (1996) claims that many older persons experience motor-sensory declines that contribute

to an inability to properly take medications.

H 1Q: Patient belief that they are taking too many medications

Patient belief that they are taking too many medicines will be included as a possible risk

factor for PDRM based on theoretical considerations. This variable may also relate to the

patient's attitude towards medications. Disagreement with the prescribed medication regimen is a

factor that contributes to poor compliance. Highly complex medication regimens are associated

with noncompliance (Haynes, Taylor, and Sackett, 1979). Also, patients who believe they are on

too many medications may have low self-efficacy for taking medications, which may also lead to

poorer health and PDRM. Again, the biopsychosocial model is important in order to consider

behavioral variables in this model. As well, patient participation in care is important to prevent

PDRM, and this includes compliance with medications.









One variable has been included based on empiric evidence, but it does not seem to

directly relate to the conceptual models used in this study:

H1 R: Female gender

Female gender has been identified as being a risk factor for drug-related morbidity in

several studies. Hurwitz (1969) noted that females had significantly more drug-related

morbidities than males. Female gender was found to be an associated factor with adverse drug

reactions in a retrospective study in Chile (Zilleruelo, Espinoza and Ruiz, 1987). The odds ratio

associated with having an ADR in older females as compared to males was found to be 1.9,

although it was not statistically significant (Hallas et al., 1991). Finally, gender was found to be

a determinant of both the frequency and characteristics of ADRs in a prospective drug

surveillance study involving 1920 patients in Chile (Domecq et al., 1980). In contrast,female

gender was not found to be a risk factor for ADRs in one study, although the odds ratio for an

ADR was 1.22 (0.987-1.54, 95 percent confidence interval) (Carbonin et al., 1991). Zadoroznyj

and Svarstad (1990) argue that by excluding female-specific drugs and conditions (e.g.;

pregnancy) there is basically no difference in drug use between males and females.

The reason why female gender may be a risk factor for PDRM does not seem to be easily

explained by any of the models used in this study but will be included for empirical reasons.



The third research question addresses whether there are general risk factors for PDRM

and risk factors which might be drug or disease specific for PDRM. This research question is

obviously closely related to the previous research question. The hypothesis proposed for this

question is as follows:

H2: There will be both general risk factors for PDRM and risk factors that are drug or

disease specific for PDRM



This proposition is based on the reports of the wide variation of risk factors associated

with different kinds of PDRMs that are found in the peer-reviewed medical literature. As the









previous review of the literature showed, there seems to be some general risk factors for PDRM

(poor health status, multiple prescribers, multiple disease states), and some drug-specific

(digoxin, antihypertensives, etc.) and disease-specific (lung disease, kidney disease, etc.) risk

factors for PDRM.



The fourth research question is whether the utilization of health care resources differs

between patients with, and without, PDRM. The specific proposition hypothesized for this

research question is the following:



H3: Older persons that have PDRM will consume more health care resources than those

who do not have PDRM



This hypothesis suggests that there will be a significant difference in the health care

resource utilization patterns between those older persons who do, and do not, have PDRM. Based

on the literature previously reviewed, there is empirical support that those older persons who

experience PDRM do consume more health care resources. Zisselman et al. (1996) concluded

that those older persons who received sedative-hypnotics with doses exceeding the Health Care

Financing Administration (HCFA) guidelines had increased hospital costs and longer lengths of

stay as compared to those who did not receive these drugs or whose dosages did not exceed the

guidelines (although the direction of causality is unknown). The very nature of PDRM often

involves the consumption of valuable resources. For example, Bates et al. (1997) determined that

those patients with a preventable adverse drug event (ADE) had an average increase of 4.6 days

in length of stay and $5857 in total cost, and the annual costs attributable to all preventable

ADEs was estimated to be $2.8 million for a 700-bed teaching hospital. Thus, based on this

evidence, the proposition will be made that those who experience PDRM will have a higher

utilization of healthcare resources.















CHAPTER 4
METHODS



The intent of this study was to (i) determine the issues in developing and using

operational definitions of PDRM, (ii) identify major risk factors for PDRM in older persons, (iii)

examine whether there are general risk factors for PDRM in older persons and drug, or disease,

specific risk factors, and (iv) determine what is the relationship between PDRM and the

utilization of healthcare resources.

To meet these objectives, this study was conducted in two phases. The first phase of the

study concentrated on the dependent variable in this study: preventable drug-related morbidity

(PDRM). In this phase, operational definitions of PDRM were created through the use of a

geriatric medicine expert panel and validated using a chart abstract reviewer panel. The second

phase of the study focused on the independent variables: hypothesized risk factors for PDRM. In

this phase, the risk factors related to PDRM in older persons were identified using the study

database: claims and quality of life data from the Florida Hospital Healthcare System Premier

Care Plan Medicare population. This second phase of the study also involved the creation of a

risk stratification system for PDRM and exploration into the relationship of PDRM and

healthcare resource utilization.


Phase I: Operational Definitions of PDRM


The first research question described above was investigated in Phase I of the study.

Phase I of the study involved the creation of operational definitions of PDRM in older persons.

This was accomplished by a review of the medical literature, and the administration of a survey

to a consensus panel of geriatric medicine experts. The validity of these definitions of PDRM









was explored through the use of a second panel: a chart abstract reviewer panel. These steps of

the Phase I methodology are displayed in Table 4.1.


Operationalization of the Study Construct PDRM


In order to have the geriatric medicine expert panel agree on what is a PDRM, and for

the purpose of the conceptual framework of this study, it was necessary to operationalize the

term PDRM. A PDRM can be defined as an unwanted consequence of the medication use

process that, with appropriate systems, adequately trained personnel and patients or caregivers,

could have detected, predicted, controlled and avoided (Hepler and Strand, 1990). PDRM results

from (a) unacceptable quality of care (e.g., failure to meet consensus guidelines) or (b) occurs

after a drug-relatedproblem (DRP). A review of the literature of PDRM in older persons was

presented in chapter 3 and its relationship to the medication use system and biopsychosocial

model was explored in chapter 2.

An extension of this conceptual definition is that PDRM has four defining

characteristics. The DRP that lead to the PDRM must be recognizable and the likelihood of a

drug-related morbidity must be foreseeable. In addition, the causes) of the DRP (and subsequent

drug-related morbidity) must be identifiable, and those causes must be controllable. Preventable

drug-related morbidity, therefore, results from unrecognized or otherwise unresolved DRPs.

An operational definition of each of these four defining characteristics is as follows:

1. Recognizable. In order for a drug-related morbidity to be recognizable, the DRP

that produced the drug-related morbidity must be observable (Hepler and Strand, 1990). This

was determined by listing specific outcomes (morbidities) and patterns of care. A geriatric

medicine expert panel was then asked to judge whether for most older persons, if health









Table 4.1 Phase I Methodology

Steps of the Phase I Methodology
Review of literature on PDRM, relationship to conceptual framework explored, and
defining characteristics of PDRM studied
Review of literature to identify specific operational definitions of PDRM in older persons
Construction of survey for the Geriatric Medicine Expert Panel, in order obtain consensus
on specific operational definitions of PDRM
Content review of survey by 8 individuals
Pilot test of survey mailed to 40 pharmacists
Revision of survey based on comments/responses from 28 respondents
Selection of Geriatric Medicine Expert Panel members
Administration of survey to Geriatric Medicine Expert Panel -Round 1 of Delphi
Administration of survey to Geriatric Medicine Expert Panel -Round 2 of Delphi
Consensus-approved operational definitions of PDRM obtained from the Geriatric
Medicine Expert Panel
Identification of consensus-approved operational definitions of PDRM in study
population
Abstracted chart reviews of a sample of patients with and without PDRM in study
population
Administration of chart reviews to Chart Abstract Reviewer Panel to test for validity








professionals (physicians, pharmacists, etc.) should be able to recognize significant problems in

this pattern of care.

2. Foreseeable. In order for a drug-related morbidity to be foreseeable, a reasonably

prudent clinician would have recognized that the drug-related morbidity might follow if the

recognized DRP were not resolved. This was determined by listing specific outcomes

(morbidities) and patterns of care. A geriatric medicine expert panel was then asked to judge

whether for most older persons, if health professionals should be able to foresee the possibility

of the outcome, given those problems were not resolved.

3. Causality must be identifiable. Formal attribution algorithms, such as the

Naranjo algorithm, DRAPE algorithm, and the Kramer algorithm, have been used in the past to

identify causality for adverse drug events. Identification of causality for the drug-related

morbidity was determined by listing specific outcomes (morbidities) and patterns of care.

Causality typically involves seeing what to change. A geriatric medicine expert panel was then

asked to judge whether most health professionals should see how to change the pattern of care to

prevent the outcome.

4. Controllable. In order for the cause of a drug-related morbidity to be controllable,

the clinician, patient, or caregiver must have been able to exercise restraint or direction over the

presumed cause of the drug-related morbidity. In order to determine whether the cause of the

drug-related morbidity was controllable, specific outcomes (morbidities) and patterns of care

were listed. A geriatric medicine expert panel was then asked to judge whether most health

professionals should actually change the pattern of care.

A drug-related morbidity was judged to be preventable only if the criteria for all four

defining characteristics were met.

In this study, the standard of care used by the health care professional to assess these

four definitions in specific clinical scenarios was not explicitly stated. It is therefore assumed








that the health care professionals used an implicit standard of care, such as their typical daily

practice or clinical practice guidelines. This approach has the advantage of letting the health care

professionals use their own professional judgement and years of clinical experience to determine

whether a specific clinical scenario is a case of PDRM.



Review of Literature to Identify Specific Operational Definitions of PDRM in Older Persons


As previously discussed in Chapter 3, the literature contains a comprehensive account of

the most commonly occurring drug-related morbidities in older persons. Many of these

morbidities have been hypothesized to be preventable. The literature on drug-related morbidity

since 1967 was reviewed for possible types of preventable drug-related morbidity. The search

was limited to those morbidities that occur in older persons. Peer-reviewed medical articles and

referenced texts were included in the literature review.

Based on the literature review, a list of 50 clinical scenarios (representing possible

PDRMs occurring in older persons) was compiled. These clinical scenarios had to meet the

following inclusion criteria: (1) well-referenced, (2) occur fairly commonly in the geriatric

population, (3) result in serious adverse outcomes, and (4) searchable in the study database.

Clinical scenarios involving specific laboratory values or drug dosages were excluded as this

information was not available in the study database.


Survey Development


A survey instrument was constructed in order to evaluate whether these 50 clinical

scenarios met the four defining characteristics of a PDRM. All clinical scenarios were listed in a

similar format to facilitate reading. In this format, the outcome (morbidity) was listed first, with

the pattern of care which lead to the outcome, listed second.

Expert review and pilot testing were used to assess the content validity of the survey.

The survey, instructions for use, and cover letter were reviewed by eight experts in health









services research from the Department of Pharmacy Health Care Administration at the

University of Florida for ease of use and to determine the relevance of the questions to existing

conceptual frameworks. Based on their feedback, slight modifications were made.

A convenience sample of community, hospital, managed care, consultant, and academic

clinical pharmacists from geographically diverse parts of the United States and Canada was

selected for the pilot test. Before receiving the survey, all individuals were contacted by e-mail,

fax or telephone to inform them to expect the survey. The list of 50 clinical scenarios was split in

half and each half was given to 20 content reviewers, along with a cover letter and instructions

for use. A total of 28 content reviewers completed the survey (15 completed the first half, 13

completed the second half), made comments, and returned the survey in time for analysis, for a

70 percent response rate.

Based on their feedback, slight modifications were made to the survey instrument. As

well, based on the pilot test results, some clinical scenarios were dropped from the survey, and

several new clinical scenarios were added to the survey instrument, leaving a total of 48 clinical

scenarios.


Geriatric Medicine Expert Panel


In order to reach consensus on which clinical scenarios listed in the survey instrument

were actual PDRMs, the Delphi technique was used. As Goodman (1987) explains:

The Delphi technique is a survey method of research which aims to structure group
opinion and discussion. It was first developed in the 1950s by the Rand Corporation in
California as an attempt to eliminate interpersonal interactions as the controlling
variables in decision making, as usually happens when groups of experts interact in
meetings. Its purpose is to generate discussion and enable ajudgement on a specified
topic to be made so that policy decisions can be taken which can claim to represent a
given group's wants and views (Goodman, 1987, pp.729).

Beers et al. (1991) argue that consensus methods, such as the Delphi technique, are useful

because: (1) differences in published opinion may be overcome, (2) they can help create criteria








that address narrow clinical scenarios, and (3) they can incorporate supplemental information,

such as physiological changes in older persons.

Duffield (1993) argues that the choice of panel members is critical in order for the

Delphi technique to work correctly. Participants should be chosen based on their willingness to

participate and their expert knowledge base (Goodman, 1987). With this in mind, a panel of

seven members was chosen by the Chief Medical Officer of the Florida Hospital Healthcare

System, with input from the principal investigator and the Director of Ambulatory Pharmacy at

the Florida Hospital. This panel consisted of physicians with recognized credentials in geriatric

medicine, physician administrators, and a geriatric specialty clinical pharmacist at the Florida

Hospital. These individuals were all thought to be opinion leaders within the Florida Hospital

Healthcare System and have extensive expertise in geriatric medicine. The Geriatric Medicine

Expert Panel members are listed in Appendix A.

The principal investigator explained the survey in depth to each panel member before

they completed the survey. This is because commitment and understanding of the Delphi

technique at the start of the technique influences the time and consideration given to the

technique by the participants (Goodman, 1987). Appendix B contains the cover letter,

instructions for use and survey instrument for the panel members. As Appendix B shows, the

panel members were asked to judge whether each of the 48 clinical scenarios met the four

defining characteristics of a PDRM. One clinical scenario was listed twice in the survey to serve

as a validity check, so there were actually 47 unique clinical scenarios. In addition, there was an

open-ended question at the end of the survey, where panel members could suggest any additional

operational definitions of PDRMs. Prior to the commencement of the Delphi rounds, the

inclusion of operational definitions of PDRM was set as those that were chosen by a majority of

panel members (at least four out of seven members). All seven panel members completed and

returned the survey.








The round two survey was sent to the same seven panel members the following month.

This survey contained the clinical scenarios that had survived round one and several new clinical

scenarios that were suggested by the panel members. For each clinical scenario, the panel

members were given their response (yes/no) from the previous round, the total group response,

and all the comments made by the panel members. By providing comments from the previous

round, consensus is reached quicker, usually in two rounds (Duffield, 1993). Round three

consisted of the results from round two and a letter thanking each expert panel member.



Identification of Consensus-Approved Operational Definitions of PDRM in Database


The consensus-approved operational definitions of PDRMs were identified by first

examining the study database for the outcomes related to the specific operational definitions of

PDRM. This was performed by searching for the diagnosis codes related to these outcomes.

Then, once the outcomes (morbidities) were identified, each patient case was individually

searched to determine whether the associated pattern of care that led to the outcome was

provided or not. If both the outcome and pattern of care matched the specific operational

definition of the PDRM, then it was judged to have been a case of PDRM.



Validation of Operational Definitions of Preventable Drug-Related Morbidity


A chart abstract reviewer panel of five clinical pharmacists was used to further validate

the Geriatric Medicine Expert Panel consensus-approved operational definitions of PDRM.

The first step was to choose the specific operational definitions of PDRM which

occurred often enough in the study database to allow adequate determination of sensitivity and

specificity (confidence intervals that did not include zero). The size was chosen to allow

determination of a sensitivity and specificity of 67 percent. This would be better than the








sensitivity (41 percent) and near the specificity (69 percent) of an existing model to detect total

adverse drug events in older adults (McElnay et al., 1997). It would also be comparable to

models developed to predict adverse drug reactions related to digoxin (sensitivity 92.9 percent,

specificity 61.8 percent) and theophylline (sensitivity 95.8 percent, specificity 84.0 percent)

(Tschepik et al., 1990). Based on this target sensitivity and specificity, only two specific

operational definitions of PDRM occurred often enough to be tested for validity: (1) patients

with secondary myocardial infarction who did not receive ASA and/or a beta-blocker, and (2)

patients with an emergency room visit and/or hospitalization due to hyperglycemia who were on

an oral hypogylcemic and did not have regular hemoglobin Alc monitoring.

Second, abstracted chart reviews of these patients were performed. These chart abstracts

were performed by a primary care pharmacy resident at the Florida Hospital. The instructions for

the chart abstracts, the patient chart abstract review form, and samples are shown in Appendix C.

The chart abstracts were all performed in the same format to allow for ease of reading by the

Chart Abstract Reviewer Panel members. The chart abstracter was given the patient medical

record numbers for all the patients who had the outcome regardless of whether they had the

pattern of care related to that defined preventability. The chart abstracter was blinded as to

whether the patient he was reviewing did, or did not, have a case of PDRM, as defined by the

specific operational definition. All chart abstracts were done from the inpatient charts at the

Florida Hospital and the outpatient laboratory computer system at the Florida Hospital. Chart

abstracts could not be completed for 4 patients with the secondary myocardial infarction

outcome and 1 patient with the hypergylcemia outcome because the medical charts could not be

located. In all, 35 chart abstracts were performed for patients with secondary myocardial

infarction and 31 patients with hypergylcemia.

All of the chart abstracts (n=66) were then given to the Chart Abstract Reviewer Panel,

consisting of five pharmacists at the Florida Hospital. The pharmacists were chosen by the








Clinical Coordinator of Pharmacy at the Florida Hospital based on their availability, willingness

to participate and experience working with the study population. Appendix D shows the

background of the panel members. The principal investigator and chart abstracter met with the

panel members to explain the instructions for use (see Appendix E), and to answer any questions

the panel members might have. These pharmacists were given the two relevant consensus-

approved operational definitions of PDRM and were asked to use them in determining whether

the patients in the chart abstracts actually experienced a PDRM. If four or more of the five panel

members judged a chart abstract to be a case of PDRM, then it was categorized as a PDRM. If

three or fewer panel members judged a chart abstract not to be a case of PDRM, then it was

categorized as not being a case of PDRM. The Fleiss measure of overall agreement was used to

calculate the degree of agreement among the five raters (pharmacists) for classifying each patient

(Fleiss, 1971). Others have advocated using this statistic in situations such as this, when there are

more than two raters (Conger, 1980; Abedi, 1996).

From the results of the Chart Abstract Reviewer Panel, the sensitivity and specificity of

these two specific operational definitions of PDRM was calculated. Sensitivity was calculated as

the percentage of true PDRMs (defined as a PDRM by four or more of the panel members) that

the operational definitions of PDRM labeled as such. Specificity was calculated as the

percentage of abstracted chart reviews that were judged by the Chart Abstract Reviewer Panel

not to be a PDRM, and the operational definition of PDRM labeled as not being a PDRM. A two-

by-two table of true PDRMs and predicted PDRMs was constructed, based on the sensitivity and

specificity.

It should be noted that for most screening tests or instruments, the sensitivity and

specificity of that test is calculated by comparing the outcome of the test to a "gold standard". In

this case there is no generally accepted "gold standard" for PDRM. However, if chart review by

experts is considered to be such a standard, then the panel of five clinical pharmacists is being








used as the "gold standard", acknowledging certain limitations with this approach (see

limitations section in chapter 6). The use of chart reviews by experts is commonly used as a

"gold standard" in many other areas of healthcare, such as peer review organizations (PROs). In

such cases, as in this study, the professional judgement of medical experts reviewing patient

charts is used as the "gold standard".



Phase II: Identification of Risk Factors for PDRM


The methodology for the independent variables will now be discussed. The final three

research questions described at the beginning of this chapter were investigated in Phase II of the

study. Phase II of the study involved the identification of risk factors for PDRM. This was

accomplished by reviewing the medical literature, identifying possible risk factors, and relating

them to the conceptual framework and models used in this study. A database, consisting of

enrollees from the Florida Hospital Healthcare System Premier Care Plan, was constructed to

allow for the measurement of these risk factors. Next, a series of statistical analyses were

performed to identify the risk factors. Finally, the relationship between PDRM and healthcare

resource utilization was explored. These steps of the Phase II methodology are outlined in Table

4.2.



Selection of Possible Risk Factors for PDRM


As previously discussed in Chapters 1 and 3, the literature contains a rich account of risk

factors for the most commonly occurring drug-related morbidities in older persons. The literature








Table 4.2 Phase II Methodology

Steps of Phase II Methodology
Review of literature on risk factors for PDRM and the relationship to conceptual framework
and models explored
Hypotheses generated related to specific risk factors for PDRM in older persons, and
semantic hierarchy developed
Study population identified
Construction of study database in order to identify and measure these hypothesized risk
factors for PDRM
Logistic regression model of all 18 hypothesized risk factors for PDRM
Factor analysis of all 18 hypothesized risk factors for PDRM
Additional logistic regression models to identify other risk factors for PDRM
Risk stratification system developed
Relationship of PDRM and healthcare resource utilization explored








on drug-related morbidity since 1967 was reviewed for possible risk factors for PDRM. Peer-

reviewed medical articles and referenced texts were included in the literature review.



Semantic Hierarchy of Risk Factors for Preventable Drug-Related Morbidity


Next, the relationship of these possible risk factors to the models (pharmaceutical care

and biopsychosocial) discussed in chapter 2 was explored. Other possible variables were also

identified, based on these models. Out of this process, 18 possible risk factors for PDRM were

selected.

Based on this, a semantic hierarchy of risk factors for PDRM was developed and

hypotheses related to the 18 risk factors were stated (chapter 3). A semantic hierarchy, as seen in

Table 4.3, relates constructs to variables, and variables to measurements. Several possible risk

factors relate to monitoring, as described by the medication use system: digoxin use,

antidepressant drug use, long-acting benzodiazepine use, antihypertensive drug use,

gastrointestinal disorders, lung conditions, kidney disease, and a history offalling. Several

possible risk factors relate to patient-provider communication, as described by the medication

use system: four or more prescribers, a previous adverse drug reaction, six or more prescription

medications, and four or more recorded diagnoses.

Several possible risk factors relate to patient-specific aspects of drug use, as described

by the biopsychosocial model: difficulty taking medications, high alcohol consumption, self-

assessment ofpoor health status, trouble paying for medications, and patient belief that they are

taking too many medications. Female gender did not seem to correspond to the conceptual

framework but was included for empirical reasons. Several risk factors also seem to fit more than

one construct: for example, six or more prescription medications may also be related to poor








Table 4.3 Semantic Hierarchy in Risk Factors for Preventable Drug-Related Morbidity


Constructs/ Preventable drug-related morbidity can be predicted by certain
Concepts risk factors.
These risk factors relate to the key concepts of the medication use
system and biopsychosocial model
Monitoring Communication Patient
Specific aspects
Variables Digoxin use Four or more Difficulty taking
Antidepressant drug prescribers medications
use A previous adverse High alcohol
Long-acting drug reaction consumption
benzodiazepine use Six or more Self-assessment of
Antihypertensive drug prescription poor health status
use medications Trouble paying for
Gastrointestinal Four or more medications
disorders recorded diagnoses Patient belief that
Lung conditions they are taking too
Kidney disease many medications
A history offalling

Measurements Digoxin use: Four or more Difficulty taking
(observables) FHHS and PCS claims prescribers: medications:
Examples data PCS claims data Personal Wellness
Dichotomous: l=yes, Dichotomous:l 1=4 or Profile, "How
no=0 more prescribers, 0=3 difficult is taking
or less medications for
you?"
Dichotomous: 1 =
difficult ...very
difficult/can't do it,
O=_________________________0not difficult_








monitoring as well as communication. A more comprehensive discussion of each risk factor is

contained in chapter 3.



Study Population


In order to measure these risk factors, a study population was selected. The study

population was drawn out of the larger pool of enrollees in the Florida Hospital Healthcare

System Premier Care Health Plan. This was a health plan offered by the Florida Hospital

Healthcare System, a provider-sponsored network with a Medicare contract. It was available to

all Medicare beneficiaries who live in Orange, Osceola, and Seminole Counties of Florida and

who were also enrolled in Medicare Part B. By U.S. federal law, however, those individuals who

elected to receive the Medicare hospice benefit and those who had end-stage renal disease were

not eligible for enrollment. Only those enrollees who completed the Personal Wellness Profile

(PWP) Senior Assessment were included in the study. Enrollment into the Premier Care Health

Plan began in January 1997, with approximately 7,000 enrollees by December 1997 and

approximately 50 percent of these individuals completing the PWP. This comparable with the

completion of health risk assessment tools in other Medicare programs (Kerekes and Thornton,

1996). Individuals who were enrolled in the plan anytime during 1997 were included in the study

population.



Data Collection and Formation of the Study Database


The data used in this study consisted of(l) claims which were already collected as a

natural part of the administration of the Premier Care Health Plan and (2) the Personal Wellness

Profile Senior Assessment instrument which was completed by the enrollees. All claims








processed and surveys completed between January 1 and December 31, 1997 were included in

the study.

More specifically, the study database consisted of three parts:

(1). Florida Hospital Healthcare System claims data. This data included all claims made

in the outpatient and inpatient settings for this population, except for prescriptions filled in the

ambulatory setting.

(2). PCS Outpatient Prescription Claims. This data includes all prescriptions filled for

the plan enrollees in the ambulatory setting.

(3). Personal Wellness Profile (PWP) Senior Assessment. The PWP is an instrument

that was given to all Premier Care plan members to complete (see Appendix F for the entire

questionnaire). It is an instrument used to identify enrollees at high risk for health-related

problems. It has been previously used by other health plans and its predictive validity has been

verified and studied by Boult et al. (1994), Pacala, Boult, and Boult (1995), and Pacala et al.

(1997). The PWP contains valuable information related to physical and functional status, which

previous authors have shown to be related to utilization of health care services by older persons

(Branch et al., 1981).

A central database containing these three data sets was constructed by a medical

artificial intelligence company in Orlando, FL called MEDai. This database was completed on

April 15, 1998 with approximately 95 percent of claims from 1997 processed at this time. A

unique patient identifier was used to link these three data sets. All patient names were masked to

protect patient confidentiality. The data set was provided to the principal investigator who

discarded irrelevant data fields. Many variables were dichotomized for the purpose of the study.

Considerable data manipulation was required for three of the study variables. While the study

database as prepared by MEDai did contain a listing of all prescription drugs the patients had

received, the drugs had to be organized into therapeutic classes for three of the study variables








(long-acting benzodiazepine use, antidepressant drug use, and antihypertensive drug use) for

ease of use. For these three variables, an on-line database called Lexicon was used to group all

these drugs by national drug code (NDC) number to the appropriate therapeutic class (Multum

Information Services, 1998). The accuracy of the Multum classification system was assessed for

one drug in each of these three therapeutic classes (triazolam, luvoxamine, and diltiazem). All

the patients in the study database who received these drugs were identified and it was determined

whether the drug was placed into a therapeutic class, and if so, whether it was the correct class.

Table 4.4 contains all 18 hypothesized risk factors and shows how they will be measured in this

database. Table 4.5 contains the additional demographic variables to be considered for inclusion

into the regression models, based on the bivariate analysis.



Statistical Analysis



Logistic Regression Model with the 18 Hypothesized Risk Factors

Initial data analysis was performed using a forward inclusion logistic regression

procedure to determine which of the 18 hypothesized risk factors were significantly associated

with PDRM. Larson et al. (1987) previously used this technique to determine which variables

were associated with global cognitive impairment adverse drug reactions in older persons.

McElnay et al. (1997) also used this technique to determine which variables were risk factors

associated with drug-related morbidity in older persons in the inpatient setting. SAS (SAS

Institute Inc., 1993) and JMP IN (Sail and Lehman, 1996) were used to create the regression

models.








Table 4.4 Hypothesized Risk Factors and Their Measurement


Hypothesized Risk Factor Measurement
Variables related to monitoring
Digoxin use Dichotomous: l=yes, 0=no
Measured from PWP drug question 1.
Antidepressant drug use Dichotomous: l=yes, O=no
~~~____~__________Measured from PCS claims data.
Long-acting benzodiazepine use Dichotomous: 1=yes, 0=no
Measured from PCS claims data.
Antihypertensive drug use Dichotomous: I =yes, 0=no
____________________Measured from PCS claims data.
Gastrointestinal disorders (ulcers or Dichotomous: l=yes, O=no
gastrointestinal bleeding) Measured from PWP question 14.
Lung conditions (emphysema, Dichotomous: I =yes, 0=no
bronchitis or asthma) Measured from PWP question 14.
Kidney disease Dichotomous: 1 =yes, O=no
Measured by PWP question 14.
A history offalling Dichotomous: l=yes, 0=no
_____________________ Measured by PWP question 24.
Variables related to communication
A previous adverse drug reaction Dichotomous: 1 =yes, O=no
Measured by PWP question 3, "Have you had a side
effect due to a medication that caused you to stop that
medication in the last 6 months?"
Four or more prescribers Dichotomous: 1= 4 or more prescribers, 0-=three or fewer
prescribers
Measured through the PCS outpatient prescription data.
Six or more prescription medications Dichotomous: I =six or more prescription medications,
0=five or fewer prescription medications
The number of medications taken by a patient was
~~~____~~______measured by PWP question 24.
Four or more recorded diagnoses Dichotomous: 1 =four or more disease states, 0=three or
fewer disease states. Measured by PWP question 14.
Variables related to biopsychosocial aspects of drug therapy
Self-Assessment ofpoor health status Dichotomous: 1 =poor, 0=better than poor (other)
____________________________ Measured by PWP question one.
Trouble paying for medicines Dichotomous: I =yes, O=no
Measured by PWP question 23, "Do you have trouble
___________________ paying for your medicines?"
Difficulty taking medications Categories: 1= Difficult or Very difficult/can't do it,
0=Not difficult
~~________~~___Measured by PWP question 31.









Table 4.4 Hypothesized Risk Factors and Their Measurement (Continued)


I Hypothesized Risk Factor Measurement
High Alcohol Consumption Dichotomous: 1=yes, 0=no
Measured by PWP question 11, "Do you often have more
_____________________ than 1 to 2 alcoholic drinks in a day?"
Patient belief that they are taking Dichotomous: 1= Patient thinks they are on too many
too many medications medications, 0= patient does not think they are on too
many medications.
Measured by PWP drug question 4, "How do you feel
about the number of medications you are taking?"
Other variables of interest
Female gender Dichotomous: l =female, 0=male
IMeasured by the FHHS data.








Table 4.5 Additional Demographic Variables and Their Measurement


Additional Demographic Variable Measurement
Arthritis Dichotomous: l=yes, 0=no
_______________________ Measured by PWP question 14.
Bladder/Bowel Control Problems Dichotomous: l=yes, 0=no
_______________________ Measured by PWP question 14.
Blind/Trouble Seeing, Even With Glasses Dichotomous: l=yes, O=no
Measured by PWP question 14.
Cancer (Non-skin) Dichotomous: l=yes, 0=no
_______________________ Measured by PWP question 14.
Congestive Heart Failure Dichotomous: l=yes, 0=no
_______________________ Measured by PWP question 14.
Coronary Disease Dichotomous: 1=yes, 0=no
_______________________ Measured by PWP question 14.
Angina Dichotomous: l=yes, 0=no
___________________ Measured by PWP question 14.
Myocardial Infarction Dichotomous: l=yes, 0=no
_______________________ Measured by PWP question 14.
Sciatica Dichotomous: 1=yes, 0=no
Measured by PWP question 14.
Deafness or Trouble Hearing Dichotomous: l=yes, O=no
Measured by PWP question 14.
Diabetes (High Blood Sugar) Dichotomous: l=yes, 0=no
Measured by PWP question 14.
High Blood Pressure Dichotomous: 1 =yes, 0-=no
~~~____~~_________Measured by PWP question 14.
Memory Problems (More Than Typical) Dichotomous: l=yes, 0=no
Measured by PWP question 14.
Stroke Dichotomous: l=yes, O=no
~~~~____~~~______Measured by PWP question 14.
Self-Assessment of Much Worse Health Dichotomous: l=much worse health status, 0=better
Status than much worse
Measured by PWP question 2.
Smoker Dichotomous: l=yes, 0=no
______________ Measured by PWP question 11.
Use of Six or More Over-The-Counter Dichotomous: l=yes, 0-five or fewer OTCs
Medications (OTCs) Measured by PWP question 22.
Warfarin use Dichotomous: 1 =yes, 0=-no
~________~~__Measured by PWP drug question 1.
Theophylline use Dichotomous: l=yes, 0=no
_______________________ Measured by PWP drug question 1.
Cimetidine use Dichotomous: 1 =yes, 0=no
_______________________ Measured by PWP drug question 1.
Phenytoin use Dichotomous: l=yes, 0=no
Measured by PWP drug question 2.
Lives Alone Dichotomous: =yes, 0=no
Measured by PWP question 16.









Table 4.5 Additional Demographic Variables and Their Measurement (Continued)


Hypothesized Risk Factor Measurement
Three or more hospitalizations in Dichotomous: 1 =yes, 0--==two or fewer
previous year hospitalizations
_______________________ Measured by PWP question 17.
Three or more ER visits in previous year Dichotomous: 1 =yes, 0=--two or fewer ER visits
Measured by PWP question 18.
Five or more MD clinic visits in previous Dichotomous: l=yes, 0=four or fewer MD clinic
year visits
Measured by PWP question 19.
Nursing home residence Dichotomous: 1 =yes, 0=no
Measured by PWP question 20.
Use of durable medical equipment Dichotomous: l=yes, 0=no
(oxygen, hospital bed, wheelchair, Measured by PWP question 25.
walker)
Use of home health services (visiting Dichotomous: 1 =yes, 0-no
nurse, physical therapy, homemaker/aide, Measured by PWP question 26.
adult day care)









Factor Analysis

The second step of the statistical analysis was a factor analysis with a varimax

orthogonall) rotation of principal components. This was done on a random selection of 2500

patients from the study population, with the remaining 835 patients serving as a validation group.

All 18 hypothesized risk factors were included in the factor analysis. After the factor analysis

was completed, the number of factors identified and factor scores were studied. While factor

analysis does make assumptions about the normality of the data, dichotomous data can be used

with factor analysis with confidence, provided that sample sizes are large enough (greater than

200 observationsXParry and McArdle, 1991).

The objective of the factor analysis was to (1) reduce the rather large number of

hypothesized variables to a relatively small number of factors, or common traits, and (2)

determine whether these factors matched the structure proposed in the semantic hierarchy. Factor

analysis accomplishes the first objective by focusing on the part of the total variance that is

shared by the variables, assuming that variables consist of common parts. The initial

communality estimates were set as one. The results were kept in perspective as factor analysis is

intended only to be used as a tool to help guide the researcher, not to be used without

consideration of the conceptual framework being used and other factors (Maraun, 1996).

One of two approaches could be used, based on the results of the factor analysis. Had the

factors matched the semantic hierarchy and shown to represent constructs with confidence, then

principal component scores based on these factors would have been entered into a logistic

regression model. However, because the factors did not match the semantic hierarchy and could

not be shown to represent constructs with confidence, the risk factors from the first regression

model were entered into another regression model with the additional variables, taking the factor

analysis results into consideration.








Logistic Regression Models with Additional Variables

Additional logistic regression models were then run, with the risk factors from the first

model entered a priori to adjust for their effects on PDRM. Other demographic variables were

also allowed to enter the model to see if they added significantly to the prediction, based on

statistical significance in a bivariate analysis between patients who did, and who did not, have

PDRM. Because there was a theoretical basis for including the risk factors from the first model,

it was felt that the final model for PDRM must include all of these risk factors, even if it

explained less of the variance of PDRM. Thus, this process incorporated both statistical and

theoretical criterion for deciding which terms to include in the model and this helped to focus

attention on those variables that fit into the conceptual framework and that had the greatest

independent effect on PDRM.

Following the creation of the final prediction model, a risk stratification system was

developed. Patients were categorized according to the number of risk factors they had and a

comparison was made between the patients with, and without, PDRM.


PDRM and Healthcare Resource Utilization


The final component of the methodology was to do another bivariate analysis to

determine the relationship of PDRM to healthcare resource utilization. Each enrollee was

classified as either having PDRM or not. Then, the utilization of health care resources was

compared for the two groups.














CHAPTER 5
RESULTS




The results of this study will be presented in two parts. First, the results of the Delphi

technique and the creation of the operational definitions of PDRM will be shared. Second, the

results of the prediction models for PDRM and risk factors identified will be presented.



Delphi Technique The Geriatric Medicine Expert Panel


As was discussed in the previous chapter, the Delphi technique was used in an attempt to

generate consensus on PDRM. Two rounds were used until consensus was obtained. Appendix G

contains the final list of consensus-approved operational definitions of PDRM. This appendix

also includes all the comments made by the Geriatric Medicine Expert Panel members in either

round I or round 2.

The expert panel agreed that 52 of the clinical scenarios presented to them were actual

PDRMs. Table 5.1 shows the opinions of the panel members after round 1 and round 2. After

round 1, the panel members' agreement with the clinical scenarios ranged from 60.4 percent to

97.9 percent. After round 2, the agreement ranged from 82.8 percent to 100 percent.

Initially, the panel members were presented with 47 unique clinical scenarios. One of

these was listed twice, as a validity check, so the panel members were actually presented with 48

outcomes and patterns of care to evaluate. The validity check received the same score from all

seven panel members for both rounds. After the first round, two clinical scenarios were rejected

(received fewer than four "yes" votes). The panel members were given the opportunity to suggest








Table 5.1 Geriatric Medicine Expert Panel Results


Expert Number Percentage (%) of clinical Percentage (%) of clinical
scenarios the expert felt were scenarios the expert felt were
PDRMs after Round 1 PDRMs after Round 2
1 95.8 100
2 97.9 82.8
3 79.2 89.7
4 60.4 91.4
5 79.2 87.9
6 77.1 91.4

7 97.9 94.8








other operational definitions of PDRMs and 12 were generated. One of these 12 new operational

definitions was, in fact, a duplicate of a previous definition. These 12 new operational definitions

were added to the remaining 46 operational definitions of PDRM and given to the panel

members in round 2. After the second round, an additional four clinical scenarios were rejected,

the two duplicates were removed, leaving 52 operational definitions of PDRM that were

approved.

After round 2 there appeared to be overwhelming consensus on which clinical scenarios

were actual PDRMs. Of the 52 clinical scenarios deemed to be PDRMs by the expert panel, 35

clinical scenarios had the agreement of all seven panel members, 15 clinical scenarios had the

agreement of six out of the seven members, and two clinical scenarios had the agreement of five

out of the seven members. There were no clinical scenarios that only had the agreement of four

panel members. Table 5.2 lists shows how each panel member voted in round 2. Throughout the

two round process, there were six clinical scenarios which did not have at least the support of

four panel members. Table 5.3 lists these clinical scenarios that were rejected as being PDRMs.



Identification of Consensus-Approved Operational Definitions of PDRM in Database


The 52 PDRMs approved by the geriatric medicine expert panel were identified by first

examining the study database for the outcomes related to the specific operational definitions of

PDRM. Overall, 1005 patients with outcomes related to one of the 52 consensus-approved

PDRMs were identified. Next, each patient with one of these outcomes was individually studied

to determine whether the pattern of care associated with a PDRM was provided or not. The

outcome and pattern of care matched a consensus-approved operational definition of PDRM in

158 cases. This represented 97 patients, as several patients had more than one specific









Table 5.2 Round 2 of the Delphi Technique


Number Expert Expert Expert Expert Expert Expert Expert Total
1 2 3 4 5 6 7 "Yes"
I Y Y Y Y Y Y Y 7
2 Y Y Y Y Y Y Y 7
3 Y Y Y Y Y Y Y 7
4 Y Y Y Y Y Y Y 7
5 Y Y Y Y Y Y Y 7
6 Y Y Y Y Y Y Y 7
7 Y Y Y Y Y Y Y 7
8 Y Y Y Y Y Y Y 7
9 Y Y Y Y Y Y Y 7
10 Y Y Y Y Y Y Y 7
11 Y Y Y Y Y Y Y 7
12 Y Y Y Y Y Y Y 7
13 Y Y Y Y Y Y Y 7
14 Y Y Y Y Y Y Y 7
15 Y Y Y Y Y Y Y 7
16 Y Y Y Y Y Y Y 7
17 Y Y Y Y Y Y Y 7
18 Y Y Y Y Y Y Y 7
19 Y Y Y Y Y Y Y 7
20 Y Y Y Y Y Y Y 7
21 Y Y Y Y Y Y Y 7
22 Y Y Y Y Y Y Y 7
23 Y Y Y Y Y Y Y 7
24 Y Y Y Y Y Y Y 7
25 Y Y Y Y Y Y Y 7
26 Y Y Y Y Y Y Y 7
27 Y Y Y Y Y Y Y 7
28 Y Y Y Y Y Y Y 7
29 Y Y Y Y Y Y Y 7
30 Y Y Y Y Y Y Y 7
31 Y Y Y Y Y Y Y 7
32 Y Y Y Y Y Y Y 7
33 Y Y Y Y Y Y Y 7
34 Y Y Y Y Y Y Y 7
35 Y Y Y Y Y Y Y 7
36 Y Y Y Y Y N Y 6
37 YV N Y Y y y Y 6
38 Y N Y Y y y y 6
39 Y N Y Y y y y 6
40 Y Y N Y Y Y Y 6
41 Y Y Y Y N Y Y 6
42 Y N Y Y y y 6








Table 5.2 Round 2 of the Delphi Technique (Continued)


Number Expert Expert Expert Expert Expert Expert Expert Total
1 2 3 4 5 6 7 "Yes"
43 Y Y N Y Y Y Y 6
44 Y Y N Y Y Y Y 6
45 Y Y Y Y N Y Y 6
46 Y Y Y N Y Y Y 6
47 Y Y Y Y Y N Y 6
48 Y N Y Y Y Y Y 6
49 Y Y Y Y N Y Y 6
50 Y N Y Y Y Y Y 6
51 Y Y N Y N Y Y 5
52 Y N Y Y Y Y N 5
53b Y N Y N N N Y 3
54b Y Y N N Y N N 3
55b Y N N N N Y N 2
56b Y N Y N N N Y 3
57c y Y y y y y y 7
58d Y___ Y Y Y Y Y Y 7


a See Appendix G for a description of the PDRM
b Rejected as an operational definition of PDRM
c Duplicate same as PDRM #2
d Duplicate same as PDRM #17








Table 5.3 Clinical Scenarios That Were Rejected As Preventable Drug-Related Morbidities

Clinical Scenario (Outcome and Pattern of Care)
Outcome: Fall and/or hip fracture and/or other bone fracture and/or bone break
Pattern of care: 1. Use of an anti-parkinsonian agent (e.g.; levodopa, bromocriptine,
Benztropine, etc.)
Outcome: Major and/or minor hemorrhagic event
Pattern of care: 1. Use of SQ heparin
2. PTT not done at least every month
Outcome: Digoxin toxicity
Pattern of care: 1. Use ofdigoxin
2. BUN/serum creatinine not done at least every 6 months
3. Digoxin level not done at least every 6 months
Outcome: Fall and/or hip fracture and/or other bone fracture and/or bone break
Pattern of care: 1. Use of a nitrate (e.g.; isosorbide)
Outcome: Acute renal failure and/or renal insufficiency
Pattern of care: 1. Use of allopurinol
2. BUN/serum creatinine not done at least every 6 months
Outcome: Asthma exacerbation and/or status asthmaticus and/or ER visit/hospitalization
Due to asthma
Pattern of care: 1. Diagnosis of asthma
2. Use of theophylline
3. Drug level not done at least every 6 months








operational definition of PDRM. Table 5.4 shows the individual breakdown of each of the 52

consensus-approved operational definitions of PDRM.

