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Utilization of care coordination among children with special ceeds in the 1994 National Health Interview Survey on Disab...

University of Florida Institutional Repository

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UTILIZATION OF CARE COORDINATI ON AMONG CHILDREN WITH SPECIAL NEEDS IN THE 1994 NATIONAL HE ALTH INTERVIEW SURVEY ON DISABILITY PHASE II By BARBARA J. KRUGER A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2004

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Copyright 2004 by Barbara J. Kruger

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To the families of children with special hea lth needs and to the professionals who help them coordinate care.

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ACKNOWLEDGMENTS Any accomplishment is accompanied by significant contributions from many individuals. Foremost, my parents, Edwin and Apolonia, had the vision to value higher education for their childrenan opportunity not available to them. Ken has particularly been a source of encouragement and support through my four academic degrees during our years together. Meanwhile, Lindsay and Michael have provided amusement, diversion, and their own style of support. Patricia, Joe, and their families have accommodated the intrusion of my laptop during family retreats and made sure I balanced work with play. The support of my family is deeply appreciated. I am grateful to my dissertation committee whose marvelous blend of expertise and collegiality provided an affirming and stimulating learning environment. I particularly acknowledge Dr. Shawn Kneipp, my dissertation chair, whose gentle guidance sustained my self-efficacy, whose challenging questions stretched me to respond critically from multiple perspectives, and whose concept of rigorous doctoral study has provided a strong foundation for future research. Each of my committee members provided valuable insights. Dr. Paul Duncan provided clarity and simplicity for the purpose of this research and its relevance. Drs. Edler and Nealis kept me faithful to my nursing heritage and its rich theoretical and research legacy. Dr. John Reiss, meanwhile, generously shared his expertise as he guided me through policy study. Everyones generosity is greatly appreciated. I also thank the University of Florida School of Nursing for fellowship support during the first 2 years of study. iv

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I acknowledge the discussions, support, encouragement, and feedback from my New England colleagues: Diane M. McCann, RN, MSN; Jane M. Hybsch, RN, BSN MHA; and Judith A. Bumbalo, RN, PhD (with NH Title V-CSHCN); and Susan G. Epstein, MSW (with New England SERVE). Their willingness to provide feedback at various stages of this doctoral program has been immensely valuable. I particularly acknowledge the editorial assistance of Diane McCann for review of numerous manuscripts. I especially thank these individuals for providing me with a practical research question and an enriching collegial practice environment. Finally, I extend gratitude to the faculty of the University of North Florida, College of Health, and School of Nursing. The leadership of Dr. Pam Chally and Dr. Lucy Trice (College of Health) and Dr. Li Loriz (School of Nursing) provided a stimulating and supportive environment for faculty development and scholarship. I also acknowledge their continuous personal support. I thank Dr. Kathleen Bloom and Dr. Kathy Robinson for their mentoring and coaching; and Dr. Barbara Olinzock for her steadfast emotional and instrumental support. I recognize all School of Nursing faculty and staff for their encouragement and humor; and for providing a professionally and personally enriching work environment. v

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TABLE OF CONTENTS page ACKNOWLEDGMENTS.................................................................................................iv LIST OF TABLES.............................................................................................................ix ABSTRACT.......................................................................................................................xi CHAPTER 1 STUDY CONTEXT.....................................................................................................1 Background and Significance.......................................................................................1 Policy Context..............................................................................................................4 Title V....................................................................................................................5 Children with Special Health Care Needs.............................................................7 Care Coordination.................................................................................................8 Service System Fragmentation............................................................................10 Community-based Care.......................................................................................13 Summary of Study Context........................................................................................15 2 LITERATURE REVIEW...........................................................................................18 Nursing Research........................................................................................................18 Use of Services by CSHCN........................................................................................21 Barriers to Service Use...............................................................................................24 Economic Barriers...............................................................................................24 Non-economic Barriers.......................................................................................26 Care Coordination.......................................................................................................27 Process.................................................................................................................28 Outcomes.............................................................................................................29 Families Who Use Care Coordination........................................................................32 Summary of Literature Review..................................................................................33 3 METHOD...................................................................................................................35 Theoretical Review.....................................................................................................35 Conceptual Framework...............................................................................................37 Predisposing Factors............................................................................................38 Enabling Factors..................................................................................................39 vi

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Need Factors........................................................................................................40 Study Design and Data Source...................................................................................40 NHIS Sampling...................................................................................................42 Data Source.........................................................................................................42 Data Collection....................................................................................................43 Creation of Dataset.....................................................................................................44 Data Cleaning......................................................................................................44 Missing Data........................................................................................................45 Study Sample Selection..............................................................................................45 Comparison of Complete and Incomplete Cases........................................................46 Measures.....................................................................................................................48 Care Coordination...............................................................................................48 Child....................................................................................................................49 Family..................................................................................................................50 Data Analyses.............................................................................................................52 Aim 1 Analyses...................................................................................................53 Aim 2 Analyses...................................................................................................53 4 RESULTS...................................................................................................................55 Sample Characteristics................................................................................................55 Utilization of Care Coordination................................................................................58 Utilization and Unmet Need for Health and Related Services...................................59 Aim 1: Determinants of Care Coordination Utilization............................................60 Predisposing........................................................................................................60 Enabling...............................................................................................................61 Need.....................................................................................................................64 Reduced Model....................................................................................................65 Aim 2: Care Coordination Utilization by Type of Provider......................................65 5 DISCUSSION.............................................................................................................70 Who Uses Care Coordination and Who Doesnt?......................................................70 Study Limitations........................................................................................................75 Implications................................................................................................................79 APPENDIX A CONSTRUCTION OF TYPE OF SERVICE VARIABLES.....................................86 Used Home Care.........................................................................................................86 Used Health Care........................................................................................................87 Used Equipment..........................................................................................................88 Used Therapy..............................................................................................................88 Used Support..............................................................................................................89 Unmet Home Care Need.............................................................................................90 Unmet Health Care Need............................................................................................90 vii

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Unmet Equipment Need.............................................................................................91 Unmet Therapy Need..................................................................................................92 Unmet Support Need..................................................................................................92 B COVARIATE CELL SIZE.........................................................................................94 LIST OF REFERENCES...................................................................................................95 BIOGRAPHICAL SKETCH...........................................................................................108 viii

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LIST OF TABLES Table Page 3-1 Model for studying care coordination utilization...................................................38 3-2 Significant differences between complete and incomplete cases..........................47 3-3 Coordination measures by NHIS-D variable and description................................49 3-4 Child measures by NHIS-D variable, description, and coding..............................50 3-5 Child service use and unmet service need categories............................................51 3-6 Family measures by NHIS-D variable, description, and coding...........................51 4-1 Characteristics of the study sample........................................................................57 4-2 Utilization of care coordination.............................................................................59 4-3 Use and unmet need for health and related services by type and variety..............60 4-4 Full model for use of care coordination by service type and variety.....................62 4-5 Reduced model for significant covariates of coordination use by service type and variety..............................................................................................................66 4-6 Full model for service type....................................................................................67 4-7 Full model for service variety................................................................................68 A-1 Home care service measure...................................................................................87 A-2 Health care service measure...................................................................................88 A-3 Equipment service measure...................................................................................89 A-4 Therapy service measure........................................................................................89 A-5 Support service measure........................................................................................90 A-6 Home care unmet need measure............................................................................91 ix