As previously mentioned, many patients experienced more than one specific operational

definition of PDRM. Table 5.5 shows the number of PDRMs each patient had, along with the

number of specific outcomes these PDRM represented. This distinction is important to make

because some patients met the criteria for more than one PDRM, but these PDRMs shared the

same outcome (e.g. there are several PDRMs related to major/minor hemorrhagic events with

different patterns of care).



Validation of Operational Definitions of Preventable Drug-Related Morbidity


Pharmacist agreement with the PDRM classification assigned by the operational

definitions of PDRM was acceptable, although it varied for the two specific operational

definitions of PDRM included in the analysis. Tables 5.6 and 5.7 show the individual panel

members' classification for each patient. One patient chart for the hyperglycemia outcome and

four patient charts for the secondary myocardial infarction outcome could not be located and

they were not included in the final analysis.

Overall, the sensitivity of the two specific operational definitions of PDRM was 87.5

percent and the specificity was 73.5 percent (Table 5.8). For the hyperglycemia outcome, the

sensitivity was 93.3 percent and the specificity was 81.3 percent (Table 5.9). For this outcome,

the chart abstracts had to be administered a second time to the Chart Abstract Reviewer Panel

because not all panel members followed the initial instructions, as will be discussed in the next

chapter. For the secondary myocardial infarction outcome, the sensitivity was 82.4 percent and

the specificity was 66.7 percent (Table 5.10).










Table 5.4 Patients with Outcomes and PDRM


PDRM Number Number Percentage PDRM Number Number Percentage
Num- of of (%) of Num- of of (%) of
ber a Patients Patients Patients ber a Patients Patients Patients
with with with the with with with the
Outcome PDRM outcome Outcome PDRM outcome
(%) (%) who have (%) (%) who have
(n=1005) (n=97) b PDRM (n=1005) (n=97) b PDRM
1 28(2.8) 8 (8.2) 28.6 27 14(1.4) 0 (0.0) 0
2 24(2.4) 6 (6.2) 25.0 28 45(4.5) 2 (2.1) 4.4
3 0(0.0) 0 (0.0) 0 29 28(2.8) 3 (3.1) 10.7
4 7(0.7) 1 (1.0) 14.3 30 13(1.3) 0 (0.0) 0
5 24(2.4) 0 (0.0) 0 31 2 (0.2) 0 (0.0) 0
6 3(0.3) 1 (1.0) 33.3 32 7(0.7) 0 (0.0) 0
7 17(1.7) 0 (0.0) 0 33 39(3.9) 2 (2.1) 5.1
8 24(2.4) 0 (0.0) 0 34 16(1.6) 7 (7.2) 43.8
9 0(0.0) 0 (0.0) 0 35 1(0.1) 0 (0.0) 0
10 0(0.0) 0 (0.0) 0 36 32(3.2) 8 (8.2) 25.0
11 45(4.5) 4 (4.1) 8.9 37 32(3.2) 6 (6.2) 18.8
12 28(2.8) 2 (2.1) 7.1 38 2(0.2) 1 (1.0) 50.0
13 27 (2.7) 0 (0.0) 0 39 3 (0.3) 0 (0.0) 0
14 2 (0.2) 0 (0.0) 0 40 0 (0.0) 0 (0.0) 0
15 2(0.2) 0 (0.0) 0 41 14(1.4) 12(12.4) 85.7
16 46(4.6) 4 (4.1) 8.7 42 31(3.1) 3 (3.1) 9.7
17 7(0.7) 1 (1.0) 14.3 43 31(3.1) 3 (3.1) 9.7
18 24(2.4) 10(10.3) 41.7 44 7(0.7) 0 (0.0) 0
19 0(0.0) 0 (0.0) 0 45 16(1.6) 0 (0.0) 0
20 0 (0.0) 0 (0.0) 0 46 24(2.4) 0 (0.0) 0
21 32(3.2) 18(18.6) 56.3 47 31(3.1) 6 (6.2) 19.4
22 45(4.5) 5 (5.2) 11.1 48 24(2.4) 6 (6.2) 25.0
23 0(0.0) 0 (0.0) 0 49 31(3.1) 1 (1.0) 3.2
24 39(3.9) 24(24.7) 61.5 50 42(4.2) 2 (2.1) 4.8
25 12(1.2) 0 (0.0) 0 51 32(3.2) 10(10.3) 31.3
26 45(4.5) 1 (1.0) 2.2 52 7 (0.7) 1 (1.0) 14.3

a See Appendix G for the description of the PDRM.
b Adds up to over 100 percent because some patients had more than one operational definition
of PDRM.








Table 5.5 Number of PDRMs and Specific Outcomes by Individual Patients

Patient Category Number of Patients
with PDRM (%) n=97
1 case of PDRM with 1 specific outcome 61(62.9)
2 cases of PDRM with 1 specific outcome 12 (12.4)
2 cases of PDRM with 2 specific outcomes 6 (6.2)
3 cases of PDRM with 1 specific outcome 4 (8.2)
3 cases of PDRM with 2 specific outcomes 8 (8.2)
3 cases of PDRM with 3 specific outcomes 0 (0.0)
4 cases of PDRM with 1 specific outcome 0 (0.0)
4 cases of PDRM with 2 specific outcomes 3 (3.1)
4 cases of PDRM with 3 specific outcomes 0 (0.0)
4 cases of PDRM with 4 specific outcomes 2 (2.1)
5 cases of PDRM with 1 specific outcome 0 (0.0)
5 cases of PDRM with 2 specific outcomes 0 (0.0)
5 cases of PDRM with 3 specific outcomes 1 (1.0)
5 cases of PDRM with 4 specific outcomes 0 (0.0)
5 cases of PDRM with 5 specific outcomes 0 (0.0)









Table 5.6 Chart Abstract Reviewer Panel Results for Hyperglycemia with no
Regular HgAlc Monitoring

Pt # RPh RPh RPh RPh RPh Total PDRM according to
____#1 #2 #3 #4 #5 "yes" definition
I Y Y Y Y Y 5 Y
2 Y Y Y Y Y 5 Y
3 N N N N Y 1 N
4 Y Y Y Y Y 5 Y
5 Y N N Y N 2 N
6 Y N N N Y 2 N
7 Y Y Y Y Y 5 Y
8 Y Y N Y Y 4 Y
9 Y Y N Y Y 4 Y
10 Y Y Y Y Y 5 Y
11 N N N N Y 1 N
12 N N N N N 0 N
13 N N N N N 0 N
14 Y Y Y Y Y 5 Y
15 Y Y Y Y Y 5 Y
16 N N N N N 0 N
17 N Y N Y N 2 N
18 Y Y Y Y N 4 Y
19 N Y IN Y N 2 N
20 N Y N Y N 2 Y
21 Y Y Y Y Y 5 N
22 N Y N N Y 2 Y
23 N N N N Y I N
24* Y
25 N N Y N N 1 N
26 Y Y Y Y Y 5 Y
27 N Y N N N I N
28 Y Y Y Y Y 5 Y
29 Y Y Y Y Y 5 Y
30 Y N N Y N 2 Y
31 Y Y Y Y Y 5 Y
32 N N N N Y 1 N

* Chart abstract could not be done because patient chart could not be located.









Table 5.7 Chart Abstract Reviewer Panel Results: Secondary Myocardial Infarction
in Patients Without ASA and/or Beta-Blocker Use

Pt # RPh RPh RPh RPh RPh Total "Yes" PDRM according
____#1 #2 #3 #4 #5 _____to definition
1 N N N N N 0 N
2 Y Y Y Y N 4 N
3 N N N N Y I N
4 Y Y N Y N 3 Y
5 Y Y Y Y Y 5 Y
6 N Y Y N N 2 N
7 Y N N Y N 2 N
8 N N Y Y Y 3 N
9 Y Y Y Y Y 5 Y
10* y____________Y
11 Y Y Y N Y 4 Y
12 Y Y Y Y Y 5 Y
13 Y N N Y Y 3 Y
14 Y Y Y Y Y 5 Y
15 Y Y Y Y Y 5 N
16 Y N N Y Y 3 Y
17 Y Y Y Y Y 5 Y
18 Y Y Y Y Y 5 Y
19 Y N Y N Y 3 Y
20 N N N Y Y 2 N
21 N N N Y N I N
22*_________ y
23 Y Y Y Y Y 5 Y
24 Y Y Y Y Y 5 Y
25 Y Y Y Y Y 5 Y
26*0___ ------- Y
27* ______ y
28 Y Y Y N N 3 Y
29 N Y N Y Y 2 Y
30 Y Y Y Y N 4 Y
31 N N N Y N I N
32 Y N Y Y Y 4 Y
33 Y N Y Y N 3 N
34 Y Y Y Y Y 5 Y
35 Y N Y Y Y 4 N
36 N N N Y Y 2 N
37 Y Y Y Y Y 5 Y
38 N Y N Y N 2 N
39 N N N Y N I N

* Chart abstract could not be done because patient chart could not be located.








Table 5.8 Sensitivity and Specificity for Both of the Operational Definitions of PDRM


Operational Definition of PDRM
Yes No
True Yes 28 4
PDRM No1 9 25

Sensitivity was calculated as [28/(28+4)] x 100 = 87.5 percent.
Specificity was calculated as [25/(25+9)] x 100 = 73.5 percent.
True PDRM (Yes) = Four or more of the panel members classified as PDRM,
True PDRM (No) = Four or more of the panel members classified as non-PDRM,
Operational definition of PDRM (Yes) = Classified as a PDRM by the operational
definition,
Operational definition of PDRM (No) Classified as a non-PDRM by the operational
definition.








Table 5.9 Sensitivity and Specificity of the Operational Definition of PDRM (Hyperglycemia
Outcome)


Operational Definition of PDRM
Yes No
True Yes 14 1
PDRM No 3 13

Sensitivity was calculated as [14/(14+1)] x 100 = 93.3 percent.
Specificity was calculated as [13/(13+3)] x 100 = 81.3 percent.
True PDRM (Yes) = Four or more of the panel members classified as PDRM,
True PDRM (No) = Four or more of the panel members classified as non-PDRM,
Operational definition of PDRM (Yes) = Classified as a PDRM by the operational
definition,
Operational definition of PDRM (No) Classified as a non-PDRM by the operational
definition.








Table 5.10 Sensitivity and Specificity of the Operational Definition of PDRM (Secondary
Myocardial Infarction Outcome)


Operational Definition of PDRM
Yes No
True Yes 14 3
PDRM No 6 12

Sensitivity was calculated as [14/(14+3)] x 100 = 82.4 percent.
Specificity was calculated as [12/(12+6)] x 100 = 66.7 percent.
True PDRM (Yes) = Four or more of the panel members classified as PDRM,
True PDRM (No) = Four or more of the panel members classified as non-PDRM,
Operational definition of PDRM (Yes) = Classified as a PDRM by the operational
definition,
Operational definition of PDRM (No) Classified as a non-PDRM by the operational
definition.








The agreement among the five pharmacists in classifying the patients into those with PDRM, and

those without PDRM, was acceptable. Fleiss's measure of overall agreement for the

hyperglycemia patients was 0.652, for the secondary myocardial infarction patients it was 0.674

and overall it was 0.664. Therefore, if a patient was selected at random and classified as either

having, or not having, PDRM by a randomly selected panel member, a second randomly selected

panel member would agree with the first panel member 66.4 percent of the time.

Therefore, because of the consensus reached by the Geriatric Medicine Expert Panel and

the high sensitivity and specificity of the two validated operational definitions, research

assumption A1 A could be met: valid operational definitions of PDRM can be developed by a

panel of geriatric medicine experts.



Phase II: Identification of Risk Factors for PDRM


Phase II involved the identification of risk factors for PDRM. First, the results of the

statistical analyses used to identify the risk factors will be presented. Second, the results related

to the hypothesis for each risk factor will be shown. Third, the results related to the hypothesis of

general risk factors and drug (and disease) specific risk factors will be presented. Finally, the

relationship between PDRM and healthcare resource utilization will be discussed.


Logistic Regression with all 18 Hypothesized Risk Factors


In order to test the first set of hypotheses regarding possible risk factors for PDRM, a

logistic regression analysis was performed. A forward inclusion procedure was used with the

entry level set at p=0.05.

A five-variable risk model was produced (Table 5.11). This model indicates that patients

withfour or more recorded diagnoses were 2.93 times more likely to have PDRM than those

with three or fewer diseases. Patients with antihypertensive drug use are at a much greater risk









Table 5.11 Logistic Regression Model With All 18 Hypothesized Variables


Variable Parameter Standard Chi-Square Odds 95 Percent
Estimate Error (SE) Probability Ratio Confidence
(b)____ ____ Interval
Four or more 0.2683 0.0602 0.0001 1.308 1.162-1.472
prescribers
Four or more 1.0758 0.2475 0.0001 2.932 1.805-4.763
recorded
diagnoses
Female gender -0.6633 0.2393 0.0056 0.515 0.823-0.322
Antihypertensive 0.7023 0.2787 0.0118 2.018 1.156-3.524
drug use
Six or more 0.6525 0.2942 0.0266 1.920 1.079-3.418
prescription
medications
Equation Constant -4.6958 0.2758 0.0001 -




Full Text
RISK ASSESSMENT OF PREVENTABLE DRUG-RELATED MORBIDITY
IN OLDER PERSONS
By
NEIL JOHN MACKINNON
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

Copyright 1999
by
NEIL JOHN MACKINNON

This dissertation is dedicated to my parents, James Elliott and Shirley MacKinnon. Thank you
for your love, support, and examples of faith.

ACKNOWLEDGMENTS
Professional maturity has much in common with maturity as a person. One attribute
common to both is a world view, an expectation that one thrives best by using one’s
power to serve something bigger than oneself. -Hepler and Strand (1990)
Completing a dissertation is one sure way to mature and grow as a person, as most
people who have obtained a Doctor of Philosophy degree would agree. It is a long process, from
the genesis of an initial idea for a research project, to the time the study comes to completion.
The process is filled with times of intense struggle, frustration and difficulty, but also with times
of excitement, learning, and finally, fulfillment. Perhaps the Apostle Paul had this in mind,
when he said in the Book of Romans that "tribulation produces perseverance; and perseverance,
character; and character, hope" (Romans 5:3b-4).
In completing a dissertation, there are many individuals who play key parts throughout
the process. Obviously one group of individuals that deserves considerable acknowledgment is
my dissertation committee. We worked together for countless hours shaping a rough research
idea into a real research project. Charles D. (Doug) Hepler, my committee chair, should take a
lot of credit. He proved to be an excellent mentor and hopefully I can have even a small portion
of the success he has had as a researcher. He also helped me balance the dual roles of graduate
student and research fellow over the past three and a half years.
Earlene Lipowski, a fellow Badger, gave invaluable advice, including encouraging me to
keep a day-by-day project journal, which I have done. Richard Segal helped me initially to
enroll in this graduate program and he always seems to be in a good mood, which is a welcomed
IV

sight on many days. Finally, Geoff Vining helped mold my perspective of statistics from a
necessary evil to a useful tool for the researcher.
There are also several other individuals who made considerable contributions to this
research project. They include several people at the Florida Hospital in Orlando, from which my
patient population came: Paul Garrett, Tim Regan, Lisa Hutchison, and Scott Neel. Two
individuals at MEDai, a medical artificial intelligence company, helped to assemble the study
database: Kathleen Costello and David Katz (without pay, I might add).
Several other individuals in the Pharmacy Health Care Administration department also
deserve recognition. The ladies in the office provided not only secretarial support, but also some
great conversation: Delayne Redding, Debbie Kemp and Jennifer Ryder. The other graduate
students also provided valuable input on my dissertation and welcomed diversions away from
my dissertation too, such as racquetball. The other faculty in the department helped to provide a
great learning environment for my three and a half years of graduate school. Finally, the faculty
also helped to directly support my dissertation through the Liberty Funds.
Finally, those individuals who have provided love and support definitely deserve special
recognition. This includes my family (thanks mom and dad!) and my fiancé, Leanne Moore, as
well as my Lord and Savior Jesus Christ. Philippians 4:6-7 is one Bible passage from which I
have drawn strength many times: "Be anxious for nothing, but in everything by prayer and
supplication, with thanksgiving, let your requests be made known to God; and the peace of God,
which surpasses all understanding, will guard your hearts and minds through Christ Jesus."
v

TABLE OF CONTENTS
page
ACKNOWLEDGMENTS iv
LIST OF TABLES x
LIST OF FIGURES xii
ABSTRACT xiii
CHAPTERS
1 INTRODUCTION 1
The Need for the Study 1
Older Persons and Healthcare 1
Drug-Related Morbidity and Mortality 2
Preventable Drug-Related Morbidity and Mortality 3
Problem Statement 5
Study Objectives 5
Rationale and Theoretical Introduction 5
Research Questions 8
Research Question 1 8
Research Question 2 8
Research Question 3 8
Research Question 4 8
2 CONCEPTUAL FRAMEWORK 9
Systems Theory and the Medication Use System 9
The System Matrix Applied to the Medication Use System 12
Feedback and Communication Loops in the Medication Use System 14
Change and the Medication Use System 19
Biopsychosocial Model of Disease Etiology and Therapy 20
Risk Factors for PDRM 24
Summary Of The Conceptual Models To Be Used In This Study 27
3 REVIEW OF THE LITERATURE 28
Drug-Related Morbidity and Mortality in Older Persons 28
Identification of Patients at High Risk of Medical Problems in Older Persons 31
Prediction Models for Drug Use in Older Persons 33
Research Assumptions and Hypotheses 34
Research Assumptions 35
VI

Research Hypotheses
36
4 METHODS 48
Phase I: Operational Definitions of PDRM 48
Operationalization of the Study Construct PDRM 49
Review of Literature to Identify Specific Operational Definitions of PDRM in
Older Persons 52
Survey Development 52
Geriatric Medicine Expert Panel 53
Identification of Consensus-Approved Operational Definitions of PDRM in
Database 55
Validation of Operational Definitions of Preventable Drug-Related Morbidity 55
Phase II: Identification of Risk Factors for PDRM 58
Selection of Possible Risk Factors for PDRM 58
Semantic Hierarchy of Risk Factors for Preventable Drug-Related Morbidity 60
Study Population 62
Data Collection and Formation of the Study Database 62
Statistical Analysis 64
Logistic Regression Model with the 18 Hypothesized Risk Factors 64
Factor Analysis 69
Logistic Regression Models with Additional Variables 70
PDRM and Healthcare Resource Utilization 70
5 RESULTS 71
Delphi Technique - The Geriatric Medicine Expert Panel 71
Identification of Consensus-Approved Operational Definitions of PDRM in
Database 73
Validation of Operational Definitions of Preventable Drug-Related Morbidity 77
Phase II: Identification of Risk Factors for PDRM 85
Logistic Regression with all 18 Hypothesized Risk Factors 85
Factor Analysis 89
Bivariate Analysis 92
Logistic Regression Models with Additional Demographic Variables 95
Additional Analyses Related to Risk Factor Identification 101
Risk Stratification System 105
Testing the Hypotheses 105
First Set of Hypotheses 105
Hypothesis H1A 107
Hypothesis H1B 107
Hypothesis H1C 107
Hypothesis HID 107
Hypothesis HIE 107
Hypothesis H1F 108
Hypothesis H1G 108
Hypothesis H1H 108
Hypothesis HI 1 108
Hypothesis H1J 108
vii

Hypothesis H1K 109
Hypothesis H1L 109
Hypothesis HIM 109
Hypothesis H1N 109
Hypothesis HIO 109
Hypothesis HIP 110
Hypothesis H1Q 110
Hypothesis H1R 110
Second Hypothesis 110
Third Hypothesis 111
6 DISCUSSION 113
Use of the Delphi Technique with the Geriatric Medicine Expert Panel 113
Consensus-Approved Operational Definitions of PDRM Observed in the Study
Population 116
Validation of Operational Definitions of Preventable Drug-Related Morbidity 119
Risk Factors for PDRM 121
Risk Factor 1: Four or More Recorded Diagnoses 122
Risk Factor 2: Antihypertensive Drug Use 122
Risk Factor 3: Male Gender 123
Risk Factor 4: Four or More Prescribers 123
Risk Factor 5: Six or More Prescription Medications 124
General Discussion on the Final Prediction Model for PDRM 124
General and Specific Risk Factors 126
PDRM and Healthcare Resource Utilization 127
Potential Limitations 127
Significance 129
Contribution to the Profession of Pharmacy 131
Contribution to Healthcare 131
Theoretical Contribution 132
Conclusions 133
APPENDICES
A GERIATRIC MEDICINE EXPERT PANEL MEMBERS 135
B EXAMPLE SURVEY FOR GERIATRIC MEDICINE EXPERT PANEL MEMBERS
- ROUND 1 OF DELPHI TECHNIQUE 136
C INSTRUCTIONS FOR PATIENT CHART ABSTRACTER, PATIENT CHART
ABSTRACT FORM, AND SAMPLE PATIENTS 151
D MEMBERS OF THE CHART ABSTRACT REVIEWER PANEL 158
E INSTRUCTIONS FOR CHART ABSTRACT REVIEWER PANEL 159
F PERSONAL WELLNESS PROFILE SENIOR ASSESSMENT 161
G ROUND 3 OF THE GERIATRIC MEDICINE EXPERT PANEL SURVEY 174
viii

LIST OF REFERENCES 189
BIOGRAPHICAL SKETCH 199
IX

LIST OF TABLES
Table page
2.1 Eight Fundamentals (Necessities) for Safe and Effective Drug Therapy 15
2.2 The Fundamentals/Human Agents Cell of the System Matrix Applied to the
Medication Use System 16
4.1 Phase I Methodology 50
4.2 Phase II Methodology 59
4.3 Semantic Hierarchy in Risk Factors for Preventable Drug-Related Morbidity 61
4.4 Hypothesized Risk Factors and Their Measurement 65
4.5 Additional Demographic Variables and Their Measurement 67
5.1 Geriatric Medicine Expert Panel Results 72
5.2 Round 2 of the Delphi Technique 74
5.3 Clinical Scenarios That Were Rejected As Preventable Drug-Related Morbidities 76
5.4 Patients with Outcomes and PDRM 78
5.5 Number of PDRMs and Specific Outcomes by Individual Patients 79
5.6 Chart Abstract Reviewer Panel Results for Hyperglycemia with no 80
Regular HgAlc Monitoring 80
5.7 Chart Abstract Reviewer Panel Results: Secondary Myocardial Infarction 81
in Patients Without ASA and/or Beta-Blocker Use 81
5.8 Sensitivity and Specificity for Both of the Operational Definitions of PDRM 82
5.9 Sensitivity and Specificity of the Operational Definition of PDRM (Hyperglycemia
Outcome) 83
5.10 Sensitivity and Specificity of the Operational Definition of PDRM (Secondary
Myocardial Infarction Outcome) 84
5.11 Logistic Regression Model With All 18 Hypothesized Variables 86
x

5.12 Correlation Matrix for Significant Variables in Regression Model With All 18
Hypothesized Variables 88
5.13 Classification Table for the Regression Model with the 18 Hypothesized Variables 90
5.14 Rotated Factor Matrix - Varimax 91
5.15 Rotated Factor Matrix - Harris-Kaiser 93
5.16 Rotated Factor Matrix - Varimax Validation Group 94
5.17 Bivariate Analysis of Patients With, and Without, PDRM Categorized by
Hypothesized Variables 96
5.18 Bivariate Analysis of Patients With, and Without, PDRM, Categorized by
Additional Demographic Variables 97
5.19 Logistic Regression Model Including Additional Demographic Variables 98
5.20 Correlation Matrix for Significant Variables in Regression Model with Additional
Variables 100
5.21 Rotated (Varimax) Factor Matrix of Five Original Risk Factors and Three New Risk
Factors 102
5.22 Logistic Regression Model Excluding Antihypertensive Drug Use 104
5.23 Bivariate Analysis of Patients With and Without PDRM, Categorized by Healthcare
Resource Utilization 112
xi

LIST OF FIGURES
Figure page
2.1 The Medication Use System 10
2.2 High Leverage Points Within the Medication Use System 18
5.1 Risk Stratification System 106
xii

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
RISK ASSESSMENT OF PREVENTABLE DRUG-RELATED MORBIDITY
IN OLDER PERSONS
By
Neil John MacKinnon
May, 1999
Chairman: Charles D. Hepler, Ph.D.
Major Department: Pharmacy Health Care Administration
This study had three primary objectives: 1) to create operational definitions of
preventable drug-related morbidity (PDRM), 2) to identify patients who are at particular risk of
PDRM and who may therefore benefit from comprehensive pharmaceutical care, and 3) to create
a risk stratification system for PDRM based on the number of risk factors present in an
individual patient. The study was conducted in two phases. In the first phase, the Delphi
technique was used with a geriatric medicine expert panel to create 52 operational definitions of
PDRM in older persons. Ninety-seven patients who matched these definitions were found in
3365 older persons in a Medicare managed care health plan. This was a health plan offered by
the Florida Hospital Healthcare System, a provider-sponsored network with a Medicare contract,
available to all Medicare beneficiaries in three counties of Central Florida. Chart abstracts from a
sample of these patients were given to a panel of five pharmacists to validate the operational
definitions. Overall, the two operational definitions of PDRM that were validated were found to
have a sensitivity of 87.5 percent and a specificity of 73.5 percent as compared to the panel of
pharmacists.
xiii

In the second phase, a prediction model was created to identify risk factors for PDRM.
The dependent variable was the occurrence of a PDRM as operationally defined, and the
independent variables were the hypothesized risk factors. Forward inclusion logistic regression
models and factor analysis were used to identify risk factors for PDRM. Five risk factors for
PDRM were identified in the final prediction model: four or more recorded diagnoses, four or
more prescribers, six or more prescription medications, antihypertensive drug use, and male
gender. A risk stratification system was developed for PDRM based on the number of risk
factors present in an individual patient. Finally, patients with PDRM were shown to use
significantly more healthcare resources then patients who did not experience a PDRM.
XIV

CHAPTER 1
INTRODUCTION
The Need for the Study
Older Persons and Healthcare
The special health care concerns of older persons are important factors in health care
policy and research. This problem is predicted to worsen as the baby boom generation becomes
older and the percentage of the total population that is elderly becomes greater. Currently, the
fastest growing segment of the United States population comprises people age 85 and older. By
the year 2030, 70 million people may be enrolled in Medicare, up from the current 33 million
(Rhinehart, 1996).
Health care resource utilization patterns are different in older persons. This is, in part,
because older persons typically have at least one chronic condition and may have multiple
disease states, experience more morbidity, and have more functional limitations than younger
people. Older persons consume a disproportionate share of health care resources. Forty percent
of all health care expenditures are related to older persons (Fincham, 1996) and older persons
account for a large percentage of all hospital stays. Thirty-eight percent of all medications are
prescribed for older persons, even though older persons only make up twelve percent of the total
population (Dalziel, Byszewski and Ross, 1996). Older persons, then, clearly make a significant
economic impact on the healthcare system.
While the utilization of healthcare services is higher for the geriatric population, a small
subset of older persons is responsible for the majority of the utilization. A study which examined
the utilization patterns of continuously enrolled Medicare beneficiaries over a four year period
concluded that ten percent of the population was responsible for 88 percent of the costs (McCall
1

2
and Wai, 1983). High users in the first year tended to be high users in the following years
(McCall and Wai, 1983). Anderson and Knickman (1984) studied the temporal patterns of
Medicare beneficiaries’ medical expenditures and concluded that unusually high expenditures
for a specific person in one year allow a good prediction of high expenditures in following years.
Therefore, this subset of older persons consistently uses more health care resources year after
year.
Drug-Related Morbidity and Mortality
While medications are prescribed to millions of older persons in an attempt to improve
their health-related quality of life, often an optimum outcome from these medications will not be
achieved. A drug-related problem (DRP) is any patient and time-specific event or situation
involving the medication regimen that interferes with the achievement of an optimum outcome
(Hepler and Strand, 1990). Eight types of DRPs have been described (Hepler and Strand, 1990;
Strand et al., 1990):
1. Untreated indication: The patient has a medical problem that required drug therapy
(an indication for drug use), but is not receiving a drug for that indication.
2. Improper drug selection: The patient has a drug indication but is taking the wrong
drug.
3. Sub-therapeutic dosage: The patient has a medical problem that is being treated with
too little of the correct drug.
4 . Failure to receive drugs: The patient has a medical problem that is the result of his or
her not receiving a drug (e.g.; for pharmaceutical, psychological, sociological, or
economic reasons).
5. Over-dosage: The patient has a medical problem that is being treated with too much
of the correct drug (toxicity).

3
6. Adverse drug reactions: The patient has a medical problem that is the result of an
adverse drug reaction or adverse effect.
7. Drug interactions: The patient has a medical problem that is the result of a drug-
drug, drug-food, or drug-laboratory interaction.
8. Drug use without indication: The patient is taking a drug for no medically valid
indication.
DRPs may lead to drug-related morbidity, which is the failure of a therapeutic agent to
produce the intended therapeutic outcome (therapeutic malfunction or miscarriage) (Hepler and
Strand, 1990). The drug-related morbidity results from either (a) the production of an adverse or
toxic effect or (b) the failure to produce the desired effect within a reasonable time.
If the DRP is unrecognized or unresolved, then drug-related mortality can occur. Recent
literature reports that the clinical, economic, and humanistic outcomes of drug-related morbidity
and mortality are substantial. Lazarou, Pomeranz and Corey (1998) conducted a meta-analysis of
prospective studies involving drug-related morbidity and mortality and concluded that the
overall incidence of serious drug-related morbidity is 6.7 percent and the incidence of drug-
related mortality is 0.32 percent. This estimate places drug-related morbidity and mortality
between the fourth and sixth top cause of death (Lazarou, Pomeranz and Corey 1998). The real
incidence may actually be higher, as their meta-analysis did not include drug-related morbidity
due to noncompliance, drug administration errors, drug abuse, poisonings or therapeutic failures.
Phillips, Christfeld and Glynn (1998) reported that this problem is growing, as between 1983 and
1993, drug-related mortality increased by 257 percent.
Preventable Drug-Related Morbidity and Mortality
Some drug-related morbidities are not preventable, including those resulting from patient
idiosyncracy, while others are preventable. As described by Hepler and Strand (1990),
preventable drug-related morbidity (PRDM) has four unique elements. Given an adverse clinical

4
outcome, a pre-existing DRP must have been recognizable and the adverse outcome or treatment
failure must have been foreseeable. In addition, the causes of the DRP and the outcome must
have been both identifiable and controllable. However, there are no published criteria to help
determine what drug-related morbidities are, and are not, preventable. Schumock and Thorton
(1992) have attempted to develop such criteria for adverse drug reactions (one type of DRP).
Although their criteria are primarily drug-oriented, a similar patient-oriented approach for drug-
related morbidity may be possible. Others have used these criteria successfully to determine
whether adverse drug reactions were preventable (Pearson et al., 1994).
The extent of the problem of PDRM was not known until recently. A recent estimate of
the total annual cost of drug-related morbidity and mortality in the ambulatory setting in the
United States by Johnson and Bootman (1995) is $76 billion, with a range from $30.1 to $136.8
billion, in their cost-of-illness model. Although they used the opinions of experts and not actual
utilization data to obtain this estimate, this figure is quite comparable with the costs of other
major diseases, such as asthma and diabetes. Schneider et al. (1995) concluded that the annual
cost to one academic medical center was approximately $1.5 million. Nelson and Talbert (1996)
reported that 16.2 percent of admissions in 452 consecutive patients were due to drug-related
morbidity, and 49.3 percent of these admissions were definitely preventable. Fifty-six percent of
all drug-related hospital admissions in older persons in a study in Denmark were judged to have
been "definitely" or "probably" avoidable (Hallas et al., 1991). Therefore, it appears that PDRM
is a serious problem, especially in older persons.
Often the problem of drug-related morbidity and mortality is under-reported and
underestimated. Nelson and Talbert (1996) concluded that the discharge summary of almost 20
percent of patients with a drug-related admission made no mention of this fact. They and others
have lamented that there have been no rigorous methods for identifying and evaluating drug-
related morbidity and mortality. Underestimation of drug-related morbidity is especially
prevalent among community-living older adults, where (1) they may fail to recognize the

5
symptoms of drug-related morbidity or (2) their clinicians may attribute these symptoms to
aging, rather than to the drugs (French, 1996).
Problem Statement
As indicated by the previous discussion, the problem of PDRM in older persons has been
long recognized. The literature continues to grow with new studies of problem drugs and new
estimates of the extent of the problem. Despite this, little quantitative information is known
about the factors associated with increased risk that an older person would experience PDRM,
especially in the ambulatory setting. A better understanding of this relationship may help to
create more effective intervention strategies and more efficient use of scarce healthcare
resources.
Study Objectives
There are three primary research objectives of this proposed study. These objectives are
1. To create operational definitions of PDRM,
2. To identify patients who are at particular risk of PDRM (as defined) and who may
therefore benefit from comprehensive pharmaceutical care, and
3. To create a risk stratification system for PDRM based on the number of risk factors
present in an individual patient.
Rationale and Theoretical Introduction
The rationale for this study will be two related models that are based on systems theory.
These two models are the medication use (pharmaceutical care) system and the biopsychosocial
model of disease etiology and therapy. The use of risk factors is the final important aspect of this
study.

6
The first of these two models the medication use system. Pharmaceutical care has been
defined by Hepler and Strand (1990) as being the cooperative, responsible provision of drug
therapy to achieve definite outcomes intended to improve a patient’s quality of life.
Pharmaceutical care is quite different from our current approach to medications use.
Pharmaceutical care differs from traditional drug treatment because ... it is an explicitly
outcome-oriented, cooperative, systematic approach to providing drug therapy, directed
not only at clinical outcomes but also activities of daily life and other dimensions of
health-related quality of life. Preventing, detecting and resolving pharmacotherapeutic
problems before they become adverse outcomes increases the effectiveness of drug
therapy. (Hepler and Grainger-Rousseau, 1995, p.8)
The development of a predictive model for PDRM in older persons may support
prospective prevention of adverse outcomes related to drug therapy.
The usual medication use process is far from the ideal pharmaceutical care system. This
process has been previously described (Hepler and Grainger-Rousseau, 1995). Communication
and cooperation between the patient, physician, and pharmacists may be unsystematic and
ineffective. The patient may develop a drug-related problem, which, if left unresolved, may
develop into drug-related morbidity or even mortality. However, no one may recognize the
problem or attribute it to drug therapy. This flow of care is described as a process, and not a
system, because there is no feedback loop after the patient receives the prescription.
Compared to a drug therapy process, a pharmaceutical care system emphasizes the
prevention, detection, and resolution of drug-related problems before they can become drug-
related morbidities. To do this, a pharmaceutical care system emphasizes monitoring of patients-
in-therapy to detect problems. Monitoring, in turn, increases communication among the patient,
physician, and pharmacist. All three parties need more information (more often) from each other,
not only to detect problems but also to prevent and resolve them.
A second dimension of the conceptual framework of this research is the biopsychosocial
model. According to this model, which is also based on systems theory, many clinical outcomes,
including those of drug therapy, result in part from complex interactions of psychological and

7
sociological factors. These factors are in addition to the physiological and chemical explanations
provided in the biomedical view. Therefore, it may be possible to identify risk factors for PDRM
from among a wider variety of variables than would be predicted by reasoning from the
biomedical model. Furthermore, this model views illness as being more continuous and less
episodic than the biomedical model and therefore it may be possibly more descriptive of the
medical problems of older persons.
The third dimension of the conceptual framework of this study is the use of risk factors.
Risk factors in this study are those variables that are statistically associated with PDRM (the
outcome event) in older persons. While general risk factors for PDRM will be identified for an
entire geriatric population, the constructed model should be applied on a patient-specific basis.
This approach follows the similar use of probability theories and the rationale behind screening
tests, although this study is not a classical epidemiological study. These tests use population-
based information to identify the risk factors, but then the information is applied on an individual
basis to identify those patients who might be at particular risk for problems. Risk factors may be
used to help allocate scarce healthcare resources and as potential indicators to identify patients
who need interventions. Careful consideration of these risk factors should be incorporated into
the therapeutic and monitoring plans of health care professionals in order to proactively prevent
PDRM in older persons. A goal of identifying older persons at risk for PDRM is to intervene in
their medical care before they experience a PDRM. Risk factors are patient characteristics and
therefore they may or may not be able to be changed (e.g.; it may be hard to change a patient's
gender or drug therapy if it is essential). If a risk factor can not be eliminated in an individual,
then at least prospective management of that risk factor should occur. This study describes an
approach to use population-based information to construct a model for PDRM in older persons
that will be applied on a patient-specific basis.

8
Research Questions
To achieve the purpose of this study, four research questions will be investigated. These
questions will explore different aspects of risk factor identification of PDRM in older persons.
Research Question 1
What are the issues in developing and using operational definitions of PDRM?
Research Question 2
What are major risk factors for PDRM in older persons?
Research Question 3
Are there general, and disease (or drug), specific risk factors for PDRM in older
persons?
Research Question 4
What is the relationship between PDRM and the utilization of healthcare resources?

CHAPTER 2
CONCEPTUAL FRAMEWORK
The conceptual framework for this study consists of two models that incorporate systems
theory. These two models are the medication use (pharmaceutical care) system and the
biopsychosocial model of disease etiology and therapy. The concept of risk factors is a third
fundamental component of this study. Brief descriptions and empirical findings of these models
will be discussed.
Systems Theory and the Medication Use System
Medication use can be described as a process. In general, model patients enter the health
care system when they recognize a health problem and see a physician. The physician then
diagnoses the patient’s problem and constructs a therapeutic plan, which is often accompanied
by a prescription. The therapeutic plan is implemented when the patient goes to a pharmacy and
a pharmacist dispenses the prescription and provides advice about the use of the medication. The
typical medication use process is completed when the patient consumes the medication.
Unintended outcomes of this process include drug-related problems, which, left unresolved or
undetected, may lead to drug-related morbidity. As discussed in chapter one, there are many such
unintended adverse outcomes from the current medication use process and several authors have
argued that this current process must be changed (Shane, 1992; Smith and Benderev, 1991).
In contrast, a medication use system emphasizes systematic monitoring and
communication. This model of medication use has been described as being a philosophy of
practice for pharmacists and its knowledge base is formed from systems theory (Hepler, 1996).
The structure of this model is seen in Figure 2.1. Patient “progress” is monitored to prevent,
9

10
Prescribing
Evaluation
Drug-Related Problem
Figure 2.1 The Medication Use System
(Adopted from Hepler and Grainger-Rousseau, 1995)

11
detect, and resolve DRPs before they develop into drug-related morbidities. Communication
between the physician, patient, pharmacist, and other health care professionals is critical to
proper functioning of the system. Prevention is important, especially in older persons, since
DRPs occur frequently and affect clinical, psychosocial, and economic outcomes. The
medication use system has a goal of patient-care outcomes, rather than performance of tasks, as
in the medication use process (Smith and Benderev, 1991).
Systems theory is the foundation upon which the medication use system is built. The
phrase “systems theory” may be deceptive, as according to some definitions, it is really a model
and not a theory (Babbie, 1983). Laszlo (1973) refers to systems theory as a philosophy.
Regardless, at the fundamental level, systems theory, or systems thinking, is a body of
knowledge that has gradually developed over the past fifty years into a model, which helps one
to see and influence things from a different, larger perspective (Senge, 1994). Key to systems
theory is the word systems itself. It is frequently used but infrequently understood. A system can
be defined as follows:
Basically, a system is (1) a group of related entities that (2) does something - receives
inputs, affects them in some way, and produces outputs to achieve some purpose. Almost
anything in the world can be called a system. A sheep can be considered a system. It
takes in fodder and produces wool and lamb chops. (Nadler and Hibino, 1994, pp.198-
199)
It is clear from this definition that one of the tenets central to systems theory is seeing the “big
picture” and “whole structures”. Senge (1994) states that systems theory is a method to (1) see
wholes, (2) interrelationships rather than things, and (3) patterns of change rather than single
elements. Nadler and Hibino (1994) call this the “systems principle”: each problem is just a
piece of a larger system. Many authors argue that these patterns, or structures, underlie complex
situations (Senge, 1994; Nadler and Hibino, 1994; Laszlo, 1973). By learning to recognize and
see them, the way by which one approaches problems will be modified. Senge (1994) elaborates
on these “structures”:
one of the most important, and potentially most empowering, insights to come from the
young field of systems thinking is that certain patterns of structure recur again and again.