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A-7 Health care unmet need measure...........................................................................91 A-8 Equipment unmet need measure............................................................................92 A-9 Therapy unmet need measure................................................................................93 A-10 Support unmet need measure.................................................................................93 x

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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 UTILIZATION OF CARE COORDINATION AMONG CHILDREN WITH SPECIAL NEEDS IN THE 1994 NATIONAL HEALTH INTERVIEW SURVEY ON DISABILITY PHASE II By Barbara J. Kruger May, 2004 Chair: Shawn M. Kneipp Major Department: Nursing Care coordination helps families of children with special needs obtain a variety of services and manage communication among providers. Some families coordinate care themselves, while others receive assistance from professionals. Health care system changes are making these children less visible, and therefore at-risk for not receiving care coordination. A key question is how to identify families who need care coordination. Research about use of care coordination is sparse, and no generalizable method exists to identify which families require coordination. One aim of our study was to explore the differences among child and family factors and the use and need for health and related services between families who do and do not use care coordination. A second aim was to identify the determinants of professional care coordination. Secondary analyses of children, birth to 18 years of age, produced a weighted sample of 7,870,264 children of which 67% used care coordination. Professionals were more frequent providers of coordination compared to families and professionals or xi

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families alone. Multivariate logistic regression showed that children who had private insurance, those who used health, support, equipment, or therapy services, and those who used a greater variety of these services were more likely to have coordination. Fair or poor child health status, co-morbidities, family financial stress, and need for services also predicted use of coordination. Children least likely to receive coordination were black, were foreign-born, lived in large families, or lived on the west coast. Multinomial logistic regression showed that highly educated, residentially stable families whose child had private or public insurance, used health services, and had good health status were more likely to have professional coordination. Families with older children, children who were black, or who lived on the west coast were less likely to use professional coordination. Our study suggests that children and families who might need care coordination may not be receiving it, and that racial/ethnic disparities exist. It supports that a focus on the family and social context, as well as the child and medical context, is necessary. Nurses are well-positioned across health and human services systems to influence policy, practice, and research. xii

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CHAPTER 1 STUDY CONTEXT Background and Significance Chronic illness and disability among children may generate a combination of physical, social, financial, psychological, and educational impacts on the child and family (Hobbs, Perrin, & Ireys, 1985). Consequently, children with special health care needs (CSHCN) require a greater volume and variety of health and related services than do children generally. These services include primary and specialty medical care, allied health, education, developmental, social, and financial services. Families continuously manage the care of their child into adulthood and over the chronic illness trajectory. They also manage communication among professionals across health and human services systems. Some families coordinate this care themselves, some report difficulty navigating the various delivery systems, and others may receive assistance from professionals. It is unknown, however, which families require care coordination from a professional. Studies suggest that care coordination connects families to information and to a range of services; and facilitates communication across those services. The process of care coordination is goal-directed and individualized for each family to meet family-identified needs. Its purpose may be to improve quality of life, child health, continuity of care, and to facilitate family capacity for self-care. Health-system outcomes related to care coordination include satisfaction with care, access to health services, and cost savings. A variety of programs and direct services for CSHCN (such as hospital discharge and transition programs, specialty medical care, early intervention, and 1

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2 Medicaid managed-care carve-out programs) include family care coordination as an approach to the delivery of heath and related clinical services (Kruger, 2002). Care coordination, however, is not a universally available or billable service and family acquisition of care coordination may be fortuitous. Furthermore, families who coordinate their own childs care do so even though they have a source of health care (namely, access to a health care provider). Currently, there are no generalizable methods that help distinguish which families need care coordination. One approach to identifying families who may require professional assistance is to determine how families who receive this service differ from those who do not. Although clinical research supports that families who receive care coordination are more likely to be referred to a variety of health and related services, there is no study that associates the use of health and related services with care coordination. Based on the literature, it is reasonable to expect that a combination of factors (that relate to the child, family, and their use and need for health and related services) can be used to differentiate among families. The primary purpose of our study is to explore the characteristics that distinguish children and families who use care coordination from those who do not. Determining how these families differ from each other could provide information that can be used for casefinding and program planning. The research questions are as follows: Aim 1: What are the differences in child and family socio-demographic factors, use of health and related services, and unmet need for health and related services, between families who use care coordination and families who do not?

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3 Aim 2: What are the determinants (child, family, use of health and related services, or unmet service need) of the use of care coordination depending on who provides it (family only, professional only, family with professional). Our study would contribute to answering a significant policy question. Federal policy (in conjunction with popular support by provider and family advocacy groups) values the development of community-based systems of care for these children that includes care coordination. However, these systems are sporadically available and there is no customary method for identifying families who require care coordination. Determining which families are more likely to use care coordination could assist agencies, who are mandated to assure the availability of these services, to better plan resource allocation. Our study would also contribute to answering a significant clinical practice question: how and where might we identify families? Studies suggest that families who use care coordination are those whose children are enrolled in categorical or specialty programs. Research, however, does not address how families who require care coordination are identified within or outside of these programs. The literature describes the care coordination process in terms of providing information, initiating referrals, and communication across service systems. Yet, the pediatric chronic illness and family research emphasis has been on how families cope with the illness, not on their interaction with health and related systems. It is well known that CSHCN use a variety of medical and nonmedical services. What is unexplored is the relationship among these services and care coordination. Nurses are employed throughout the health, education, and

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4 developmental service systems and are well-positioned to identify families for care coordination services. The proliferation and emphasis on care coordination by policymakers, families, and health care professionals intensifies the demand for research. Yet, studies regarding care coordination for CSHCN are limited in volume and are largely based on convenience samples of families who receive these services in clinical programs. Non-users of care coordination are not included in this research. The 1994 National Health Interview Survey on Disability (NHIS-D) Phase II for children was used as the data source for answering the questions posed in our study. This nationally representative household survey contains a sample of families of children who use and who do not use care coordination. This is important because non-users of care coordination have not been included in prior research. The NHIS-D Phase II also contains information about the child and family and their utilization of a variety of health and related services. This exploratory study proposes to identify child and family characteristics associated with the use of care coordination and with a wide variety of medical and nonmedical services. This is an initial step in a program of research that seeks to develop methods to identify families who require care coordination and understand how families use external resources. Policy Context This section discusses factors that influence the development of systems of care, including care coordination, for CSHCN. It will also define the population of CSHCN and care coordination. Finally, family-expressed needs and changes in the health care system are discussed to provide a rationale for why care coordination has been and will continue to be an integral component of service delivery for these families.