12
These ‘systems archetypes’ or ‘generic structures’ embody the key to learning to see
structures in our personal and organizational lives. The systems archetypes - of which
there are only a relatively small number - suggest that not all...problems are unique.
(Senge, 1994, p.94)
These structures, then, are an important concept in systems theory and allow one to view
problems with a larger perspective.
The medication use system has built upon many of the central principles of systems
theory. These include the interrelationship of the various elements and dimensions of the
medication use system, the importance of communication and feedback loops, identification of
key areas of change, and the individualization of drug therapy goals and monitoring for specific
patients. Systems theory also emphasizes that the medication use system can be viewed as being
a subset of a larger system - the health care system - or subsets of the medication use system can
be thought of systems in their own right. For example, the act of a physician writing a
prescription for a patient is a complex system that has been extensively studied. The physician
uses multiple inputs when deciding which drug to prescribe, such as the price of the prescription,
the condition of the patient, the information given to him by a pharmaceutical representative,
whether the drug is in his evoked set, and other factors. Similarly, there are many outputs and
feedback mechanisms after he/she has written the prescription, such as whether the patient has
improved, drug use evaluation, and any adverse effects experienced by the patient. One way of
viewing these aspects of the medication use system is through the use of the system matrix.
The System Matrix Applied to the Medication Use System
While it is useful to recognize that structures exist, the size of most systems could
quickly cause one to become overwhelmed by the amount of information in these structures. One
way of organizing this information is to use a method called the system matrix (Nadler and
Hibino, 1994). According to the system matrix, a system consists of elements and dimensions.
The elements of a system are (1) purpose (the mission, aim, need, primary concern, or results
sought from a system), (2) inputs (physical items, information or human beings on which work,

13
conversion, or processing takes place), (3) outputs (desired and undesired physical items,
information, humans and services that result from processing inputs), (4) sequence (the
conversion, work, process, or transformation by which the inputs become the outputs), (5)
environment (the physical and sociological factors within which the other factors operate), (6)
human agents (those who aid in the steps of the sequence without becoming part of the outputs),
(7) physical catalysts (resources that aid in the steps of the sequence without becoming part of
the outputs), and (8) information aids (include knowledge and data resources that help in the
steps of the sequence without becoming part of the outputs) (Nadler and Hibino, 1994).
Dimensions help clarify the conditions for each element in a specific situation (Nadler and
Hibino, 1994). The dimensions of a system are (1) fundamentals (tangible, overt, observable,
physical, or basic structural characteristics), (2) values and goals (motivating beliefs, human
expectations, global desires, ethics, equity, and moral concerns that can be ascribed in some form
to each element, (3) measures (translate the fundamentals and values dimensions into particular
performance factors and operational objectives), (4) control (comprises methods for ensuring
that the fundamentals, measures, and even value specifications, are maintained as desired during
the operation of the system), (6) interface (the relationships of the fundamentals, values,
measures, and control specifications to other elements and to external systems), and (7) future
(changes in each specification of the other dimensions in the future) (Nadler and Hibino, 1994).
A system matrix clearly delineates the relationships of elements and their interdependences and,
most importantly according to Nadler and Hibino (1994), it prevents the omission of critical
components of the system.
The system matrix can be used to describe the medication use system. As previously
discussed, a system matrix helps to view a system by listing the elements and dimensions of that
system. Chiefly, this allows one to see the structure of a system; in this case, the medication use
system by specifying each element and dimension cell in the system matrix. For example, the

14
fundamentals (one of the dimensions of a system) have been previously described by Grainger-
Rousseau et al. (1997). As seen in Table 2.1, these eight fundamentals, or necessities, must be
present to ensure that drug therapy will be safe, effective, and humane (Grainger-Rousseau et al.,
1997). The human agents (one of the elements of a system) can be described for these eight
fundamentals for the medication use system. In the medication use system, the main human
agents are the prescriber, the pharmacist, and the patient (and caregiver). Table 2.2 shows how
each of the eight fundamentals of a safe and effective medication use system relates to these
three human agents. The boxes with checkmarks indicate an important relationship between an
element and a dimension. For example, fundamental one (timely recognition of signs and
symptoms) often depends on the action of the prescriber, pharmacist, or patient/caregiver. This
example shows how the system matrix can be applied to one cell for the medication use system.
Feedback and Communication Loops in the Medication Use System
An important concept in systems theory is feedback, also referred as communication
loops. Feedback is defined by Senge (1994) as being a broad concept meaning any reciprocal
flow of influence, and every influence acts as both a cause and an effect. Senge (1994) continues,
The practice of systems thinking starts with understanding a simple concept called
‘feedback’ that shows how actions can reinforce or counteract (balance) each other. It
builds to learning to recognize types of ‘structures’ that recur again and again: the arms
race is a generic or archetypal pattern of escalation, at its heart no different from turf
warfare between two street gangs, the demise of a marriage, or the advertising battles of
two consumer goods companies fighting for market share. (Senge, 1994, p.73)
Two types of feedback have been described. The first type is reinforcing (also called amplifying
or positive) feedback which causes growth. The second type is balancing (also called stabilizing
or negative) feedback which counteracts the reinforcing feedback. These feedback cycles or
communication loops often may have “delays”, in which the flow of influence is interrupted,
resulting in a slowing down of events (Senge, 1994).

15
Table 2.1 Eight Fundamentals (Necessities) for Safe and Effective Drug Therapy
(Adopted from Grainger-Rousseau et al., 1997)
1. Timely recognition of drug indications and other signs and symptoms relevant to drug
use with accurate identification of underlying disease. “Correct” therapy for a late or
incorrect diagnosis cannot improve a patient’s quality of life.
2. Safe, accessible, and cost-effective medicines. Safe and cost-effective (efficient) drug
products must be legally and financially available.
3. Appropriate prescribing for explicit (clear, measurable, and communicable) objectives.
Explicit therapeutic objectives simplify the assessment of prescribing appropriateness and
are necessary for assessing (monitoring) therapeutic outcomes.
4. Drug product distribution, dispensing, and administration with appropriate patient
advice. Including: (a) ensuring that a patient actually obtained the medicine, (b) negotiating
a regimen that the patient can tolerate and afford, (c) ensuring that a patient (or caregiver)
can correctly use the medicine and administration devices, (d) advising to empower the
patient or caregiver to cooperate in his or her own care as much as possible.
5. Patient participation in care (intelligent adherence). The ambulatory patient or caregiver
should consent to therapeutic objectives and know the signs of therapeutic success, side
effects and toxicities; when to expect them; and what to do if they appear.
6. Monitoring (problem detection and resolution). Many failures can be detected while they
are still problems and before they become adverse outcomes or treatment failures.
7. Documentation and communication of information and decisions. Communication and
documentation are necessary for cooperation in a system.
8. Product and system performance evaluation and improvement. Practice guidelines,
performance indicators, and databases are a useful approach to achieving and maintaining
improved system performance (outcomes).

16
Table 2.2 The Fundamentals/Human Agents Cell of the System Matrix Applied to the
Medication Use System
Fundamental for safe and effective
drug therapy
Fluman Agents of the Medication Use System
Prescriber
Pharmacist
Patient/ Caregiver
Timely recognition of signs and
symptoms
/
/
y
Safe, accessible, and cost-effective
medicines
Appropriate prescribing
/
Distribution, dispensing,
administration , and patient advice
/
y
Patient participation
y
Monitoring
/
V
y
Documentation and communication
/
y
y
System evaluation

17
The acknowledgement of the importance of feedback in systems theory allows one to
accurately discern the role of individuals in a system. In systems theory, individuals are seen as
simply part of the feedback process, and not as a separate component of a system. Individuals
can act as either reinforcing or balancing feedback. This is different from the common perception
that individuals are somehow special and are different from the other elements of a system.
Senge (1994) notes that considering individuals as a form of feedback implies that an individual
is usually not solely responsible for an action, but that everyone shares in the responsibility for
the event occurring. Individuals may have different levels of influence but most systems
problems are solved by looking at all the types of feedback.
Several feedback loops occur in the medication use system. As seen in Figure 2.2, there
is a feedback loop for aggregated prescribing that includes prescribing data, prescribing (drug
use) evaluation, and prescribing influences such as a formulary and education. A second
feedback loop exists for a single patient's prescription or drug regimen. This is when information
obtained by a pharmacist or another health care professional from the patient can be used to alter
the therapeutic plan. A third feedback loop, which is not formalized in the medication use
process, involves monitoring the patient for drug-related problems, resolving any problems
which exist, and using this information to revise the therapeutic plan as appropriate.
The medication use system contains feedback loops that are reinforcing and it may also
contain delays. Patient monitoring by the pharmacist can be used to reinforce or strengthen the
therapeutic plan. Monitoring includes the determination of which information to collect for the
evaluation of the progress of therapy, the evaluation of achieving the therapeutic objectives, and
responding to evaluations (Grainger-Rousseau et al., 1997). This constant, critical re-evaluation
of the therapeutic plan is an important reinforcing feedback loop that helps facilitate the
provision of pharmaceutical care (Strand et ah, 1991). Delays can occur at many places in the
medication use system, such as when pharmacists or patients do not provide timely data to the
physician, such that the therapeutic plan is not revised and poor patient care results.

18
Prescribing
Evaluation
Figure 2.2 High Leverage Points Within the Medication Use System
(Adopted from Hepler and Grainger-Rousseau, 1995)

19
Change and the Medication Use System
The next logical step is to ask how one can influence and change the various types of
feedback that exist in a given system. This skill of knowing where to affect a system to produce
the greatest possible intended effect is known as leverage. Senge (1994) explains this concept,
The bottom line of systems thinking is leverage - seeing where actions and changes in
structures can lead to significant, enduring improvements...our nonsystemic ways of
thinking are so damaging specifically because they consistently lead us to focus on low-
leverage changes: we focus on symptoms where the stress is greatest. We repair or
ameliorate the symptoms. But such efforts only make matters better in the short run, at
best, and worse in the long run. (Senge, 1994, p.l 14)
In order to change systems the most effectively, then, high-leverage changes should be sought.
By recognizing which feedback mechanisms result in the high-leverage changes, one can target
those feedback points and obtain the intended effect.
In order to most effectively change the medication use system such that PDRM is
minimized, high leverage change points must be identified and successful intervention strategies
must be implemented. Two such high leverage points for the prevention of PDRM within the
medication use system are identified in Figure 2.2: the therapeutic plan and patient monitoring.
The creation and re-evaluation of the therapeutic plan is a key point in the medication use
process. Without a therapeutic plan that includes explicit, realistic objectives, therapy
management is practically impossible (Hepler and Grainger-Rousseau, 1995). Also, the patient
risks for PDRM are generally unknown during the formation of the therapeutic plan (Strand et
al., 1991):
It seems evident that we have accumulated very little hard information that describes
pharmaceutical-care needs, patient risks related to pharmacotherapy, and the drug-
related problems present. Before the concept of... pharmaceutical care can be developed
any further, this information needs to be collected and evaluated. (Strand et ah, 1991,
p.550)
Some medications, such as digoxin, have been identified in the literature as being commonly
prescribed inappropriately in the older population (Aronow, 1996). There is also some evidence

20
that those medications that are prescribed inappropriately lead to PDRM in older adults. French
(1996) argues that up to 25 percent of community-living older persons are at risk for drug-related
morbidity due to inappropriate prescribing, a key part of the therapeutic plan. Ideally, a method
that could prospectively identify these high-risk medications, and incorporate this information
into the therapeutic plans for them, would be of tremendous value.
The monitoring and management of patient outcomes according to the therapeutic plan is
a second key point in the medication use process. As early as 1981, (Campbell, 1981) an
advisory committee on pharmaceutical needs in older persons recommended that identification
of toxic drug effects and drug monitoring be priority functions of the pharmacist. In 1988,
Grymonpre et al. (1988) stated that their study on drug-related adverse patient events confirmed
the need for increased caution and monitoring of all consequences and outcomes of medication
use in older persons. More recently, Johnson and Bootman (1995) recommended that urgent
attention be given to this problem and monitoring should be emphasized to help lessen PDRM.
Patient monitoring is also required to ensure that the desired patient outcomes and goals are
attained (Strand et al., 1991; McDonough, 1996). Identification of medications for which
monitoring is extremely important may greatly assist the pharmacist during patient monitoring
and management to prevent PDRM in older persons.
Biopsvchosocial Model of Disease Etiology and Therapy
The previous discussions on (1) the importance of feedback and communication within
the medication use system, and (2) the high leverage points (the therapeutic plan and patient
monitoring), show the importance of accounting for individual patient differences. As well, as
was seen in Table 2.2, many of the fundamentals of safe and effective drug therapy rely on the
cooperative actions of the prescriber, pharmacist, and patient. This holistic, individualized
approach to medication use is complimented by the biopsychosocial model of disease etiology
and therapy.

21
The biopsychosocial model was first proposed by Engel (1977), in response to the
biomedical model. The biomedical model of disease had molecular biology as its basic scientific
discipline (Engel, 1977) and tried to explain disease as being deviations from ‘norms’-
measurable biological variables (DiMatteo, 1991). Engel (1977, 1981) argued that such a model
did not consider the person as a whole, and failed to incorporate psychological, social or
behavioral aspects of illness.
Engel turned to nature, and biology in particular, for an alternative. The knowledge base
from which the biopsychosocial model is built upon is the use of systems theory in biology
(Engel, 1981; Sadler and Hulgus, 1992). Engel (1981) describes this foundation,
systems theory, by providing a conceptual framework within which both organized
wholes and component parts can be studied, overcomes this centuries-old limitation and
broadens the range of the scientific method to the study of life and living systems,
including health and illness (Engel, 1981, p. 103)
and,
systems theory is best approached through the common sense observation that nature is a
hierarchically arranged continuum, with its more complex larger units being
superordinate to the less complex smaller units. (Engel, 1981, pp. 103-104)
As previously discussed, systems theory emphasizes this hierarchy and views each component of
the system, whether it be a person or the biosphere, as not existing in isolation, but being
influenced by its environment. A bee, for example, can not truly be studied without studying its
environment (flowers, other bees, honeycomb, etc.). Systems theory, then, was useful to help
explain how things are ordered in nature and how these different “systems” in nature interacted.
Engel advocated taking this knowledge base of systems theory in biology and applying it
to the medical care of individuals, as well as medical research and teaching. This would allow
for a holistic evaluation of patients - considering cultural, social, psychological, and behavioral
dimensions of illness (DiMatteo, 1991), humane, empathic and rigorous medical care (Sadler and
Hulgus, 1992), and patient-centered and patient-specific medical care (Howell, 1992;

22
Zimmermann and Tansella, 1996). This is especially important in older persons, where medical
information and diagnosis are insufficient in predicting their health care needs and consideration
of these other dimensions are critical (Rock et al., 1996).
This distinction between the biomedical and biopsychosocial models can be seen in the
medication use system. Traditional drug treatment follows a population-based approach to care,
whereas a pharmaceutical care system follows a patient-based approach. In order to improve the
medications use process, the current approach utilizes population-based methods such as drug
formularies and drug use evaluation. These methods follow a biomedical model of disease,
whereby the disease becomes the emphasis and a mechanistic, reductionistic, and dualistic
perspective is employed. Patients who have illness must be deviating from objective somatic
norms and thus there is always a drug of choice that can be chosen for a whole population.
In contrast, a biopsychosocial model approach states that deviation from the somatic
norm is insufficient to explain disease, and biological relationships to illness are complex
interactions with the mind and environment. This model accounts for patient-specific differences
in illness because it holds a monistic perspective that states the mind and body are intimately
related. While a population basis can be employed to change a system, the biopsychosocial
requires that individual patient differences be considered. Variation between patients is expected
and is not necessarily negative. It even allows for patient disagreement with the health
professional’s plan of care (Kasahara, Shemon and Holzschuh, 1994). This model incorporates
psychological aspects of illness (such as anxiety or depression), the individual’s cultural
expectations about illness, and the present social context of the illness as well as the biological
parameters (DiMatteo, 1991).
The biopsychosocial model fits well with the approach to be used in this proposed
investigation of PDRM in older persons, which will consider these other dimensions of illness.
First, illness presentation in older persons is different from other age groups and this raises some

23
unique concerns. One of these unique factors is that many older persons have atypical disease
presentations that do not fit a classic disease model. Jarrett et al. (1995) concluded that atypical
disease presentation is associated with adverse hospital outcomes. This makes the issues of
disease diagnosis and treatment far more complex in older persons and further strengthens the
need to account for patient differences. Second, PDRM has been reported and analyzed on a
patient-specific basis. Third, drug-related morbidity often has atypical or paradoxical
manifestations in older persons (French, 1996; Harper, Newton and Walsh, 1989). This may be
because patient-specific differences play a large role in the development of drug-related
morbidity and mortality. Gurwitz and Avorn (1991) state that patient-specific physiologic and
functional patient characteristics, such as pharmacokinetic and pharmacodynamic changes, are
often very important to predict drug-related morbidity in older persons due to the inter-individual
variability of the aging process. As Strand et al. (1991) explain,
each patient must receive individualized treatment not only because of information
derived from scientific knowledge but also because he or she must be consistently
respected as a unique individual with specific needs. (Strand et al., 1991, p.549)
The biopsychosocial model allows for the inclusion of social, psychological, and behavioral
factors in a prediction model of PDRM in older persons. This includes such things as trouble
paying for medications, difficulty taking medications, patient belief that he/she is on too many
medications, and the patient’s own perception of their health. This is important, as, for example,
an older person’s self-assessment of their health as being poor has been shown to be associated
with mortality in a study of recipients of community-based long-term care (Fried, Pollack, and
Tinetti, 1998). A prediction model that includes this and other social, psychological, and
behavioral factors may be the best way to identify PDRM in older persons.

24
Risk Factors for PDRM
The medication use system emphasizes an individualized approach to care. However,
population-based information can be useful in planning for a specific patient. One such type of
population-based information is risk factors. Risk factors are variables that are statistically
associated with an outcome event. Prediction models use risk factors identified from a
population and apply this information to the individual patient. The individual patient is then told
the probability, or risk, of having a certain disease or medical condition.
Probability theory and its applications play a central role in the development and
function of screening tests and risk factors. Medical tests results are rarely positive or negative,
instead they lie on a continuum; test results that are beyond a cutoff threshold are called positive.
This cutoff threshold separates the two separate, but usually overlapping Gaussian distributions
of patients with, and without, the disease (condition). This relates to the concepts of sensitivity
and specificity. Sensitivity is the percentage of patients with a disease (condition) who are
labeled “positive” by the test, whereas specificity is the percentage of patients without the
disease who are labeled “negative” by the test. A test or prediction model with a high specificity
and sensitivity will have a low rate of false negatives and false positives.
Risk factor identification is an important issue, but equally important is the actual use of
these risk factors in an assessment or screening program. Risk assessment generally involves a
prospective investigation of a person’s health risks to facilitate interventions before the
occurrence of a preventable health crisis (Kerekes and Thornton, 1996). Such a risk assessment
program may utilize the risk factors previously identified to target certain patients proactively.
Nikolaus et al. (1995) were able to develop an instrument to assess nutritional risk in older
persons. They argue that risk assessment is a major component of the medical management of
older persons. Interventions are often designed based on the risk factors identified. For example,

25
an intervention program which targets older persons who are frail and who have chronic illness
has demonstrated a significant decrease in emergency room visits, admissions, and length of stay
in a risk assessment program in St. Joseph Medical Center (Swindle, Weyant and Mar, 1994).
Cargill (1992) developed criteria to help identify those patients at highest risk for
problems related to medication noncompliance. These criteria included: multiple medications
(more than three medications per day prescribed), medication regimen changes (a change in the
past six months), multiple prescribers, and memory, sensory, and cognitive deficits (unable to
verbalize name or purpose and frequency of medication, unable to read the label, unable to
calculate how many lOmg pills in a 20mg dose, and unable to judge appropriately administration
times for twice a day dosing regimens)(Cargill, 1992). Cargill (1992) reported that those older
persons with more risk factors were older and had more medications prescribed, but they did not
have different compliance patterns than those with fewer risk factors.
Identifying possible risk factors for drug-related morbidity based on theory has been a
difficult problem. Strand et al. (1991) used the following approach to identify possible risk
factors,
we identify three categories of risk factors that can affect the type and level of
pharmacotherapeutic risk: (1) risk factors associated with the patient’s clinical
characteristics, (2) risk factors associated with the patient’s disease, and (3) risk
factors associated with the patient’s pharmacotherapy. The interaction of these three
types of risk factors ultimately determines the level of risk associated with a patient’s
pharmacotherapy and therefore the level of pharmaceutical care required of the
pharmacist. (Strand et al., 1991, p.549)
The approach used in this study will be different. Possible risk factors for PDRM in older adults
will be identified by careful consideration of the high leverage points within the medication use
system and the biopsychosocial model of disease etiology and therapy. Based on this conceptual
basis and empirical evidence, possible risk factors for PDRM will be entered into logistic
regression models for PDRM. Therefore, risk factors in this study will be those variables that
help to explain some proportion of the variance of PDRM in older persons. Variables with odds

26
ratios greater than one will be positive risk factors for PDRM, while variables with odds ratios
less than one will be negative risk factors for PDRM. This approach will also allow estimation of
the proportion of variance in PDRM in older persons that can be accounted for by these risk
factors.
Risk factors for PDRM may be used in a variety of ways. First, if health care
professionals know that an individual has one or more risk factors for PDRM, a proactive change
in the medical care of that patient may be made to help prevent the occurrence of PDRM in the
future. This knowledge will hopefully allow health care professionals to anticipate the possibility
of PDRM occurring in individual patients. Boult et al. (1998) argue that high-risk older persons
should be identified so that a comprehensive assessment of their health-related needs may be
performed, and interventions planned to meet these needs. In the medication use system,
physicians, in particular, should consider these risk factors when creating a therapeutic plan for
the patient. Risk factors may be considered as indicators that help direct the health care
professional to patients with potential problems. Koecheler et al. (1989) used six prognostic
indicators (or risk factors without the empirical evidence) for patients who might warrant
pharmacist monitoring. Second, the risk factors identified can be related to the medication use
system for interpretability in order to determine the best possible manner of use. For example, if
a risk factor is known to relate to patient monitoring, then every attempt should be made to
ensure that the patient receives proper monitoring. This is important because often risk factors
can not be eliminated from the individual patient (e.g. gender), so proper management of the
patient becomes paramount. Finally, risk factors may be used to help in the allocation of scarce
healthcare resources.

27
Summary Of The Conceptual Models To Be Used In This Study
In summary, the conceptual framework that will be used for the study risk assessment of
preventable drug-related morbidity in older persons is based on the application of systems
theory in two models. The first model that incorporates systems theory is the medication use
system. This model applies many of the central themes of systems theory, including the
interdependency of related things, identification of recurring structures, feedback and
communication loops, system goals, and identification of main change (or high leverage) points
(Senge, 1994; Laszlo, 1973; Nadler and Hibino, 1994). In the medication use system itself, a
structure has been proposed and essential elements identified, which includes key
communication and feedback loops between the physician, patient, and pharmacist, a system
goal of improving patient outcomes, and high leverage points such as patient monitoring (Hepler
and Grainger-Rousseau, 1995; Hepler and Strand, 1990). The biopsychosocial model, also based
on systems theory, permits a holistic evaluation of patients that includes cultural, social,
psychological, and behavioral dimensions of illness. Risk factors for PDRM in older persons will
be identified based on these first two models.

CHAPTER 3
REVIEW OF THE LITERATURE
The following review of the literature will focus on drug-related morbidity and mortality
in older persons, identification of older persons at high risk of medical problems, and finally, a
review of prediction models for drug use in older persons.
Drug-Related Morbidity and Mortality in Older Persons
Drug-related morbidity and mortality is a problem of special consideration in older
persons. The incidence of drug-related morbidity seems to be higher in older persons, although it
declines in the last decades of life (80 plus years) and age does not appear to be an independent
risk factor for drug-related morbidity (Carbonin et al., 1991; Gurwitz and Avorn, 1991). Still,
Campbell (1981) argues the increasing number of older persons and their prominent utilization
of healthcare services, particularly medications, naturally extend itself to concern about the high
risk drug-related morbidity and mortality in this population.
Fincham (1996) gives a succinct statement as to the extent of this problem in older
persons:
providing for consistent and appropriate use of drugs is exceedingly important for the
ambulatory elderly. Studies have shown that when it does not occur, hospitalizations
occur due to noncompliance and to the occurrence of avoidable adverse drug reactions.
Significant predictors of preventable hospital readmission for the elderly include the
occurrence of preventable adverse drug reactions, noncompliance, overdose, lack of a
necessary drug therapy, and underdose. Others have noted that 50% of drug-induced
illnesses that require hospitalization could have been avoided. Elsewhere, researchers
have estimated that 75% of medications are misprescribed for the elderly, with overuse
and underuse rampant. There must be increasing efforts to ensure continuity of care for
the ambulatory elderly to avoid these and other drug-related problems. Because drug use
28

29
in the elderly is dynamic and increases with proximity to death, pharmacists are key
players to help the elderly avoid these drug-related problems. (Fincham, 1996, p.525)
The literature is rich with examples to substantiate these concerns. Grymonpre et al. (1988)
determined that 19 percent of all hospital admissions (23 percent of all admissions that involved
prescription drugs) of patients aged 50 and older exhibited at least one type of drug-related
morbidity and mortality in a tertiary care hospital in Manitoba, Canada. The major types of drug-
related morbidity and mortality identified were adverse drug reactions (48 percent), intentional
noncompliance (27 percent), treatment failures (19 percent), alcohol-related problems (14
percent) and medication errors (10 percent) (Grymonpre et al., 1988).
Ostrom et al. (1985) studied medication use in 183 independently living seniors in
Seattle and reported the prevalence of many medication problems. Seventy-five percent of the
older persons had at least one potential medication problem, with a label discrepancy (37
percent), potential drug interaction (27 percent), underuse of medication (24 percent), inability to
read label (14 percent), and failure to open prescription vial (12 percent) being the most common
problems (Ostrom et al., 1985).
Some researchers have identified drugs that have a particularly high risk for drug-related
morbidity and mortality in older persons. Dalziel, Byszewski and Ross (1996) constructed a list
of the top ten problem drugs in the older persons (they failed to provide any empirical evidence
as to why these certain medications made the list, however): Non-steroidal anti-inflammatory
drugs (NSAIDS), benzodiazepines, amitriptyline, fluoxetine, anticholingerics/antihistamines,
over-the-counter drugs/alcohol, cimetidine, centrally-acting antihypertensives/beta-blockers,
digoxin, and irritant laxatives/colacel. The most commonly implicated drugs in 162 cases of
drug-related morbidity and mortality identified in another study were: systemic steroids, digoxin,
nonsteroidal anti-inflammatory agents, methyldopa, calcium channel blockers, beta-blockers,
theophylline, furosemide, sympathomimetics, thiazides, and benzodiazepines (Grymonpre et al.,

30
1988). In a study of drug-related hospital admissions, hypoglycemic and diuretic agents were the
two most implicated drugs (Nelson and Talbert, 1996).
Beers et al. (1991) used the Delphi technique to develop 30 factors defining
inappropriate medication use in the nursing home setting. Using modified versions of these
criteria, other authors determined that 14.0 to 23.5 percent of older adults living in the
community were using at least one inappropriate drug (Stuck et al., 1994; Wilcox, Himmelstein
and Woolhandler, 1994). Beers' criteria was limited by its failure to include specific reasons why
these “inappropriate” drugs should be avoided, which newer lists have attempted to include
(Buerger, 1998).
Several authors have studied specific types of PDRM in older persons, such as falls and
hip fractures. Ray, Griffin and Downey (1989) studied a population of older persons in
Saskatchewan, Canada, and determined that the risk relative risk of hip fracture was higher for
users of long half-life benzodiazepines (1.7) as compared to those who used short half-life drugs
(1.1). Prudham and Grimley-Evans (1981) concluded that older persons who reported falls in a
one-year period were taking statistically significantly more tranquilizers and diuretics than older
persons who did not report having at least one fall. A case-control study assessed the risk of hip
fractures associated with four classes of psychotropic drugs and determined that there was an
increased risk with concomitant use of long-half-life hypnotic-anxiolytics, tricyclic
antidepressants, and antipsychotics (Ray et al., 1987).
Drug-related morbidity and mortality in older persons has been documented to occur in
many different locations, such as the ambulatory, nursing home, emergency room, and inpatient
settings. In a study conducted in the early 1960’s in 178 older persons ambulatory patients with
chronic illness, 59 percent were found to have at least one type of drug-related morbidity and
mortality, with 26 percent of the cases viewed as being serious (Schwartz et al., 1962). Aronow
(1996) looked at the use of digoxin in 500 consecutive nursing home admissions and concluded

31
that 47 percent of the patients had an inappropriate indication for use. In the nursing home
setting, it has been estimated that the total cost of drug-related morbidity and mortality without
the services of consultant pharmacists is $7.6 billion annually (Bootman, Harrison and Cox,
1997). Adams et al. (1987) found that a large percentage of older persons who had an
emergency room visit at a hospital in England had a DRP in the categories of drug interaction
and improper drug selection. Six percent of older persons had a serious drug-diagnosis or drug-
laboratory contraindication and 19.7 percent of patients had a drug-drug interaction, although not
all of these were deemed to be clinically significant (Adams et al., 1987). Ray, Federspeil and
Schaffner's (1980) study of antipsychotic drug use in Tennessee nursing homes suggested that
many older persons were using drugs without an indication. A study of the use of sedative-
hypnotics in hospitalized older persons revealed that 20 percent of the prescriptions exceeded
recognized dosing guidelines and this was associated with a greater severity of illness (Zisselman
et al., 1996).
Identification of Patients at High Risk of Medical Problems in Older Persons
A risk factor is a variable statistically associated with an outcome event. Many
researchers have attempted to identify specific risk factors for health care resource utilization or
morbidity in older persons. These researchers reason that because health care resources are
limited it may be best to find those patients who are at particular risk and concentrate on those
individuals. For example, Nikolaus et al. (1995) identified risk factors associated with
malnutrition in older persons. The main risk factors were a high number of prescription drugs,
social isolation, chronic and painful diseases, and high consumption of alcohol or cigarettes
(Nikolaus et al., 1995). Their study was limited to hospitalized older persons so the authors
admit that further research must be done in other settings. Fowles et al. (1996) compared self-

32
reported health status (ShortForm-36) and diagnosis (Ambulatory Care Groups) to demographic
information and found that the former two were much better predictors of health care
expenditures in older adults. Therefore, it appears that demographic information is not sufficient
to predict high utilizers of health care and other factors must be considered.
Kramer, Fox, and Morgenstern (1992) describe the approaches taken by seven health
maintenance organizations (HMOs) with Medicare-risk contracts to identify high risk patients.
One of these HMOs, Kaiser Permanente Southern California, identified approximately 35
percent of all inpatient admissions as being a high risk group through the use of the following
criteria: age 80 or above, cerebral vascular accident, new fracture, admitted from a nursing
home, a hospital readmission within ninety days that was unplanned, immobility, activities of
daily living impairment, malnutrition, incontinence, confusion, prolonged bed rest, history of
falls, depression, or existence of social problems (Kramer, Fox and Morgenstern, 1992). Kramer,
Fox and Morgenstern (1992) did not state whether these initiatives to identify high risk patients
were successful.
Stuck et al. (1994) studied patient factors associated with a risk of using an
“inappropriate medication” in older persons by performing a multivariate logistic regression
analysis. It was determined that a depression score was a risk factor, however, age, gender,
income, number of chronic diseases, and activities of daily living score were not predictors
(Stuck et al., 1994). Wilcox, Himmelstein, and Woolhandler (1994) also examined the risk of
using an inappropriate medication in older persons and determined that patient risk factors were
a high number of prescription medications, female gender, people living in the Southern United
States, poor self-rated health status, and Medicaid beneficiaries.
Risk factors associated with drug-related morbidity have also been identified for older
persons. Grymonpre et al. (1988) determined that the risk of a drug-related morbidity in patients
aged 50 and older was related to the number of diseases and number of drugs used, but not to

33
age, health score, or gender. Hurwitz (1969) reported that predisposing risk factors associated
with drug-related morbidity included an age of 60 or greater, female gender, previous adverse
drug reaction, and history of allergic disease. In a review of the English-language literature,
Gurwitz and Avom (1991) found that age is not an independent risk factor for drug-related
morbidity, but rather patient-specific and functional characteristics are more important.
Therefore, it appears that the literature is rich in examples of possible risk factors for PDRM in
older persons.
Prediction Models for Drug Use in Older Persons
There is some evidence in the literature that prediction models can be created to identify
individuals who are at risk of DRPs and these models can facilitate the development of
interventions. Beers et al. (1992) developed an operational definition of inappropriate medication
use in older persons in the nursing home setting and subsequent studies were able to use this
definition to determine the degree of inappropriate use and develop intervention strategies to
help correct this problem. Koecheler et al. (1989) developed six prognostic indicators for
patients who might warrant pharmacist monitoring: (1) five or more medications in present drug
regimen, (2) 12 or more medication doses per day, (3) medication regimen changed four or more
times during the past 12 months, (4) more than three concurrent disease states present, (5)
history of noncompliance, and (6) presence of drugs that require therapeutic drug monitoring.
Evidence of adverse outcomes related to drug therapy was identified in 33.1 percent of charts,
based on the use of these indicators (Koecheler et al., 1989). McGhan, Wertheimer, and Rowland
(1982) used Medicaid data to develop multivariate predictive equations to identify patients with
drug therapy problems.
In a recent study, McElnay et al. (1997) developed a risk model for predicting drug-
related morbidity and mortality in older persons, similar to the approach used in this study. Their
model was able to predict drug-related mortality and morbidity in older persons with a

34
specificity of 69 percent, a sensitivity of 41 percent, and an overall accuracy of 63 percent
(McElnay et al., 1997). McElnay et al. (1997) identified seven variables which influenced the
risk of drug-related morbidity and mortality: digoxin, antidepressants, chronic obstructive
airways disease, angina, abnormal potassium level, and patient belief that their medication was
in some way responsible for their hospital admission (McElnay et al., 1997).
Wilcox, Himmelstein, and Woolhandler (1994) state that measuring preventable drug-
related morbidity and mortality in the community setting is difficult but it is extremely important
since only a small proportion of drug-related morbidity results in hospital admissions and many
problems may be unreported or unrecognized. Therefore, there appears to be a continued need
for better prediction models of drug-related morbidity and mortality in older persons. Also, the
McElnay study did not consider preventability. Furthermore, there are no operational definitions
of PDRM in the peer-reviewed medical literature. The development of such definitions would
contribute to the conceptual framework of a systems approach to the medication use system.
Research Assumptions and Hypotheses
The research assumptions and hypotheses that will serve as the basis for this study are
proposed within the context of systems theory applied to the medication use system, the
biopsychosocial model of disease etiology and therapy, and the ability to use conditional
probabilities to identify at-risk individuals. As described earlier, while PDRM has been
previously determined to be an important problem within the medication use process, operational
definitions have not been adequately developed and the method to best identify those individuals
at-risk is unknown. Four research questions that address these unresolved problems will be
investigated. Based on the literature reviewed earlier in this chapter, and the conceptual
framework discussed in chapter two, propositions were made for all four of these research
questions.

35
Research Assumptions
The first research question is directed at the creation of operational definitions of PDRM.
A research assumption related to this research question was proposed. This research assumption
had to be met before the other research questions could be investigated. This research
assumption is as follows:
A1A: Valid operational definitions of PDRM can be developed by a panel of geriatric
medicine experts.
This assumption proposes that operational definitions of PDRM, consisting of criteria for
specific types of PDRMs in older persons, can be developed and tested for validity. Previous
authors have succeeded in creating algorithms for the assessment of adverse drug reactions (one
type of DRP that can lead to PDRM). These algorithms have been tested for validity (Karch and
Lasagna, 1977; Hutchinson et al., 1979; Kramer et al., 1979; Leventhal et al., 1979; Naranjo et
al., 1981). Schumock and Thornton (1992) have created criteria to determine the preventability
of adverse drug reactions, which has been subsequently used by others (Pearson et al., 1994). As
explained in the next chapter, an attempt will be made to demonstrate the validity of the
operational definitions of PDRM, although the lack of an accepted “gold standard” for PDRM is
a limitation.
The consensus method to be used is the Delphi technique. Therefore, research question
one was refined to focus on the usefulness of developing operational definitions of PDRM with
the Delphi technique. The utilization of expert panels via the Delphi technique to generate
consensus on healthcare issues has been quite extensive (Roberts, Sek Khee and Philp, 1994;
Butterworth and Bishop, 1995; Megel, Barna Elrod, and Rausch, 1996). This includes its use to
determine criteria for medication use in older persons. Fouts et al. (1997) used the Delphi
technique to identify risk factors for DRPs in older persons. Beers et al. (1991) used the Delphi
technique with 13 experts to reach consensus on explicit criteria for determining inappropriate
medication use in nursing home residents. Therefore, it seems there is sufficient evidence that

36
the use of an expert panel in the creation of operational definitions for PDRM in older persons is
reasonable and not without precedent.
Research Hypotheses
The second research question deals with the identification of major risk factors for
PDRM in older persons. The specific hypotheses proposed to address this research question are
the following:
H1A: Digoxin use will be a risk factor for PDRM in older persons.
H1B: Antidepressant drug use will be a risk factor for PDRM in older persons.
H1C: Long-acting benzodiazepine use will be a risk factor for PDRM in older persons.
HID: Antihypertensive drug use will be a risk factor for PDRM in older persons.
HIE: Gastrointestinal disorders will be a risk factor for PDRM in older persons.
H1F: Lung conditions (lung disease, emphysema, bronchitis and asthma) will be a risk
factor for PDRM in older persons.
H1G: Kidney disease will be a risk factor for PDRM in older persons.
H1H: A history of falling will be a risk factor for PDRM in older persons.
H1I: Four or more preservers will be a risk factor for PDRM in older persons.
H1J: Six or more prescription medications will be a risk factor for PDRM in older
persons.
H1K: Four or more recorded diagnoses will be a risk factor for PDRM in older persons.
H1L: A previous adverse drug reaction will be a risk factor for PDRM in older persons.
HIM: High alcohol consumption will be a risk factor for PDRM in older persons.
H1N: Self-assessment of poor health status will be a risk factor for PDRM in older
persons.
HIO: Trouble paying for medications will be a risk factor for PDRM in older persons.
HIP: Difficulty taking medications will be a risk factor for PDRM in older persons.

37
H1Q: Patient belief that they are taking too many medications will be a risk factor for
PDRM in older persons.
H1R: Female gender will be a risk factor for PDRM in older persons.
This proposition is based on (1) reports of the risk factors associated with different kinds
of drug-related problems and drug-related morbidity that are found in the peer-reviewed medical
literature, and (2) identification of possible risk factors as they might relate to the medication use
system and the biopsychosocial model of disease etiology and therapy. The rationale for each
individual risk factor is as follows.
Several variables relate monitoring of drug therapy:
HI A: Digoxin use
There is considerable evidence in the medical literature that digoxin use is a risk factor
for adverse drug reactions and drug-related morbidity and mortality. Williamson and Chopin
(1980) determined that digoxin is one of the highest risk drugs for drug-related hospital
readmissions in older persons. Digoxin was the most implicated drug in an inpatient study
involving 193 adverse drug reactions, accounting for 21 percent of all adverse drug reactions
(Ogilvie and Ruedy, 1967b). Digoxin has been labeled one of the top ten problem drugs in older
persons (Dalziel, Byszewski and Ross, 1996), and it was also identified as a risk factor for drug-
related morbidity in older persons (McElnay et al., 1997). A multidisciplinary panel of health
professionals in long-term care listed it as a risk factor for drug-related problems in elderly
nursing home residents (Fouts et ah, 1997).
There are several reasons why digoxin use may be a risk factor for PDRM in older
persons. It is a water-soluble drug and has a smaller volume of distribution in older persons,
therefore, it requires a lower dose (Harper, Newton and Walsh, 1989). Digoxin is also implicated
in many drug-interactions and elimination of the drug may be a problem in older persons since
there is an age-related loss of renal function. Digoxin can also cause failure to thrive in older
persons through a diminished appetite (Harper, Newton and Walsh, 1989). Finally, digoxin

38
toxicity manifestations are often subtle or atypical in older persons (Dalziel, Byszewski and
Ross, 1996).
Many of these problems with digoxin use in older persons may also relate to
inappropriate prescribing, lack of patient advice or poor patient monitoring. As discussed in
chapter two, all these factors can impact PDRM. This is especially true in older persons, as
inadequate patient education on prescribed drugs was a factor that increased the risk of drug-
related morbidity in older persons (French, 1996).
H1B: Antidepressant drug use
There is also considerable evidence in the medical literature for including antidepressant
drug use as a possible risk factor for PDRM in older persons. Certain antidepressants
(amitriptyline and fluoxetine) were identified as being two of the top ten problem drugs in older
persons (Dalziel, Byszewski and Ross, 1996), and McElnay et al. (1997) also determined that
antidepressant use was a risk factor for drug-related morbidity in older persons.
Like digoxin, patient monitoring is extremely important for antidepressants in older
persons. In particular, tricyclic antidepressants are highly bound and there are lower albumin
levels in older persons so the free fraction is greater, increasing the likelihood of drug toxicity
(Harper, Newton and Walsh, 1989). Tricyclic antidepressants have anticholingeric/
antihistaminic side effects that are more evident in older persons.
H1C: Long-acting benzodiazepine use
There is considerable evidence in the medical literature that long-acting benzodiazepine
use is a risk factor for adverse drug reactions and drug-related morbidity and mortality.
Williamson and Chopin (1980) determined that long-acting benzodiazepines are one of the
highest risk drug classes for drug-related hospital readmissions in older persons, and they were
identified as one of the top ten problem drugs in older persons (Dalziel, Byszewski and Ross,
1996). They were also a risk factor for adverse drug reactions associated with global cognitive
impairment in older persons (Larson et al., 1987). A multidisciplinary panel of health

39
professionals in long-term care listed it as a risk factor for drug-related problems in elderly
nursing home residents (Fouts et al., 1997).
There are many physiological reasons why long-acting benzodiazepine use may be a risk
factor for PDRM in older persons. Long-acting benzodiazepines are fat-soluble and have a larger
volume of distribution in older persons, leading to increased storage and prolonged half-life
(Harper, Newton and Walsh, 1989). Also, older persons are more sensitive to the effects of
benzodiazepines (Harper, Newton and Walsh, 1989). The oxidative metabolism of long-half life
benzodiazepines is often impaired in older persons (Harper, Newton and Walsh, 1989). As a
result, they can cause depression, falls, confusion and withdrawal symptoms. French (1996)
argues that age-related physiological changes that alter drug kinetics and pharmacological
responses to the prescribed medication are factors that increase the risk of drug-related morbidity
in older persons. Therefore, for long-acting benzodiazepine use, it appears that dosing and drug
monitoring are two critical elements needed to diminish PDRM in older persons.
HID: Antihypertensive drug use
At least two studies have identified antihypertensive drug use as a possible risk factor for
drug-related morbidity. This drug class was a risk factor for adverse drug reactions associated
with global cognitive impairment in older persons in the Larson study (Larson et al., 1987), and
it was one of the highest risk drugs for drug-related hospital readmissions in older persons in
another study (Williamson and Chopin, 1980). Also, centrally-acting antihypertensives/ beta-
blockers was identified as being one of the top ten problem drugs in older persons (Dalziel,
Byszewski and Ross, 1996).
Antihypertensive drugs have been well documented to cause drug-related morbidity in
older persons. Many antihypertensives have central nervous system side effects and may cause
acute confusion, hallucinations, impairment of memory, and reduced ability to perform complex
psychomotor tasks (Harper, Newton and Walsh, 1989). Older persons are also particularly
susceptible to depression and postural hypotension from certain antihypertensives. Therefore, it

40
appears that monitoring is also an important element of care for older persons who are taking this
class of drugs.
H1E: Gastrointestinal disorders
Gastrointestinal disorders was previously identified in one study has being a risk factor
for drug-related morbidity in older persons (McElnay et ah, 1997). This could be because
medications used for gastrointestinal disorders are often used improperly (Tamblyn et ah, 1997;
Moride et ah, 1997) and many of these medications have been associated with drug-related
morbidity in older persons (Harper, Newton and Walsh, 1989; Dalziel, Byszewski and Ross,
1996). Therefore, it appears that elements three (appropriate prescribing for explicit objectives)
and six (monitoring) of the eight necessities for safe and effective drug therapy may also be
potential problem areas for patients with gastrointestinal disorders.
H1F: Lung conditions (emphysema, bronchitis or asthma)
Chronic obstructive airways disease was previously identified in one study has being a
risk factor for drug-related morbidity in older persons (McElnay et ah, 1997). Patients with lung
diseases such as asthma are often on medications that require special monitoring and it has been
argued that these diseases can not be adequately explained by the biomedical model. For
example, severity of asthma symptoms may depend on such things as cleanliness of living
conditions and activities of daily living, which are better explained by the biopsychosocial
model. Therefore including lung conditions as a risk factor appears to be compatible with the
biopsychosocial model of disease and with the importance of monitoring drug therapy.
H1G: Kidney disease
The presence of kidney disease will be included as a possible risk factor for PDRM
based on both the medical literature and theoretical considerations. A multidisciplinary panel of
health professionals in long-term care listed decreased kidney (renal) function as a risk factor for
drug-related problems in elderly nursing home residents (Fouts et al., 1997). Renal failure was
found to be an associated factor to drug-related morbidity in a retrospective study in Chile
(Zilleruelo, Espinoza and Ruiz, 1987). Older persons may be at a special risk of this. Renal blood

41
flow and glomerular filtration rate decrease with age (Harper, Newton and Walsh, 1989). This
leads to an elevated drug level and prolonged half-life for drugs excreted by the kidney. This can
cause drugs to accumulate and toxicity to develop. Therefore, prescribing proper doses of many
medications and drug level monitoring for PDRM are very important. As well, patient-specific
differences in renal function are great in older persons, which the biopsychosocial model
considers.
H1H: A history of falling
A history of falling was found to be a risk factor for drug-related morbidity associated
with global cognitive impairment in older persons in one study (Larson et al., 1987). In contrast,
a history of falling was not found to be a risk factor for drug-related morbidity in a later study
(Carbonin et al., 1991).
A history of falling may be a risk factor for PDRM in older persons because many
different drug classes, such as long-acting benzodiazepines, antihypertensives, and others have
been documented to cause falls. Therefore, it seems that appropriate prescribing, patient advice,
patient participation in care, monitoring, and appropriate documentation of previous falls may be
key elements to help prevent PDRM in older persons.
Several variables relate to the importance of communication for optimal drug therapy:
HII: Four or moreprescribers
Four or more prescribers will be included as a possible risk factor, mainly based on
theoretical considerations. French (1996) did document that several providers prescribing
therapy independently was a factor that increased the risk of drug-related morbidity in older
persons. Theoretically, if a patient has multiple prescribers, PDRM could develop from
competing prescribing objectives, and poor documentation and communication of information
and therapy decisions.
HI J: Six or more prescription medications
There is considerable evidence in the medical literature that the risk of drug-related
morbidity increases with an increase in the number of medications in the drug regimen. As early

42
as 1969, Hurwitz observed that patients with drug reactions had significantly more drugs during
their hospital stay then those who did not develop drug reactions. Five or more medications was
found to be the primary risk factor in a study of potential drug-drug interactions (Braverman et
al., 1996), and taking more than four drugs was found to be a risk factor of drug-related
morbidity (Carbonin et al., 1991). A multidisciplinary panel of health professionals in long-term
care said elderly nursing home patients who take nine or more medications are at risk for drug-
related problems (Fouts et al., 1997). Larson et al. (1987) looked at the relationship between the
number of medications and cognitive impairment and determined that the relative odds for
adverse drug reactions related to cognitive impairment was 9.3 for patients taking four or five
drugs, and 13.7 for patients taking six or more drugs. Finally, five or more drugs in a regimen
was a prognostic indicator chosen to identify ambulatory patients who warranted special
pharmacist monitoring (Koecheler et al., 1989).
It appears that as the number of medications in a regimen increases, the opportunity for
inappropriate prescribing, dispensing/administration errors, inadequate patient advice, lack of
patient participation in care, inadequate monitoring, and poor documentation and communication
all increase.
H1K: Four or more recorded diagnoses
Patients with several diseases appear to be at greater risk for drug-related mortality.
Carbonin et al. (1991) found that more than four active medical problems was a risk factor for
drug-related morbidity. "Patients having more than three diseases" was used as a prognostic
indicator chosen to identify ambulatory patients who warranted special pharmacist monitoring
(Koecheler et al., 1989). A multidisciplinary panel of health professionals in long-term care
listed “more than six active chronic medical diagnoses” as a risk factor for drug-related problems
in elderly nursing home residents (Fouts et al., 1997).
The biopsychosocial model may be important in understanding why patients with several
diseases may be at an increased risk of PDRM. Gurwitz and Avom (1991), upon reviewing the
medical literature on drug-related morbidity, stated that patient-specific physiologic and

43
functional characteristics are important in predicting drug-related morbidity. The
biopsychosocial model allows for consideration of the patient-specific differences. As well,
timely recognition of signs and symptoms and appropriate documentation may be even more
important for patients with several concurrent diseases. Patients with several diseases are often in
the care of a general practitioner and one or more specialists, who may not always communicate
their therapeutic plans, and which may be in conflict.
H1L: A previous adverse drug reaction (ADR)
It appears that patients who had a previous adverse drug reaction are at increased risk for
experiencing another event in the future. A previous adverse drug reaction was previously found
to be a predisposing factor in adverse reactions to drugs (Hurwitz, 1969). A previous adverse
drug reaction was found to be an associated factor with adverse drug reactions in a retrospective
study in Chile (Zilleruelo, Espinoza and Ruiz, 1987). A multidisciplinary panel of health
professionals in long-term care listed it as a risk factor for drug-related problems in elderly
nursing home residents (Fouts et al., 1997). Also, in one study involving 177 patients who had
suffered adverse reactions during hospitalization, 32 percent had a second reaction (Ogilvie and
Ruedy, 1967a).
There are several reasons why these individuals may be at particular risk of PDRM.
They may have physicians, prescribers or patient-specific factors that contribute to poor
prescribing, dispensing, administration, patient advice, patient participation in care, monitoring
or documentation. As well, the biopsychosocial model may be useful to help explain why
specific patients are at risk of experiencing a second drug-related morbidity. The most important
factor, however, may be poor documentation and communication of their previous adverse drug
reaction.
Several variables relate to the biopsychosocial model and patient-specific differences:
HIM: High Alcohol Consumption
There is some evidence in the medical literature that high alcohol consumption is
associated with drug-related morbidity. Alcohol consumption was found to be a risk factor of

44
drug-related morbidity (Carbonin et al., 1991) and alcohol was listed as one of the top ten
problem drugs in older persons (Dalziel, Byszewski and Ross, 1996). The side effects of alcohol
intake in older persons are potentiated because both metabolism and excretion of alcohol are
altered with aging. In older persons, recognition of alcoholism is often difficult and delayed
because the manifestations may be subtle or erroneously attributable to normal aging (Dalziel,
Byszewski and Ross, 1996). Alcohol, when combined with many medications, can be dangerous
and lead to such events as falls. When a patient consumes alcohol and is taking medications,
patient advice and monitoring are both critical elements. As well, high alcohol consumption is
associated with sociological, behavioral, and psychological factors that can be best explained by
the psychosocial model of disease.
H1N: Self-assessment of poor health status
Self-assessment of poor health status will be included as a possible risk factor, also
primarily based on theoretical considerations. There is some evidence in the literature that self-
assessment of health as poor is associated with mortality (Fried, Pollack and Tinetti, 1998). In
addition, patient-specific physiologic and functional characteristics are important in predicting
drug-related morbidity (Gurwitz and Avorn, 1991). The biopsychosocial model is important in
helping to explain this as a possible risk factor for PDRM. This model allows the inclusion of
psychological and behavioral elements in illness.
HIO: Trouble paying for medications
Trouble paying for medications will be included as a possible risk factor for PDRM
based on theoretical considerations. A patient who reports that they are having trouble paying for
medications may exhibit poor medication compliance with their medication regimen due to the
cost of the medications. The expense of the drug is a factor that contributes to poor compliance.
Schneider et al. (1991) showed that noncompliance is related to a belief that taking medications
will not result in a successful medical outcome. Thus, trouble paying for medications may also
relate to a reluctance to take medications because of a belief that they will not help to improve
the health of the patient. O'Neil and Poirer (1998) showed that patients with poor perceptions of

45
their drug regimen had more adverse drug therapy outcomes. One study did determine that older
persons who do not comply with prescribed medicines are at an increased risk of drug-related
morbidity (French, 1996). Again, the biopsychosocial model is important in order to consider
sociological variables in this model. As well, patient participation in care is important to prevent
PDRM, and this includes compliance with medications and attributing medications with an
improved health status.
HIP: Difficulty taking medications
Difficulty taking medications will be included as a possible risk factor for PDRM based
on theoretical considerations. A patient's self-report that they are having difficulty taking
medications may also relate to their attitude of taking medication. Grembowski et al. (1993)
discovered that older persons with a low self-efficacy in health behaviors had poorer health.
Therefore, it is conceivable that many older persons have low self-efficacy in taking medications
and thus develop PDRM. The biopsychosocial model is important in order to consider
behavioral variables in this model. As with the previous variable, patient participation in care is
important to prevent PDRM, and this includes self-efficacy with taking medications. In addition,
French (1996) claims that many older persons experience motor-sensory declines that contribute
to an inability to properly take medications.
H1Q: Patient belief that they are taking too many medications
Patient belief that they are taking too many medicines will be included as a possible risk
factor for PDRM based on theoretical considerations. This variable may also relate to the
patient's attitude towards medications. Disagreement with the prescribed medication regimen is a
factor that contributes to poor compliance. Highly complex medication regimens are associated
with noncompliance (Haynes, Taylor, and Sackett, 1979). Also, patients who believe they are on
too many medications may have low self-efficacy for taking medications, which may also lead to
poorer health and PDRM. Again, the biopsychosocial model is important in order to consider
behavioral variables in this model. As well, patient participation in care is important to prevent
PDRM, and this includes compliance with medications.

46
One variable has been included based on empiric evidence, but it does not seem to
directly relate to the conceptual models used in this study:
H1R: Female gender
Female gender has been identified as being a risk factor for drug-related morbidity in
several studies. Hurwitz (1969) noted that females had significantly more drug-related
morbidities than males. Female gender was found to be an associated factor with adverse drug
reactions in a retrospective study in Chile (Zilleruelo, Espinoza and Ruiz, 1987). The odds ratio
associated with having an ADR in older females as compared to males was found to be 1.9,
although it was not statistically significant (Hallas et al., 1991). Finally, gender was found to be
a determinant of both the frequency and characteristics of ADRs in a prospective drug
surveillance study involving 1920 patients in Chile (Domecq et al., 1980). In contrast, female
gender was not found to be a risk factor for ADRs in one study, although the odds ratio for an
ADR was 1.22 (0.987-1.54, 95 percent confidence interval) (Carbonin et al., 1991). Zadoroznyj
and Svarstad (1990) argue that by excluding female-specific drugs and conditions (e.g.;
pregnancy) there is basically no difference in drug use between males and females.
The reason why female gender may be a risk factor for PDRM does not seem to be easily
explained by any of the models used in this study but will be included for empirical reasons.
The third research question addresses whether there are general risk factors for PDRM
and risk factors which might be drug or disease specific for PDRM. This research question is
obviously closely related to the previous research question. The hypothesis proposed for this
question is as follows:
H2: There will be both general risk factors for PDRM and risk factors that are drug or
disease specific for PDRM
This proposition is based on the reports of the wide variation of risk factors associated
with different kinds of PDRMs that are found in the peer-reviewed medical literature. As the

47
previous review of the literature showed, there seems to be some general risk factors for PDRM
(poor health status, multiple prescribers, multiple disease states), and some drug-specific
(digoxin, antihypertensives, etc.) and disease-specific (lung disease, kidney disease, etc.) risk
factors for PDRM.
The fourth research question is whether the utilization of health care resources differs
between patients with, and without, PDRM. The specific proposition hypothesized for this
research question is the following:
H3: Older persons that have PDRM will consume more health care resources than those
who do not have PDRM
This hypothesis suggests that there will be a significant difference in the health care
resource utilization patterns between those older persons who do, and do not, have PDRM. Based
on the literature previously reviewed, there is empirical support that those older persons who
experience PDRM do consume more health care resources. Zisselman et al. (1996) concluded
that those older persons who received sedative-hypnotics with doses exceeding the Health Care
Financing Administration (HCFA) guidelines had increased hospital costs and longer lengths of
stay as compared to those who did not receive these drugs or whose dosages did not exceed the
guidelines (although the direction of causality is unknown). The very nature of PDRM often
involves the consumption of valuable resources. For example, Bates et al. (1997) determined that
those patients with a preventable adverse drug event (ADE) had an average increase of 4.6 days
in length of stay and $5857 in total cost, and the annual costs attributable to all preventable
ADEs was estimated to be $2.8 million for a 700-bed teaching hospital. Thus, based on this
evidence, the proposition will be made that those who experience PDRM will have a higher
utilization of healthcare resources.

CHAPTER 4
METHODS
The intent of this study was to (i) determine the issues in developing and using
operational definitions of PDRM, (ii) identify major risk factors for PDRM in older persons, (iii)
examine whether there are general risk factors for PDRM in older persons and drug, or disease,
specific risk factors, and (iv) determine what is the relationship between PDRM and the
utilization of healthcare resources.
To meet these objectives, this study was conducted in two phases. The first phase of the
study concentrated on the dependent variable in this study: preventable drug-related morbidity
(PDRM). In this phase, operational definitions of PDRM were created through the use of a
geriatric medicine expert panel and validated using a chart abstract reviewer panel. The second
phase of the study focused on the independent variables: hypothesized risk factors for PDRM. In
this phase, the risk factors related to PDRM in older persons were identified using the study
database: claims and quality of life data from the Florida Hospital Healthcare System Premier
Care Plan Medicare population. This second phase of the study also involved the creation of a
risk stratification system for PDRM and exploration into the relationship of PDRM and
healthcare resource utilization.
Phase I: Operational Definitions of PDRM
The first research question described above was investigated in Phase I of the study.
Phase I of the study involved the creation of operational definitions of PDRM in older persons.
This was accomplished by a review of the medical literature, and the administration of a survey
to a consensus panel of geriatric medicine experts. The validity of these definitions of PDRM
48

49
was explored through the use of a second panel: a chart abstract reviewer panel. These steps of
the Phase I methodology are displayed in Table 4.1.
Operationalization of the Study Construct PDRM
In order to have the geriatric medicine expert panel agree on what is a PDRM, and for
the purpose of the conceptual framework of this study, it was necessary to operationalize the
term PDRM. A PDRM can be defined as an unwanted consequence of the medication use
process that, with appropriate systems, adequately trained personnel and patients or caregivers,
could have detected, predicted, controlled and avoided (Hepler and Strand, 1990). PDRM results
from (a) unacceptable quality of care (e.g., failure to meet consensus guidelines) or (b) occurs
after a drug-related problem (DRP). A review of the literature of PDRM in older persons was
presented in chapter 3 and its relationship to the medication use system and biopsychosocial
model was explored in chapter 2.
An extension of this conceptual definition is that PDRM has four defining
characteristics. The DRP that lead to the PDRM must be recognizable and the likelihood of a
drug-related morbidity must be foreseeable. In addition, the cause(s) of the DRP (and subsequent
drug-related morbidity) must be identifiable, and those causes must be controllable. Preventable
drug-related morbidity, therefore, results from unrecognized or otherwise unresolved DRPs.
An operational definition of each of these four defining characteristics is as follows:
1. Recognizable. In order for a drug-related morbidity to be recognizable, the DRP
that produced the drug-related morbidity must be observable (Hepler and Strand, 1990). This
was determined by listing specific outcomes (morbidities) and patterns of care. A geriatric
medicine expert panel was then asked to judge whether for most older persons, if health

50
Table 4.1 Phase I Methodology
Steps of the Phase l Methodology
Review of literature on PDRM, relationship to conceptual framework explored, and
defining characteristics of PDRM studied
Review of literature to identify specific operational definitions of PDRM in older persons
Construction of survey for the Geriatric Medicine Expert Panel, in order obtain consensus
on specific operational definitions of PDRM
Content review of survey by 8 individuals
Pilot test of survey mailed to 40 pharmacists
Revision of survey based on comments/responses from 28 respondents
Selection of Geriatric Medicine Expert Panel members
Administration of survey to Geriatric Medicine Expert Panel -Round 1 of Delphi
Administration of survey to Geriatric Medicine Expert Panel -Round 2 of Delphi
Consensus-approved operational definitions of PDRM obtained from the Geriatric
Medicine Expert Panel
Identification of consensus-approved operational definitions of PDRM in study
population
Abstracted chart reviews of a sample of patients with and without PDRM in study
population
Administration of chart reviews to Chart Abstract Reviewer Panel to test for validity

51
professionals (physicians, pharmacists, etc.) should be able to recognize significant problems in
this pattern of care.
2. Foreseeable. In order for a drug-related morbidity to be foreseeable, a reasonably
prudent clinician would have recognized that the drug-related morbidity might follow if the
recognized DRP were not resolved. This was determined by listing specific outcomes
(morbidities) and patterns of care. A geriatric medicine expert panel was then asked to judge
whether for most older persons, if health professionals should be able to foresee the possibility
of the outcome, given those problems were not resolved.
3. Causality must be identifiable. Formal attribution algorithms, such as the
Naranjo algorithm, DRAPE algorithm, and the Kramer algorithm, have been used in the past to
identify causality for adverse drug events. Identification of causality for the drug-related
morbidity was determined by listing specific outcomes (morbidities) and patterns of care.
Causality typically involves seeing what to change. A geriatric medicine expert panel was then
asked to judge whether most health professionals should see how to change the pattern of care to
prevent the outcome.
4. Controllable. In order for the cause of a drug-related morbidity to be controllable,
the clinician, patient, or caregiver must have been able to exercise restraint or direction over the
presumed cause of the drug-related morbidity. In order to determine whether the cause of the
drug-related morbidity was controllable, specific outcomes (morbidities) and patterns of care
were listed. A geriatric medicine expert panel was then asked to judge whether most health
professionals should actually change the pattern of care.
A drug-related morbidity was judged to be preventable only if the criteria for all four
defining characteristics were met.
In this study, the standard of care used by the health care professional to assess these
four definitions in specific clinical scenarios was not explicitly stated. It is therefore assumed

52
that the health care professionals used an implicit standard of care, such as their typical daily
practice or clinical practice guidelines. This approach has the advantage of letting the health care
professionals use their own professional judgement and years of clinical experience to determine
whether a specific clinical scenario is a case of PDRM.
Review of Literature to Identify Specific Operational Definitions of PDRM in Older Persons
As previously discussed in Chapter 3, the literature contains a comprehensive account of
the most commonly occurring drug-related morbidities in older persons. Many of these
morbidities have been hypothesized to be preventable. The literature on drug-related morbidity
since 1967 was reviewed for possible types of preventable drug-related morbidity. The search
was limited to those morbidities that occur in older persons. Peer-reviewed medical articles and
referenced texts were included in the literature review.
Based on the literature review, a list of 50 clinical scenarios (representing possible
PDRMs occurring in older persons) was compiled. These clinical scenarios had to meet the
following inclusion criteria: (1) well-referenced, (2) occur fairly commonly in the geriatric
population, (3) result in serious adverse outcomes, and (4) searchable in the study database.
Clinical scenarios involving specific laboratory values or drug dosages were excluded as this
information was not available in the study database.
Survey Development
A survey instrument was constructed in order to evaluate whether these 50 clinical
scenarios met the four defining characteristics of a PDRM. All clinical scenarios were listed in a
similar format to facilitate reading. In this format, the outcome (morbidity) was listed first, with
the pattern of care which lead to the outcome, listed second.
Expert review and pilot testing were used to assess the content validity of the survey.
The survey, instructions for use, and cover letter were reviewed by eight experts in health

53
services research from the Department of Pharmacy Health Care Administration at the
University of Florida for ease of use and to determine the relevance of the questions to existing
conceptual frameworks. Based on their feedback, slight modifications were made.
A convenience sample of community, hospital, managed care, consultant, and academic
clinical pharmacists from geographically diverse parts of the United States and Canada was
selected for the pilot test. Before receiving the survey, all individuals were contacted by e-mail,
fax or telephone to inform them to expect the survey. The list of 50 clinical scenarios was split in
half and each half was given to 20 content reviewers, along with a cover letter and instructions
for use. A total of 28 content reviewers completed the survey (15 completed the first half, 13
completed the second half), made comments, and returned the survey in time for analysis, for a
70 percent response rate.
Based on their feedback, slight modifications were made to the survey instrument. As
well, based on the pilot test results, some clinical scenarios were dropped from the survey, and
several new clinical scenarios were added to the survey instrument, leaving a total of 48 clinical
scenarios.
Geriatric Medicine Expert Panel
In order to reach consensus on which clinical scenarios listed in the survey instrument
were actual PDRMs, the Delphi technique was used. As Goodman (1987) explains:
The Delphi technique is a survey method of research which aims to structure group
opinion and discussion. It was first developed in the 1950s by the Rand Corporation in
California as an attempt to eliminate interpersonal interactions as the controlling
variables in decision making, as usually happens when groups of experts interact in
meetings. Its purpose is to generate discussion and enable a judgement on a specified
topic to be made so that policy decisions can be taken which can claim to represent a
given group’s wants and views (Goodman, 1987, pp.729).
Beers et al. (1991) argue that consensus methods, such as the Delphi technique, are useful
because: (1) differences in published opinion may be overcome, (2) they can help create criteria

54
that address narrow clinical scenarios, and (3) they can incorporate supplemental information,
such as physiological changes in older persons.
Duffleld (1993) argues that the choice of panel members is critical in order for the
Delphi technique to work correctly. Participants should be chosen based on their willingness to
participate and their expert knowledge base (Goodman, 1987). With this in mind, a panel of
seven members was chosen by the Chief Medical Officer of the Florida Hospital Healthcare
System, with input from the principal investigator and the Director of Ambulatory Pharmacy at
the Florida Hospital. This panel consisted of physicians with recognized credentials in geriatric
medicine, physician administrators, and a geriatric specialty clinical pharmacist at the Florida
Hospital. These individuals were all thought to be opinion leaders within the Florida Hospital
Healthcare System and have extensive expertise in geriatric medicine. The Geriatric Medicine
Expert Panel members are listed in Appendix A.
The principal investigator explained the survey in depth to each panel member before
they completed the survey. This is because commitment and understanding of the Delphi
technique at the start of the technique influences the time and consideration given to the
technique by the participants (Goodman, 1987). Appendix B contains the cover letter,
instructions for use and survey instrument for the panel members. As Appendix B shows, the
panel members were asked to judge whether each of the 48 clinical scenarios met the four
defining characteristics of a PDRM. One clinical scenario was listed twice in the survey to serve
as a validity check, so there were actually 47 unique clinical scenarios. In addition, there was an
open-ended question at the end of the survey, where panel members could suggest any additional
operational definitions of PDRMs. Prior to the commencement of the Delphi rounds, the
inclusion of operational definitions of PDRM was set as those that were chosen by a majority of
panel members (at least four out of seven members). All seven panel members completed and
returned the survey.

55
The round two survey was sent to the same seven panel members the following month.
This survey contained the clinical scenarios that had survived round one and several new clinical
scenarios that were suggested by the panel members. For each clinical scenario, the panel
members were given their response (yes/no) from the previous round, the total group response,
and all the comments made by the panel members. By providing comments from the previous
round, consensus is reached quicker, usually in two rounds (Duffield, 1993). Round three
consisted of the results from round two and a letter thanking each expert panel member.
Identification of Consensus-Approved Operational Definitions of PDRM in Database
The consensus-approved operational definitions of PDRMs were identified by first
examining the study database for the outcomes related to the specific operational definitions of
PDRM. This was performed by searching for the diagnosis codes related to these outcomes.
Then, once the outcomes (morbidities) were identified, each patient case was individually
searched to determine whether the associated pattern of care that led to the outcome was
provided or not. If both the outcome and pattern of care matched the specific operational
definition of the PDRM, then it was judged to have been a case of PDRM.
Validation of Operational Definitions of Preventable Drug-Related Morbidity
A chart abstract reviewer panel of five clinical pharmacists was used to further validate
the Geriatric Medicine Expert Panel consensus-approved operational definitions of PDRM.
The first step was to choose the specific operational definitions of PDRM which
occurred often enough in the study database to allow adequate determination of sensitivity and
specificity (confidence intervals that did not include zero). The size was chosen to allow
determination of a sensitivity and specificity of 67 percent. This would be better than the

56
sensitivity (41 percent) and near the specificity (69 percent) of an existing model to detect total
adverse drug events in older adults (McElnay et al., 1997). It would also be comparable to
models developed to predict adverse drug reactions related to digoxin (sensitivity 92.9 percent,
specificity 61.8 percent) and theophylline (sensitivity 95.8 percent, specificity 84.0 percent)
(Tschepik et al., 1990). Based on this target sensitivity and specificity, only two specific
operational definitions of PDRM occurred often enough to be tested for validity: (1) patients
with secondary myocardial infarction who did not receive ASA and/or a beta-blocker, and (2)
patients with an emergency room visit and/or hospitalization due to hyperglycemia who were on
an oral hypogylcemic and did not have regular hemoglobin Ale monitoring.
Second, abstracted chart reviews of these patients were performed. These chart abstracts
were performed by a primary care pharmacy resident at the Florida Hospital. The instructions for
the chart abstracts, the patient chart abstract review form, and samples are shown in Appendix C.
The chart abstracts were all performed in the same format to allow for ease of reading by the
Chart Abstract Reviewer Panel members. The chart abstracter was given the patient medical
record numbers for all the patients who had the outcome regardless of whether they had the
pattern of care related to that defined preventability. The chart abstracter was blinded as to
whether the patient he was reviewing did, or did not, have a case of PDRM, as defined by the
specific operational definition. All chart abstracts were done from the inpatient charts at the
Florida Hospital and the outpatient laboratory computer system at the Florida Hospital. Chart
abstracts could not be completed for 4 patients with the secondary myocardial infarction
outcome and 1 patient with the hypergylcemia outcome because the medical charts could not be
located. In all, 35 chart abstracts were performed for patients with secondary myocardial
infarction and 31 patients with hypergylcemia.
All of the chart abstracts (n=66) were then given to the Chart Abstract Reviewer Panel,
consisting of five pharmacists at the Florida Hospital. The pharmacists were chosen by the

57
Clinical Coordinator of Pharmacy at the Florida Hospital based on their availability, willingness
to participate and experience working with the study population. Appendix D shows the
background of the panel members. The principal investigator and chart abstracter met with the
panel members to explain the instructions for use (see Appendix E), and to answer any questions
the panel members might have. These pharmacists were given the two relevant consensus-
approved operational definitions of PDRM and were asked to use them in determining whether
the patients in the chart abstracts actually experienced a PDRM. If four or more of the five panel
members judged a chart abstract to be a case of PDRM, then it was categorized as a PDRM. If
three or fewer panel members judged a chart abstract not to be a case of PDRM, then it was
categorized as not being a case of PDRM. The Fleiss measure of overall agreement was used to
calculate the degree of agreement among the five raters (pharmacists) for classifying each patient
(Fleiss, 1971). Others have advocated using this statistic in situations such as this, when there are
more than two raters (Conger, 1980; Abedi, 1996).
From the results of the Chart Abstract Reviewer Panel, the sensitivity and specificity of
these two specific operational definitions of PDRM was calculated. Sensitivity was calculated as
the percentage of true PDRMs (defined as a PDRM by four or more of the panel members) that
the operational definitions of PDRM labeled as such. Specificity was calculated as the
percentage of abstracted chart reviews that were judged by the Chart Abstract Reviewer Panel
not to be a PDRM, and the operational definition of PDRM labeled as not being a PDRM. A two-
by-two table of true PDRMs and predicted PDRMs was constructed, based on the sensitivity and
specificity.
It should be noted that for most screening tests or instruments, the sensitivity and
specificity of that test is calculated by comparing the outcome of the test to a "gold standard". In
this case there is no generally accepted "gold standard" for PDRM. However, if chart review by
experts is considered to be such a standard, then the panel of five clinical pharmacists is being

58
used as the "gold standard", acknowledging certain limitations with this approach (see
limitations section in chapter 6). The use of chart reviews by experts is commonly used as a
"gold standard" in many other areas of healthcare, such as peer review organizations (PROs). In
such cases, as in this study, the professional judgement of medical experts reviewing patient
charts is used as the "gold standard".
Phase 11: Identification of Risk Factors for PDRM
The methodology for the independent variables will now be discussed. The final three
research questions described at the beginning of this chapter were investigated in Phase II of the
study. Phase II of the study involved the identification of risk factors for PDRM. This was
accomplished by reviewing the medical literature, identifying possible risk factors, and relating
them to the conceptual framework and models used in this study. A database, consisting of
enrollees from the Florida Hospital Healthcare System Premier Care Plan, was constructed to
allow for the measurement of these risk factors. Next, a series of statistical analyses were
performed to identify the risk factors. Finally, the relationship between PDRM and healthcare
resource utilization was explored. These steps of the Phase II methodology are outlined in Table
4.2.
Selection of Possible Risk Factors for PDRM
As previously discussed in Chapters 1 and 3, the literature contains a rich account of risk
factors for the most commonly occurring drug-related morbidities in older persons. The literature

59
Table 4.2 Phase II Methodology
Steps of Phase II Methodology
Review of literature on risk factors for PDRM and the relationship to conceptual framework
and models explored
Hypotheses generated related to specific risk factors for PDRM in older persons, and
semantic hierarchy developed
Study population identified
Construction of study database in order to identify and measure these hypothesized risk
factors for PDRM
Logistic regression model of all 18 hypothesized risk factors for PDRM
Factor analysis of all 18 hypothesized risk factors for PDRM
Additional logistic regression models to identify other risk factors for PDRM
Risk stratification system developed
Relationship of PDRM and healthcare resource utilization explored

60
on drug-related morbidity since 1967 was reviewed for possible risk factors for PDRM. Peer-
reviewed medical articles and referenced texts were included in the literature review.
Semantic Hierarchy of Risk Factors for Preventable Drug-Related Morbidity
Next, the relationship of these possible risk factors to the models (pharmaceutical care
and biopsychosocial) discussed in chapter 2 was explored. Other possible variables were also
identified, based on these models. Out of this process, 18 possible risk factors for PDRM were
selected.
Based on this, a semantic hierarchy of risk factors for PDRM was developed and
hypotheses related to the 18 risk factors were stated (chapter 3). A semantic hierarchy, as seen in
Table 4.3, relates constructs to variables, and variables to measurements. Several possible risk
factors relate to monitoring, as described by the medication use system: digoxin use,
antidepressant drug use, long-acting benzodiazepine use, antihypertensive drug use,
gastrointestinal disorders, lung conditions, kidney disease, and a history of falling. Several
possible risk factors relate to patient-provider communication, as described by the medication
use system: four or more prescribers, a previous adverse drug reaction, six or more prescription
medications, and four or more recorded diagnoses.
Several possible risk factors relate to patient-specific aspects of drug use, as described
by the biopsychosocial model: difficulty taking medications, high alcohol consumption, self-
assessment of poor health status, trouble paying for medications, and patient belief that they are
taking too many medications. Female gender did not seem to correspond to the conceptual
framework but was included for empirical reasons. Several risk factors also seem to fit more than
one construct: for example, six or more prescription medications may also be related to poor

61
Table 4.3 Semantic Hierarchy in Risk Factors for Preventable Drug-Related Morbidity
Constructs/
Concepts
Preventable drug-related morbidity can be predicted by certain
risk factors.
These risk factors relate to the key concepts of the medication use
system and biopsychosocial model
Monitoring Communication Patient
Specific aspects
Variables
Digoxin use
Antidepressant drug
use
Long-acting
benzodiazepine use
Antihypertensive drug
use
Gastrointestinal
disorders
Lung conditions
Kidney disease
A history of falling
Four or more
prescribers
A previous adverse
drug reaction
Six or more
prescription
medications
Four or more
recorded diagnoses
Difficulty taking
medications
High alcohol
consumption
Self-assessment of
poor health status
Trouble paying for
medications
Patient belief that
they are taking too
many medications
Measurements
(observables)
Examples
Digoxin use:
FHHS and PCS claims
data
Dichotomous: l=yes,
no=0
Four or more
prescribers:
PCS claims data
Dichotomous: 1=4 or
more prescribers, 0=3
or less
Difficulty taking
medications:
Personal Wellness
Profile, “How
difficult is taking
medications for
you?”
Dichotomous: 1 =
difficult ...very
difficult/ can’t do it,
0=not difficult

62
monitoring as well as communication. A more comprehensive discussion of each risk factor is
contained in chapter 3.
Study Population
In order to measure these risk factors, a study population was selected. The study
population was drawn out of the larger pool of enrollees in the Florida Hospital Healthcare
System Premier Care Health Plan. This was a health plan offered by the Florida Hospital
Healthcare System, a provider-sponsored network with a Medicare contract. It was available to
all Medicare beneficiaries who live in Orange, Osceola, and Seminole Counties of Florida and
who were also enrolled in Medicare Part B. By U.S. federal law, however, those individuals who
elected to receive the Medicare hospice benefit and those who had end-stage renal disease were
not eligible for enrollment. Only those enrollees who completed the Personal Wellness Profile
(PWP) Senior Assessment were included in the study. Enrollment into the Premier Care Health
Plan began in January 1997, with approximately 7,000 enrollees by December 1997 and
approximately 50 percent of these individuals completing the PWP. This comparable with the
completion of health risk assessment tools in other Medicare programs (Kerekes and Thornton,
1996). Individuals who were enrolled in the plan anytime during 1997 were included in the study
population.
Data Collection and Formation of the Study Database
The data used in this study consisted of (1) claims which were already collected as a
natural part of the administration of the Premier Care Health Plan and (2) the Personal Wellness
Profile Senior Assessment instrument which was completed by the enrollees. All claims

63
processed and surveys completed between January 1 and December 31, 1997 were included in
the study.
More specifically, the study database consisted of three parts:
(1). Florida Hospital Healthcare System claims data. This data included all claims made
in the outpatient and inpatient settings for this population, except for prescriptions filled in the
ambulatory setting.
(2). PCS Outpatient Prescription Claims. This data includes all prescriptions filled for
the plan enrollees in the ambulatory setting.
(3). Personal Wellness Profile (PWP) Senior Assessment. The PWP is an instrument
that was given to all Premier Care plan members to complete (see Appendix F for the entire
questionnaire). It is an instrument used to identify enrollees at high risk for health-related
problems. It has been previously used by other health plans and its predictive validity has been
verified and studied by Boult et al. (1994), Pacala, Boult, and Boult (1995), and Pacala et al.
(1997). The PWP contains valuable information related to physical and functional status, which
previous authors have shown to be related to utilization of health care services by older persons
(Branch et al., 1981).
A central database containing these three data sets was constructed by a medical
artificial intelligence company in Orlando, FL called MEDai. This database was completed on
April 15, 1998 with approximately 95 percent of claims from 1997 processed at this time. A
unique patient identifier was used to link these three data sets. All patient names were masked to
protect patient confidentiality. The data set was provided to the principal investigator who
discarded irrelevant data fields. Many variables were dichotomized for the purpose of the study.
Considerable data manipulation was required for three of the study variables. While the study
database as prepared by MEDai did contain a listing of all prescription drugs the patients had
received, the drugs had to be organized into therapeutic classes for three of the study variables

64
(long-acting benzodiazepine use, antidepressant drug use, and antihypertensive drug use) for
ease of use. For these three variables, an on-line database called Lexicon® was used to group all
these drugs by national drug code (NDC) number to the appropriate therapeutic class (Multum
Information Services, 1998). The accuracy of the Multum classification system was assessed for
one drug in each of these three therapeutic classes (triazolam, luvoxamine, and diltiazem). All
the patients in the study database who received these drugs were identified and it was determined
whether the drug was placed into a therapeutic class, and if so, whether it was the correct class.
Table 4.4 contains all 18 hypothesized risk factors and shows how they will be measured in this
database. Table 4.5 contains the additional demographic variables to be considered for inclusion
into the regression models, based on the bivariate analysis.
Statistical Analysis
Logistic Regression Model with the 18 Hypothesized Risk Factors
Initial data analysis was performed using a forward inclusion logistic regression
procedure to determine which of the 18 hypothesized risk factors were significantly associated
with PDRM. Larson et al. (1987) previously used this technique to determine which variables
were associated with global cognitive impairment adverse drug reactions in older persons.
McElnay et al. (1997) also used this technique to determine which variables were risk factors
associated with drug-related morbidity in older persons in the inpatient setting. SAS® (SAS
Institute Inc., 1993) and JMP IN® (Sail and Lehman, 1996) were used to create the regression
models.

65
Table 4.4 Hypothesized Risk Factors and Their Measurement
Hypothesized Risk Factor
Measurement
Variables related to monitoring
Digoxin use
Dichotomous: l=yes, 0=no
Measured from PWP drug question 1.
Antidepressant drug use
Dichotomous: l=yes, 0=no
Measured from PCS claims data.
Long-acting benzodiazepine use
Dichotomous: l=yes, 0=no
Measured from PCS claims data.
Antihypertensive drug use
Dichotomous: l=yes, 0=no
Measured from PCS claims data.
Gastrointestinal disorders (ulcers or
gastrointestinal bleeding)
Dichotomous: l=yes, 0=no
Measured from PWP question 14.
Lung conditions (emphysema,
bronchitis or asthma)
Dichotomous: 1 =yes, 0=no
Measured from PWP question 14.
Kidney disease
Dichotomous: l=yes, 0=no
Measured by PWP question 14.
A history of falling
Dichotomous: 1 =yes, 0=no
Measured by PWP question 24.
Variables related to communication
A previous adverse drug reaction
Dichotomous: l=yes, 0=no
Measured by PWP question 3, “Have you had a side
effect due to a medication that caused you to stop that
medication in the last 6 months?”
Four or more prescribers
Dichotomous: 1= 4 or more prescribers, 0=three or fewer
prescribers
Measured through the PCS outpatient prescription data.
Six or more prescription medications
Dichotomous: l=six or more prescription medications,
0=five or fewer prescription medications
The number of medications taken by a patient was
measured by PWP question 24.
Four or more recorded diagnoses
Dichotomous: l=four or more disease states, 0=three or
fewer disease states. Measured by PWP question 14.
Self-Assessment of poor health status
Dichotomous: l=poor, 0=better than poor (other)
Measured by PWP question one.
Trouble paying for medicines
Dichotomous: l=yes, 0=no
Measured by PWP question 23, “Do you have trouble
paying for your medicines?”
Difficulty taking medications
Categories: 1= Difficult or Very difficult/can’t do it,
0=Not difficult
Measured by PWP question 31.