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5 Title V Care coordination for CSHCN in the United States has been supported under the policy context of Title V of the Social Security Act of 1935. Title V, known as the Maternal and Child Health Block Grant, authorizes federal funds to states for maternal and child health services, including CSHCN. A revision to this federal mandate in 1989, in conjunction with ongoing changes in health policy, has continued to influence the delivery of services to this population (Kruger, 2001). A significant change was the emphasis on creating systems of care for all CSHCN, not just those enrolled in state programs. States are required to provide and promote family-centered, culturally competent, community-based, coordinated care (including care coordination) and to facilitate the development of systems of care in communities where families and children live (USDHHS, 1998). Healthy People 2010 (the nations prevention agenda) supports this federal mandate by calling for an increase in the proportion of states that have community-based systems of care for CSHCN (USDHHS, 2000). Care coordination for CSHCN has a long history that has been linked to the work of the Childrens Bureau in the early part of the century (Perrin, Shayne, & Bloom, 1993). In 1984, most State Title V-CSHCN programs reported that coordinating patient care services was very important to their mission and an activity they did not spend enough time doing (Ireys & Eichler, 1988). A recent survey of state Title V-CSHCN programs reported that 30 of 46 states have increased the intensity of provision of care coordination for CSHCN, expanded eligibility for these services, and/or are providing a comprehensive model of services (Zimmerman, Schwalberg, Gallagher, Harkins, & Sines, 2000). Advocacy groups are echoing the importance of defining goals and principles for care coordination to guide Title VCSHCN programs (Association of

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6 Maternal and Child Health Programs, 2000, 2002). Family needs assessments, as required by the Maternal and Child Health Block Grant, also validate that care coordination is a priority for families (Reiss, 2000). The most recent development in the evolution of federal policy has been the been the dissemination of a 10-year action plan (McPherson & Honberg, 2002). This plan identifies six core goals to be achieved by the year 2010, and numerous action steps related to improving coordination of care. These strategies include developing models of coordination between primary and specialty health care providers, developing medical home models, coordinating services with community professionals, improving pediatric to adult medical transition, supporting tele-health initiatives, determining the cost of care coordination, developing financing models, and establishing adequate reimbursement for care coordination (USDHHS, 2001). A requirement of the Maternal and Child Health Block Grant is that all state Title V-CSHCN programs must annually report the percent of CSHCN who have a medical home. The medical home was recommended as an approach for pediatricians to assure coordinated care for CSHCN in response to the OBRA 89 impetus to move the care of CSHCN to community-based primary care (Brewer, McPherson, Magrab, & Hutchins, 1989). However, it has only recently received heightened national attention due to the combined efforts of the Maternal and Child Health Bureau and the American Academy of Pediatrics. A medical home is described as an approach to providing quality and cost-effective health care services at the primary care level (AAP Medical Home Initiatives for Children with Special Needs Project Advisory Committee, 2002). Professionals and

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7 parents are envisioned as working together to identify and access medical and nonmedical services required by CSHCN. The sustained and escalating emphasis on care coordination for CSHCN and their families is unmistakable. The interest in assuring that these services are provided at the community level, particularly in primary care settings, is a more recent development. For families to receive care coordination, they must first be identified. Currently no approach exists that helps to identify which families require care coordination. This is different than the approaches that attempt to identify which children are CSHCN. Care coordination is targeted to families of children (not to the child alone). Comparing characteristics of children and families who use and dont use care coordination may provide information about potential predictors of need for care coordination. To date, this type of study has not been reported in the literature. Children with Special Health Care Needs The definition of CSHCN includes children ". . who have or are at increased risk for a chronic physical, developmental, behavioral, or emotional conditions and who also require health and related services of a type or amount beyond that required by children generally (McPherson et al., 1998, p. 138). The revision to the Title V-CSHCN mandate in OBRA 89 created a change in how children were defined. CSHCN are children first. The label of crippled child and use of diagnoses were replaced in favor of a non-categorical approach because children share similar needs across medical diagnoses. CSHCN are a heterogeneous group with more than 200 conditions ranging from prevalent to very rare and from mild to severe conditions. These children are included in three overlapping groups: 1) children with developmental delays or disabilities; 2) children with ongoing medical disorders and chronic illness; and 3) children with

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8 emotional and behavioral problems. Approximately 18% of children (12.6 million) under 18 years of age in the United States in 1994 were reported to have a chronic condition (physical, developmental, behavioral or emotional) and service use or presumed need for greater health or related services beyond that generally required by children (Newacheck, Strickland et al., 1998). The prevalence of a special health care need increases with age, with boys more likely than girls, and African-American children more likely than other minority children. Additionally, the occurrence of co-morbidity affects about 5% of children who have one chronic condition; and places them at risk for developmental, learning, and behavioral problems, along with a progressive increase in physician and hospital services (Newacheck & Stoddard, 1994). There has been much emphasis in the literature on the development of approaches to operationalize a consistent definition of CSHCN. This is necessary in order to collect population-based data for the purposes of epidemiology, program planning, quality assurance, and risk adjustment. None of these approaches for identifying CSHCN were intended to identify families who require care coordination (Epstein & Walker, 2002). Care Coordination The use of the term care coordination (versus case management) is consistent with the family-centered care values of the Title V legislation. Care coordination has been defined specifically in relationship to CSHCN by the Title V legislation and the American Academy of Pediatrics (AAP); and referred to as case management within nursing (by the American Nurses Association) and within the case management industry (by the Case Management Society of America). Care coordination services, as defined in OBRA 1989 ". . promote the effective and efficient organization and utilization of resources to assure access to necessary

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9 comprehensive services for children with special health care needs and their families (USDHHS, 1998, Section 2, p. 3). This definition is focused on the health care system and access to services. In comparison, the AAP defines care coordination as a process . . that links children with special health care needs and their families to services and resources in a coordinated effort to maximize the potential of the children and provide them with optimal health care (Committee on Children with Disabilities, 1999, p. 978). This definition considers the context of the family and the long-term effects on the child. Nursing and allied health case management focus their definitions on all populations. The purpose of nursing case management is to . . integrate, coordinate, and advocate for individuals, families and groups requiring extensive services. The ultimate goal is to achieve planned care outcomes by brokering services across the health care continuum (Bower, 1992, p. 3). This focus of case management is on high users of services and it suggests that brokering is the mechanism of coordinating care. The international allied health case management industry identifies the components of a . . collaborative process which assesses, plans, implements, coordinates, monitors and evaluates the options and services to meet an individual's health needs through communication and available resources to promote quality, cost-effective outcomes (Case Management Society of America, 2002, p. 5). All of these definitions are focused on health care services delivery and explicitly or implicitly identify the functions of assessment, service planning, implementation, monitoring, and evaluation. The Vanderbilt Study, the first comprehensive assessment of this population of children and families in the U.S., emphasized the value of coordination as a role for nursing (Hymovich, 1985) and as a method to improve child and family functioning and