66
Table 4.4 Hypothesized Risk Factors and Their Measurement (Continued)
Hypothesized Risk Factor
Measurement
High Alcohol Consumption
Dichotomous: l=yes, 0=no
Measured by PWP question 11, “Do you often have more
than 1 to 2 alcoholic drinks in a day?”
Patient belief that they are taking
too many medications
Dichotomous: 1= Patient thinks they are on too many
medications, 0= patient does not think they are on too
many medications.
Measured by PWP drug question 4, “How do you feel
about the number of medications you are taking?”
Other variables of interest
Female gender
Dichotomous: l=female, 0=male
Measured by the FHHS data.
66

67
Table 4.5 Additional Demographic Variables and Their Measurement
Additional Demographic Variable
Measurement
Arthritis
Dichotomous: l=yes, 0=no
Measured by PWP question 14.
Bladder/Bowel Control Problems
Dichotomous: l=yes, 0=no
Measured by PWP question 14.
Blind/Trouble Seeing, Even With Glasses
Dichotomous: l=yes, 0=no
Measured by PWP question 14.
Cancer (Non-skin)
Dichotomous: l=yes, 0=no
Measured by PWP question 14.
Congestive Heart Failure
Dichotomous: l=yes, 0=no
Measured by PWP question 14.
Coronary Disease
Dichotomous: l=yes, 0=no
Measured by PWP question 14.
Angina
Dichotomous: l=yes, 0=no
Measured by PWP question 14.
Myocardial Infarction
Dichotomous: l=yes, 0=no
Measured by PWP question 14.
Sciatica
Dichotomous: l=yes, 0=no
Measured by PWP question 14.
Deafness or Trouble Hearing
Dichotomous: l=yes, 0=no
Measured by PWP question 14.
Diabetes (High Blood Sugar)
Dichotomous: l=yes, 0=no
Measured by PWP question 14.
High Blood Pressure
Dichotomous: l=yes, 0=no
Measured by PWP question 14.
Memory Problems (More Than Typical)
Dichotomous: l=yes, 0=no
Measured by PWP question 14.
Stroke
Dichotomous: l=yes, 0=no
Measured by PWP question 14.
Self-Assessment of Much Worse Health
Status
Dichotomous: l=much worse health status, 0=better
than much worse
Measured by PWP question 2.
Smoker
Dichotomous: l=yes, 0=no
Measured by PWP question 11.
Use of Six or More Over-The-Counter
Medications (OTCs)
Dichotomous: l=yes, 0=five or fewer OTCs
Measured by PWP question 22.
Warfarin use
Dichotomous: l=yes, 0=no
Measured by PWP drug question 1.
Theophylline use
Dichotomous: l=yes, 0=no
Measured by PWP drug question 1.
Cimetidine use
Dichotomous: l=yes, 0=no
Measured by PWP drug question 1.
Phenytoin use
Dichotomous: l=yes, 0=no
Lives Alone
Measured by PWP drug question 2.
Dichotomous: l=yes, 0=no
Measured by PWP question 16.

68
Table 4.5 Additional Demographic Variables and Their Measurement (Continued)
Hypothesized Risk Factor
Measurement
Three or more hospitalizations in
previous year
Dichotomous: l=yes, 0=two or fewer
hospitalizations
Measured by PWP question 17.
Three or more ER visits in previous year
Dichotomous: l=yes, 0=two or fewer ER visits
Measured by PWP question 18.
Five or more MD clinic visits in previous
year
Dichotomous: l=yes, 0=four or fewer MD clinic
visits
Measured by PWP question 19.
Nursing home residence
Dichotomous: l=yes, 0=no
Measured by PWP question 20.
Use of durable medical equipment
(oxygen, hospital bed, wheelchair,
walker)
Dichotomous: l=yes, 0=no
Measured by PWP question 25.
Use of home health services (visiting
nurse, physical therapy, homemaker/aide,
adult day care)
Dichotomous: l=yes, 0=no
Measured by PWP question 26.

69
Factor Analysis
The second step of the statistical analysis was a factor analysis with a varimax
(orthogonal) rotation of principal components. This was done on a random selection of 2500
patients from the study population, with the remaining 835 patients serving as a validation group.
All 18 hypothesized risk factors were included in the factor analysis. After the factor analysis
was completed, the number of factors identified and factor scores were studied. While factor
analysis does make assumptions about the normality of the data, dichotomous data can be used
with factor analysis with confidence, provided that sample sizes are large enough (greater than
200 observations)(Parry and McArdle, 1991).
The objective of the factor analysis was to (1) reduce the rather large number of
hypothesized variables to a relatively small number of factors, or common traits, and (2)
determine whether these factors matched the structure proposed in the semantic hierarchy. Factor
analysis accomplishes the first objective by focusing on the part of the total variance that is
shared by the variables, assuming that variables consist of common parts. The initial
communality estimates were set as one. The results were kept in perspective as factor analysis is
intended only to be used as a tool to help guide the researcher, not to be used without
consideration of the conceptual framework being used and other factors (Maraun, 1996).
One of two approaches could be used, based on the results of the factor analysis. Had the
factors matched the semantic hierarchy and shown to represent constructs with confidence, then
principal component scores based on these factors would have been entered into a logistic
regression model. However, because the factors did not match the semantic hierarchy and could
not be shown to represent constructs with confidence, the risk factors from the first regression
model were entered into another regression model with the additional variables, taking the factor
analysis results into consideration.

70
Logistic Regression Models with Additional Variables
Additional logistic regression models were then run, with the risk factors from the first
model entered a priori to adjust for their effects on PDRM. Other demographic variables were
also allowed to enter the model to see if they added significantly to the prediction, based on
statistical significance in a bivariate analysis between patients who did, and who did not, have
PDRM. Because there was a theoretical basis for including the risk factors from the first model,
it was felt that the final model for PDRM must include all of these risk factors, even if it
explained less of the variance of PDRM. Thus, this process incorporated both statistical and
theoretical criterion for deciding which terms to include in the model and this helped to focus
attention on those variables that fit into the conceptual framework and that had the greatest
independent effect on PDRM.
Following the creation of the final prediction model, a risk stratification system was
developed. Patients were categorized according to the number of risk factors they had and a
comparison was made between the patients with, and without, PDRM.
PDRM and Healthcare Resource Utilization
The final component of the methodology was to do another bivariate analysis to
determine the relationship of PDRM to healthcare resource utilization. Each enrollee was
classified as either having PDRM or not. Then, the utilization of health care resources was
compared for the two groups.

CHAPTER 5
RESULTS
The results of this study will be presented in two parts. First, the results of the Delphi
technique and the creation of the operational definitions of PDRM will be shared. Second, the
results of the prediction models for PDRM and risk factors identified will be presented.
Delphi Technique - The Geriatric Medicine Expert Panel
As was discussed in the previous chapter, the Delphi technique was used in an attempt to
generate consensus on PDRM. Two rounds were used until consensus was obtained. Appendix G
contains the final list of consensus-approved operational definitions of PDRM. This appendix
also includes all the comments made by the Geriatric Medicine Expert Panel members in either
round 1 or round 2.
The expert panel agreed that 52 of the clinical scenarios presented to them were actual
PDRMs. Table 5.1 shows the opinions of the panel members after round 1 and round 2. After
round 1, the panel members' agreement with the clinical scenarios ranged from 60.4 percent to
97.9 percent. After round 2, the agreement ranged from 82.8 percent to 100 percent.
Initially, the panel members were presented with 47 unique clinical scenarios. One of
these was listed twice, as a validity check, so the panel members were actually presented with 48
outcomes and patterns of care to evaluate. The validity check received the same score from all
seven panel members for both rounds. After the first round, two clinical scenarios were rejected
(received fewer than four "yes" votes). The panel members were given the opportunity to suggest
71

72
Table 5.1 Geriatric Medicine Expert Panel Results
Expert Number
Percentage (%) of clinical
scenarios the expert felt were
PDRMs after Round 1
Percentage (%) of clinical
scenarios the expert felt were
PDRMs after Round 2
1
95.8
100
2
97.9
82.8
3
79.2
89.7
4
60.4
91.4
5
79.2
87.9
6
77.1
91.4
7
97.9
94.8

73
other operational definitions of PDRMs and 12 were generated. One of these 12 new operational
definitions was, in fact, a duplicate of a previous definition. These 12 new operational definitions
were added to the remaining 46 operational definitions of PDRM and given to the panel
members in round 2. After the second round, an additional four clinical scenarios were rejected,
the two duplicates were removed, leaving 52 operational definitions of PDRM that were
approved.
After round 2 there appeared to be overwhelming consensus on which clinical scenarios
were actual PDRMs. Of the 52 clinical scenarios deemed to be PDRMs by the expert panel, 35
clinical scenarios had the agreement of all seven panel members, 15 clinical scenarios had the
agreement of six out of the seven members, and two clinical scenarios had the agreement of five
out of the seven members. There were no clinical scenarios that only had the agreement of four
panel members. Table 5.2 lists shows how each panel member voted in round 2. Throughout the
two round process, there were six clinical scenarios which did not have at least the support of
four panel members. Table 5.3 lists these clinical scenarios that were rejected as being PDRMs.
Identification of Consensus-Approved Operational Definitions of PDRM in Database
The 52 PDRMs approved by the geriatric medicine expert panel were identified by first
examining the study database for the outcomes related to the specific operational definitions of
PDRM. Overall, 1005 patients with outcomes related to one of the 52 consensus-approved
PDRMs were identified. Next, each patient with one of these outcomes was individually studied
to determine whether the pattern of care associated with a PDRM was provided or not. The
outcome and pattern of care matched a consensus-approved operational definition of PDRM in
158 cases. This represented 97 patients, as several patients had more than one specific

74
Table 5.2 Round 2 of the Delphi Technique
Number3
Expert
1
Expert
2
Expert
3
Expert
4
Expert
5
Expert
6
Expert
7
Total
“Yes”
1
Y
Y
Y
Y
Y
Y
Y
7
2
Y
Y
Y
Y
Y
Y
Y
7
3
Y
Y
Y
Y
Y
Y
Y
7
4
Y
Y
Y
Y
Y
Y
Y
7
5
Y
Y
Y
Y
Y
Y
Y
7
6
Y
Y
Y
Y
Y
Y
Y
7
7
Y
Y
Y
Y
Y
Y
Y
7
8
Y
Y
Y
Y
Y
Y
Y
7
9
Y
Y
Y
Y
Y
Y
Y
7
10
Y
Y
Y
Y
Y
Y
Y
7
11
Y
Y
Y
Y
Y
Y
Y
7
12
Y
Y
Y
Y
Y
Y
Y
7
13
Y
Y
Y
Y
Y
Y
Y
7
14
Y
Y
Y
Y
Y
Y
Y
7
15
Y
Y
Y
Y
Y
Y
Y
7
16
Y
Y
Y
Y
Y
Y
Y
7
17
Y
Y
Y
Y
Y
Y
Y
7
18
Y
Y
Y
Y
Y
Y
Y
7
19
Y
Y
Y
Y
Y
Y
Y
7
20
Y
Y
Y
Y
Y
Y
Y
7
21
Y
Y
Y
Y
Y
Y
Y
7
22
Y
Y
Y
Y
Y
Y
Y
7
23
Y
Y
Y
Y
Y
Y
Y
7
24
Y
Y
Y
Y
Y
Y
Y
7
25
Y
Y
Y
Y
Y
Y
Y
7
26
Y
Y
Y
Y
Y
Y
Y
7
27
Y
Y
Y
Y
Y
Y
Y
7
28
Y
Y
Y
Y
Y
Y
Y
7
29
Y
Y
Y
Y
Y
Y
Y
7
30
Y
Y
Y
Y
Y
Y
Y
7
31
Y
Y
Y
Y
Y
Y
Y
7
32
Y
Y
Y
Y
Y
Y
Y
7
33
Y
Y
Y
Y
Y
Y
Y
7
34
Y
Y
Y
Y
Y
Y
Y
7
35
Y
Y
Y
Y
Y
Y
Y
7
36
Y
Y
Y
Y
Y
N
Y
6
37
Y
N
Y
Y
Y
Y
Y
6
38
Y
N
Y
Y
Y
Y
Y
6
39
Y
N
Y
Y
Y
Y
Y
6
40
Y
Y
N
Y
Y
Y
Y
6
41
Y
Y
Y
Y
N
Y
Y
6
42
Y
N
Y
Y
Y
Y
Y
6

75
Table 5.2 Round 2 of the Delphi Technique (Continued)
Number3
Expert
1
Expert
2
Expert
3
Expert
4
Expert
5
Expert
6
Expert
7
Total
“Yes”
43
Y
Y
N
Y
Y
Y
Y
6
44
Y
Y
N
Y
Y
Y
Y
6
45
Y
Y
Y
Y
N
Y
Y
6
46
Y
Y
Y
N
Y
Y
Y
6
47
Y
Y
Y
Y
Y
N
Y
6
48
Y
N
Y
Y
Y
Y
Y
6
49
Y
Y
Y
Y
N
Y
Y
6
50
Y
N
Y
Y
Y
Y
Y
6
51
Y
Y
N
Y
N
Y
Y
5
52
Y
N
Y
Y
Y
Y
N
5
53b
Y
N
Y
N
N
N
Y
3
54b
Y
Y
N
N
Y
N
N
3
55b
Y
N
N
N
N
Y
N
2
56b
Y
N
Y
N
N
\w~
Y
hr~
57c
Y
Y
Y
Y
Y
Y
Y
7
58d
Y
Y
Y
Y
Y
Y
Y
7
a See Appendix G for a description of the PDRM
b Rejected as an operational definition of PDRM
c Duplicate - same as PDRM #2
d Duplicate - same as PDRM #17

76
Table 5.3 Clinical Scenarios That Were Rejected As Preventable Drug-Related Morbidities
Clinical Scenario (Outcome and Pattern of Care)
Outcome: Fall and/or hip fracture and/or other bone fracture and/or bone break
Pattern of care: 1. Use of an anti-parkinsonian agent (e.g.; levodopa, bromocriptine,
Benztropine, etc.)
Outcome: Major and/or minor hemorrhagic event
Pattern of care: 1. Use of SQ heparin
2. PTT not done at least every month
Outcome: Digoxin toxicity
Pattern of care: 1. Use of digoxin
2. BUN/serum creatinine not done at least every 6 months
3. Digoxin level not done at least every 6 months
Outcome: Fall and/or hip fracture and/or other bone fracture and/or bone break
Pattern of care: 1. Use of a nitrate (e.g.; isosorbide)
Outcome: Acute renal failure and/or renal insufficiency
Pattern of care: 1. Use of allopurinol
2. BUN/serum creatinine not done at least every 6 months
Outcome: Asthma exacerbation and/or status asthmaticus and/or ER visit/hospitalization
Due to asthma
Pattern of care: 1. Diagnosis of asthma
2. Use of theophylline
3. Drug level not done at least every 6 months

77
operational definition of PDRM. Table 5.4 shows the individual breakdown of each of the 52
consensus-approved operational definitions of PDRM.
As previously mentioned, many patients experienced more than one specific operational
definition of PDRM. Table 5.5 shows the number of PDRMs each patient had, along with the
number of specific outcomes these PDRM represented. This distinction is important to make
because some patients met the criteria for more than one PDRM, but these PDRMs shared the
same outcome (e.g. there are several PDRMs related to major/minor hemorrhagic events with
different patterns of care).
Validation of Operational Definitions of Preventable Drug-Related Morbidity
Pharmacist agreement with the PDRM classification assigned by the operational
definitions of PDRM was acceptable, although it varied for the two specific operational
definitions of PDRM included in the analysis. Tables 5.6 and 5.7 show the individual panel
members' classification for each patient. One patient chart for the hyperglycemia outcome and
four patient charts for the secondary myocardial infarction outcome could not be located and
they were not included in the final analysis.
Overall, the sensitivity of the two specific operational definitions of PDRM was 87.5
percent and the specificity was 73.5 percent (Table 5.8). For the hyperglycemia outcome, the
sensitivity was 93.3 percent and the specificity was 81.3 percent (Table 5.9). For this outcome,
the chart abstracts had to be administered a second time to the Chart Abstract Reviewer Panel
because not all panel members followed the initial instructions, as will be discussed in the next
chapter. For the secondary myocardial infarction outcome, the sensitivity was 82.4 percent and
the specificity was 66.7 percent (Table 5.10).

78
Table 5.4 Patients with Outcomes and PDRM
PDRM
Num¬
ber a
Number
of
Patients
with
Outcome
(%)
(n=1005)
Number
of
Patients
with
PDRM
(%)
(n=97)b
Percentage
(%) of
Patients
with the
outcome
who have
PDRM
PDRM
Num¬
ber a
Number
of
Patients
with
Outcome
(%)
(n=1005)
Number
of
Patients
with
PDRM
(%)
(n=97)b
Percentage
(%)of
Patients
with the
outcome
who have
PDRM
1
28 (2.8)
8 (8.2)
28.6
27
14(1.4)
0 (0.0)
0
2
24 (2.4)
6 (6.2)
25.0
28
45 (4.5)
2 (2.1)
4.4
3
0 (0.0)
0 (0.0)
0
29
28 (2.8)
3 (3.1)
10.7
4
7 (0.7)
1 (1.0)
14.3
30
13 (1.3)
0 (0.0)
0
5
24 (2.4)
0 (0.0)
0
31
2 (0.2)
0 (0.0)
0
6
3 (0.3)
1 (1.0)
33.3
32
7 (0.7)
0 (0.0)
0
7
17(1.7)
0 (0.0)
0
33
39 (3.9)
2 (2.1)
5.1
8
24 (2.4)
0 (0.0)
0
34
16(1.6)
7 (7.2)
43.8
9
0 (0.0)
0 (0.0)
0
35
1(0.1)
0 (0.0)
0
10
0 (0.0)
0 (0.0)
0
36
32(3.2)
8 (8.2)
25.0
11
45 (4.5)
4 (4.1)
8.9
37
32(3.2)
6 (6.2)
18.8
12
28 (2.8)
2 (2.1)
7.1
38
2 (0.2)
1 (1.0)
50.0
13
27 (2.7)
0 (0.0)
0
39
3 (0.3)
0 (0.0)
0
14
2 (0.2)
0 (0.0)
0
40
0 (0.0)
0 (0.0)
0
15
2 (0.2)
0 (0.0)
0
41
14(1.4)
12(12.4)
85.7
16
46 (4.6)
4 (4.1)
8.7
42
31 (3.1)
3 (3.1)
9.7
17
7 (0.7)
1 (1.0)
14.3
43
31 (3.1)
3 (3.1)
9.7
18
24 (2.4)
10(10.3)
41.7
44
7 (0.7)
0 (0.0)
0
19
0 (0.0)
0 (0.0)
0
45
16(1.6)
0 (0.0)
0
20
0 (0.0)
0 (0.0)
0
46
24 (2.4)
0 (0.0)
0
21
32 (3.2)
18(18.6)
56.3
47
31 (3.1)
6 (6.2)
19.4
22
45 (4.5)
5 (5.2)
11.1
48
24 (2.4)
6 (6.2)
25.0
23
0 (0.0)
0 (0.0)
0
49
31 (3.1)
1 (1.0)
3.2
24
39 (3.9)
24(24.7)
61.5
50
42 (4.2)
2 (2.1)
4.8
25
12(1.2)
0 (0.0)
0
51
32 (3.2)
10(10.3)
31.3
26
45 (4.5)
1 (1.0)
2.2
52
7 (0.7)
1 (1.0)
14.3
a See Appendix G for the description of the PDRM.
b Adds up to over 100 percent because some patients had more than one operational definition
of PDRM.

79
Table 5.5 Number of PDRMs and Specific Outcomes by Individual Patients
Patient Category
Number of Patients
with PDRM (%) n=97
1 case of PDRM with 1 specific outcome
61 (62.9)
2 cases of PDRM with 1 specific outcome
12(12.4)
2 cases of PDRM with 2 specific outcomes
6 (6.2)
3 cases of PDRM with 1 specific outcome
4 (8.2)
3 cases of PDRM with 2 specific outcomes
8 (8.2)
3 cases of PDRM with 3 specific outcomes
0 (0.0)
4 cases of PDRM with 1 specific outcome
0 (0.0)
4 cases of PDRM with 2 specific outcomes
3 (3.1)
4 cases of PDRM with 3 specific outcomes
0 (0.0)
4 cases of PDRM with 4 specific outcomes
2 (2.1)
5 cases of PDRM with 1 specific outcome
0 (0.0)
5 cases of PDRM with 2 specific outcomes
0 (0.0)
5 cases of PDRM with 3 specific outcomes
1 (1.0)
5 cases of PDRM with 4 specific outcomes
0 (0.0)
5 cases of PDRM with 5 specific outcomes
0 (0.0)

80
Table 5.6 Chart Abstract Reviewer Panel Results for Hyperglycemia with no
Regular HgAlc Monitoring
Pt#
RPh
#1
RPh
#2
RPh
#3
RPh
#4
RPh
#5
Total
"yes"
PDRM according to
definition
1
Y
Y
Y
Y
Y
5
Y
2
Y
Y
Y
Y
Y
5
Y
3
N
N
N
N
Y
1
N
4
Y
Y
Y
Y
Y
5
Y
5
Y
N
N
Y
N
2
N
6
Y
N
N
N
Y
2
N
7
Y
Y
Y
Y
Y
5
Y
8
Y
Y
N
Y
Y
4
Y
9
Y
Y
N
Y
Y
4
Y
10
Y
Y
Y
Y
Y
5
Y
11
N
N
N
N
Y
1
N
12
N
N
N
N
N
0
N
13
N
N
N
N
N
0
N
14
Y
Y
Y
Y
Y
5
Y
15
Y
Y
Y
Y
Y
5
Y
16
N
N
N
N
N
0
N
17
N
Y
N
Y
N
2
N
18
Y
Y
Y
Y
N
4
Y
19
N
Y
N
Y
N
2
N
20
N
Y
N
Y
N
2
Y
21
Y
Y
Y
Y
Y
5
N
22
N
Y
N
N
Y
2
Y
23
N
N
N
N
Y
1
N
24*
Y
25
N
N
Y
N
N
1
N
26
Y
Y
Y
Y
Y
5
Y
27
N
Y
N
N
N
1
N
28
Y
Y
Y
Y
Y
5
Y
29
Y
Y
Y
Y
Y
5
Y
30
Y
N
N
Y
N
2
Y
31
Y
Y
Y
Y
Y
5
Y
32
N
N
N
N
Y
1
N
* Chart abstract could not be done because patient chart could not be located.

81
Table 5.7 Chart Abstract Reviewer Panel Results: Secondary Myocardial Infarction
in Patients Without ASA and/or Beta-Blocker Use
Pt#
RPh
#1
RPh
#2
RPh
#3
RPh
#4
RPh
#5
Total "Yes"
PDRM according
to definition
1
N
N
N
N
N
0
N
2
Y
Y
Y
Y
N
4
N
3
N
N
N
N
Y
1
N
4
Y
Y
N
Y
N
3
Y
5
Y
Y
Y
Y
Y
5
Y
6
N
Y
Y
N
N
2
N
7
Y
N
N
Y
N
2
N
8
N
N
Y
Y
Y
3
N
9
Y
Y
Y
Y
Y
5
Y
10*
Y
11
Y
Y
Y
N
Y
4
Y
12
Y
Y
Y
Y
Y
5
Y
13
Y
N
N
Y
Y
3
Y
14
Y
Y
Y
Y
Y
5
Y
15
Y
Y
Y
Y
Y
5
N
16
Y
N
N
Y
Y
3
Y
17
Y
Y
Y
Y
Y
5
Y
18
Y
Y
Y
Y
Y
5
Y
19
Y
N
Y
N
Y
3
Y
20
N
N
N
Y
Y
2
N
21
N
N
N
Y
N
1
N
22*
Y
23
Y
Y
Y
Y
Y
5
Y
24
Y
Y
Y
Y
Y
5
Y
25
Y
Y
Y
Y
Y
5
Y
26*
Y
27*
Y
28
Y
Y
Y
N
N
3
Y
29
N
Y
N
Y
Y
2
Y
30
Y
Y
Y
Y
N
4
Y
31
N
N
N
Y
N
1
HÑ
32
Y
N
Y
Y
Y
4
Y
33
Y
N
Y
Y
N
3
N
34
Y
Y
Y
Y
Y
5
Y
35
Y
N
Y
Y
Y
4
N
36
N
N
N
Y
Y
2
N
37
Y
Y
Y
Y
Y
5
Y
38
N
Y
N
Y
N
2
N
39
N
N
N
Y
N
N
* Chart abstract could not be done because patient chart could not be located.

82
Table 5.8 Sensitivity and Specificity for Both of the Operational Definitions of PDRM
Operational Definition of PDRM
Yes
No
True
PDRM
Yes
28
4
No
9
25
Sensitivity was calculated as [28/(28+4)] x 100 = 87.5 percent.
Specificity was calculated as [25/(25+9)] x 100 = 73.5 percent.
True PDRM (Yes) = Four or more of the panel members classified as PDRM,
True PDRM (No) = Four or more of the panel members classified as non-PDRM,
Operational definition of PDRM (Yes) = Classified as a PDRM by the operational
definition,
Operational definition of PDRM (No) - Classified as a non-PDRM by the operational
definition.

83
Table 5.9 Sensitivity and Specificity of the Operational Definition of PDRM (Hyperglycemia
Outcome)
Operational Definition of PDRM
Yes
No
True
PDRM
Yes
14
1
No
3
13
Sensitivity was calculated as [14/(14+1)] x 100 = 93.3 percent.
Specificity was calculated as [13/(13+3)] x 100 = 81.3 percent.
True PDRM (Yes) = Four or more of the panel members classified as PDRM,
True PDRM (No) = Four or more of the panel members classified as non-PDRM,
Operational definition of PDRM (Yes) = Classified as a PDRM by the operational
definition,
Operational definition of PDRM (No) - Classified as a non-PDRM by the operational
definition.

84
Table 5.10 Sensitivity and Specificity of the Operational Definition of PDRM (Secondary
Myocardial Infarction Outcome)
Operational Definition of PDRM
Yes
No
True
PDRM
Yes
14
3
No
6
12
Sensitivity was calculated as [14/(14+3)] x 100 = 82.4 percent.
Specificity was calculated as [12/(12+6)] x 100 = 66.7 percent.
True PDRM (Yes) = Four or more of the panel members classified as PDRM,
True PDRM (No) = Four or more of the panel members classified as non-PDRM,
Operational definition of PDRM (Yes) = Classified as a PDRM by the operational
definition,
Operational definition of PDRM (No) - Classified as a non-PDRM by the operational
definition.

85
The agreement among the five pharmacists in classifying the patients into those with PDRM, and
those without PDRM, was acceptable. Fleiss's measure of overall agreement for the
hyperglycemia patients was 0.652, for the secondary myocardial infarction patients it was 0.674
and overall it was 0.664. Therefore, if a patient was selected at random and classified as either
having, or not having, PDRM by a randomly selected panel member, a second randomly selected
panel member would agree with the first panel member 66.4 percent of the time.
Therefore, because of the consensus reached by the Geriatric Medicine Expert Panel and
the high sensitivity and specificity of the two validated operational definitions, research
assumption A1A could be met: valid operational definitions of PDRM can be developed by a
panel of geriatric medicine experts.
Phase II: Identification of Risk Factors for PDRM
Phase II involved the identification of risk factors for PDRM. First, the results of the
statistical analyses used to identify the risk factors will be presented. Second, the results related
to the hypothesis for each risk factor will be shown. Third, the results related to the hypothesis of
general risk factors and drug (and disease) specific risk factors will be presented. Finally, the
relationship between PDRM and healthcare resource utilization will be discussed.
Logistic Regression with all 18 Hypothesized Risk Factors
In order to test the first set of hypotheses regarding possible risk factors for PDRM, a
logistic regression analysis was performed. A forward inclusion procedure was used with the
entry level set at p=0.05.
A five-variable risk model was produced (Table 5.11). This model indicates that patients
with four or more recorded diagnoses were 2.93 times more likely to have PDRM than those
with three or fewer diseases. Patients with antihypertensive drug use are at a much greater risk

86
Table 5.11 Logistic Regression Model With All 18 Hypothesized Variables
Variable
Parameter
Estimate
(b)
Standard
Error (SE)
Chi-Square
Probability
Odds
Ratio
95 Percent
Confidence
Interval
Four or more
prescribers
0.2683
0.0602
0.0001
1.308
1.162-1.472
Four or more
recorded
diagnoses
1.0758
0.2475
0.0001
2.932
1.805-4.763
Female gender
-0.6633
0.2393
0.0056
0.515
0.823-0.322
Antihypertensive
drug use
0.7023
0.2787
0.0118
2.018
1.156-3.524
Six or more
prescription
medications
0.6525
0.2942
0.0266
1.920
1.079-3.418
Equation Constant
-4.6958
0.2758
0.0001
-
-

87
(2.02 times) for having PDRM, as are patients taking six or more prescription medications (1.92
times). One other variable, four or more prescribers, also placed patients at a greater risk (1.31
times) for developing a PDRM. Finally, the odds ratio for female gender was less than one
(0.52), meaning that females were at a far lower risk of developing PDRM than males. None of
the 95 percent confidence intervals for the odds ratios contained 1, and the confidence intervals
were quite small.
The fit of this prediction model appears to be quite good. To measure how well the
estimated model fits the data, two times the log of the likelihood (-2LL) is used. The difference
between the -2LL for the intercept (constant) only and for the constant plus the covariates
(variables) is referred to as the G statistic (Menard, 1995). Here the G statistic, which tests
whether the information about the independent variables allows one to make a better prediction
of the dependent variable, is significant (chi square = 101.402 with 5 df, p =0.0001). Therefore,
addition of these variables did significantly increase the model chi square. The Score statistic,
which tests the statistical significance of the combined effects of the independent variables in the
model, confirms these findings (Menard, 1995). It is significant as well (chi square = 130.166
with 5 df, p=0.0001). Therefore, overall, the fit of the model is good as evidenced by these tests.
The amount of variance explained by the prediction model is also quite good. While R^
is not recommended for use in logistic regression, an analogue called Rj^ has been proposed
(Menard, 1995). This statistic measures the proportional reduction on chi-square and it varies
between 0 and 1 (where 1= the model predicts the dependent variable perfectly) (Menard, 1995).
For this prediction model, the Rj^ is 0.562.
Other attributes of this model suggest that it is free of multicollinearity and that it is
quite accurate. Table 5.12 shows the correlation matrix for the final variables in the model.
Overall, the correlations (pairwise relationships) between the variables are quite low. Only two
correlations were greater than +/- 0.20: four or more prescribers and antihypertensive drug use

88
Table 5.12 Correlation Matrix for Significant Variables in Regression Model With All 18
Hypothesized Variables
Con¬
stant
Female
Gender
Four or
more
recorded
diagno¬
ses
Six or more
prescription
medications
Four or
more
prescribers
Antihyper¬
tensive
drug use
Constant
1.00
Female Gender
-0.3520
1.00
Four or more
recorded
diagnoses
-0.2899
0.0274
1.00
Six or more
prescription
medications
0.0524
-0.0323
0.2965
1.00
Four or more
prescribers
-0.3626
0.0042
0.0500
0.1556
1.00
Antihyper¬
tensive drug
use
-0.4854
0.0224
0.0894
0.0894
0.2995
1.00

89
(0.2995), and four or more recorded diagnoses and six or more prescription medications
(0.2965). A backward elimination procedure was also performed, which yielded identical
results. The classification accuracy of the model is quite good, as determined by the jack-knife
method (Table 5.13). The sensitivity was 56.1 percent, the specificity was 86.8 percent, the
positive predictive value was 11.5 percent and the negative predictive value was 98.5 percent.
Factor Analysis
The second step of the statistical analysis was a factor analysis (orthogonal) of principal
components. This was done on a random selection of 2500 patients out of the 3365 patients in
the study population, with the remaining 835 patients serving as the validation group. This factor
analysis included all 18 hypothesized risk factors.
The factor analysis suggested that there are six factors. Several things were considered
when coming to this conclusion. There were seven factors with eigenvalues greater than one,
although the eigenvalues down to the tenth factor were all quite large (the eigenvalue for factor
10 was 0.9176). Cattell's Scree Plot showed a large separation between factors 4 and 5, a smaller
separation between factors 6 and 7 and then another separation between factors 10 and 11. To
avoid underextracting the number of factors, up to ten factors were specified in the factor
analysis. Great factor stability was observed in the factor analyses done with ten to six specified
factors. Therefore, six factors were chosen for simplicity and to avoid the dangers of
overextraction in factor analysis (Fava and Velicer, 1992).
The results of the factor analysis with six factors can be seen in Table 5.14. These six
factors were subjected to a varimax (orthogonal) rotation. Several other results seem to support
that the factors seen in Table 5.14 are reliable. First, the final communality estimates, which are
squares of the factor patterns, were at least 0.50 (acceptable) for all the variables except for lung

90
Table 5.13 Classification Table for the Regression Model with the 18 Hypothesized Variables
Predicted
Yes
No
Actual
Yes
46
36
No
354
2318
Sensitivity was calculated as [46/(46+36)] x 100 = 56.1 percent.
Specificity was calculated as [2318/(2318+354)] x 100 = 86.8 percent.
Positive Predictive Value [46/(46+354)] x 100 =11.5 percent.
Negative Predictive Value [2318/(2318+36)] x 100 = 98.5 percent
Overall Percentage accuracy in Classification [2364/(390+2364)] x 100 = 85.8 percent

91
Table 5.14 Rotated Factor Matrix - Varimax
Variable
Factor 1
Factor 2
Factor 3
Factor 4
Factor 5
Factor 6
Kidney disease
.83
Gastrointestinal
disorders
.79
Four or more
recorded diagnoses
.71
Lung conditions
.60
Four or more
prescribers
.78
Antihypertensive
drug use
.72
Antidepressant
drug use
.49
Six or more
prescription
medications
.66
Digoxin use
.53
Self-assessment of
poor health status
.51
A history of falling
.70
Difficulty taking
drugs
.67
Long-acting
benzodiazepine use
.40
Patient belief that
they are taking too
many medications
.68
Trouble paying for
medications
.46
A previous adverse
drug reaction
.43
High alcohol
consumption
.70
Female gender
-.71
Eigenvalue
2.58
1.57
1.37
1.17
1.07
1.05
Only factor scores greater than +/- 0.40 are shown.

92
disease (0.40). Second, a factor analysis done with a Harris Kaiser rotation (oblique) yielded
almost identical results, given that the factors were in different order (Table 5.15). In a Harris
Kaiser rotation the rotated factors become correlated and sometimes produce more useful factor
patterns. In cases like this, where the Harris Kaiser rotation yields similar results to the
orthogonal rotation, it can be concluded that the correlations among the factors are minimal and
the orthogonal rotation may be used for interpretation (Pedhazur and Schmelkin, 1991). Third,
the validation group of 835 patients also yielded very similar results (Table 5.16).
Constructs could not be assigned to the six factors with certainty. While the factor
groupings do not seem to relate to definite constructs in the medication use system, the variables
that were grouped together in the same factor do appear reasonable. Because of this inability to
assign each factor a construct, factor scores were not used in a logistic regression equation.
Instead, the five risk factors identified in the regression model with all 18 hypothesized risk
factors were used. Of the five risk factors, one was contained in factor 1 {four or more recorded
diagnoses), two were contained in factor 2 (four or more prescribers and antihypertensive drug
use), one was contained in factor 3 (six or more prescription medications), and one was
contained in factor 6 (female gender). There was no risk factor contained in factors 4 or 5. A
more detailed discussion of the factor analysis is contained in the next chapter.
Bivariate Analysis
A bivariate analysis was conducted for the purpose of identifying variables in the study
database, other than the 18 hypothesized variables, that might be associated with PDRM. These
other variables represent a wide variety of demographics related to healthcare (diseases, history
of healthcare resource utilization, medications, etc.) and were not chosen because of any
empirical or theoretical evidence that they are risk factors for PDRM. In comparing patients

93
Table 5.15 Rotated Factor Matrix - Harris-Kaiser
Variable
Factor 1
Factor 2
Factor 3
Factor 4
Factor 5
Factor 6
High alcohol
consumption
.71
Female gender
-.71
Four or more
prescribers
.80
Antihypertensive
drug use
.74
Antidepressant
drug use
.45
Kidney disease
.82
Gastrointestinal
disorders
.79
Four or more
recorded diagnoses
.73
.46
Lung conditions
.61
Six or more
prescription
medications
.68
Digoxin use
.56
Self-assessment of
poor health status
.52
Patient belief that
they are taking too
many medications
.69
A previous adverse
drug reaction
.49
Trouble paying for
medications
.45
A history of falling
.70
Difficulty taking
medications
.67
Long-acting
benzodiazepine use
.40
Eigenvalue
2.58
1.57
1.37
1.17
1.07
1.05
Only factor scores greater than +/- 0.40 are shown.

94
Table 5.16 Rotated Factor Matrix - Varimax Validation Group
Variable
Factor 1
Factor 2
Factor 3
Factor 4
Factor 5
Factor 6
Kidney disease
.82
Gastrointestinal
disorders
.78
Four or more
recorded diagnoses
.76
Lung conditions
.61
Antihypertensive
drug use
.81
Four or more
prescribers
.75
Antidepressant
drug use
.62
Self-assessment of
poor health status
.62
Digoxin use
.62
Six or more
prescription
medications
.42
Female gender
-.63
Patient belief that
they are taking too
many medications
.59
High alcohol
consumption
.48
A history of falling
.69
Difficulty taking
medications
.67
A previous adverse
drug reaction
.73
Trouble paying for
medications
.54
Eigenvalue
2.58
1.57
1.37
1.17
1.07
1.05
Only factor scores greater than +/- 0.40 are shown.

95
with, and without PDRM, a chi-square test was used to test for any significant difference in the
variables. If the expected frequency was less than five in either cell, then the Fisher's Exact Test
was used. The results of the bivariate analysis for the original 18 hypothesized variables can be
seen in Table 5.17. The results of the analysis for the 28 additional demographic variables can be
seen in Table 5.18. If the chi square value for a variable was significantly associated with PDRM
in the bivariate analysis (p<0.05) then it was included in following regression model.
Logistic Regression Models with Additional Demographic Variables
It was thought that there might be some additional demographic variables that are risk
factors for PDRM. If a variable was significantly associated with PDRM in the bivariate analysis
(p<0.05) then it was included in the regression model along with the five variables that were
identified as risk factors in the original model. A forward inclusion procedure was again used
with the entry level set at p=0.05.
A seven-variable risk model was produced (Table 5.19). As before, patients with four or
more recorded diagnoses were more likely to have PDRM (odds ratio = 2.32), as well as those
with antihypertensive drug use (odds ratio = 2.29), and those with four or more presenters
(odds ratio = 1.27). Also, the odds ratio for female gender was again less than one (0.60),
meaning that females were at a far lower risk of developing PDRM than males. New variables
included in the final model were angina (odds ratio = 1.87), three or more hospitalizations in the
previous year (odds ratio = 2.02), and use of durable medical equipment (odds ratio = 2.23). Six
or more prescription medications was not contained in this model. None of the 95 percent
confidence intervals for the odds ratios contained 1, and the confidence intervals were quite
small.
The fit of this prediction model appears to be quite good. The G statistic is significant
(chi square = 126.032 with 7 df, p =0.0001). Therefore, the addition of these seven explanatory
variables did significantly increase the model chi square. The Score statistic confirms these

96
Table 5.17 Bivariate Analysis of Patients With, and Without, PDRM Categorized by
Hypothesized Variables
Variable
No. (%) with
PDRM (n=97)
No. (%)
without PDRM
(n=3268)
P value
Digoxin use
12(12.4)
212 (6.5)
0.022a
Antidepressant drug use
15(15.5)
235 (7.2)
0.002b
Long-acting benzodiazepine use
2 (2.1)
80 (2.5)
0.808d
Antihypertensive drug use
74 (76.3)
1375 (42.1)
0.001C
Gastrointestinal disorders
15(15.5)
182 (5.6)
0.001C
Lung conditions
21 (21.7)
436(13.4)
0.019a
Kidney disease
12(12.4)
120 (3.7)
0.001c
A history of falling
4 (4.2)
78 (2.4)
0.299d
Four or more prescribers
32 (33.0)
388 (11.9)
0.001c
Six or more prescription medications
24 (25.5)
235 (7.3)
0.001c
Four or more recorded diagnoses
56 (57.7)
667 (20.4)
0.001C
A previous adverse drug reaction
9 (9.8)
405 (13.0)
0.362
High alcohol consumption
6 (6.5)
222 (7.1)
0.822
Self-assessment of poor health status
8 (8.3)
74 (2.3)
0.001c
Trouble paying for medications
8 (8.5)
158 (5.0)
0.126
Difficulty taking medications
2 (2.2)
51 (1.7)
0.668d
Patient belief that they are taking too
many medications
10(10.6)
186 (5.9)
0.055
Female gender
42 (43.3)
1808 (55.3)
0.019a
a Significance difference in variable between patients with, and without, PDRM, p < 0.05
b Significance difference in variable between patients with, and without, PDRM, p < 0.01
c Significance difference in variable between patients with, and without, PDRM, p < 0.001
d Probability value for Fisher's Exact Test

97
Table 5.18 Bivariate Analysis of Patients With, and Without, PDRM, Categorized by Additional
Demographic Variables
Variable
No. (%) with
PDRM (n=97)
No. (%)
without PDRM
(n=3268)
P value
Arthritis
61 (62.9)
1497 (45.8)
0.001C
Bladder/Bowel Disease
30 (30.9)
560(17.1)
0.001c
Vision Problems
24 (24.7)
323 (9.9)
0.001C
Cancer (Non-skin)
20 (20.6)
314 (9.6)
0.001c
Heart Failure
19(19.6)
189 (5.8)
0.001C
Coronary Disease
32 (33.0)
384(11.6)
0.001C
Angina
29 (29.9)
280 (8.6)
0.001C
Myocardial Infarction
10(10.3)
148 (4.5)
0.008b
Sciatica
32 (33.0)
666 (20.4)
0.003 b
Deafness
35 (36.1)
708 (21.7)
0.001C
Diabetes
34 (35.1)
445 (13.6)
0.00 ic
High Blood Pressure
58 (59.8)
1202 (36.8)
0.001C
Memory Problems
20 (20.6)
274 (8.4)
0.001C
Stroke
19(19.6)
191 (5.9)
0.001C
Much Worse Health Status
2 (2.1)
27 (0.8)
0.209d
Smoker
7 (7.5)
225 (7.1)
0.882
Use of six or more OTCs
1 (1.1)
14 (0.4)
0.357d
Warfarin use
7 (7.2)
110 (3.4)
0.041a
Theophylline use
3 (3.1)
94 (2.9)
0.758d
Cimetidine use
8 (8.3)
149 (4.6)
0.090
Phenytoin use
0 (0.0)
12 (0.4)
1.000d
Lives Alone
9 (9.5)
328 (10.3)
0.796
Three or more hospitalizations in
previous year
17(18.1)
120 (3.7)
0.001C
Three or more ER visits in previous year
4 (4.3)
26 (0.8)
0.001 c,d
Five or more MD clinic visits in previous
year
68 (71.6)
1413 (43.8)
0.001C
Nursing home residence
2 (2.1)
28 (0.9)
0.209d
Use of durable medical equipment
22 (23.2)
219 (6.8)
0.001C
Use of home health care services
6 (6.3)
49 (1.5)
0.001C
a Significance difference in variable between patients with, and without, PDRM, p < 0.05
b Significance difference in variable between patients with, and without, PDRM, p < 0.01
c Significance difference in variable between patients with, and without, PDRM, p < 0.001
d Probability value for Fisher's Exact Test

98
Table 5.19 Logistic Regression Model Including Additional Demographic Variables
Variable
Parameter
Estimate (b)
Standard
Error (SE)
Chi Square
Probability
Odds
Ratio
95 Percent
Confidence
Interval
Four or more
prescribers
0.2387
0.0590
0.0001
1.270
1.131-1.425
Four or more
recorded
diagnoses
0.8428
0.2621
0.0013
2.323
1.390-3.883
Antihypertensive
drug use
0.8271
0.2746
0.0026
2.287
1.335-3.917
Female gender
-0.5105
0.2276
0.0055
0.600
0.384-0.937
Use of durable
medical equipment
0.8021
0.2887
0.0249
2.230
1.266-3.927
Angina
0.6230
0.2812
0.0267
1.865
1.074-3.235
Three or more
hospitalizations in
the previous year
0.7029
0.3325
0.0345
2.020
1.053-3.875
Equation Constant
-4.9206
0.2739
0.0001
-
-

99
findings (chi square = 174.511 with 7 df, p=0.0001). Therefore, overall, the fit of the model is
good as evidenced by these tests.
The amount of variance explained by the prediction model is slightly greater then for the
regression model with the only the 18 hypothesized risk factors. For this prediction model, the
RL2 is 0.581.
This regression model also seems to be free of multicollinearity. Table 5.20 shows the
correlation matrix for the final variables in the model. Overall, the correlations between the
variables are quite low, with the highest one being between four or more recorded diagnoses and
angina (0.42594). A backward elimination procedure was also performed, which yielded
identical results.
Although this last model did explain slightly more variance (Rl2 = 0.581) than the
original model (Rl2 = 0.562), the increased complexity of the new model and the elimination of
one of the original risk factors from the first model {six or more prescription medications) were
cause for further analysis. Therefore, an attempt was made to determine the relationship between
six or more prescription medications and the new risk factors since six or more prescription
medications dropped from this last model.
Three regression models were performed with the five risk factors from the first model
and each of the three new risk factors from the last model. A simultaneous regression approach,
rather than forward inclusion or backward elimination techniques, were used as the intent was to
keep all six variables in the final model. Six or more prescription medications was not a
statistically significant predictor of PDRM in any of the three models. The correlation between
six or more prescription medications and the three new risk factors was relatively small {six or
more prescription medications and use of durable medical equipment = 0.2562, six or more
prescription medications and angina =0.0842, and six or more prescription medications and
three or more hospitalizations in the previous year = 0.1002). The parameter estimate for six or
more prescription medications remained stable in all three models. The odds ratio for six or
more prescription medications was also relatively stable in all three models and it was nearly as

100
Table 5.20 Correlation Matrix for Significant Variables in Regression Model with Additional
Variables
Constant
Female
gender
Four or
more
record¬
ed
diagno¬
ses
Four or
more
pres-
cribers
Anti¬
hyper¬
tensive
drug use
Ang¬
ina
Three or
more
hospit¬
alizat¬
ions in
the
previous
year
Use of
dur¬
able
med¬
ical
equip¬
ment
Constant
1.00
Female
gender
-0.3591
1.00
Four or
more
recorded
diagnoses
-0.2268
0.0202
1.00
Four or
more
preserv¬
ers
-0.3330
-0.0056
0.0597
1.00
Antihyper¬
tensive
drug use
-0.4946
0.0146
0.0767
0.2993
1.00
Angina
-0.0367
-0.0028
0.4259
0.0043
0.0624
1.00
Three or
more
hospital¬
izations in
the
previous
year
-0.0255
0.0052
0.0984
0.1336
0.0301
0.041
9
1.00
Use of
durable
medical
equipment
-0.0564
-0.0130
0.2252
0.0528
0.0274
0.113
1
0.1702
1.00

101
large as it was in the original model (OR = 1.463, 1.594, and 1.640 in these three models, and
OR=1.920 in the original model).
Finally, to further clarify the relationship of six or more prescription medications and
these three new risk factors, a factor analysis (orthogonal rotation) of principal components was
performed with the five original risk factors and the three new risk factors. Table 5.21 contains
the results of the factor analysis. Four factors were identified. Several things were considered
when coming to this conclusion. There were three factors with eigenvalues greater than one,
although the eigenvalue for factor four was very close to one (0.9899). Cattell's Scree Plot
showed a large separation between factors 1 and 2, and a smaller separation between factors 4
and 5.
All three new risk factors were contained in a factor that was already represented by one
of the five original risk factors. Angina was contained in factor 1, as was four or more recorded
diagnoses. Use of durable medical equipment and three or more hospitalizations in the previous
year were contained in factor 2, as was six or more prescription medications. Therefore, the
three new risk factors did not represent any additional factors than what was already represented
by the original five variables.
Based on these results, it was decided that the original regression model with the five
risk factors was the optimal model for predicting PDRM. Although this model explained slightly
less of the variance of PDRM than the second regression model with the seven risk factors, the
original model is less complex and it contains variables that were hypothesized to be risk factors
on either empirical findings or the conceptual framework used in this study.
Additional Analyses Related to Risk Factor Identification
Two additional analyses related to risk factor identification were performed. First,
because it could be argued that one of the significant risk factors in the original model
(
102
Table 5.21 Rotated (Varimax) Factor Matrix of Five Original Risk Factors and Three New Risk
Factors
Variable
Factor 1
Factor 2
Factor 3
Factor 4
Angina
0.87
Four or more recorded
diagnoses
0.78
Use of durable medical
equipment
0.83
Six or more prescription
medications
0.57
Three or more
hospitalizations in the
previous year
0.52
Four or more prescribers
0.79
Antihypertensive drug use
0.73
Female gender
0.99
Eigenvalue
2.0059
1.0645
1.0155
0.9899
Only factor scores greater than +/- 0.40 are shown.

103
PDRM, an additional regression analysis was done, removing patients who had a PDRM with an
antihypertensive drug specified in the operational definition from the analysis. This was
performed to see if the model remained stable with the same risk factors, or if new risk factors
would appear. Twenty-five patients were found to have a PDRM that contained an
antihypertensive drug specified in the operational definition (representing PDRM #6, 15, 18, 37,
38, 42, 49, and 50). However, 20 of these 25 patients also experienced at least one other PDRM
that did not contain an antihypertensive drug in the operational definition. Therefore, only five
patients were removed from the analysis.
The results of this analysis can be seen in Table 5.22. The regression analysis included
all 18 hypothesized risk factors. The resulting model contained all five risk factors from the
original model: four or more prescribers, four or more recorded diagnoses, female gender,
antihypertensive drug use, and six or more prescription medications. No new risk factors
appeared in this new model. Therefore, because of the stability of the model, it was felt that the
full model that includes antihypertensive drug use was acceptable.
A second additional analysis was performed to help delineate the relationship between
antihypertensive drug use and four or more prescribers. This is because both of these risk factors
fell into the same factor in the original factor analysis. Two regression models were performed
taking either antihypertensive drug use or four or more prescribers out of the model and
observing any change in the parameter estimate or odds ratio. When antihypertensive drug use
was removed from the model, the parameter estimate and odds ratio for four or more prescribers
remained fairly constant. In the original model, the parameter estimate was 0.2683 and the odds
ratio was 1.308, while in this new model the parameter estimate was 0.3176 and the odds ratio
was 1.374. When four or more prescribers was removed from the model, the parameter estimate
and odds ratio for antihypertensive drug use did increase. In the original model, the parameter
estimate was 0.7023 and the odds ratio was 2.018, while in this new model the parameter
estimate was 1.0766 and the odds ratio was 2.935. Therefore, although the parameter estimate
and odds ratio for antihypertensive drug use did increase, it did not account for the full effect of

104
Table 5.22 Logistic Regression Model Excluding Antihypertensive Drug Use
Variable
Parameter
Estimate (b)
Standard
Error (SE)
Chi Square
Probability
Odds
Ratio
95 Percent
Confidence
Interval
Four or more
prescribers
0.2753
0.0612
0.0001
1.317
1.168-1.485
Four or more
recorded
diagnoses
1.0184
0.2546
0.0001
2.769
1.681-4.560
Female gender
-0.6824
0.2463
0.0056
0.505
0.312-0.819
Antihypertensive
drug use
0.6183
0.2824
0.0286
1.856
1.067-3.228
Six or more
prescription
medications
0.6465
0.3050
0.0340
1.909
1.050-3.471
Equation Constant
-4.6792
0.2772
0.0001
-
-

105
four or more prescribers on PDRM. Thus, because of this, and the fact that these two variables
are not highly correlated (0.2995), it was decided to include both variables in the final risk
model. Overall, then, the original logistic regression model, which yielded five risk factors: four
or more prescribers, four or more recorded diagnoses, female gender, antihypertensive drug use,
and six or more prescription medications appears to be the most appropriate model for predicting
PDRM.
Risk Stratification System
A risk stratification system was developed based on the five risk factors for PDRM.
Patients were grouped into categories, based on the number of risk factors they had from the
original regression model. Three categories were used: Stratum I, 0-1 risk factors; Stratum II, 2-3
risk factors; and Stratum III, 4-5 risk factors. The results of this risk stratification system can be
seen in Figure 5.1. The Mantel-Haenszel Test was significant (chi square in 2 x 3 table = 63.81,
p<0.0001), indicating that a definite trend was evident in the data; those patients who had more
risk factors were associated with a greater risk of PDRM.
Testing the Hypotheses
Three sets of hypotheses were proposed and tested. The first set of hypotheses deals with
the identification of risk factors for PDRM. The second hypothesis deals with general and drug
(and disease) specific risk factors for PDRM. The third and final hypothesis deals with the
relationship of PDRM to healthcare resource utilization. These three sets of hypotheses will be
discussed in turn.
First Set of Hypotheses
The first set of hypotheses deals with the identification of major risk factors for PDRM
in older persons.

106
| â–¡ Rate of PDRM (%)
35
30 .
25 .
20 .
15.
10.
5.
0 .
7.8
1.8
l l
I
I II
PDRM pts 51 42
Total pts 2811 540
Risk Stratum
1 = 0 - 1 risk factors
2 = 2-3 risk factors
3=4-5 risk factors
Figure 5.1 Risk Stratification System

107
Hypothesis HI A
This hypothesis stated: "Digoxin use will be a risk factor for PDRM in older persons."
While digoxin use was significantly associated with PDRM in the bivariate analysis (p value =
0.022), it was not identified as a risk factor in the final prediction model for PDRM. Hypothesis
H1A is rejected.
Hypothesis H1B
This hypothesis stated: "Antidepressant drug use will be a risk factor for PDRM in older
persons." While antidepressant drug use was significantly associated with PDRM in the
bivariate analysis (p value = 0.002), it was not identified as a risk factor in the final prediction
model for PDRM. Hypothesis H1B is rejected.
Hypothesis H1C
This hypothesis stated: "Long-acting benzodiazepine use will be a risk factor for PDRM
in older persons." Long-acting benzodiazepine use was not identified as a risk factor in the final
prediction model for PDRM. Hypothesis H1C is rejected.
Hypothesis HID
This hypothesis stated: "Antihypertensive drug use will be a risk factor for PDRM in
older persons." Antihypertensive drug use was identified as a risk factor in the final prediction
model for PDRM. Hypothesis HID is supported.
Hypothesis HIE
This hypothesis stated: "Gastrointestinal disorders will be a risk factor for PDRM in
older persons." While gastrointestinal disorders was significantly associated with PDRM in the
bivariate analysis (p value = 0.001), it was not identified as a risk factor in the final prediction
model for PDRM. Hypothesis HIE is rejected.

108
Hypothesis H1F
This hypothesis stated: "Lung conditions (lung disease, emphysema, bronchitis and
asthma) will be a risk factor for PDRM in older persons." While lung conditions was
significantly associated with PDRM in the bivariate analysis (p value = 0.019), it was not
identified as a risk factor in the final prediction model for PDRM. Hypothesis H1F is rejected.
Hypothesis H1G
This hypothesis stated: "Kidney disease will be a risk factor for PDRM in older persons."
While kidney disease was significantly associated with PDRM in the bivariate analysis (p value
= 0.001), it was not identified as a risk factor in the final prediction model for PDRM.
Hypothesis H1G is rejected.
Hypothesis HIH
This hypothesis stated: "A history of falling will be a risk factor for PDRM in older
persons." A history of falling was not identified as a risk factor in the final prediction model for
PDRM. Hypothesis H1H is rejected.
Hypothesis Hll
This hypothesis stated: "Four or more prescribers will be a risk factor for PDRM in
older persons." Four or more prescribers was identified as a risk factor in the final prediction
model for PDRM. Hypothesis Hll is supported.
Hypothesis HI J
This hypothesis stated: "Six or more prescription medications in a drug regimen will be a
risk factor for PDRM in older persons." Six or more prescription medications was identified as a
risk factor in the final prediction model for PDRM. Hypothesis H1J is supported.

109
Hypothesis H1K
This hypothesis stated: "Four or more recorded diagnoses will be a risk factor for
PDRM in older persons." Four or more recorded diagnoses was identified as a risk factor in the
final prediction model for PDRM. Hypothesis H1K is supported.
Hypothesis H1L
This hypothesis stated: "A previous adverse drug reaction will be a risk factor for PDRM
in older persons." A previous adverse drug reaction was not identified as a risk factor in the final
prediction model for PDRM. Hypothesis H1L is rejected.
Hypothesis HIM
This hypothesis stated: "High alcohol consumption will be a risk factor for PDRM in
older persons." High alcohol consumption was not identified as a risk factor in the final
prediction model for PDRM. Hypothesis HIM is rejected.
Hypothesis H1N
This hypothesis stated: "Self-assessment of poor health status will be a risk factor for
PDRM in older persons." While self-assessment of poor health status was significantly
associated with PDRM in the bivariate analysis (p value = 0.001), it was not identified as a risk
factor in the final prediction model for PDRM. Hypothesis H1N is rejected.
Hypothesis H1Q
This hypothesis stated: "Trouble paying for medications will be a risk factor for PDRM
in older persons." Trouble paying for medications was not identified as a risk factor in the final
prediction model for PDRM. Hypothesis HIO is rejected.

110
Hypothesis HIP
This hypothesis stated: "Difficulty taking medications will be a risk factor for PDRM in
older persons." Difficulty taking medications was not identified as a risk factor in the final
prediction model for PDRM. Hypothesis HIP is rejected.
Hypothesis HIO
This hypothesis stated: “Patient belief that they are taking too many medications will be
a risk factor for PDRM in older persons." Patient belief that they are taking too many
medications was not identified as a risk factor in the final prediction model for PDRM.
Hypothesis H1Q is rejected.
Hypothesis H1R
This hypothesis stated: "Female gender will be a risk factor for PDRM in older persons."
Female gender was identified as a risk factor in the final prediction model for PDRM, however,
its odds ratio was less than 1, meaning that females are actually at less risk for a PDRM.
Hypothesis H1R is rejected.
Second Hypothesis
The second hypothesis deals with the identification of general risk factors and disease or
drug-specific risk factors for PDRM in older persons. This hypothesis stated, "there will be
general and specific risk factors for PDRM in older persons."
The logistic regression analysis previously discussed was used as the basis to test this
hypothesis. In the final logistic regression model, one drug-specific risk factor was identified
(iantihypertensive drug use), and four risk factors pertaining to general patient attributes related

Ill
to healthcare were identified: four or more recorded diagnoses, male gender, four or more
presenters, and six or more prescription medications. Hypothesis H2 is supported.
Third Hypothesis
The third hypothesis deals with the relationship of PDRM and healthcare resource
utilization. This hypothesis stated, "Older persons that have PDRM will consume more health
care resources than those who do not have PDRM." To test this hypothesis, each enrollee was
classified as either having PDRM or not. Then, the utilization of health care resources was
compared for the two groups. The results of this bivariate analysis can be seen in Table 5.23.
Patients with PDRM had statistically significantly more admissions to skilled nursing
facilities, use of home health services, hospital admissions, emergency room visits, and use of
one to two OTC medications. Patients with PDRM also had significantly more physician office
visits and use of prescription medications in the higher end of these two categories (three or
more visits, three to five medications, six or more medications). There was not a statistically
significant difference between patients with, and without, PDRM in three areas of healthcare
utilization: nursing home admissions, use of three to five OTC medications, and use of six or
more OTC medications. The trend was in the direction of patients with PDRM for these three
areas, though. Overall, then, patients with PDRM appear to use more healthcare resources then
patients who do not have PDRM. Hypothesis H3 is supported.

112
Table 5.23 Bivariate Analysis of Patients With and Without PDRM, Categorized by Healthcare
Resource Utilization
Healthcare Resource
No. (%) with
PDRM (n=97)
No. (%)
without PDRM
(n=3268)
P value
Admission to a Skilled Nursing Facility
13 (13.4)
14 (0.4)
0.001C
Use of Home Health Services
6 (6.3)
49 (1.5)
0.001C
At Least One Hospital Admission
41 (42.3)
470(14.4)
0.001c
At Least One Emergency Room Visit
40 (43.0)
550(17.1)
0.001c
One to Two MD Office Visits
3 (3.2)
425 (13.2)
*0.001c>d
Three or More MD Office Visits
91 (95.8)
2644 (81.8)
0.001C
Nursing Home Admission
2 (2.1)
28 (0.9)
0.209d
One to Two Prescription Medications
28 (29.8)
1284 (40.0)
*0.047a
Three to Five Prescription Medications
40 (42.6)
969 (30.2)
0.010b
Six or More Prescription Medications
24 (25.5)
235 (7.3)
0.001c
One to Two OTC Medications
59 (62.1)
1490 (46.8)
0.003b
Three to Five OTC Medications
6 (6.3)
163 (5.1)
0.605
Six or More OTC Medications
1 (1.1)
14 (0.4)
0.357d
a Significance difference in variable between patients with, and without, PDRM, p < 0.05
b Significance difference in variable between patients with, and without, PDRM, p < 0.01
c Significance difference in variable between patients with, and without, PDRM, p < 0.001
d Probability value for Fisher's Exact Test
* Significance in opposite direction (Patients without PDRM were greater)
A

CHAPTER 6
DISCUSSION
This chapter will discuss the findings reported in the previous chapter. First, the Delphi
technique and operational definitions of PDRM will be discussed. Second, the prediction models
and risk factors for PDRM will be described in more detail. Third, the limitations, significance
and implications of this research will be explored. Finally, some concluding remarks will be
made.
Use of the Delphi Technique with the Geriatric Medicine Expert Panel
Drug-related morbidity is a widely recognized problem in older persons today. However,
the phrase preventable drug-related morbidity has been far more controversial and less
understood. When the term has been used in the medical literature, typically authors do not
provide explicit definitions of what they mean. Others have different opinions of exactly which
drug-related morbidities are preventable. This study represents the first attempt to develop
explicit, operational definitions of PDRM.
Research question one asked what are the issues in developing operational definitions of
PDRM with the Delphi technique. Several observations can be made about the usefulness of the
Delphi technique with a panel of geriatric medicine experts to reach consensus on operational
definitions of PDRM. This study showed it is possible to promote convergence of opinion on
PDRM. Consensus was reached after only two rounds and opinion did not widely vary between
the two rounds. Goodman (1987) states that it is the stability of the group response to an item
113

114
between the rounds which is important in the Delphi technique. There is some evidence that the
comments written by other panel members in round 1 did influence the opinion of the panel
members. There was only one written comment that indicated the group opinion had moved the
panel member to change his opinion against his will: "I'll go with the group, but this is generally
not the practice." Other than this sole comment, there was no evidence of "groupthink." Other
written comments indicate that some members with strong opinions on certain types of PDRMs
were not willing to change their opinion, e.g.: "Still stick with my original comments despite
being in the minority." Overall, however, the process did promote consensus, which is one of the
chief advantages of the Delphi technique (Delbecq, de Ven and Gustafson, 1986).
Using the Delphi technique also provided insight into the decision-making process that
the panel members used. The comments made by the panel members suggest that they did take
into account all four of the defining characteristics of PDRM. The first defining characteristic of
a PDRM is whether health professionals should recognize significant problems in this pattern of
care. There were not many comments related to this question, as one might expect. The patterns
of care were included in the survey with the belief that there was a problem with them, and the
panel members were asked to make comments only if they did not agree there was a significant
problem in the pattern of care. Still, some comments indicated that the panel members believed
the problem in the pattern of care may not have been that great. For example, one panel member
suggested that failure to give cytoprotective agents (the pattern of care) would be not be
inappropriate: "But cytoprotective agents may not prevent the adverse outcome."
The second question the panel members were requested to consider was whether health
professionals should foresee the possibility of the outcome. Some panel members commented
that the outcome was so infrequent they may not foresee the possibility of it occurring: "Very
uncommon" and "Very rare in healthy patient."

115
The third question the panel members were asked pertained to identifying the cause of
the outcome. This defining characteristic of a PDRM seemed to generate the most comments.
The majority of these comments indicated that for certain clinical scenarios, they were unable to
distinguish the real cause, due to the complexity of the scenario: "depends on other drugs in use,"
"Concomitant hypertension? Concomitant diuretics?," and "Too many other factors usually
involved." Some of the comments stated that the cause-effect relationship, as outlined in the
pattern of care and outcome presented, was not clear enough: “the role of NSAIDs may be
minor," and "Beta blockers not uniformally or clearly significantly associated with depression."
The final question the panelists were requested to consider asked if the cause of the
outcome was controllable. There were a few comments related to this question. These comments
addressed the issue that often the pattern of care which lead to the outcome is necessary (risk
versus benefit) or the outcome may still occur even if the pattern of care were changed: "Still
feel that can occur no matter how diligent the care."
Therefore, in answer to research question one, it appears that the Delphi technique was
useful and successful in obtaining consensus on operational definitions of PDRM.
Two other issues that merit discussion are the characteristics of those clinical scenarios
that were rejected and those that were added by the expert panel. Six clinical scenarios were
rejected by the expert panel. All four defining characteristics of a PDRM seem to have been
influential in the panel's decision to reject these clinical scenarios. For example, one panel
member did not recognize a problem with failure to monitor theophylline because, "theophylline
isn't potent enough to prevent exacerbation [of asthma]." The group consensus on rejecting acute
renal failure due to poor allopurinol monitoring is that they would not foresee the possibility of
the outcome because it is so rare. One panel member rejected digoxin toxicity with inappropriate
digoxin monitoring as a PDRM because that member felt the cause of the toxicity could not be
identifiable - the patient may be on other medications that interact with digoxin. Finally, falls

116
due to use of antiparkinsonian agents appears to have been rejected by the panel because (1) the
cause of the falls could not identifiable (both the drugs and the disease itself cause falls) and, (2)
the panel could not change the pattern of care (people with Parkinson's disease need to be on
these drugs, even though they cause falls, because there are no alternatives).
The additional types of PDRM proposed by the expert panel represent a wide array of
clinical conditions and outcomes. Only three of the proposed types would not have met the initial
inclusion criteria; they represented relatively minor clinical outcomes (one outcome was rebound
congestion and two were acute urinary retention). The initial list of clinical scenarios in older
persons was not intended to be an all-inclusive list of PDRM, so the fact that so many additional
clinical scenarios were suggested by the expert panel (who work in a wide variety of clinical
settings) should not be too surprising.
The creation of these operational definitions of PDRM is an important first step - but it is
just that - a first step. Over time, these definitions will need to be updated and revised as clinical
practice and standards of care progress. As well, this exercise was limited to only PDRM in older
persons. It would also be useful to replicate this study with a different panel of geriatric medicine
experts and to compare the results. Finally, the creation of operational definitions on PDRM in
older persons may help to establish new standards of care in geriatric medicine.
Consensus-Approved Operational Definitions of PDRM Observed in the Study Population
A wide variety of outcomes that matched the consensus-approved operational definitions
of PDRM were identified in the study population. As stated in the results section, 1005 such
outcomes were identified. The outcome that occurred with the greatest frequency was the
outcome for PDRM 16 (congestive heart failure and/or heart block: see Appendix G). However,
this outcome occurred in only 46 patients, or 4.6 percent of all outcomes. There were seven

117
outcomes associated with operational definitions of PDRM that were not found at all in the study
population.
While a wide variety of outcomes were found in the study population, a few specific
operational definitions of PDRM were responsible for a large percentage of total PDRMs found.
The most frequently occurring operational definition of PDRM was found 24 times (15.2 percent
of all PDRMs): secondary myocardial infarction without ASA and/or beta-blocker use. The
second most frequently occurring operational definition of PDRM was found 18 times (11.4
percent of all PDRMs): hospitalization due to hypogylcemia for those patients taking an oral
hypogylcemic without regular hemoglobin Ale monitoring. Overall, the top five operational
definitions of PDRM were responsible for almost half of all PDRMs found (46.8 percent). There
were 23 consensus-approved operational definitions of PDRMs that did not occur even once in
the study population. It appears that just as a small proportion of patients and diseases are
responsible for a large proportion of healthcare costs, a small proportion of PDRMs are
responsible for most PDRMs.
As presented in the last chapter, 11 out of the 52 consensus-approved operational
definitions of PDRM were suggested by the Geriatric Medicine Expert Panel after the first round
of the Delphi technique. These 11 additional operational definitions of PDRM occurred less
frequently than the other 41 types of PDRM. Although the 11 operational definitions accounted
for 21.2 percent of all 52 operational definitions of PDRM, they only accounted for 16 (10.1
percent) out of all 97 PDRMs identified in the database. Only one of these 11 additional
operational definitions of PDRM occurred in more than three patients (acute urinary retention in
patients with a history/diagnosis of benign prostatic hypertropy with use of an anticholingeric
agent). Therefore, while the large number of additional PDRMs proposed and accepted by the
Geriatric Medicine Expert Panel suggests that there are many additional PDRMs in older persons
other than the initial list, the initial list may represent the most frequently occurring operational

118
definitions of PDRM. One of the inclusion criteria for a clinical scenario being on the initial list
was that it occurs commonly in older persons.
A majority (62.9 percent) of the patients who had PDRM experienced only a single
specific operational definition of PDRM. Still, a considerable percentage (37.1 percent) of
patients with PDRM experienced more than one specific operational definition. Almost nineteen
percent (18.6 percent) of patients with PDRM experienced three or more specific operational
definitions of PDRM. One patient experienced five specific operational definitions of PDRM.
Many of these patients who experienced more than one PDRM had PDRMs which
shared the same outcome. For example, one patient had an emergency room visit/hospitalization
due to congestive heart failure (the outcome), but met the pattern of care for three specific
operational definitions of PDRMs related to this outcome (#16, 42, and 43). Overall, 28 (28.9
percent) out of 97 patients with PDRM had multiple operational definitions of PDRM that shared
the same outcome. This finding should not be too surprising. First, the operational definitions of
PDRM were not designed to be mutually exclusive. That is, patients may have two or more
inappropriate patterns of care that led to the same outcome. Several of the operational definitions
of PDRM contain the same outcome. For example, emergency room visit/hospitalization due to a
major or minor hemorrhagic event is the outcome in four specific operational definitions of
PDRM. Second, there is some empirical evidence that patients who experience an adverse drug
event are at higher risk for a second adverse drug event (Hurwitz, 1969; Zilleruelo, Espinoza and
Ruiz, 1987), therefore the same could be true for PDRM. For this reason, a past history of an
adverse drug reaction was included in the list of hypothesized risk factors for PDRM in this
study. Third, some patients may suffer from general medical mismanagement. Their medical
care may be lacking in one or more of the eight necessities of the medication use process
(Grainger-Rousseau et al., 1997). For example, if the patient has multiple prescribers who are not
communicating their therapeutic plans to one another, the patient may be taking a dangerous

119
combination of medications, placing that patient at greater risk for a PDRM. This could be the
cause of the problem for the patient identified with three inappropriate patterns of care related to
congestive heart failure, where all three patterns may have contributed to the outcome.
Validation of Operational Definitions of Preventable Drug-Related Morbidity
This study showed that classification of PDRM, based on operational definitions of
PDRM, is valid as compared to the clinical judgements of a panel of pharmacists. The overall
sensitivity of 87.5 percent and specificity of 73.5 percent for both operational definitions of
PDRM tested actually exceeded the initial target sensitivity and specificity. These results are
encouraging for several reasons. First, the high specificity indicates that most people who do not
have PDRM are correctly classified as such by the operational definitions. Although the
implementation of these definitions in patient care is beyond the scope of this study, if they were
used clinically, the high specificity will prevent a lot of unnecessary work. Pharmacists and other
health care professionals can be fairly certain that patients identified by the definitions as having
PDRM, will be found to have PDRM upon review. Second, the sensitivity is high enough to
correctly identify over 87 percent of patients who actually have PDRM. This finding suggests
that more of the study population has PDRM than the 2.9 percent identified by the operational
definitions. This would be more consistent with other estimates of the incidence of PDRM
(Lazarou et al., 1998). This finding, along with the fact that there may be other operational
definitions of PDRM approved by other panels, further emphasizes that this 2.9 percent is really
a lower-bound estimate of the incidence of PDRM. Further investigations may focus on refining
the definitions in an attempt to identify more patients with PDRM.
As was mentioned in the results section, the chart abstracts for the patients with the
hyperglycemia outcome had to be re-administered to the Patient Chart Abstract Panel. This is

120
because the pharmacists did not appear to follow the specific operational definition, but instead
looked for other cases of PDRM related to the outcome (hyperglycemia) in these patients. This
conclusion was made based on verbal and written comments by the panel members.
There are two factors that might help to explain why the pharmacists deviated from the
operational definition in the first validation exercise for the hyperglycemia outcome. First,
several patients who did not meet this operational definition for a PDRM (outcome =
hypergylcemia with pattern of care = patient on an oral hypoglycemic with no regular HgAlc
monitoring) did meet the operational definition for another PDRM with the same outcome. This
overlapping definition of PDRM is outcome = hyperglycemia or hypogylcemia with pattern of
care = use of insulin and no regular HgAlc monitoring (see #51 in Appendix G). Therefore, it
was very possible that the pharmacists felt that although the patient did not meet the first
operational definition of PDRM, since the patient was also on insulin, and it was related to the
same outcome (hypergylcemia), they classified the patient as having PDRM. In future
investigations, the operational definitions of PDRM that do not overlap in the "outcome" with
other operational definitions should be chosen when testing sensitivity and specificity (such as
the PDRM related to secondary myocardial infarction). A second factor to explain the
differences between the pharmacist and operational definition classification could be the
pharmacists' clinical judgement. Many of the patients classified as having PDRM by the
pharmacists were not on insulin or an oral hypoglycemic. Hemoglobin Ale monitoring is not
typically done on diabetes with mild disease, which these patients probably had. Still, most panel
members felt they should have regular HgAlc monitoring. This could mean either the
pharmacists did not completely understand the questions related to PDRM or they were lacking
in their medical knowledge. The pharmacists on this panel were not chosen for their expertise
in diabetes so a possible future investigation could involve repeating this exercise with experts in
the care of diabetics (diabetic educators, etc.). Upon re-administration of the chart abstracts,

121
however, pharmacist agreement with the operational definition was very high so this second
factor seems unlikely.
Many of the discrepancies between the pharmacists' and operational definition
classification of patients with the secondary myocardial infarction outcome seem to be related to
details in patient care that the operational definition does not address. The pharmacists listed
many reasons why they felt patients should not be on either ASA or a beta-blocker in specific
situations. For ASA, reasons the pharmacists felt the patient should not be on it (and therefore it
would not be a case of PDRM) include: the patient was on warfarin (drug interaction), aspirin
allergy, documented nose bleeds with high-dose ASA, history of gastrointestinal bleeding,
patient was on ibuprofen and naproxen (drug interaction), and patient had peptic ulcer disease.
The panel members also listed several reasons why the risk of being on a beta-blocker would
outweigh benefits of preventing a secondary myocardial infarction: patient had second degree
heart block, sinus bradycardia, right bundle branch block, hyperlipidemia, congestive heart
failure, peripheral vascular disease, and patient was taking diltiazem (combined negative intropic
effects with a beta-blocker). Several pharmacists noted that although the patient was on a beta-
blocker, the dosage was too low and therefore even though the operational definition did not
classify it as a PDRM, they felt it was a PDRM. One pharmacist noted that the patient's previous
myocardial infarction was over five years ago, thus that pharmacist felt the patient did not need
to be on preventive medicine (beta-blocker or ASA).
Risk Factors for PDRM
Phase II of this study involved identifying risk factors for PDRM in older persons. Five
risk factors for PDRM were identified in the final prediction model. Each of these risk factors
will be discussed in turn.

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Risk Factor 1: Four or More Recorded Diagnoses
Four or more recorded diagnoses was one of the 18 hypothesized risk factors that was a
significant risk factor for PDRM in the final prediction model. As was discussed in chapter three,
this variable was included for empirical reasons (Carbonin et al., 1991; O'Neil and Poirer, 1998)
and because patients with multiple diseases often receive care from multiple physicians, who
may have differing or conflicting therapeutic plans due to poor communication, which may
result in PDRM.
Risk Factor 2: Antihvnertensive Drus Use
Antihypertensive drug use was also one of the 18 hypothesized risk factors that was a
significant risk factor for PDRM in the final prediction model. Antihypertensive drug use was
included for empirical (Larson et al., 1987; Williamson and Chopin, 1980) and theoretical
reasons. It was felt that because of the numerous side effects, drug interactions, and
contraindications with the use of antihypertensive medications, proper therapeutic monitoring
(one of the eight essential elements of drug use) is very important.
It could be argued that antihypertensive drug use is a risk factor because it is contained
in the some of the operational definitions of PDRM. This does not appear to be the case, as when
patients who experienced a PDRM with an antihypertensive drug in the operational definition
were removed from the analysis, antihypertensive drug use remained in the prediction model.
While some of the PDRMs did include antihypertensive drugs in the operational definition, the
inappropriate pattern of care for the top kind of PDRM identified (secondary myocardial
infarction with no ASA and/or beta-blocker use) actually specifies that beta-blockers (an
antihypertensive) were not used.

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Risk Factor 3: Male Gender
Female gender was one of the 18 hypothesized risk factors for PDRM. However, while
female gender was identified in the final prediction model, the odds ratio was less than one,
meaning that male gender was the actual risk factor. This result is opposite the hypothesized
relationship, but it may not be too surprising. It was acknowledged in chapter three that the
empirical evidence offemale gender being a risk factor was mixed, and, in fact, some authors
argue that by excluding pregnancy and female-specific drugs, females are not at a higher risk
(Zadoroznyj and Svarstad, 1990). In this population of older persons, pregnancy is obviously not
a consideration. As well, a review of the final 52 types of PDRM reveals that there are no
female-specific types of PDRM listed, while there are some types of PDRM that are either
exclusive to males (acute urinary retention for patients with benign prostatic hypertropy and the
use of anticholingeric agents), or either occur predominantly in males (secondary myocardial
infaction without the use of ASA and/or a beta-blocker). There is some evidence in the literature
that males adhere less to medical instructions than females, which may also lead to PDRM
(Oldenburg, MacDonald and Perkins, 1988). Finally, it was acknowledged in chapter three that
female gender did not seem to fit within the conceptual framework (medication use system and
biopsychosocial model).
Risk Factor 4: Four or More Prescribers
Four or more prescribers was one of the 18 hypothesized risk factors that was a
significant risk factor for PDRM in the final prediction model. It was included as a possible risk
factor mainly based on theoretical considerations. Theoretically, if a patient has multiple
prescribers, PDRM could develop from competing prescribing objectives, and poor
documentation and communication of information and therapy decisions.