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10 reduce unnecessary use of medical care (Perrin, Ireys, Hobbs, Shayne, & Moynihan, 1985). Nurses trace the origins of care coordination to the settlement-house movement in the 1800's and turn-of-the century community health services coordination performed by public health nurses (Kersbergen, 1996; Tahan, 1998) as the forerunner to contemporary nursing case management in primary, hospital and managed-care settings (Lyon, 1993). The nursing literature vacillates between the terms care coordination and case management. The term, coordinator of care, is a core competency for baccalaureate prepared nurses as identified by the American Association of Colleges of Nursing (1998). A distinction between the terms care coordination and case management was made in a study of state Medicaid managed-care programs. Rosenbach and Young (2000) described care coordination as a social service model whose goal was to facilitate access to quality care across a broad range of programs in the community for vulnerable populations. Case management, in contrast, was described as containing costs within a medical model of service delivery for high users of costly services. Whether these distinctions are universal is moot for families who prefer the term care coordination. Service System Fragmentation Hundreds of categorical programs that fund services for children and families have developed over 30 years of legislation (Grason & Guyer, 1995). Categorical programs are developed to meet a particular human service need, are affiliated with a particular lead agency; and have specific eligibility criteria and scope of services, and designated providers (including coordinators). A family who has a young child may complete an application for early intervention, Title V-CSHCN, Medicaid or a childrens health insurance program, nutrition services through WIC, medical/health services through a primary care office or clinic, and a family support program through mental health (all in

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11 different agencies, in different places across town). This is a relatively mild example of the extent of service requirements by some families. Even if families have been deemed eligible for any or all of these services, some may require annual re-application. Knowing what to ask for and where to find it (and the associated extra work and hassles) is a challenge for families who seek a variety of services to support themselves and their children. Furthermore, some of these services may actually duplicate each other, yet paradoxically leave gaps where families "don't fit" the eligibility criteria for age, condition, or financial status. For example, grandparents who are primary caregivers of children have a particularly difficult time fitting into eligibility criteria and may be denied benefits such as social security-disability, Medicaid, or special education for their grandchildren. Case managers who accompanied grandparents to appeal denials for services referred 25% of the cases to legal assistance before a benefit could be received (McCallion, Janicki, Grant-Griffin, & Kolomer, 2000). A decade of studies that have identified the needs of families with CSHCN validate that families require help locating and obtaining medical and nonmedical services, and communicating across multiple systems (Davis & Steele, 1991; Diehl, Moffitt, & Wade, 1991; Gabor & Farnham, 1996; Garwick, Patterson, Bennett, & Blum, 1998; Horner, Rawlins, & Giles, 1987; Krauss, Wells, Gulley, & Anderson, 2001; New England SERVE, 1997; Saywell, Zollinger, Schafer, Schmit, & Ladd, 1993; Walker, Epstein, Taylor, Crocker, & Tuttle, 1989). As an aggregate, these studies, based on convenience samples, represent thousands of single, two-parent, and minority families of children with a variety of conditions, living in urban and rural areas across the country. They identify

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12 that some families want and/or use care coordination, but they do not identify what distinguishes families who use care coordination from those who do not. Coordinating care for CSHCN is not an exclusively U.S. phenomenon. A link person who assists families to access services is common in Canada and the United Kingdom where the lack of health insurance is a less significant barrier to care (Baine, Rosenbaum, & King, 1995; Ray, 1997; Sloper & Turner, 1992). Navigating the health care system is described by Ray (1997) as working the system and was identified by Canadian parents as the worst aspect of having a CSHCN and a large part of parental caregiving. Parents spent a considerable amount of time learning, phoning, following-up, searching, and adjusting their employment hours to assume the role of their childs coordinator of care. The work of parents included researching medical treatments, monitoring their childs quality of care, participating in advocacy groups, learning the politics of the health care system, and ultimately learning how to work around the system to get what they required. Parent work and expertise, however, was reported to remain invisible to many health care providers (Ray, 1997). Care coordination is an individual response to service system fragmentation in that it works at the level of the family to assure access to a broad range of medical and nonmedical services. This is distinct from a service systems response to fragmentation that seeks to integrate services by pooling funding streams and creating single points of entry into service delivery systems (Hughes, Halfon, Brindis, & Newacheck, 1996). Systems coordination is focused on agency networks and the structure within which care coordination takes place. System outcomes are concerned with cost-effective use of services and encouraging service integration. Care coordination and systems responses

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13 are interdependent, and both approaches are necessary to assist families to access services. Community-based Care The expansion of managed-care during the 1990s has precipitated a shift from specialty care to primary care for many populations. Historically, CSHCN have been higher users of specialty medical services in contrast to primary care (Perrin & Ireys, 1984). Furthermore, children attending specialty clinics did not always have a source of primary care (Palfrey, Levy, & Gilbert, 1980). The movement to community-based services, close to where families live, is consistent with the Title V-CHSCN mission; however, this has been a change for families and providers. Managed-care offers families the opportunity to access comprehensive pediatric primary care with minimal out-of-pocket expense. However, concerns about Medicaid managed-care were found to include the disruption of long-term provider relationships, restrictions placed on referrals, and disincentives to refer children to the services they may require (Mele & Flowers, 2000). For example, 3 years after disbanding a 25-year-old team clinic for children with myelomeningocele, one-half of the clients reported no orthopedic or urologic follow-up and two-thirds reported no neurological or pediatric follow-up (Kaufman et al., 1994). One third of the children reported no primary pediatric provider. This means that they lost their connection to specialty care and did not make a connection to primary care. Clients from the disbanded clinic also were observed to undergo fewer proactive surgical procedures and more preventable and serious surgical procedures compared to a comparison site. The role of the coordinator, central to a multidisciplinary team, was not assumed by the local physician or the family.

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14 Federal expansions of Medicaid and State Childrens Health Insurance Programs have enabled CSHCN to access primary care. Consequently, more CSHCN are enrolled in Medicaid versus employer-insured plans than in the past (Shatin, Levin, Ireys, & Haller, 1998). Furthermore, CSHCN in Medicaid now receive most of their care from generalist providers at the primary care level (Kuhlthau, Ferris, Beal, Gortmaker, & Perrin, 2001). Although care coordination has been incorporated into some of these financing initiatives, and within private managed-care plans for high-risk populations, for CSHCN it is more often provided in public programs (Krauss, Gulley, Leiter, Minihan, & Sciegaj, 2000). Specialists, in comparison to generalists, appear to have more experience in providing care coordination. For example, specialists more than primary care providers met family needs related to care coordination (Scholle & Kelleher, 1995), connected them with other parents for support (Ireys & Perry, 1999), and were better informed about community resources (Liptak & Revell, 1989). Furthermore, families report that primary care physicians underestimate family desire for care coordination (Liptak & Revell, 1989; Perrin, Lewkowicz, & Young, 2000). Pediatricians identify constraints related to coordinating care. Primary care providers who were highly committed to providing services to CSHCN reported very low satisfaction with the time available to care for CSCHN, to assist with coordinating school services, and to transition older children to adult care (Davidson, Silva, Sofis, Gantz, & Palfrey, 2002). These pediatricians identified training about public programs, time, and financial constraints as barriers to coordinating care for CSHCN.