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Risk Factor 5: Six or More Prescription Medications
Six or more prescription medications was also one of the 18 hypothesized risk factors
that was a significant risk factor for PDRM in the final prediction model. It was included as a
possible risk factor mainly based on empirical considerations. As was discussed in chapter three,
there is considerable empirical evidence in the medical literature that the risk of drug-related
morbidity increases with an increase in the number of medications in the drug regimen.
(Hurwitz, 1969; Braverman et al., 1996; Carbonin et al., 1991; Fouts et al., 1997; Larson et al.,
1987).
General Discussion on the Final Prediction Model for PDRM
The final prediction model for PDRM merits discussion. The final model consisted of
five risk factors, four of which were hypothesized to be risk factors and one that had a
relationship opposite to the hypothesized direction (gender). This model demonstrates that a
wide variety of factors influence PDRM, not just drugs themselves or certain diseases. This
supports the idea that the medication use system is influenced by numerous factors. In fact, only
one of the five risk factors is a drug class (antihypertensive drug use). This also supports the use
of the biopsychosocial model, which says health outcomes are not just related to a patient's
physiological status (Engel, 1977). The final prediction model contains risk factors that include
things other than just drugs and measures of health status.
There may be some surprise that so few of the initial 18 hypothesized risk factors ended
up in the final prediction model. Although there was both empirical and theoretical evidence for
making hypotheses about all 18 risk factors, few studies have included all these variables (and
the 27 additional variables) in one prediction model. In the past, most authors have considered
only a few variables in each study. Therefore, the amount of variance explained for PDRM in the
literature would exceed 100 percent if each risk factor explained independent proportions of
variance. It was clear that not all of the 18 hypothesized risk factors would end up in the final

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model. Future investigations may focus on still more possible risk factors for PDRM that were
not included in this investigation.
Although the factor analysis did not yield factors that related to definite constructs, and
therefore an analysis incorporating factor scores was not used, the factor analysis still yielded
some noteworthy results. First, some of the factors do appear to relate to constructs within the
medication use system and biopsychosocial model. Factor 1, which contained kidney disease,
gastrointestinal disorders, four or more recorded diagnoses, and lung conditions, may to relate
to the patient's disease. Factor 2, which contained four or more preservers, antihypertensive
drug use, and antidepressant drug use, may relate to long-term prescriptions, although this was
not clear. Factor 3, which contained six or more prescription medications, digoxin use, and self-
assessment of poor health status, was difficult to relate to one particular construct. Some
possibilities are illness, frailty, poor monitoring, or drug interactions. Factor 4, which contained
a history of falling, difficulty taking drugs, and long-acting benzodiazepine use, may relate to
cognitive difficulties/impairment. Factor 5, which contained a patient belief that they are taking
too many medications, trouble paying for medications, and a previous adverse drug reaction,
may relate to the patient's attitude toward medications. Finally, factor 6, which contained high
alcohol consumption and female gender, may relate to female gender.
The factor analysis also failed to show an exact relationship to the proposed underlying
structure for this study (see Table 4.3). Although some possible similarities may be observed
between the factors identified and the proposed structure, overall there were many differences.
This result should not be too surprising. The proposed structure contained constructs whose
relationships have been relatively untested to date. The factor analysis performed in this study
may help guide future research in determining the nature of these relationships.
In the factor analysis, two of the final five risk factors were contained in factor 2
(iantihypertensive drug use and four or more preservers). Both factors were left in the final
prediction model based on the their relatively low correlation, stability of their odds ratios and
parameter estimates when one of them was removed from the regression models (see previous

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chapter), and the inability to definitely assign a construct to factor 2. The risk stratification
system developed in this study treated them as separate risks, with the acknowledgement that
further research may prove that they really do represent the same construct.
The application of the prediction model for PDRM is beyond the scope of this study, but
some general principles may still be stated. As was discussed in chapter two, the risk factors in
this study are those variables that are statistically associated with PDRM (the outcome event) in
older persons. They represent patient characteristics, mainly healthcare demographics. Some of
the patient characteristics, or risk factors, can not be easily changed, such as gender, while others
can be modified (e.g.; by reducing the number of prescribers for a given patient). Using the risk
stratification system developed in this study, patients with multiple risk factors for PDRM could
be identified by health plans or individual physicians and then proactively managed to help
prevent PDRM and allocate resources in the most efficient manner. The types of patient
management will be varied, but regardless, the interventions should be based on how the risk
factors relate to the key constructs in the medication use system and the biopsychosocial model.
For example, we know that physician-pharmacist-patient communication is a necessity of the
drug use system, and therefore if a patient has four or more prescribers, attempts should be made
to improve communication by reducing the number of prescribers or better coordination of
therapy. Other researchers in the future may investigate whether the risk factors identified are
truly causal or predictive and they may attempt to see how these risk factors relate to other
healthcare models and theories. For example, as discussed, further research may be conducted to
determine the causal relations that may underlie the identification of antihypertensive drug use
as a risk factor.
General and Specific Risk Factors
Both the specific and general risk factors are a function of the operational definitions
used to identify patients with PDRM. As was previously discussed, antihypertensive drug use is

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contained within some of the operational definitions of PDRM. The general risk factors
identified are only general in the sense that they are risk factors for the 52 types of PDRM
approved by the expert panel. However, there can be some confidence that these represent the
main types of PDRM since (1) round 1 of the survey contained PDRMs believed to be common
and serious, (2) the panel members had the opportunity to add additional PDRMs, and (3) a small
percentage of the 52 operational definitions of PDRMs were responsible for the majority of the
PDRMs identified in the study population. Therefore, the general risk factors identified may be
general risk factors for PDRM in older persons, not just for these 52 operational definitions. Still,
further research will be needed to determine whether these general risk factors remain the same
in other patient populations (pediatrics, younger adults, etc.) and settings (other than Medicare
managed care plans).
PDRM and Healthcare Resource Utilization
As was hypothesized, patients with PDRM use significantly more healthcare resources
than those patients without PDRM. This relationship does not imply causality, however. For
example, while patients with PDRM had more hospitalizations and emergency room visits,
perhaps being in those settings places people at greater risk for PDRM. However, the idea that
PDRM increases resource utilization is supported by the literature reviewed in chapters one and
three. There have been many estimates of the additional costs incurred to the healthcare system
by improper drug therapy management and drug-related morbidity and mortality (Johnson and
Bootman, 1995; Bates etal., 1997).
Potential Limitations
Some potential limitations of this study relate to the creation and validation of the
operational definitions of PDRM. Although the method for selecting PDRMs has been described,

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perhaps another panel would select different operational definitions of PDRM. As well, this
study defined only a subset of PDRM. The operational definitions of PDRM that were
developed will require testing in other settings and populations. As previously mentioned, a
potential limitation of this study is the lack of an accepted "gold standard" to determine what is,
and is not, PDRM. In this study, the Chart Abstract Reviewer Panel of five pharmacists was
used. Although their judgements were influenced by the presentation of the patient in the chart
abstract and their own biases, it was felt that this was the best validity check available for the
operational definitions of PDRM.
Some potential limitations pertain to the identification of risk factors for PDRM. Only
risk factors associated with PDRM in older persons were considered. Risk factors may differ for
other populations and it may differ for PDRMs in older persons that were not investigated in this
study. Patterns related to definite constructs for risk factor groupings could not be recognized
from the factor analysis. While the list of possible risk factors considered in this study was more
thorough than any other study in the peer-reviewed medical literature, there could potentially be
additional risk factors that may contribute to the regression models. Some potential risk factors
for PDRM, such as an abnormal potassium level, drug interactions, and a patient belief that the
drugs were responsible for hospitalization, have been previously shown to be risk factors for
adverse drug events, but could not be tested in this study due to limitations of the study database.
For example, although the study database listed all laboratory tests performed on any given
patient, it did not contain the actual value of the test.
Finally, there are some potential limitations with the study population used in this study.
The study population was limited to those individuals enrolled in the Florida Hospital Healthcare
System Premier Plan and who completed the PWP Senior Assessment tool. Since enrollment in
the plan was optional, the study population may not be totally representative of the older
population in general. As well, those older persons who elected to complete the PWPs may be

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different from those who did not complete the instrument. However, a previous study of
Medicare beneficiaries concluded that older persons who participate in screening services (such
as completing a risk assessment questionnaire) did not differ significantly in their health
behaviors from older persons not participating in the preventive services (Schweitzer et al.,
1994). A study that included 217 noninstitutionalized older persons in Sweden also concluded
that those older persons who do, and do not, participate in health promotion activities do not
differ in health status (Augustsson et al., 1994). Older persons enrolled in a managed care
Medicare-risk health plan may differ demographically and may have different health care
resource utilization and outcomes that those older persons not in these plans. One study that
compared older persons with joint or chest pain in traditional Medicare and Medicare-risk health
plans found little significant difference, although the patients in the managed care environment
had reduced utilization of services and poorer improvement of symptoms in one of four
outcomes considered (Clement et al., 1994). In contrast, another study which compared ten
HMOs with Medicare risk contracts to ten traditional fee-for-service plans found that enrollment
status was not significantly associated with functional status or medical visits (Retchin et al.,
1992).
Significance
Healthcare administrators who are interested in reducing healthcare costs should be
particularly interested in the relationship between healthcare resource utilization and PDRM, and
the risk stratification system developed in this study. The prediction model and risk stratification
system developed may allow identification of high-risk patients and potential high utilizers of
valuable health care resources, thus contributing to an efficient allocation of scarce “preventive”
resources. This would concur with Anderson and Knickman’s (1984) argument that focused

130
attention must be placed on older persons who are high utilizers of medical services. McCall and
Wai (1983) also argue for the study of patterns of utilization in older persons and add that
strategies targeted to high cost enrollees have the potential for a strong impact on resource
utilization. Boult et al. (1995) claim that the first step of any geriatric evaluation and
management program is the identification of high risk. Others have argued that risk
identification, and proposing interventions based on those risks, are essential elements to
increase the span of healthy life in older persons (Collier, Kinion and Brodbeck, 1996). Johnson
and Bootman (1995) state that older persons are at particular risk for drug-related problems,
which can lead to PDRM. Plushner and Helling (1996) argue that studies should be performed in
the future to identify risk factors for PDRM in older persons. That was the intent of this study.
Pharmacy managers and administrators may be able to use the results of this study to reallocate
resources in the most effective and efficient way possible.
The identification of the significant risk factors for PDRM should support the
development of a rational basis for planning and implementing interventions to reduce drug-
related morbidity and mortality in older persons. Hopefully, this system will allow the
identification of individuals who have the greatest need of pharmaceutical care. Future studies
may be able to use this knowledge to perform pharmaceutical care interventions that will have
the potential to deliver the greatest good, and test the results of these “targeting” pharmaceutical
care interventions. Pharmacists working in settings other than hospitals and nursing homes will
especially benefit from this study, as PDRM has been inadequately studied in the community
setting (French, 1996). Fincham (1996) argues that meeting the health care needs of older
persons will involve restructuring health care delivery and an essential component of this
restructuring will include preventing adverse drug-related episodes. He adds that pharmacists
must take a more active role in decreasing therapeutic failure and the occurrence of PDRM and
one way of doing this is the proactive avoidance of adverse drug effects (Fincham, 1996).

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Finally, healthcare administrators in captitated Medicare plans may be particularly
interested in the relationship of PDRM to resource utilization and the risk stratification system.
Here, need for adequate identification of high-risk patients is essential. An additional challenge
is the management of pharmacy benefits within these types of plans (Nee and Schwab, 1997). It
is hoped that this project may allow for better management of the pharmacy benefit and the total
health care benefit in general. The ability to target certain individuals at risk for PDRM may be a
valuable tool for health care clinicians and administrators.
Overall, then, if PDRM in older persons is to be decreased, operational definitions of
PDRM are needed and those older persons who are at high risk must be identified prospectively.
It is hoped by addressing these important issues, this study will add significantly to the medical
literature and to medical practice.
Contribution to the Profession of Pharmacy
While many authors have agreed that PDRM is an important problem and the resolution
of this problem fits into the mandate of the profession of pharmacy, there is a lack of agreement
as how to best approach this issue. Hopefully this study will give pharmacists the tools they need
to identify which individuals should receive additional monitoring.
Contribution to Healthcare
Numerous healthcare organizations have argued for more research in the area of geriatric
medicine and, in particular, for PDRM. One of the areas recommended for more research by the
Institute of Medicine's Committee to Develop an Agenda for Health Outcomes Research for
Elderly People was how different treatments, including drugs, affect older individuals' health
outcomes (Feasley, 1996). Identifying and preventing drug-related morbidity in older persons is

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one goal of Healthy People 2000 (U.S. Department of Health and Human Services, 1991).
Finally, “When Medicine Hurts Instead of Helps”, a recent report by the Alliance for Aging
Research, recommended that more research be done to determine which older persons are at risk
for medication-related problems (Alliance for Aging Research, 1998).
This study will hopefully help these organizations and healthcare professionals through
the creation of operational definitions of PDRM and potential indicators for PDRM.
Theoretical Contribution
This study has created operational definitions of PDRM in older persons. This is
currently lacking in healthcare. There is no consensus about exactly which drug-related problems
would have the characteristics of being recognizable, foreseeable, identifiable, and controllable,
and few authors have explicitly described the standard that they used in judging events as
preventable. Ultimately, judgements of preventability may depend on the development of
consensus standards for medication use. By developing explicit operational definitions and
testing their association to other elements of care, this research project will promote further
research and advance the development of improved standards. Such definitions will not only help
to distinguish preventable from nonpreventable drug-related morbidity and mortality, but should
foster more research in this area by providing others with readily available definitions to use in
other research studies. This research could be in the form of program evaluation or testing
“targeting’ pharmaceutical care interventions.

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Conclusions
This study, risk assessment of preventable drug related morbidity in older persons, had
three initial research objectives. The first research objective was to create operational definitions
of preventable drug-related morbidity (PDRM) in older persons. This research objective was
accomplished through using the Delphi technique with a geriatric medicine expert panel to create
52 operational definitions of PDRM in older persons. These operational definitions were then
validated through the use of a chart abstract reviewer panel. This is the contribution to
methodology of this research, since such a consensus on the operationalization of PDRM was
previously lacking in the medical literature.
The second research objective was to identify patients who are at particular risk of
PDRM and who may therefore benefit from comprehensive pharmaceutical care. Here, a
prediction model was created to identify risk factors for PDRM. Eighteen initial hypothesized
variables were proposed based on empirical findings and the medication use system and
biopsychosocial model. The dependent variable was the existence of PDRM (as defined) and the
independent variables were the hypothesized risk factors. Factor analysis and logistic regression
models were used to identify risk factors for PDRM. Five risk factors for PDRM were identified
in the final prediction model: four or more prescribers,four or more recorded diagnoses,
antihypertensive drug use, male gender, and six or more prescription medications.
The third research objective was to create a risk stratification system for PDRM. A risk
stratification system of three categories was developed for PDRM based on the number of risk
factors present in an individual patient. This system will hopefully aid health care professionals
to determine the risk of PDRM in an individual patient, based on how many of the five risk
factors are present in that patient.

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While it was not a primary research objective, patients with PDRM were shown to use
significantly more healthcare resources then patients who did not experience a PDRM in many
key areas.
The outcome of this research project will ultimately be measured by two things. First,
the results of this study will either be confirmed or rejected by subsequent studies in the future.
Other studies will need to be conducted to answer some of the new questions raised by this
study. This includes determining what are the risk factors for PDRM in other settings and patient
populations. Second, the outcome of this study will also be determined by the incorporation of
the study results into actual patient care. It is intended that the prediction model and risk
stratification system developed in this study will help pharmacists and other healthcare
professionals by providing them a tool for the identification of older persons who may be at
particular risk of PDRM. For example, knowing that patients taking antihypertensives are at a
two-fold increased risk of developing a PDRM will be valuable information for physicians,
pharmacists, and other health care professionals. It is hoped that implementation of the
prediction model created in this study will help prevent patients from developing PDRM in the
future.

APPENDIX A
GERIATRIC MEDICINE EXPERT PANEL MEMBERS
Expert Panel Member
Title
Additional qualifications in
geriatric medicine
Lee Adler, DO
Medical Director, FHHS
John Fleming, MD
Assistant Director of Internal
Medicine Florida Hospital
Family Practice Residency
Board certified in Geriatrics
Paul Garrett, MD
Senior Medical Director, Florida
Hospital Healthcare System
Manoucher Manoucheri, MD
Associate Director of Internal
Medicine Florida Hospital
Family Practice Residency
Gary Miller, MD
Medical Director of several
long-term care facilities
Board certified in family
practice & geriatrics
Hang Nguyen, Pharm.D.
Clinical pharmacist,
Premier Care Plan
David Pocock, MD, MBBS
Internal Medicine Physician
Board certified in geriatrics
135

APPENDIX B
EXAMPLE SURVEY FOR GERIATRIC MEDICINE EXPERT PANEL MEMBERS -
ROUND 1 OF DELPHI TECHNIQUE
July 24, 1998
Dear ,
Congratulations on being appointed a member of the Geriatric Medicine Expert Panel by Dr.
Paul Garrett! As you know, a major problem in the health of older persons is preventable drug-
related morbidity. Your expertise will be used to determine whether the following drug-related
morbidities in older persons are recognizable, foreseeable, and if causality can be identified
and controlled.
Please see the following Survey Instructions for details on how you can help to define
preventable drug-related morbidities in older persons. Based on the experience of others, it will
take approximately 20 minutes to complete the survey.
Your input is important, as there are only seven members of the Geriatric Medicine Expert Panel.
Please return the list of possible preventable drug-related morbidities with your comments by
Friday, July 31,1998 to ensure that your input is considered. Approximately one week after
that, you will receive a revised version of the survey, based on the comments of all panel
members. Please do not distribute or reproduce this survey without permission.
You can fax the survey back at (352) 392-7782. Feel free to call with any questions at (352)
846-0163 or use the following e-mail address:neil@cop3.health.ufl.edu. Again, thank you.
Sincerely, Neil MacKinnon, M.S., R.Ph.
136

137
Survey Instructions
You will be helping to define preventable drug-related morbidities in older persons. First, here
are a couple definitions:
(1) A drug-related morbidity (adverse drug event, drug misadventure) is defined as a clinical
outcome in which drug therapy has not produced a reasonable intended result either by (a)
producing a noxious, unintended and undesired drug effect, or (b) by failing to produce the
intended effect within a reasonable time.
(2) A preventable drug-related morbidity (a) results from unacceptable quality of care (e.g.,
failure to meet consensus guidelines) or (b) occurs after a drug-related problem. There are four
defining characteristics of a preventable drug-related morbidity. The drug-related problem must
be recognizable and the likelihood of a drug-related morbidity must be foreseeable. In addition,
the cause(s) of the problem (and subsequent drug-related morbidity) must be identifiable, and
those causes must be controllable. Preventable drug-related morbidity, therefore, results from
unrecognized or unresolved drug-related problems.
• A drug-related problem might be recognizable and interpretable by the patient in whom the
event occurs, a lay caretaker, or a health professional (physician, pharmacist, etc.).
Here are the survey instructions:
• The objective of this exercise, given a specific drug-related morbidity, is to develop criteria
for the four defining characteristics. Each of the following examples will describe a drug-related
morbidity and additional information that may or may not (in your judgement) relate to quality
standards or describe a drug-related problem.
• In order to evaluate whether the following examples are types of preventable drug-related
morbidities, you should read the outcomes and patterns of care and answer the following
questions:
1. For most older persons, should health professionals (MDs, pharmacists, etc.) be
able to recognize significant problems in this pattern of care?
2. For most older persons, should health professionals be able to foresee the
possibility of the outcome, if those problems were not resolved?
3. Should most health professionals see how to change the pattern of care to prevent
the outcome?
4. Should most health professionals actually change the pattern of care?
• If you answer “Yes” to all four of these questions, then this is a type of preventable drug-
related morbidity and you should check the “Yes” box.
• If you answer “No” to one or more of these questions, then this is not a type of preventable
drug-related morbidity and you should check the “No” box. If you answered “No”, please
specify why. You may wish to describe whether there is some other element that could be added
to the pattern of care that would make it a stronger, clearer or less ambiguous definition of
preventable drug-related morbidity. This section is very important to complete. Please be as
specific as you possibly can. For example, if you believe the pattern of care should be changed
(e.g. a lab value should be monitored every 2 months instead of every 3 months) or something is

138
missing (e.g.; a duration of use of a drug should be specified) then please write that in the space
provided. Also, if you felt the possible preventable drug-related morbidity did not meet any one
of the four criteria, then please let me know what caused you to answer the way you did (e.g.;
you feel additional laboratory tests would be needed in order to identify causality).
If, for some reason, you answer “Yes” to all four questions, but you still think you should check
the “No” box, please describe your reservations or concerns.
Finally, there is space at the end of the list for any additional preventable drug-related
morbidities in older adults that you feel are important to add.
Thank you.

139
Geriatric Medicine Expert Panel Survey
Please read the following outcomes and patterns of care and answer the following questions:
1. For most older persons, should health professionals (MDs, pharmacists, etc.) be
able to recognize significant problems in this pattern of care?
2. For most older persons, should health professionals be able to foresee the
possibility of the outcome, if those problems were not resolved?
3. Should most health professionals see how to change the pattern of care to prevent
the outcome?
4. Should most health professionals actually change the pattern of care?
If you answer “Yes” to all four of these questions, then this is a type of preventable drug-related
morbidity and you should check the “Yes” box.
If you answer “No” to one or more of these questions, then this is not a type of preventable drug-
related morbidity and you should check the “No” box. If you answered “No”, please specify
why.
Start of Survey
1.This outcome has occurred after the pattern of care below:
Gastritis and/or upper GI bleed and/or GI perforation and/or GI ulcer and anemia
This is the pattern of care:
1. NSAID (e.g.; diclofenac, ibuprofen, ketoprofen, etc.) use for at least 1 month
2. No concurrent use of a cytoprotective agent (misoprostol)
3. Hemoglobin/ hematocrit/C BC not done within 30 days of start of therapy or
not done at least every 3 months thereafter
Is this a type of preventable drug-related morbidity? Yes â–¡ No â–¡
If no, then please explain:
2. This outcome has occurred after the pattern of care below:
ER visit/ hospitalization due to congestive heart failure and/or fluid overload
This is the pattern of care:
1. History/diagnosis of high blood pressure (over 140/90) and/or congestive
heart failure
2. NSAID (e.g.; diclofenac, indomethacin, ketoprofen, etc.) use for at least 3
months
Is this a type of preventable drug-related morbidity? Yes â–¡ No â–¡
If no, then please explain:

140
3.This outcome has occurred after the pattern of care below:
Acute renal failure and/or renal insufficiency
This is the pattern of care:
1. NSAID (e.g.; diclofenac, ibuprofen, ketoprofen, etc.) use for at least 3
months
2. BUN/Serum creatinine not done at least every 3 months.
Is this a type of preventable drug-related morbidity? Yes â–¡ No â–¡
If no, then please explain:
4.This outcome has occurred after the pattern of care below:
ER visit/hospitalization due to extreme hypogylcemia
This is the pattern of care:
1. History/diagnosis of diabetes
2. Use of a beta-adrengeric blocking agent (e.g.; propranolol, nadolol, etc.)
Is this a type of preventable drug-related morbidity? Yes â–¡ No â–¡
If no, then please explain:
5.This outcome has occurred after the pattern of care below:
ER visit/hospitalization due to depression and/or increase in dosage of
antidepressant
This is the pattern of care:
1. History/diagnosis of depression
2. Use of a moderate to high lipophilic beta-adrengeric blocking agent (e.g.;
propranolol, pindolol, etc.)
Is this a type of preventable drug-related morbidity? Yes â–¡ No â–¡
If no, then please explain:
6.This outcome has occurred after the pattern of care below:
ER visit/hospitalization due to depression and/or increase in dosage of
antidepressant
This is the pattern of care:
1. History/diagnosis of depression
2. Use of a long-acting benzodiazepine (e.g.; Librium, Valium, Centrax,
Paxipam, Dalmane, Azaene/Tranxene, etc.)
Is this a type of preventable drug-related morbidity? Yes â–¡ No â–¡
If no, then please explain:

141
7.This outcome has occurred after the pattern of care below:
Digoxin toxicity
This is the pattern of care:
1. Use of digoxin
2. BUN/serum creatinine not done at least every 6 months
3. Digoxin level not done at least every 6 months
Is this a type of preventable drug-related morbidity? Yes â–¡ No â–¡
If no, then please explain:
8.This outcome has occurred after the pattern of care below:
Fall and/or hip fracture and/or other bone fracture and/or bone break
This is the pattern of care:
1.Use of a tricyclic antidepressant (e.g.; amitriptyline, doxepin, imipramine,
etc.)
Is this a type of preventable drug-related morbidity? Yes â–¡ No â–¡
If no, then please explain:
9.This outcome has occurred after the pattern of care below:
Fall and/or hip fracture and/or other bone fracture and/or bone break
This is the pattern of care:
1. Use of an anti-parkinsonian agent (e.g.; levodopa, bromocriptine, benztropine,
etc.)
Is this a type of preventable drug-related morbidity? Yes â–¡ No â–¡
If no, then please explain:
10.This outcome has occurred after the pattern of care below:
Fall and/or hip fracture and/or other bone fracture and/or bone break
This is the pattern of care:
1. Use of a nitrate (e.g.; isosorbide, etc.)
Is this a type of preventable drug-related morbidity? Yes â–¡ No â–¡
If no, then please explain:
11.This outcome has occurred after the pattern of care below:
ER visit/hospitalization for hypokalemia
This is the pattern of care:
1. Use of a potassium-wasting diuretic (e.g.; hydrochlorothiazide, etc.)
2. No concurrent use of potassium chloride supplement
3. Electrolytes not checked at least every 2 months
Is this a type of preventable drug-related morbidity? Yes â–¡ No â–¡
If no, then please explain:

142
12.This outcome has occurred after the pattern of care below:
ER visit/hospitalization due to worsening renal impairment and/or acute renal
failure and/or renal insufficiency
This is the pattern of care:
1. Diagnosis/history of moderate to severe renal impairment and/or history of
kidney disease
2. Use of tetracycline
3. BUN/serum creatinine not done within 30 days of initiation of therapy and at
least every 6 months
Is this a type of preventable drug-related morbidity? Yes â–¡ No â–¡
If no, then please explain:
13.This outcome has occurred after the pattern of care below:
ER visit/hospitalization due to hyperkalemia
This is the pattern of care:
1. Use of an ACE inhibitor (e.g.; captopril, enalapril, etc.)
2. Electrolytes/CBC not done at least every 6 months
Is this a type of preventable drug-related morbidity? Yes â–¡ No â–¡
If no, then please explain:
14.This outcome has occurred after the pattern of care below:
Blood dyscrasias and/or hyponatremia and/or excessive water retention and/or
syndrome of inappropriate antidiuretic hormone (SIADH)
This is the pattern of care:
1. Use of carbamazepine
2. Electrolytes/CBC not done before therapy initiated, at least weekly during
the first month of therapy, at least monthly during the next five months of
therapy, and at least every 6 months thereafter
Is this a type of preventable drug-related morbidity? Yes â–¡ No â–¡
If no, then please explain:
15.This outcome has occurred after the pattern of care below:
Acute renal failure and/or renal insufficiency
This is the pattern of care:
1. Use of allopurinol
2. BUN/serum creatinine not done at least every 6 months
Is this a type of preventable drug-related morbidity? Yes â–¡ No â–¡
If no, then please explain:

143
16.This outcome has occurred after the pattern of care below:
Acute renal failure and/or renal insufficiency
This is the pattern of care:
1. Use of lithium
2. BUN/serum creatinine not done at least every 3 months
Is this a type of preventable drug-related morbidity? Yes â–¡ No â–¡
If no, then please explain:
17.This outcome has occurred after the pattern of care below:
Anticonvulsant drug toxicity
This is the pattern of care:
1. Use of an anticonvulsant requiring drug level monitoring (e.g.; phenytoin,
carbamazepine, valproic acid)
2. Drug level not done at least every 6 months
Is this a type of preventable drug-related morbidity? Yes â–¡ No â–¡
If no, then please explain:
18.This outcome has occurred after the pattern of care below:
Theophylline toxicity
This is the pattern of care:
1. Use of theophylline
2. Drug level not done at least every 6 months
Is this a type of preventable drug-related morbidity? Yes â–¡ No â–¡
If no, then please explain:
19.This outcome has occurred after the pattern of care below:
Bipolar exacerbation and/or ER visit/hospitalization due to bipolar disorder
This is the pattern of care:
1. Diagnosis/history of bipolar disorder
2. Use of lithium
3. Drug level not done at least every 3 months
Is this a type of preventable drug-related morbidity? Yes â–¡ No â–¡
If no, then please explain:
20.This outcome has occurred after the pattern of care below:
ER visit/hospitalization due to hypoglycemia or hyperglycemia
This is the pattern of care:
1. Use of insulin
2. Hemoglobin Ale level not done at least every 6 months
Is this a type of preventable drug-related morbidity? Yes â–¡ No â–¡
If no, then please explain:

144
21.This outcome has occurred after the pattern of care below:
Major and/or minor hemorrhagic event
This is the pattern of care:
1. Use of SQ heparin
2. PTT not done at least every month
Is this a type of preventable drug-related morbidity? Yes â–¡ No â–¡
If no, then please explain:
22.This outcome has occurred after the pattern of care below:
Major and/or minor hemorrhagic event
This is the pattern of care:
1. Use of IV heparin
2. PTT not done at least every day
Is this a type of preventable drug-related morbidity? Yes â–¡ No â–¡
If no, then please explain:
23.This outcome has occurred after the pattern of care below:
ER visit/hospitalization due to hypothyroidism
This is the pattern of care:
1. Use of a thyroid or antithyroid agent (e.g.; levothyroxine, propylthiouracil,
etc.)
2. T4/TSH not done before therapy starts and at least every 12 months
thereafter
Is this a type of preventable drug-related morbidity? Yes â–¡ No â–¡
If no, then please explain:
24.This outcome has occurred after the pattern of care below:
ER visit/hospitalization due to systolic heart failure
This is the pattern of care:
1. History/diagnosis of systolic heart failure
2. Use of a beta-adrengeric blocking agent (e.g.; propranolol, nadolol, etc.)
Is this a type of preventable drug-related morbidity? Yes â–¡ No â–¡
If no, then please explain:
25.This outcome has occurred after the pattern of care below:
ER visit/hospitalization due to congestive heart failure
This is the pattern of care:
1. History/diagnosis of congestive heart failure
2. Use of an antiarrhythmic agent (e.g.; disopyramide, procainamide, etc.)
Is this a type of preventable drug-related morbidity? Yes â–¡ No â–¡
If no, then please explain:

145
26.This outcome has occurred after the pattern of care below:
Gastritis and/or upper GI bleeds and/or GI perforations and/or GI ulcers and
anemia
This is the pattern of care:
1. History/diagnosis of ulcers and/or gastrointestinal bleeding
2. NSAID (e.g.; diclofenac, ibuprofen, ketoprofen, etc.) use for at least 1 month
Is this a type of preventable drug-related morbidity? Yes â–¡ No â–¡
If no, then please explain:
27.This outcome has occurred after the pattern of care below:
Acute renal failure and/or renal insufficiency
This is the pattern of care:
1 .NSAID (e.g.; diclofenac, ibuprofen, ketoprofen, etc.) use for at least 3 months
2.BUN/serum creatinine not done when therapy starts and at least every 3
months thereafter
Is this a type of preventable drug-related morbidity? Yes â–¡ No â–¡
If no, then please explain:
28.This outcome has occurred after the pattern of care below:
Gastritis and/or upper GI bleeds and/or GI perforations and/or GI ulcers and
anemia
This is the pattern of care:
1. History/diagnosis of ulcers and/or gastrointestinal bleeding
2. Use of an oral corticosteroid (e.g., prednisone) for at least 3 months
Is this a type of preventable drug-related morbidity? Yes â–¡ No â–¡
If no, then please explain:
29.This outcome has occurred after the pattern of care below:
ER visit/hospitalization due to depression and/or increase in dosage of
antidepressant
This is the pattern of care:
1. History/diagnosis of depression
2. Use of a barbiturate (e.g.; butalbital)
Is this a type of preventable drug-related morbidity? Yes â–¡ No â–¡
If no, then please explain:

146
30.This outcome has occurred after the pattern of care below:
ER visit/hospitalization due to depression and/or increase in dosage of
antidepressant
This is the pattern of care:
1. History/diagnosis of depression
2. Use of a sympatholytic antihypertensive (e.g.; resperine, methyldopa,
clonidine, etc.)
Is this a type of preventable drug-related morbidity? Yes â–¡ No â–¡
If no, then please explain:
31.This outcome has occurred after the pattern of care below:
ER visit/hospitalization due to congestive heart failure and/or heart block
This is the pattern of care:
1. History/diagnosis of congestive heart failure with heart block or advanced
bradycardia
2. Use of digoxin
Is this a type of preventable drug-related morbidity? Yes â–¡ No â–¡
If no, then please explain:
32.This outcome has occurred after the pattern of care below:
Fall and/or hip fracture and/or other bone fracture and/or bone break
This is the pattern of care:
1. Use of a long-half-life hypnotic-anxiolytic (e.g.; flurazepam, diazepam,
chlordiazepoxide, etc.)
Is this a type of preventable drug-related morbidity? Yes â–¡ No â–¡
If no, then please explain:
33.This outcome has occurred after the pattern of care below:
Fall and/or hip fracture and/or other bone fracture and/or bone break
This is the pattern of care:
1. Use of an antipsychotic (e.g.; thioridazine, haloperidol, chlorpromazine, etc.)
Is this a type oí preventable drug-related morbidity? Yes □ No □
If no, then please explain:
34.This outcome has occurred after the pattern of care below:
Asthma exacerbation and/or status asthmaticus and/or ER visit/hospitalization
due to asthma
This is the pattern of care:
1. Diagnosis of asthma
2. Use of a bronchodilator
3. No use of a maintenance corticosteriod (e.g.; beclomethasone, etc.)
Is this a type of preventable drug-related morbidity? Yes â–¡ No â–¡
If no, then please explain:

147
35.This outcome has occurred after the pattern of care below:
Hospitalization/ER visit due to worsening renal impairment and/or acute renal
failure and/or renal insufficiency
This is the pattern of care:
1. Diagnosis/history of moderate to severe renal impairment/history of kidney
disease
2. Use of a select urinary antiinfective agent (nalidixic acid, nitrofurantoin or
methenamine complexes)
3. BUN/serum creatinine not done within 30 days of initiation of therapy and at
least every six months
Is this a type of preventable drug-related morbidity? Yes â–¡ No â–¡
If no, then please explain:
36.This outcome has occurred after the pattern of care below:
ER visit/hospitalization due to congestive heart failure
This is the pattern of care:
1. Diagnosis/history of congestive heart failure
2. Not on an ACE inhibitor (e.g.; captopril, enalapril, etc.)
Is this a type of preventable drug-related morbidity? Yes â–¡ No â–¡
If no, then please explain:
37.This outcome has occurred after the pattern of care below:
Acute renal failure and/or renal insufficiency
This is the pattern of care:
1. Use of an ACE inhibitor (e.g.; captopril, enalapril, etc.)
2. BUN/serum creatinine not done at initiation of therapy and at least every 3
months thereafter
Is this a type of preventable drug-related morbidity? Yes â–¡ No â–¡
If no, then please explain:
38.This outcome has occurred after the pattern of care below:
Aminoglycoside toxicity (acute renal failure and/or renal insufficiency and/or
vestibular damage and/or auditory damage)
This is the pattern of care:
1. Use of an aminoglycoside
2. Serum creatinine not done before and after therapy (and if therapy longer
than 7 days, not done at least every 7 days)
3. At least one drug level not done
Is this a type of preventable drug-related morbidity? Yes â–¡ No â–¡
If no, then please explain:

148
39.This outcome has occurred after the pattern of care below:
Status epilepticus and/or ER visit/hospitalization due to seizure activity
This is the pattern of care:
1. Use of an anticonvulsant requiring drug level monitoring (e.g.; phenytoin,
carbamazepine, valproic acid)
2. Drug level not done upon initiation of therapy and at least every 6 months
thereafter
Is this a type of preventable drug-related morbidity? Yes â–¡ No â–¡
If no, then please explain:
40.This outcome has occurred after the pattern of care below:
Asthma exacerbation and/or status asthmaticus and/or ER visit/hospitalization
due to asthma
This is the pattern of care:
1. Diagnosis of asthma
2. Use of theophylline
3. Drug level not done at least every 6 months
Is this a type of preventable drug-related morbidity? Yes â–¡ No â–¡
If no, then please explain:
41.This outcome has occurred after the pattern of care below:
Lithium toxicity
This is the pattern of care:
1. Use of lithium
2. Lithium level not done at least every month
Is this a type of preventable drug-related morbidity? Yes â–¡ No â–¡
If no, then please explain:
42.This outcome has occurred after the pattern of care below:
ER visit/hospitalization due to hyperglycemia
This is the pattern of care:
1. Use of an oral hypoglycemic agent (e.g.; chlorpropamide, etc.)
2. Hemoglobin A1 c level not done at least every 6 months
Is this a type of preventable drug-related morbidity? Yes â–¡ No â–¡
If no, then please explain:

149
43.This outcome has occurred after the pattern of care below:
Major and/or minor hemorrhagic event
This is the pattern of care:
1. Use of warfarin
2. Prothrombin time not done before therapy starts and at least every month
thereafter
Is this a type of preventable drug-related morbidity? Yes â–¡ No â–¡
If no, then please explain:
44.This outcome has occurred after the pattern of care below:
ER visit/hospitalization due to hyperthyroidism
This is the pattern of care:
1. Use of a thyroid or antithyroid agent (e.g.; levothyroxine, propylthiouracil,
etc.)
2. T4/TSH not done within 6 weeks after initiation of therapy and at least every
12 months thereafter
Is this a type of preventable drug-related morbidity? Yes â–¡ No â–¡
If no, then please explain:
45.This outcome has occurred after the pattern of care below:
ER visit/hospitalization due to congestive heart failure
This is the pattern of care:
1. History/diagnosis of congestive heart failure
2. Use of a calcium channel blocker (e.g.; diltiazem, etc.)
Is this a type of preventable drug-related morbidity? Yes â–¡ No â–¡
If no, then please explain:
46.This outcome has occurred after the pattern of care below:
Secondary myocardial infarction
This is the pattern of care:
1. History/diagnosis of myocardial infarction
2. No use of ASA and/or a beta-blocker (e.g.; metoprolol, etc.)
Is this a type of preventable drug-related morbidity? Yes â–¡ No â–¡
If no, then please explain:
47.This outcome has occurred after the pattern of care below:
Blood dyscrasias
This is the pattern of care:
1. Concurrent use of trimethoprim/ sulfamethoxazole (Bactrim, Septra) and
methotrexate
2. WBC/platelets/CBC not done at least every 4 weeks
Is this a type of preventable drug-related morbidity? Yes â–¡ No â–¡
If no, then please explain:

150
48. This outcome has occurred after the pattern of care below:
COPD exacerbation and/or ER visit/hospitalization due to COPD
This is the pattern of care:
1. Diagnosis/history of COPD
2. Use of a beta-blocker (e.g.; propranolol, etc.)
Is this a type of preventable drug-related morbidity? Yes â–¡ No â–¡
If no, then please explain:
Please describe any additional preventable drug-related morbidities that you believe occur
in older persons:
1. Outcome;
Pattern of care;
2. Outcome;
Pattern of care:
3.Outcome;
Pattern of care:
Thank you. Please fax to Neil MacKinnon by Friday, July 31 at (352) 392-7782.

APPENDIX C
INSTRUCTIONS FOR PATIENT CHART ABSTRACTER, PATIENT CHART ABSTRACT
FORM, AND SAMPLE PATIENTS
Instructions for Patient Chart Abstracter
1. I have provided you with a list of Premier Care Plan patients who were found to have
diagnosis codes related to either hyperglycemia or secondary myocardial infarction. In this
list, you were given the patient’s medical record number and the date of the outcome event.
Some of these patients met our criteria for preventable drug-related morbidity and some do
not. You were not told whether these patients did, in fact, have a case of preventable drug-
related morbidity.
2. Write down the outcome event (either hyperglycemia or secondary MI). The patient will
likely have other diagnoses/procedures but do not list them here.
3. Write a description of the pattern of care that proceeded the outcome event. This description
can follow the same format that you would use if you were presenting a case study to
medical grand rounds or a pharmacy in-service. Make sure to include the following things:
(1) any relative patient demographics, (2) chief compliants of the patient, (3) patient’s past
medical history, (4) the diagnoses made /procedures done, (5) the patient’s medications upon
admission, and (6) any other information you deem to be necessary or relevant, using your
clinical judgement. Total length of the description should not exceed one page.
4. For patients with the outcome of hyperglycemia, be sure to include the following
information: (1) use, if any, of oral hypoglycemic agents prior to admission, and (2) dates
when a hemoglobin Ale level was performed. Specifically, we are interested in whether the
patient had this test done at least every 6 months. Please document this clearly in the chart
abstract.
5. For patients with the outcome of secondary MI, be sure to document (1) whether the patient
did indeed have a previous MI, (2) use, if any, of ASA prior to admission, and (3) use, if any,
of a beta-blocker prior to admission.
6. On the reverse side of the form, please document the patient’s medical record number.
7. I will review two sample cases with you before you begin the patient chart abstracting
process to familiarize you with the chart abstract form.
8. Feel free to contact me if you have any questions:
Neil MacK.innon, M.S., R.Ph.
Work Phone: (352) 846-0163
Home Phone: (352) 336-8348
Fax: (352)392-7782
E-mail:neil@cop3 .health.ufl.edu
151

152
Drug Therapy Risk Assessment and Management Program
(DT-RAMP) Patient Chart Abstract Form
Outcome Event]
Description of the pattern of care that preceded the outcome event]
Please review the above patient chart abstract and answer the following questions to judge
whether this was a case of preventable drug-related morbidity:
1. Should health professionals (MDs, pharmacists, etc.) be able to recognize significant
problems in this pattern of care (related to the above outcome)?
2. Should health professionals be able to foresee the possibility of the above outcome in this
patient?

153
3. Should most health professionals see how to change the pattern of care to prevent the above
outcome in this patient?
4. Should most health professionals actually change the pattern of care in this patient?
If you answer “Yes” to all four of these questions, then this is a case of preventable drug-related
morbidity and you should check the “Yes” box.
If you answer “No” to one or more of these questions, then this is not a case of preventable drug-
related morbidity and you should check the “No” box. If you answered “No”, please specify
why.
Is this a case of preventable drug-related morbidity? Yes â–¡ No â–¡
If no, then please explain:

154
Drug Therapy Risk Assessment and Management Program (DT-RAMP) Patient
Chart Abstract Form - Example of a preventable drug-related morbidity
Outcome Event: LM admitted to Florida Hospital on 10/10/97 with an upper GI bleed
Description of the pattern of care that preceded the outcome event: LM is a 68 v/o WF with
a history of arthritis and hypertension. She is admitted to Florida Hospital with an upper GI
bleed. Her physical examination is within normal limits. Her medications upon admission are
diclofenac 50 mg tid for arthritis and furosemide 20 mg qam for hypertension. She is not
receiving any cvtoprotective agents. Her hemoglobin and hematocrit are done upon admission
and are 14.2 gm/dl and 44%. respectively. Before that, her last documented hematocrit is from
11/11/96. LM noticed some blood in her urine over the past few weeks and noted recently that
her stools have become black and tarry in appearance but she has no other symptoms consistent
with peptic ulcer disease.
Please review the above patient chart abstract and answer the following questions to judge
whether this was a case of preventable drug-related morbidity:
1. Should health professionals (MDs, pharmacists, etc.) be able to recognize significant
problems in this pattern of care (related to the above outcome)? (yes- NSAID use without a
cytoprotective agent or regular hematocrits)
2. Should health professionals be able to foresee the possibility of the above outcome in this
patient? (yes- patient is an older adult and is therefore at an increased risk of a GI bleed)
3. Should most health professionals see how to change the pattern of care to prevent the above
outcome in this patient? (yes - could add a cytoprotective agent, do regular hematocrits or
even change diclofenac to a different drug)
4. Should most health professionals actually change the pattern of care in this patient? (yes -
could do any of the above things)

155
If you answer “Yes” to all four of these questions, then this is a case of preventable drug-related
morbidity and you should check the “Yes” box.
If you answer “No” to one or more of these questions, then this is not a case of preventable drug-
related morbidity and you should check the “No” box. If you answered “No”, please specify
why.
Is this a case of preventable drug-related morbidity? Yes / No â–¡
If no, then please explain:

156
Drug Therapy Risk Assessment and Management Program (DT-RAMP) Patient
Chart Abstract Form - Example of a patient without preventable drug-related morbidity
Outcome Events LM admitted to Florida Hospital on 10/10/97 with an upper GI bleed
Description of the pattern of care that preceded the outcome event: LM is a 68 v/o WF
with a history of arthritis and hypertension. She is admitted to Florida Hospital with an upper GI
bleed. Her physical examination is within normal limits. Her medications upon admission are
diclofenac 50 mg tid for arthritis, misoprostol 200 meg qid for GI protection and furosemide 40
mg qam for hypertension. Her hemoglobin and hematocrit are done upon admission and are 14.2
gm/dl and 44%. respectively. Her physician. Dr. Perry Colace has ordered hematocrits every 3
months, the last one being done on 09/09/99. LM noticed some blood in her urine over the past
few weeks and noted recently that her stools have become black and tarry in appearance but she
has no other symptoms consistent with peptic ulcer disease.
Please review the above patient chart abstract and answer the following questions to judge
whether this was a case of preventable drug-related morbidity:
1. Should health professionals (MDs, pharmacists, etc.) be able to recognize significant
problems in this pattern of care (related to the above outcome)? (no- pt on a NSAID but also
receiving a cytoprotective agent and regular hematocrits - proper pattern of care)
2. Should health professionals be able to foresee the possibility of the above outcome in this
patient? (yes- patient is an older adult and is therefore at an increased risk of a GI bleed)
3. Should most health professionals see how to change the pattern of care to prevent the above
outcome in this patient? (no- already on a cytoprotective agent and getting regular
hematocrits - could possibly change diclofenac to a different drug)
4. Should most health professionals actually change the pattern of care in this patient? (yes -
could change diclofenac to a different drug - may work)

157
If you answer “Yes” to all four of these questions, then this is a case of preventable drug-related
morbidity and you should check the “Yes” box.
If you answer “No” to one or more of these questions, then this is not a case of preventable drug-
related morbidity and you should check the “No” box. If you answered “No”, please specify
why.
Is this a case of preventable drug-related morbidity? Yes â–¡ No V
If no, then please explain:
GI bleed may be due to NSAID use, but Dr. Colace took precautions with adding misoprostol for
GI protection and regular hematocrits for monitoring; therefore it was not a case of preventable
drug-related morbidity.

APPENDIX D
MEMBERS OF THE CHART ABSTRACT REVIEWER PANEL
Category
Pharmacist # 1
Pharmacist #2
Pharmacist #3
Name
Helen Hsu
Robert Vandervort
John (Bill)
Kennedy
Degree(s)
Pharm.D.
Pharm.D.
Pharm.D.
Years of Clinical
Experience
5
3
18
Areas with clinical
expertise
Internal Medicine
Family Practice
Family Practice
Experience reading chart
abstracts
Y
Y
Y
Experience completing
ADR forms (MedWatch,
etc.)
Y
Y
N
Category
Pharmacist #4
Pharmacist #5
Name
Sandra Newman
Simone Minto-
Pennant
Degree(s)
Pharm.D.
Pharm.D., C.Ph.
Years of Clinical
Experience
7
1
Areas with clinical
expertise
Geriatrics
Geriatrics,
Inpatient &
Family Practice
Experience reading chart
abstracts
Y
Y
Experience completing
ADR forms (MedWatch,
etc.)
Y
Y
158

APPENDIX E
INSTRUCTIONS FOR CHART ABSTRACT REVIEWER PANEL
November 6, 1998
Dear Chart Abstract Reviewer Panel Member,
As you know, a major problem in the health of older persons is preventable drug-related
morbidity. Your expertise will be used to determine whether the patients described in the
following chart abstracts experienced a preventable drug-related morbidity.
Your input is important, as there are only four members of the Chart Abstract Reviewer Panel.
Please return the patient chart abstract forms with your responses to Scott Neel by Tuesday,
November 10,1998 to ensure that your input is considered.
Feel free to call Scott Neel with any questions at extension 6338 or Neil MacKinnon at
(352) 846-0163. Again, thank you.
Sincerely,
Scott A. Neel, Pharm.D.
Primary Care Resident
Neil J. MacKinnon, M.S., R.Ph.
Ph.D. Candidate and Research Fellow
159

160
Instructions
You have been given chart abstracts from older persons who were admitted to the Florida
Hospital in 1997 with one of two diagnoses: hyperglycemia or secondary myocardial infarction.
Some of these patients met our criteria for a preventable drug-related morbidity and some did
not.
• In order to evaluate whether these patients experienced a preventable drug-related morbidity
or not, you should read the outcome event and description of the pattern of care that
preceded the outcome event and answer the following questions:
1. Should health professionals (MDs, pharmacists, etc.) be able to recognize significant
problems in this pattern of care (related to the above outcome)?
2. Should health professionals be able to foresee the possibility of the above outcome in
this patient?
3. Should most health professionals see how to change the pattern of care to prevent the
above outcome in this patient?
4. Should most health professionals actually change the pattern of care in this patient?
• If you answer “Yes” to all four of these questions, then this is a type of preventable drug-
related morbidity and you should check the “Yes” box.
• If you answer “No” to one or more of these questions, then this is not a type of preventable
drug-related morbidity and you should check the “No” box. If you answered “No”, please
specify why. This section is very important to complete. Please be as specific as you possibly
can.
If, for some reason, you answer “Yes” to all four questions, but you still think you should check
the “No” box, please describe your reservations or concerns.
Thank you.

APPENDIX F
PERSONAL WELLNESS PROFILE SENIOR ASSESSMENT
Gender
(1)
Male
(2)
Female
years
Height Feet, inches
Weight Pounds
Questions:
1. Health. In general, would you say your health is:
(1) excellent
(2) very good
(3) good
(4) fair
(5) poor
2. General Health. Compared to one year ago, how would you rate your health in
general now?
(1) much better now than one year ago
(2) somewhat better now than one year ago
(3) about the same
(4) somewhat worse now than one year ago
(5) much worse now than one year ago
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3. Bodily pain. How much bodily pain have you had during the past four weeks?
(1) none
(2) very mild
(3) mild
(4) moderate
(5) severe
(6) very severe
4 . Health view. Mark any of the following that apply to you.
(1) I’m as healthy as anybody I know.
(2) 1 seem to get sick a little easier than other people.
(3) I expect my health to get worse.
(4) I have a serious health problem.
5. Health limitations. During the past four weeks, how much difficulty did you have
doing your work or other regular daily activities as a result of your physical health?
(1) none at all
(2) a little bit
(3) some
(4) quite a bit
(5) could not do daily work
6. Emotional problems. During the past four weeks, to what extent have you
accomplished less than you would like in your work or other daily activities as a
result of emotional problems, such as feeling depressed or anxious?
(1) none at all
(2) slightly
(3) moderately
(4 ) quite a bit
(5)extremely

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7 . Social activity. During the past four weeks, to what extent has your physical health
or emotional problems interfered with your normal social activities with family,
friends, neighbors, or groups?
(1) not at all
(2) slightly
(3) moderately
(4) quite a bit
(5) extremely
8 . Daily activities. The following items are about activities you might do during a
typical day. Does your health now limit you in these activities? If so, how much?
1: yes, limited a lot
2: yes, limited a little
3: no ,not limited at all
1 - (1)(2)(3) lifting or carrying groceries
2 - (1)(2)(3) climbing several flights of stairs
3 - (1)(2)(3) walking several blocks
9. Feelings. The next questions are about how you feel things have been with you
during the past four weeks. For each question, please give the one answer that comes
the closest to the way you have been feeling. How much of the time during the past
four weeks...
1: all the time
2: most of the time
3: a good bit of the time
4: some of the time
5: a little of the time
6: none of the time
1 - (1 )(2)(3)(4)(5)(6) Have you felt calm and peaceful?

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2 - (1)(2)(3)(4)(5)(6) Did you have a lot of energy?
3 - (1)(2)(3)(4)(5)(6) Have you felt downhearted and blue?
4 - (1 )(2)(3)(4)(5)(6) Have you been a happy person?
10. Eating habits. The following items relate to your eating habits. Mark any item that
is true for you or describes your current situation.
1 -1 have an illness or condition that has made me change the kind or amount of
food I eat.
2 -1 eat fewer than two meals per day.
3 -1 eat few fruits or vegetables, or milk products.
4 -1 have three or more drinks of beer, liquor, or wine almost every day.
5 -1 have tooth or mouth problems that make it hard for me to eat.
6-1 don’t always have enough money to buy the food I need.
7 -1 eat alone most of the time.
8 - Without wanting to, I have lost or gained 10 pounds in the last 6 months.
9 -1 am not always physically able to shop, cook, and/or feed myself.
11. Health practices. The following questions refer to common health practices.
Please answer yes or no. Do you...
1: yes 2: no
1 - (1)(2) currently smoke?
2 - (1)(2) often use medicines that affect your mood, help you relax or sleep?
3 - (1)(2) often have more than 1 to 2 alcoholic drinks in a day?
4 - (1)(2) usually get 7 to 8 hours sleep daily (including naps)?
5 - (1 )(2) ever drive after drinking?
6 - (1)(2) consider yourself more than 20 pounds overweight?
7 - (1)(2) always wear your seat belt when driving or riding in a car?
8 - (1)(2) usually eat breakfast?
9 - (1)(2) eat primarily whole-grain breads and cereals?

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10 - (1)(2) make a serious attempt to eat primarily low fat foods?
11 - (1)(2) regularly snack on junk foods (chips, doughnuts, cookies, candy)?
12 - (1)(2) have a working smoke alarm at home?
13 - (1)(2) always do heavy lifts with legs and not back?
14 - (1)(2) cope well with stress in your life?
12. Exercise. How many days per week do you engage in aerobic exercise of at least 20
to 30 minutes duration (Brisk walking, golf [walking, no cart] dancing, active gardening,
cycling, swimming, etc.)?
(1) none
(2) one to two
(3) three to four
(4) five or more
13. Fruits and vegetables. How many servings of fruits and vegetables do you usually
eat per day?
(1) one or less
(2) two
(3) three
(4) four
(5) five or more
14 . Personal health history. Has a doctor informed you that you currently have any of
the following health problems? If yes, mark either yes, but not taking or yes, and
taking medication, otherwise leave blank.
1: yes, not taking medication 2: yes, taking medication
1 - (1 )(2) arthritis
2 - (1)(2) bladder or bowel control problems
3 - (1)(2) blind/trouble seeing, even with glasses

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4 - (1)(2) cancer, other than skin cancer (within last 5 years)
5 - (1)(2) congestive hear failure
6 - (1)(2) heart attack, angina, by-pass surgery, or angioplasty
7 - (1)(2) chronic back problems or sciatica
8 - (1)(2) deafness or trouble hearing
9 - (1)(2) diabetes (high blood sugar)
10 - (1)(2) high blood pressure (140/90 or higher)
11 - (1)(2) lung disease, emphysema, bronchitis, or asthma
12 - (1)(2) memory problems (more than typical)
13 - (1)(2) kidney disease
14-(1X2) stroke
15 - (1)(2) ulcer or gastrointestinal bleeding
15. Social health. Answer the following questions with a yes or no.
1: yes 2: no
1 - (1)(2) Do you participate in church or community activities regularly with
your friends?
2 - (1)(2) Do you have a plan for getting emergency help if you need it?
3 - (1)(2) Do you feel overwhelmed by a recent crisis or loss?
4 - (1)(2) Have you recently considered ending your life?
16. Living arrangements. I live with...
(1) my spouse
(2) by myself
(3) a paid caregiver
(4) family/relatives
(5) a friend
(6)other

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17 . Hospital. How many times were you hospitalized in the last 12 months?
(1) none
(2) one to two
(3) three or more
18. Emergency Room. How many times did you seek care in the emergency room in
the last 12 months?
(1) none
(2) one to two
(3) three or more
19. Doctor visits. How many times did you visit a doctor in the last 12 months?
(1) none
(2) one to two
(3) three to four
(4) five or more
20. Nursing Home. In the past year, have you been admitted to a nursing or
convalescent home?
(1) yes
(2) no
21. Prescription drugs. How many different prescribed drugs do you take daily?
(1) none
(2) one to two
(3) three to five
(4) six or more
22. Over-the-counter drugs. How many different over-the-counter drugs do you take
daily? (mark one)
(1) none
(2) one to two

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(3) three to five
(4) six or more
23. Medicines. Do you have trouble paying for your medicines?
(1) yes
(2) only some times
(3) no or not applicable
24 . Falling. Have you fallen in the last year (how often)?
(1) no
(2) yes, 1 to 2 times
(3) yes, 3 or more times
25. Medical equipment. Are you currently using medical equipment, for example:
oxygen, hospital bed, wheelchair, walker.
(1) yes
(2) no
26. Home health services. Are you currently receiving home health services, such as:
visiting nurse, physical therapy, homemaker/aids, adult day care, etc.
(1) yes
(2) no
27. Physical health. Think about your physical health, including illness and injury.
How many days during the last 30 days was your physical health not good?
(1) none
(2) one to two
(3) three to five
(4) six or more
28 . Mental health. Think about your mental health, including stress, depression, and
problems with emotions. How many days during the last 30 days was your mental
health not good?

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(1) none
(2) one to two
(3) three to five
(4) six or more
29. Blood pressure. Do you have a blood pressure of 140/90 or higher?
(1) yes
(2) no
(3) don’t know
30. Cholesterol. Do you have a cholesterol level of 200 or higher?
(1) yes
(2) no
(3) don’t know
31. Activities of daily living. How difficult are the following activities and tasks for
you?
1: not difficult 2: difficult 3: very difficult or can’t do it
1-(1X2X3) bathing
2 - (1)(2)(3) dressing
3 - (1)(2)(3) getting out of bed
4 - (1)(2)(3) getting out of a chair
5-(1X2X3) eating
6 - (1)(2)(3) using the toilet
7 - (1)(2)(3) walking around the house
8 - (1)(2)(3) shopping and errands
9 - (1)(2)(3) housekeeping
10 - (1)(2)(3) preparing meals
11 - (1)(2)(3) doing laundry
12 - (1)(2)(3) using the telephone

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13 - (1)(2)(3) taking medications
14 - (1)(2)(3) using transportation
15 - (1)(2)(3) managing your money
32. Physical exam. When was your last physical examination? Within the last...
(1) year
(2) 2 years
(3) 3 years
(4) 4 years
(5) 5 years or more
33. Preventive exams. Mark the preventive exams you have had during the time frame
listed (leave blank if you are not sure).
1 - cholesterol check, within the last 2-5 years
2 - blood pressure check, every 1 -2 years
3 - check for blood in stool yearly, or bowel exam with flexible sigmoidoscopy
every 3-10 years
4 - dental exam, yearly
5 - vision, every 2 years
6 - hearing, periodically as needed
7 - health-lifestyle assessment, every 1-2 years
8 - tetanus shot, every 10 years
9 - pneumonia immunization, once
10 - flu shot, yearly
11 - PAP smear, every 1-3 years
12 - mammogram with breast exam, every 1-2 years up to age 69
13 - breast self-exam, monthly
14 - PSA and rectal exam every 1-2 years
34 . Race. Mark one.

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(1) Native American
(2) Asian
(3) African-American, black
(4) Caucasian, white
(5) Hispanic
(6) Other
35. Living will. Have you completed a living will or advance directive? (A statement
prepared when a person is well and competent to give guidance to the health care
team concerning the person’s wishes regarding heroics for life support in a
terminally ill condition when such treatment will not permit recovery, or give
authority to another person to make these decisions if they are in a coma.)
(1) yes
(2) no
36. Readiness to change. Mark the response below that best describes where you are
currently in relation to adopting a healthier lifestyle.
(1) I don’t think I will make any changes this year.
(2) I plan on adopting a healthier lifestyle in the next six months.
(3) I plan on adopting a healthier lifestyle this month.
(4) I’ve adopted a healthier lifestyle in the last six months.
(5) I’ve lived a healthy lifestyle for over six months.
37. Health interest survey. Mark any of the following health improvement
opportunities that you would like to be notified about if available.
(1) Help quitting smoking
(2) Weight management
(3) Aerobic exercise to music
(4) A walking group
(5) A jogging group

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(6) a fitness evaluation
(7) Nutrition improvement
(8) Cholesterol reduction
(9) Blood pressure control
(10) Coronary risk reduction
(11) Cancer risk reduction
(12) Alcohol/drug awareness
(13) Healthy back program
(14) Medical self-care
(15) Stress management
(16) CPR training
(17) First aid
(18) Comprehensive health evaluation
(19) Women’s health programs
(20) Relationship enrichment
(21) Retirement planning
(22) Living wills/advance directives
1 - Do not notify me about health improvement opportunities
38. When is the best time to contact you?
(1) morning
(2) afternoon
(3) evening
Additional Drug-Related Questions
1. Are you taking any of the following medications? (Mark all that apply)
(1) Warfarin (Coumadin®)
(2) Theophylline (Theodur®, Slophyllin®, Theo-24®, Uniphyl®, Theolair®)
(3) Digoxin (Lanoxin®, Lanoxicap®)

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(4) Cimetidine (Tagamet®)
(5) Not on any of these medications
2. Are you taking of the following medications? (Mark all that apply.)
(1) Phenytoin (Dilantin®)
(2) Carbamazepine (Tegretol®)
(3) Phenobarbital
(4) Valproic Acid (Depakote®, Depakene®)
(5) Not on any of these medications.
3. Have you had a side effect due to a medication that caused you to stop that
medication in the last 6 months?
(1) Yes
(2) No
4. How do you feel about the number of medications that you are taking?
(1) Not currently taking any medications.
(2) The number of medications I am taking is about right.
(3) I think the number of medications I am taking is too many.

APPENDIX G
ROUND 3 OF THE GERIATRIC MEDICINE EXPERT PANEL SURVEY
Geriatric Medicine Expert Panel Survey - Round 3: Final Results
The following, outcomes & patterns of care were agreed to be preventable druz-related
morbidities by all 7 geriatric medicine expert panel members:
1. This outcome has occurred after the pattern of care below:
Gastritis and/or upper GI bleed and/or GI perforation and/or GI ulcer and anemia
This is the pattern of care:
1. NSAID (e.g.; diclofenac, ibuprofen, ketoprofen, etc.) use for at least 1 month
2. No concurrent use of a cytoprotective agent (misoprostol)
3. Hemoglobin/ hematocrit/C BC not done within 30 days of start of therapy or
not done at least every 3 months thereafter
Comments from round 1:
But cytoprotective agents may not prevent the adverse outcome.
(2) and (3) not necessary in every instance.
2. This outcome has occurred after the pattern of care below:
Acute renal failure and/or renal insufficiency
This is the pattern of care:
1. NSAID (e.g.; diclofenac, ibuprofen, ketoprofen, etc.) use for at least 3 months
2. BUN/Serum creatinine not done at least every 3 months.
Comments from round 1:
Serum creatinine more than every 3 months.
Not so neatly predictable - prefer baseline labs and then monitor clinically.
What's baseline BUN/creatinine?
Comments from round 2:
Should not apply to sporadic or very low dose NSAID use.
3. This outcome has occurred after the pattern of care below:
ER visit/hospitalization due to depression and/or increase in dosage of
antidepressant
This is the pattern of care:
1. History/diagnosis of depression
2. Use of a long-acting benzodiazepine (e.g.; Librium, Valium, Centrax,
Paxipam, Dalmane, Azaene/Tranxene, etc.)
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4. This outcome has occurred after the pattern of care below:
Fall and/or hip fracture and/or other bone fracture and/or bone break
This is the pattern of care:
1.Use of a tricyclic antidepressant (e.g.; amitriptyline, doxepin, imipramine,
etc.)
Comments from round 1:
May not apply to low-dose analgesic doses.
5. This outcome has occurred after the pattern of care below:
ER visit/hospitalization due to worsening renal impairment and/or acute renal
failure and/or renal insufficiency
This is the pattern of care:
1. Diagnosis/history of moderate to severe renal impairment and/or history of
kidney disease
2. Use of tetracycline
3. BUN/serum creatinine not done within 30 days of initiation of therapy and at
least every 6 months
Comments from round 1:
Chronic tetracycline? E.g.; For acne?
Use serum creatinine.
6. This outcome has occurred after the pattern of care below:
ER visit/hospitalization due to hyperkalemia
This is the pattern of care:
1. Use of an ACE inhibitor (e.g.; captopril, enalapril, etc.)
2. Electrolytes/CBC not done at least every 6 months
Comments from round 1:
Should be done baseline, 10-14 days, then pm.
7. This outcome has occurred after the pattern of care below:
Blood dyscrasias and/or hyponatremia and/or excessive water retention and/or
syndrome of inappropriate antidiuretic hormone (SIADH)
This is the pattern of care:
1. Use of carbamazepine
2. Electrolytes/CBC not done before therapy initiated, at least weekly during the
first month of therapy, at least monthly during the next five months of therapy,
and at least every 6 months thereafter
Comments from round 1:
I monitor only CBC and drug levels routinely; others pm.

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8. This outcome has occurred after the pattern of care below:
Acute renal failure and/or renal insufficiency
This is the pattern of care:
1. Use of lithium
2. BUN/serum creatinine not done at least every 3 months
Comments from round 1:
Monitor lithium levels.
The pattern of prevention should be used in those patients with baseline
creatinine elevation.
Use serum creatinine.
Comments from round 2:
I’ll go with the group, but this is not generally the practice.
Need to follow electrolytes also.
9. This outcome has occurred after the pattern of care below:
Theophylline toxicity
This is the pattern of care:
1. Use of theophylline
2. Drug level not done at least every 6 months
Comments from round 1:
Addition of other medications that interact with theophylline?
10. This outcome has occurred after the pattern of care below:
Bipolar exacerbation and/or ER visit/hospitalization due to bipolar disorder
This is the pattern of care:
1. Diagnosis/history of bipolar disorder
2. Use of lithium
3. Drug level not done at least every 3 months
Comments from round 1:
Lithium level is not an all-inclusive monitoring parameter (i.e.; level may be
normal).
11. This outcome has occurred after the pattern of care below:
Major and/or minor hemorrhagic event
This is the pattern of care:
1. Use of IV heparin
2. PTT not done at least every day
Comments from round 1:
Usually requires more frequent monitoring, at least initially.
12. This outcome has occurred after the pattern of care below:
Gastritis and/or upper GI bleeds and/or GI perforations and/or GI ulcers and
anemia
This is the pattern of care:
1. History/diagnosis of ulcers and/or gastrointestinal bleeding
2. NSAID (e.g.; diclofenac, ibuprofen, ketoprofen, etc.) use for at least 1 month
Comments from round 1:
Cytoprotective agent!!

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13. This outcome has occurred after the pattern of care below:
Gastritis and/or upper GI bleeds and/or GI perforations and/or GI ulcers and
anemia
This is the pattern of care:
1. History/diagnosis of ulcers and/or gastrointestinal bleeding
2. Use of an oral corticosteroid (e.g., prednisone) for at least 3 months
Comments from round 1:
No proof.
The prednisone may be essential and some antiulcer medications may interfere
with its absorption - thus might want to use antiulcer medications with
prednisone.
Comments from round 2:
Long-term corticosteriods warrant close monitoring of patient period!
14. This outcome has occurred after the pattern of care below:
ER visit/hospitalization due to depression and/or increase in dosage of
antidepressant
This is the pattern of care:
1. History/diagnosis of depression
2. Use of a barbiturate (e.g.; butalbital)
15. This outcome has occurred after the pattern of care below:
ER visit/hospitalization due to depression and/or increase in dosage of
antidepressant
This is the pattern of care:
1. History/diagnosis of depression
2. Use of a sympatholytic antihypertensive (e.g.; resperine, methyldopa,
clonidine, etc.)
16. This outcome has occurred after the pattern of care below:
ER visit/hospitalization due to congestive heart failure and/or heart block
This is the pattern of care:
1. History/diagnosis of congestive heart failure with heart block or advanced
bradycardia
2. Use of digoxin
Comments from round 1:
What kind of monitoring? Concomitant medications? Dosage?
17. This outcome has occurred after the pattern of care below:
Fall and/or hip fracture and/or other bone fracture and/or bone break
This is the pattern of care:
1. Use of a long-half-life hypnotic-anxiolytic (e.g.; flurazepam, diazepam,
chlordiazepoxide, etc.)

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18. This outcome has occurred after the pattern of care below:
Acute renal failure and/or renal insufficiency
This is the pattern of care:
1. Use of an ACE inhibitor (e.g.; captopril, enalapril, etc.)
2. BUN/serum creatinine not done at initiation of therapy and at least every 3
months thereafter
Comments from round 1:
Initial monitoring should be 10-14 days after starting.
Unless patient has renal insufficiency with concomitant diabetes.
Check BUN/serum creatinine every 3-6 months.
19. This outcome has occurred after the pattern of care below:
Status epilepticus and/or ER visit/hospitalization due to seizure activity
This is the pattern of care:
1. Use of an anticonvulsant requiring drug level monitoring (e.g.; phenytoin,
carbamazepine, valproic acid)
2. Drug level not done upon initiation of therapy and at least every 6 months
thereafter
Comments from round 1:
Once seizure control achieved, not necessary to obtain blood levels.
20. This outcome has occurred after the pattern of care below:
Lithium toxicity
This is the pattern of care:
1. Use of lithium
2. Lithium level not done at least every month
Comments from round 1:
Monthly too frequent for most cases.
Electrolyte (chemistry lab test) needed.
Comments from round 2:
Still think monthly too frequent in stable patient.
21. This outcome has occurred after the pattern of care below:
ER visit/hospitalization due to hyperglycemia
This is the pattern of care:
1. Use of an oral hypoglycemic agent (e.g.; chlorpropamide, etc.)
2. Hemoglobin Ale level not done at least every 6 months
Comments from round 1:
Should try to avoid “older ” hypoglycemics in elderly.
22. This outcome has occurred after the pattern of care below:
Major and/or minor hemorrhagic event
This is the pattern of care:
1. Use of warfarin
2. Prothrombin time not done before therapy starts and at least every month
thereafter

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23. This outcome has occurred after the pattern of care below:
ER visit/hospitalization due to hyperthyroidism
This is the pattern of care:
1. Use of a thyroid or antithyroid agent (e.g.; levothyroxine, propylthiouracil,
etc.)
2. T4/TSH not done within 6 weeks after initiation of therapy and at least every
12 months thereafter
Comments from round 1:
TSH level sufficient.
24. This outcome has occurred after the pattern of care below:
Secondary myocardial infarction
This is the pattern of care:
1. History/diagnosis of myocardial infarction
2. No use of ASA and/or a beta-blocker (e.g.; metoprolol, etc.)
25. This outcome has occurred after the pattern of care below:
Blood dyscrasias
This is the pattern of care:
1. Concurrent use of trimethoprim/ sulfamethoxazole (Bactrim, Septra) and
methotrexate
2. WBC/platelets/CBC not done at least every 4 weeks
Comments from round 1:
Also electrolytes in HIV patients (potassium increasesj.
Need baseline CBC also.
Also need to check hepatic function periodically for methotrexate.
26. This outcome has occurred after the pattern of care below:
ER visit/hospitalization due to a major/minor hemorrhagic event
This is the pattern of care:
1. Warfarin use
2. NSAID (e.g.; diclofenac, ibuprofen, ketoprofen, etc.) use
3. PT not done within 10 days
Comments from round 2:
Would use cytoprotection or proton-pump inhibitor
Should not use NSAIDS and warfarin unless unusual reasons to do so.
27. This outcome has occurred after the pattern of care below:
ER visit/hospitalization due to hypothyroidism
This is the pattern of care:
1. Lithium use for at least 6 months
2. TSH not done at least every 6 months
Comments from round 2:
Or hyperthyroidism

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28. This outcome has occurred after the pattern of care below:
ER visit/hospitalization due to a major/minor hemorrhagic event
This is the pattern of care:
1. Warfarin use
2. Antibiotic use (Bactrim, etc.)
3. PT not done within 5 days
29. This outcome has occurred after the pattern of care below:
Gastritis and/or upper GI bleed and/or G1 perforation and/or GI ulcer and anemia
This is the pattern of care:
1. Use of two or more NSAIDs concurrently for at least 2 weeks
Comments from round 2:
Who does this?
30. This outcome has occurred after the pattern of care below:
Blood dyscrasias/ thrombocytopenia
This is the pattern of care:
1. Use of Ticlopidine (Ticlid)
2. CBC/platelets not done at baseline, within 2 weeks of start of therapy and
within 2 months
31. This outcome has occurred after the pattern of care below:
Rebound congestion
This is the pattern of care:
1. Use of a long-acting nasal spray (e.g.; oxymetazoline) for more than 3 days
32. This outcome has occurred after the pattern of care below:
Acute urinary retention
This is the pattern of care:
1. Diagnosis/ history of bladder atony due to diabetes.
2. Use of Imipramine
33. This outcome has occurred after the pattern of care below:
Acute respiratory failure
This is the pattern of care:
1. History/ diagnosis of severe COPD
2. Use of a medium to long-acting benzodiazepine
Comments from round 2:
Probably true in C02 retainers. Should not use long-acting benzodiazepines in
elderly anyway.
34. This outcome has occurred after the pattern of care below:
Acute urinary retention
This is the pattern of care:
1. History / diagnosis of benign prostatic hypertropy (BPH)
2. Use of an anticholinergic agent
Comments from round 2:
Or antihistamine

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35. This outcome has occurred after the pattern of care below:
ER visit/hospitalization due to liver toxicity
This is the pattern of care:
1. Use of troglitazone (Rezulin)
2. Liver function tests not done at baseline and at least monthly for the first 8
months of therapy and at least every 2 months for the remainder of the first year.
The following outcomes & patterns of care were agreed to be preventable drus-related
morbidities by 6 geriatric medicine expert panel members:
36. This outcome has occurred after the pattern of care below:
ER visit/ hospitalization due to congestive heart failure and/or fluid overload
This is the pattern of care:
1. History/diagnosis of high blood pressure (over 140/90) and/or congestive heart failure
2. NSAID (e.g.; diclofenac, indomethacin, ketoprofen, etc.) use for at least 3 months
Comments from round 1:
Would shorten interval to 10-14 days.
ACE inhibitors, carvedilol key prophylactics. NSAÍDS & uncontrolled BP are two risk
factors that can also be modified.
How frequent is monitoring of blood pressure?
Comments from round 2:
As long as it is accepted that the role of NSAIDS may be minor in this setting of
hypertensive cardiomyopathy or the key prophylactics may prevent the drug morbidity in
the first place.
Agree with comments regarding BP control and NSAIDS.
37. This outcome has occurred after the pattern of care below:
ER visit/hospitalization due to extreme hypogylcemia
This is the pattern of care:
1. History/diagnosis of diabetes
2. Use of a beta-adrengeric blocking agent (e.g.; propranolol, nadolol, etc.)
Comments from round 1:
Recent data tends to indicate that beta-blockers may not reduce hypoglycemic
awareness to the degree previously claimed. Thus this is a controversial area at
present.
Selective versus non-selective beta-blocker? At what dosage? Dietary habits.
Comments from round 2:
See New Engl J Med article (Aug 20, 1998) on use of beta-blockers post Mi¬
not everyone has same degree of diabetes, severity of illness, or type.

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38. This outcome has occurred after the pattern of care below:
ER visit/hospitalization due to depression and/or increase in dosage of
antidepressant
This is the pattern of care:
1. History/diagnosis of depression
2. Use of a moderate to high lipophilic beta-adrengeric blocking agent (e.g.;
propranolol, pindolol, etc.)
Comments from round 1:
Discontinuing beta-blocker in lieu of stronger antidepressant treatment.
The role in beta-blocker causation of depression is probably not as significant
as previously claimed - thus it is a controversial area.
Comments from round 2:
If you can avoid using a med, then it should be done.
Beta blockers not uniformally or clearly significantly associated with
depression.
39. This outcome has occurred after the pattern of care below:
ER visit/hospitalization for hypokalemia
This is the pattern of care:
1. Use of a potassium-wasting diuretic (e.g.; hydrochlorothiazide, etc.)
2. No concurrent use of potassium chloride supplement
3. Electrolytes not checked at least every 2 months
Comments from round 1:
Except very low dose (6.25 or 12.5mg) HCTZ.
I would do q 4 months for thiazides and q2-3 months for loop diuretics.
Once stable, checking electrolytes every 4-6 months is adequate.
Comments from round 2:
Agree with except low-dose HCTZ.
40. This outcome has occurred after the pattern of care below:
Anticonvulsant drug toxicity
This is the pattern of care:
1. Use of an anticonvulsant requiring drug level monitoring (e.g.; phenytoin,
carbamazepine, valproic acid)
2. Drug level not done at least every 6 months
Comments from round 1:
Routine levels are not necessary once seizure control achieved and no evidence
of clinical toxicity.
Addition of medications that interact with anticonvulsant?
Comments from round 2:
Still stick with my original comments despite being in the minority.

183
41. This outcome has occurred after the pattern of care below:
ER visit/hospitalization due to hypothyroidism
This is the pattern of care:
1. Use of a thyroid or antithyroid agent (e.g.; levothyroxine, propylthiouracil,
etc.)
2. T4/TSH not done before therapy starts and at least every 12 months thereafter
Comments from round 1:
Check TSH in 6-8 weeks and every 4 months after prescription.
Monitoring should be more frequent (TSH only).
Monitoring more frequently required.
TSH is sufficient.
Comments from round 2:
Interval too long. Many patients seen with iatrogenic hypothyroidism following
their annual TSH screen.
42. This outcome has occurred after the pattern of care below:
ER visit/hospitalization due to systolic heart failure
This is the pattern of care:
1. History/diagnosis of systolic heart failure
2. Use of a beta-adrengeric blocking agent (e.g.; propranolol, nadolol, etc.)
Comments from round 1:
Low dose beta-blockers now recommended in Class III or IV.
Comments from round 2:
Preventable if identified as the only variable.
Dosage?
Recent literature suggests beta blockers may have value in heart failure (Aug 20,
NEJM, 1998)
43. This outcome has occurred after the pattern of care below:
ER visit/hospitalization due to congestive heart failure
This is the pattern of care:
1. History/diagnosis of congestive heart failure
2. Use of an antiarrhythmic agent (e.g.; disopyramide, procainamide, etc.)
Comments from round 1:
Too many other factors usually involved.
There is no answer to this - is preferable to use antiarrhythmics without the
significant negative inotropic effects of the agents - but the rhythm may dictate
their use.
Comments from round 2:
See above.

184
44. This outcome has occurred after the pattern of care below:
Fall and/or hip fracture and/or other bone fracture and/or bone break
This is the pattern of care:
1.Use of an antipsychotic (e.g.; thioridazine, haloperidol, chlorpromazine, etc.)
Comments from round 1:
Benefit may outweigh risk - perhaps no choice.
The medication may be necessary for therapy and thus the adverse event
potential is mitigated by the requirement for use of the agent.
Comments from round 2:
See above comments.
45. This outcome has occurred after the pattern of care below:
Asthma exacerbation and/or status asthmaticus and/or ER visit/hospitalization
due to asthma
This is the pattern of care:
1. Diagnosis of asthma
2. Use of a bronchodilator
3. No use of a maintenance corticosteriod (e.g.; beclomethasone, etc.)
Comments from round 1:
Inhaled not oral
This one is patient-dependent. Could apply in some.
Mild intermittent asthma does not require anti-inflammatory medication.
Comments from round 2:
Agree with comment ‘‘could apply in some mild intermittant - no, mild, mod,
severe persistant - yes
46. This outcome has occurred after the pattern of care below:
Hospitalization/ER visit due to worsening renal impairment and/or acute renal
failure and/or renal insufficiency
This is the pattern of care:
1. Diagnosis/history of moderate to severe renal impairment/history of kidney
disease
2. Use of a select urinary antiinfective agent (nalidixic acid, nitrofurantoin or
methenamine complexes)
3. BUN/serum creatinine not done within 30 days of initiation of therapy and at
least every six months
Comments from round 1:
These drugs shouldn 't be used long term - sometimes low dose nitrofurantoin is
for suppression but shouldn’t need monitoring.
Checking of renal status for use of these drugs is not a usual procedure.
Can t remember the last time the above three antibiotics were prescribed.
Comments from round 2:
CrCl <50 nitrofurantoin and urinary antiseptics don 7 work.

185
47. This outcome has occurred after the pattern of care below:
ER visit/hospitalization due to congestive heart failure
This is the pattern of care:
1. Diagnosis/history of congestive heart failure
2. Not on an ACE inhibitor (e.g.; captopril, enalapril, etc.)
Comments from round 1:
Patient-dependent; depends on type of heart disease, renal function, etc.
Could be diuretics (but maybe not an adequate dosage).
Comments from round 2:
Could be on inadequate diuretics
48. This outcome has occurred after the pattern of care below:
Aminoglycoside toxicity (acute renal failure and/or renal insufficiency and/or
vestibular damage and/or auditory damage)
This is the pattern of care:
1. Use of an aminoglycoside
2. Serum creatinine not done before and after therapy (and if therapy longer than
7 days, not done at least every 7 days)
3. At least one drug level not done
Comments from round 1:
With single dose aminoglycoside in pt with short course of treatment and normal
renal baseline function, probably not required.
Comments from round 2:
Same explanation
49. This outcome has occurred after the pattern of care below:
ER visit/hospitalization due to congestive heart failure
This is the pattern of care:
1. History/diagnosis of congestive heart failure
2. Use of a calcium channel blocker (e.g.; diltiazem, etc.)
Comments from round 1:
Only with the calcium channel blockers with negative inotropy (e.g.; verapamil).
Concomitant hypertension? Concomitant diuretics?
Comments from round 2:
Negative inotropy comment as above.
50.This outcome has occurred after the pattern of care below:
COPD exacerbation and/or ER visit/hospitalization due to COPD
This is the pattern of care:
1. Diagnosis/history of COPD
2. Use of a beta-blocker (e.g.; propranolol, etc.)
Comments from round 1:
Use of beta-agonist? Use of steroid inhaler? Selective versus non-selective beta-
blocker? At what dosage of beta-blockers?
Comments from round 2:
In Aug 20, 1998 NEJM- moderate to severe COPD - beta blockers used to
reduce repeat Ml (? Severity and correlation beta-blocker use still ill-defined
and needs more study)

186
The following outcomes & patterns of care were agreed to be preventable drus-related
morbidities by 5 geriatric medicine expert panel members:
51. This outcome has occurred after the pattern of care below:
ER visit/hospitalization due to hypoglycemia or hyperglycemia
This is the pattern of care:
1. Use of insulin
2. Hemoglobin Ale level not done at least every 6 months
Comments from round 1:
Apply to hyper, not hypo
Ale is more for long term control. Symptoms and home blood testing are the best
way to prevent acute episodes with some references to Ale.
Tight control of diabetes can result in hypoglycemia despite the best of care.
Comments from round 2:
Home blood testing is the preventive mechanism.
Still feel that can occur no matter how diligent the care.
A1C is for long-term hyperglycemia control
52. This outcome has occurred after the pattern of care below:
Fall and/or hip fracture and/or other bone fracture and/or bone break
This is the pattern of care:
1. Use of an anticholinergic agent
Comments from round 2:
Depending on indication for anticholinergic, alternative meds may be used.
Difficult to anticipate the problem.
What anticholinergic agent?
The following outcomes & patterns of care were rejected as preventable drus-related
morbidities by 4 or more geriatric medicine expert panel members:
53. This outcome has occurred after the pattern of care below:
Digoxin toxicity
This is the pattern of care:
1. Use of digoxin
2. BUN/serum creatinine not done at least every 6 months
3. Digoxin level not done at least every 6 months
Comments from round 1:
(2) not necessary in every case; (3) may not always be necessary.
Most important thing to monitor is symptomatology - anorexia, nausea, vision -
ifpositive then do digoxin level. Creatinine indicated as a baseline test but if no
reason for renal insufficiency then every 6 month checks not indicated.
Was there addition of other meds that interact with digoxin? Other chemistry
labs?
Comments from round 2:
(2) & (3) the interval could be one year in most cases.
Dig level is warranted if symptomology indicates so - should warn patient of
potential symptoms.
Why monitor both renal function and dig level?
As already stated by me. Agree with above comment.

187
54. This outcome has occurred after the pattern of care below:
Fall and/or hip fracture and/or other bone fracture and/or bone break
This is the pattern of care:
1. Use of a nitrate (e.g.; isosorbide, etc.)
Comments from round 1:
Very uncommon, other symptoms limit dosage, depends on other drugs in use.
Despite the potential risk ofpostural changes in blood pressue the use of these
agents would be indicated and a physician would continue the use of nitrates
despite the potential of an adverse event.
Comments from round 2:
Uncommon occurrence, difficult to predict unless patient has aortic stenosis or
HOC
Patient must be warned against getting up quickly.
As above.
most likely required for patient use despite potential risks - i.e. necessary
medication.
55. This outcome has occurred after the pattern of care below:
Acute renal failure and/or renal insufficiency
This is the pattern of care:
1. Use of allopurinol
2. BUN/serum creatinine not done at least every 6 months
Comments from round 1:
Very rare in healthy pt.
Is the incidence of renal insufficiency high enough to mandate regular testing of
CR in all patients on allopurinol?
The hypersensitivity syndrome from allopurinol is very infrequent and would not
expect routine BUN/creatinine monitoring
Comments from round 2:
Rare occurrence.
Allopurinol is not commonly used unless patient has high uric acid secondary to
gout...you need to follow BUN/creatinine and UA level
As above.
Rare event and would not routinely monitor renal status.
Routine BUN/Cr not warranted because of rarity of the adverse outcome.
Allopurinol induced renal failure uncommon.

188
56. This outcome has occurred after the pattern of care below:
Asthma exacerbation and/or status asthmaticus and/or ER visit/hospitalization
due to asthma
This is the pattern of care:
1. Diagnosis of asthma
2. Use of theophylline
3. Drug level not done at least every 6 months
Comments from round 1:
Theophylline isn ’t potent enough to prevent excerbation.
Theophylline is mostly an adjunct to anti-inflammatory therapy and is used to
smooth out dip in peak flow in early hours of morning. Otherwise it is not a very
useful drug.
Theophylline and level is not an all-inclusive method of treatment and level is
not an all-inclusive monitoring parameter.
Comments from round 2:
More importantly, is patient also taking beta-agonist and steroid inhaler?
As above.
All of the reasons recorded above.
Agree that anti-inflammatory therapy is most useful and drug levels may not
clearly indicate toxicity or effectiveness of treatment.

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BIOGRAPHICAL SKETCH
Neil John MacKinnon was bom on February 8, 1971 in New Glasgow, Nova Scotia,
Canada. He received a Bachelor of Science degree in Pharmacy from Dalhousie University in
1993. He completed a Master of Science degree in hospital pharmacy and an advanced
administrative residency at the University of Wisconsin-Madison in 1995. He has worked as a
relief pharmacist in the community setting and has trained pharmacists in eight states in an
infectious disease pharmaceutical care program for Simkin, a healthcare technology company
based in Gainesville, FL.
Following his doctoral work at the University of Florida, he plans to move back to Nova
Scotia where he will be Assistant Professor of Pharmacy at the Dalhousie University College of
Pharmacy. He also plans to be married later in 1999 to Miss Leanne Moore.
199

I certify that I have read this study and that in my opinion it conforms to acceptable
standards of scholarly presentation and is fully adequate, in scope and quality, as a dissertation
for the degree of Doctor of Philosophy.
Charles D. Hfipler, Chair
Distinguished Professor of
Pharmacy Health Care Administration
I certify that I have read this study and that in my opinion it conforms to accer
standards of scholarly presentation and is fully adequate, in scope^;
for the degree of Doctor of Philosophy.
ion
Richard Segal
Professor of Pharm
Administration
y Health Care
I certify that I have read this study and that in my opinion it conforms to acceptable
standards of scholarly presentation and is fully adequate, in scope and quality, as a dissertation
for the degree of Doctor of Philosophy.
$
Earlene E. Lipowsk#
Associate Professor of Pharmacy Health
Care Administration
I certify that I have read this study and that in my opinion it conforms to acceptable
standards of scholarly presentation and is fully adequate, in scope and quality, as a dissertation
for the degree of Doctor of Philosophy.
nAJrJ/ZL.
G. Geoffrey fining
Associate Professor of Statistics
This dissertation was submitted to the Graduate Faculty of the College of Pharmacy and
to the Graduate School and was accepted as partial fulfillment of the requirements for the degree
of Doctor of Philosophy.
May, 1999
Dean, Graduate School





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