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15 In a primary care setting, CSHCN are dispersed among many providers (in contrast to being grouped within diagnostic-related specialty teams). This means that providers at the primary care level deliver services to children with a wider diversity of conditions and are expected to link families to generic as well as specialty services. This is compounded by the potential lack of provider awareness about the value of care coordination to families, the time to do this, and the lack of knowledge of community resources. The shift to primary care may make CSHCN and families less visible, and intensifies the need to develop methods to identify families. Summary of Study Context CSHCN, who represent hundreds of heterogeneous conditions, use a greater volume and variety of medical and nonmedical services than do children generally. Families describe a common and prevailing desire for information and services from educational, developmental, and social service systems; not just medical care. Families report difficulty finding, getting, and navigating the maze of available programs which occurs in spite of having an identified primary care provider. Some families report coordinating care for themselves. Federal policymakers, advocacy groups, providers, and families agree on the value of care coordination to assure that families and children are linked to a broad range of services, across a variety of delivery systems; and to assure that services are easy to use. Care coordination, however, is not universally available. The shift to community-based primary care is a change for families who were accustomed to receiving health care from specialists. System development initiatives such as the medical home may improve access to care coordination in the future, but current evidence shows that families perceive that primary care providers are less knowledgeable about resources than specialty care providers. Also, the barrier of

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16 reimbursement for the time and cost of providing care coordination at the primary care level has not been addressed. As CSHCN become integrated into primary care systems they compete with the larger population of children who do not have special needs for provider time. Families who need care coordination may be at-risk for becoming invisible to inexperienced or uninterested providers. All of these factors (the high use of services, family need to find resources, the changes in health care financing, the shift to community-based care, and the emphasis on care coordination by policymakers) intensify the urgency to assure that families receive care coordination. A key policy question is, which families require professional care coordination and how will they be identified? The answer to this question would provide information to agencies responsible for assuring service delivery and for allocating resources for care coordination. The answer to this question would also assist providers to casefind families from within any practice setting. Nurses are particularly well positioned across the health and education systems to identify families and to provide care coordination. Currently, there is no universal method for identifying families who require professional care coordination; nor is there research that associates family use of care coordination with a variety of health and related services. Yet that is what care coordination has been purported to do (link families to services). Our study proposes to explore the relationship among child and family characteristics and child and family use of medical and nonmedical services to distinguish between families who use and dont use care coordination. The NHIS-D, a national household survey, provides the advantage

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17 of a randomized sample of users and non-users of care coordination and contains information about child and family use of health and related services.

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CHAPTER 2 LITERATURE REVIEW This literature review examines three areas of research related to families and CSHCN; and identifies gaps in the literature related to care coordination and family use of a variety of services. The first section discusses nursing research related to families and children with chronic illness (which has largely focused on the impact of the childs condition on family life). The second section identifies the variety and volume of health services used by this population, and identifies barriers that may be mediated by care coordination. The third section discusses the process and outcome of care coordination, specifically as it relates to family use of services, and which families are reported to use care coordination. Nursing Research A review of nursing literature between 1966 and 1981 discovered much descriptive research of how nurses were providing care to families and children with chronic illness (Hymovich, 1985). Nurses were also developing assessment and intervention frameworks to guide their practice with these children and families (Wright & Leahey, 1987). A research review during the 1980s reported that nursing was still in the early stages of knowledge development and although there was evidence of theory building and reflection on practice, the evidence was insufficient to guide nursing interventions (Burke & Roberts, 1990). The decade of the 1990s witnessed an intensification of interest in carving out nursings contribution to care of families in theory development and research (Broome, Knafl, Pridham, & Feetham, 1998; Feetham, Meister, Bell, & Gilliss, 1993; 18

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19 Whall & Fawcett, 1991). Nurses were broadening their focus from the individual child to the family as a unit of care. The challenge to theory development and research that guides practice with families as a unit of analysis vis--vis individuals within a family has been discussed extensively in the literature. A 50-year review highlighted that nursing has followed CSHCN and their families from institutional to community settings; and has modified the view of families from needful to competent, and from a deficit model of how families cope with stress to a family-strengths-and-transformation perspective (Faux, 1998). Exploration of how families respond to the chronic illness experience included examining and re-visiting concepts such as chronic sorrow (Clubb, 1991), adaptation (Austin, 1991), hardiness and stress (Huang, 1995), normalization (Deatrick, Knafl, & Murphy-Moore, 1999), family empowerment (Hulme, 1999), and uncertainty (Stewart & Mishel, 2000). The program of research related to Family Management Styles provides a framework for assessing family response to chronic illness; and a future basis for the development and testing of nursing interventions (Knafl, Breitmayer, Gallo, & Zoeller, 1996; Knafl, Gallo, Zoeller, Breitmayer, & Ayres, 1993). Stress and coping are the predominant underlying concepts within nursing research related to families and children with chronic illness. Family stress theories suggest that resources help families cope and adapt to stresses (Hill and McCubbin as cited in Sterling, 1990). Family resources were derived in Sterlings study from interviews with families who provided home care to infants with severe respiratory diseases requiring professional monitoring. Interview questions asked families about home caregiving experience. Themes related to parental resource needs were identified as knowledge and

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20 skills, time, coping strategies, support from family and friends, and professional and material resources. Frequency or intensity of resources was not measured, and professional resources were identified simply as health care professionals. A follow-up study tested psychometric properties of the Home Care Resources Inventory and assessed family resource availability as indicators of the ability to care for the child at home (Sterling, Jones, Johnson, & Bowen, 1996). Family resources were conceptualized as psychological, social, interpersonal, and material (finances, equipment, transportation) means by which individuals gain control over their lives. The two main resource categories were support and assets (internal to the family unit) and health or community services. The studies were not related to care coordination, they did not address a broad range of medical and nonmedical services, and they were focused on infant home care. Researchers recognize that the absence of community resources may interfere with family resilience and promotion of strengths (Patterson, 2002). The family and children's chronic illness literature has contributed to clarifying concepts, has provided direction for theory development, and has given nurses a perspective into the life of the family as the primary caregiver. The themes of service system barriers, need for community resources or services, and information is often reported in the results or discussion sections of qualitative studies that explore the impact of chronic illness on family life. However, nursing conceptualization of resources has been focused on internal family dynamics. Family researchers acknowledge that the interaction between family and the health care system is of interest to nurses (Gilliss & Knafl, 1999). However, child and family use of health and related services is understudied in the nursing literature (Knafl & Gilliss, 2002).

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21 Use of Services by CSHCN CSHCN use a greater volume and wider variety of health and related services than do children generally. Consequently, families desire information about these services and assistance to access them. The following section examines what is known about the use of health services by CSHCN and the barriers which may affect service utilization. This provides a rationale for why care coordination may be necessary for some families. Studies which examine the use of health services by CSHCN indicate that no matter how the definition of the condition is conceptualized (diagnosis, severity, non-categorical) these children use a variety and greater volume of services than do children generally. Medical care, especially hospitalization, may vary widely by condition and can be 2.5 to 20 times more costly than for children in general (Ireys, Anderson, Shaffer, & Neff, 1997). Furthermore, a small proportion of CSHCN (10%) accounted for 80% of Medicaid payments, with the largest expenditures attributable to hospitalization. A 12-month clinical study examined unscheduled hospital ICU admissions by children with chronic conditions and children with no chronic conditions (Dosa, Boeing, Ms, & Kanter, 2001). Children with chronic illness had a 3-fold risk of unscheduled admission compared to children in general. The risk was higher for children who were technology dependent but one-third of the preventable admissions occurred among children with chronic illness who were not technology-dependent. Factors associated with the preventable admission included family stress and delay in seeking care and inadequate coordination of care. CSHCN have typically been higher users of specialty medical services in contrast to primary care (Perrin & Ireys, 1984). One specialty clinic estimated that 31% of enrolled children considered the clinic as their source of primary care; particularly if they

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22 lived close to the medical center (Palfrey, Levy, & Gilbert, 1980). National household studies verify that children with chronic conditions made 15 visits per year to a physician compared to 4 visits per year for children with no chronic condition (Newacheck & Stoddard, 1994). CSHCN make twice as many physician visits and have five times as many hospitalization compared to children in general (Newacheck, Stoddard et al., 1998). Similarly, children with limitations in activity (disabilities) were reported to use 8.8 physician contacts compared to 3 contacts for children who are not disabled, and were more likely to be hospitalized (Newacheck & Halfon, 1998). Children with a chronic condition enrolled in a state children health insurance program were also found to use a greater volume of out-patient services over a 12-month period; 6 outpatient visits in comparison to 2 outpatient visits by children without chronic illness (Lin & Lave, 2000). The predominant theme among these studies is that higher utilization of health care is significantly associated with chronic illness / special need / disability and exceeds use by children generally. Fewer studies report the use of allied health or nonmedical services. One clinical study examined the use of rehabilitation and support services by a convenience sample of children with cystic fibrosis, myelodysplasia, cerebral palsy and multiple physical handicaps (Smyth-Staruch, Breslau, Weitzman, & Gortmaker, 1984). Comparison to a control group of children without special needs found that CSHCN used a greater volume of hospital, specialty medical, therapy, mental health, and social services that varied widely by type of condition. A distinction among diagnoses found that children with cerebral palsy and myelodysplasia were higher users of services compared to children with cystic fibrosis and multiple handicaps. CSHCN also had a greater volume of contact

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23 with the school nurse and contact with office nurses, nurse practitioners, and public health nurses was 5-times greater compared to children in general. Children with impaired mobility and severe functional limitations were also identified as having extensive use of equipment, primary and specialty providers; and moderate use of therapy and counseling (Walker, Palfrey, Butler, & Singer, 1988). This study pointed out that children used a variety of public and private agencies in the community to secure services; while family support, respite, homemaker, summer camp and after-school programs were less often used and more often not available. The need for care coordination as a strategy to help families locate community services is emphasized in this study. A recent study identified predictors of health care use among 93 children with cerebral palsy enrolled in a state Medicaid program (Balkrishnan, Naughton, Smith, Manuel, & Koman, 2002). A caregiver survey found that children with cerebral palsy used a higher volume of inpatient, out-patient, home health, and orthopedic services. The caregiver behavioral factors that were associated with higher service use were the willingness by the family to use home health care and respite care and family perception that their child was receiving adequate care. Families who used fewer services were associated with having more years of caregiving experience (older child), having financial difficulties, and a caregiver who was employed. One conclusion drawn from this study was that families who used allied health services may be better informed about the availability of services. Clinical experience supports this observation. Once families gain entry into the system of providers who specialize in this population and are familiar with the resources; the door opens to a wider range of services.

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24 These studies underscore that CSHCN use a greater volume of health and medical services than do children generally. Only a few studies reported utilization of allied health or community supports. In some of these studies, care coordination is acknowledged as a necessary accessory to assure linkage to services, yet none of these studies investigated the relationship between use of health or community services and care coordination. Barriers to Service Use Epidemiologic study indicates that CSHCN are more likely to include those with incomes below the poverty level and living in single-parent families where the education level of the head of household is lower compared to families where children do not have a special health care need (Newacheck, Strickland et al., 1998). This may place some families at particular risk of not being able to access health or community services. The following section identifies some of the common economic and non-economic barriers to accessing services. Economic Barriers The economic impact of caring for CSHCN is well established. Family access to financial assistance to pay for care is an enabling factor discussed in the health service utilization literature. Having health insurance, and a usual source of health care, has been associated with increased access to health care, however, it is estimated that 11.2% or 1.3 million CSHCN did not have any form of insurance coverage during 1994-1995 (Newacheck, McManus, Fox, Hung, & Halfon, 2000). The major barrier to access to medical care was the cost of insurance followed by employment issues. Children who were uninsured were less likely to have a regular source of primary care and children with public insurance were twice as likely to be without a regular clinician. Furthermore,

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25 children with public insurance did not have access to after-hours medical care from their usual source compared to children with private insurance. Likewise, children who were either uninsured or publicly insured were more likely to have an unmet need such as dental care, prescription medications, eyeglasses and medical care. Under-insurance, therefore, creates gaps in coverage for CSHCN. Regardless of insurance status, children who are minorities or poor have reduced access to primary care (Newacheck, Stoddard, Hughes, & Pearl, 1998). Children enrolled in Medicaid managed-care may be particularly vulnerable since they may not receive the broad range of services they may require due to ". . referral barriers, financial disincentives that discourage the use of appropriate specialty care and medications, and disruption of long-standing providers relationship" (Mele & Flowers, 2000, p. 70). Meanwhile, a higher percentage of children enrolled in Medicaid versus employer-insured plans were reported to have a special need and consequently use more services, which varied by diagnosis (Shatin, Levin, Ireys, & Haller, 1998). The shift in where health care access now occurs, primary versus specialty level, is affecting CSHCN. Unlike in the past, when services were predominately delivered by specialty care providers, children with chronic illness enrolled in Medicaid were reported to receive a majority of their care from generalist physicians (Kuhlthau, Ferris, Beal, Gortmaker, & Perrin, 2001). The availability of health insurance, the predominance of Medicaid use for this population, and the shift to primary care are important factors to consider when determining where to target families for care coordination. Economic impact also includes out-of-pocket costs and decreased employment opportunities related to the time spent caring for the child. Out of pocket expenses among

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26 families of children who were severely disabled increased with the time spent caring for the child and consumed 12.2 % of family income (Leonard, Brust, & Sapienza, 1992). Caregiving time in the home, escorting the child to appointments, and coordinating medical care has been shown to be associated with the childs medical condition and to occur at the expense of womens leisure or career time (Breslau, 1983; Brust, Leonard, & Sielaff, 1992; Leonard, Brust, & Sapienza, 1992). Mothers (at or below poverty level income) who were caring for a child with a severe disability had a 15% lower probability of employment, worked an average of 15 fewer hours per month, and a had higher likelihood of incurring out-of pocket expenses (Lukemeyer, Meyers, & Smeeding, 2000). Higher income families also report financial hardship with out-of-pocket expenses, increased hours providing home care, and interference with employment (Krauss et al.; 2000; Krauss et al., 2001). This triple effect (home care, reduced employment, out-of-pocket costs) particularly subjects low-income families to deeper poverty and is an important consideration when distinguishing among families who use and do not use care coordination. Non-economic Barriers Factors such as minority status, language, poverty, lack of support, and low educational level of parents are recognized as increasing the risk that vulnerable populations will not be able to access health and related services (Halfon, Inkelas, & Wood, 1995). This may apply to the use of care coordination as well. Families may be aware of their eligibly for assistance from public programs that include care coordination, such as Medicaid, State Children's Health Insurance Programs (SCHIP), Title V-CSHCN, or Supplemental Security Income Benefits (SSI). However, families may have difficulty

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27 negotiating the eligibility determination process, gaining entry, and/or learning how to use the system to meet their needs. The current national concern with disparities in heath care has highlighted the role of ethnicity and culture in mediating access to care. Variables such as socio-economic status, education, and race were found to not explain the differences in health care service use by Native American, Black, and Hispanic children who had fewer physician visits (Flores, Bauchner, Feinstein, & Nguyen, 1999). The researchers suggest that cultural differences such as language, health beliefs, and provider practices may account for the disparities in service use. Language has been identified as a significant factor differentiating access to primary care for children (Weinick & Krauss, 2000). Interviews conducted in English were 2.6-times more likely to be associated with a child who had a usual source of health care compared to interviews for children that were conducted in Spanish. The interaction of family economic and socio-cultural factors may exacerbate barriers for families who try to access services for their child. The literature supports that care coordination has been targeted to lower-income and ethnic populations thereby acknowledging that these families may particularly require assistance. Care Coordination This section synthesizes the literature in relation to the process and outcomes of care coordination as it relates specifically to families of CSHCN. It emphasizes what is known and unknown about the relationship between care coordination and the use of resources; and which families are reported to use care coordination. It concludes by identifying the gaps in the current literature related to the identification of families who may require care coordination.

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28 Process Care coordination for CSHCN is an individualized and goal-directed process of how care is delivered in conjunction with other service providers (Kruger, 2002). Coordination generally accompanies the transition of a child from hospital to home, specialty team diagnosis and treatment; as well as early intervention or developmental services. The process of care coordination typically includes child and family assessment, individualized service planning, implementation, monitoring and evaluation. For example, assessment in the community-based Florida REACH program revealed that nurses who made home visits performed physical (52%), nutritional (49%), educational (44%), psychosocial (44%), environmental (25%), and developmental (18%) assessments of children and/or families (Schwab & Pierce, 1986). The nurses identified that 55% of their family encounters included providing direct care to the child while 63% of the encounters were related to educating the family (such as how to access services). A study conducted in 20 states reported that 50% of families with CSHCN have a care coordinator (Krauss et al., 2000). The coordinator helped families to identify community-based programs and services (63%), coordinate care among different providers and services (56%), find ways to pay for services/equipment (47%), access public programs (43%), and help understand child's health insurance plan benefits (39%). The low frequency response to these structured questions raises questions about how else coordinators assisted families. Referral is a common mechanism by which coordinators link families to services. An insurance-based coordinator reported that 40% of her time was spent identifying a wide spectrum of available resources for families within a 250 mile area (Gehl, 1993). The nurse made 363 referrals for unmet needs among 67 families (who had a primary

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29 care provider) to 91 different public and private programs. The average referral per family was 5.5 which covered 13 different service categories (counseling, dental, equipment, health education, financial, legal, medical, social/recreation, school, support, developmental /therapy, prescription assistance and vision). The highest frequency referrals were made for financial assistance (30%), followed by dental services (11%), and health education (11%). Furthermore, 55 % of the families accessed additional referrals upon subsequent contact by the nurse. This underscores that families who had a primary care health provider still required assistance to obtain additional medical and nonmedical services and that the need for care coordination may be continuous. This is the only study that measured the variety and volume of referrals made for families as a result of care coordination (Gehl, 1993). No studies have measured the relationship between care coordination and health or related services using a randomized sample. Outcomes Service delivery programs for CSHCN consistently identify that the purpose of care coordination is to improve overall quality of life, child health, continuity of care, and family capacity for self-care (Kruger, 2002). Yet, the outcomes related to these indicators have not been studied. Research related to care coordination outcomes have reported positive change in relation to parent perceived child health/mental health status, health and/or child development, increased service use satisfaction with care or quality, and savings from higher cost inpatient care by displacement to lower cost of community services (Kruger, 2002). A limitation of these studies is that most did not clearly identify the components of the care coordination intervention or they did not use comparison groups. Interpretation of coordination outcomes, therefore, is problematic.

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30 Research related to family outcomes is sparse. The CHOICES national demonstration project (Shriners Hospital) asked families how care coordination benefited them and their child (Presler, 1998). An evaluation of 1,094 respondents found that children benefited through fewer doctor visits (43%), improved function (36%), more community-based services (35%), and fewer complications (29%) and hospitalizations (22%). The family benefited from care coordination through ease in obtaining equipment and supplies (49%), they better understood services and agencies (48%), received help from people who cared (45%), and saved time finding health care and worried less about their child (41%). Gehl (1993) reported that her families received information about services (50%), referrals (43%); and benefited from a caring provider attitude (38%), having telephone calls returned and by home visits (21%). These descriptive studies used convenience samples and measured respondent perception at one point in time. A more recent evaluation of the Rare and Expensive Case Management (REM), Marylands Medicaid managed-care program for individuals participating in Medical Assistance, reports that care coordination lowered overall health care costs (Pandey, Mussman, Moore, Folkemer, & Kaelin, 2000). Acute care hospital in-patient costs decreased while non-acute care special services and pharmacy costs increased. The displacement of the more expensive hospital to less expensive community services resulted in the overall net savings. Special services were delivered in the school such as transportation, therapies, and school health nursing as well as durable medical equipment and disposable medical supplies. The estimated reduction in medical costs, per-member-per-month, was calculated to be $607 and adjusted by the cost of case management ($253 per member per month) to produce a net lowering attributable to case management of

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31 $354 per-member-per-month. This study quantified the costs but not the actual variety or volume of services attributable to case management. The Pediatric Ambulatory Care Treatment Study (PACTS), a randomized control trial, suggested that there was variability in how families acquired and retained care coordination and related services over time (Stein & Jessop, 1984). Care coordination was integrated into the PACTS delivery approach that included direct primary and specialty care, health education, and psychosocial services for families across all care settings, including the home (Stein, 1978). A majority of families in the PACTS intervention and control groups reported acquiring the following services over the 18-month study period: a usual source of health care, acute care, health maintenance, coordination with sub-specialists, as well as, someone who discussed family risk, gave advice, and listened to family concerns (Jessop & Stein, 1994). A statistically significant difference was reported between intervention and control groups when coordination with other agencies (school, daycare or Medicaid) was provided at enrollment and retained at 6-months but was not sustained at 12-months. Furthermore, coordination had decreased for the intervention group and increased for the control group. The reason why the treatment group lost their source of care coordination was not provided. This longitudinal study points out the potential for variability in the use of care coordination. To date, one nationally representative study has examined the impact of care coordination on the use of mental health care by school-aged CSHCN (Witt, 2001). Coordination of medical care by a physician or a physician in conjunction with the family significantly increased the likelihood that children received outpatient mental health care and decreased the likelihood that children used inpatient care. Families who coordinated

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32 their own care or who did not have anyone coordinating care did not show these positive results. This study is an important contribution to the literature about the mediating effect of care coordination on the use of mental health services within a population at-risk for psychosocial morbidity. Families Who Use Care Coordination We do not know, at a population level, which families receive care coordination services. We do know that if children are enrolled in specific publicly funded programs then the probability that they are receiving care coordination increases. Studies of care coordination for CSHCN infrequently and incompletely described the socio-demographics of children and families included in the study sample (Kruger, 2002). When reported, the ages of children in the samples ranged from birth through young adulthood while the diagnoses covered a range of heterogeneous conditions from medically fragile to more prevalent conditions such as asthma. Only a couple of studies in this literature reported a high representation from Hispanic and Latino populations. Socio-economically, these families demonstrated a consistently low income population. Characteristics of children and families who had a care coordinator (50% of all families) were reported by the Family Partners survey (Krauss et al., 2000; Krauss et al., 2001). The children were more likely to be younger, a minority, have a more severe and/or less stable condition, and a poorer overall health rating. Parents of the children were less likely to be employed or have a bachelors degree and more likely to be lower income. No statistically significant differences were noted for sex of child, age of parent, health status of parent, family structure, or number of CSHCN in the family. A sub-population of CSHCN, those who were technology dependent, used care coordination more frequently (70%) than children who were not technology dependent

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33 (Krauss et al., 2000). Children with technology dependence were more likely to be younger with a more compromised and severe health status and less likely to be a minority. These children also used more primary care, outpatient specialties, emergency room, hospitalization, disposable medical supplies, durable medical equipment, special diets, nutrition counseling, and physical, occupational, speech therapies and consumed more parent time providing in-home health care. Almost half (49%) of the parents of children with technology dependence provided 20 or more hours per week of home care compared to 12% of parents who children had special needs but were not technology dependent. Consequently, the former parents reported a greater impact on their finances and employment; and also spent five or more hours per week coordinating care. Most all of these studies used convenience samples and/or did not use comparison or control groups of families who did not use care coordination. They emphasized that care coordination was important to families and was targeted to populations enrolled in the demonstration programs. Missing from the population demographics was representation from families with varied socio-economic status, a broader range of minority groups, and children who were uninsured. Also, children who appear absent from these studies are those who might have been medically stable. Summary of Literature Review Four decades of nursing research have largely focused on the impact of the childs condition on family life or family functioning. Nurses have acknowledged that resources play a role in assisting families; however, the focus has been on resources within the family unit. Nurses have not studied how families use health and related services. Health care utilization studies identify that CSHCN use primary, specialty, and hospital care, namely medical services, in greater volume than do children generally.

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34 Studies of child use of health related or community services are less frequent. Meanwhile, care coordination process studies report that families are referred to many different services while separate outcome studies suggest that this positively affects the child (developmental and mental health status, increased use of services), family (satisfaction with care), and health care system (cost savings). Research also supports that care coordination has been targeted to lower-income minority populations and is also received by higher income families. A major limitation is that most of the research about care coordination is based on convenience samples and not population or household survey data which limits generalizability. This research also does not examine the relationship among care coordination, health and related service use, and child and family characteristics. Consequently, it does not provide sufficient information that could help identify families for care coordination. Our exploratory study proposes to address this gap.

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CHAPTER 3 METHOD This chapter provides a brief overview of conceptual models applied to care coordination and confirms that none of them sufficiently describe, predict, or explain the relationship between the use of care coordination and health and related services. However, the Behavioral Model of Health Service Utilization (Andersen & Newman, 1973; Andersen, 1995) that is frequently applied to health care utilization studies does provide a heuristic for the study of care coordination. The application of this model, study design, data source, sampling, and data collection procedures, development of measures, and data analysis are discussed in this section. Theoretical Review A theory or conceptual model which explains or predicts relationships among care coordination, the use of and need for health and related services, and child and family characteristics, has not been reported in the literature. In fact, few studies of care coordination identified a theoretical framework (Kruger, 2002). Models used in care coordination studies included Bronfenbrenner's ecological human development (Gillette, Hansen, Robinson, Kirkpatrick, & Grywalski, 1991), the enabling and empowering framework of Dunst, Trivette and Deal (Farel & Rounds, 1998), the family partnership model of Tunbull and Turnbull (Jackson, Finkler, & Robinson, 1992) and Orems self-care deficit theory (Pierce & Giovinco, 1983). These conceptual frameworks were predominately applied to the delivery of clinical interventions rather than care coordination services. 35

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36 Meanwhile, models that identify health and related services as being important to families do not relate acquisition or the need for these services with care coordination. For example, the Family Adjustment and Adaptation Response Model (FAAR) associates community resources with family adaptation (Patterson & Garwick, 1994). Resources help families to balance demands and promote adaptation while a lack of resources undermines family capacity to be resilient (Patterson, 2002). Similarly, research with vulnerable populations relates resource availability with health status risk (Flaskerud & Winslow, 1998). Populations that do not receive services or resources have an increased risk of poor health. How families acquire resources or services was not discussed. Only one model was found that relates care coordination with family use of services. Dunst and colleagues conceptualize case management as . . a particular set of functions for linking what is needed with what resources are provided (Dunst, Trivette, & Deal, 1994, p. 187). Principles and beliefs about families that guide the model include the family as the unit of intervention, family empowerment and self-identification of needs, family as having strengths and capabilities, and social support. Resources are conceptualized as concentric circles surrounding the family beginning from the center with informal supports and extending outward to include professional services. The intent is not to supplant informal supports with professional services (Dunst, Trivette, & Deal, 1988). The informal supports and professional services are thought to interact with each other to strengthen family functioning and help families gain mastery. Identification of which families use or need which services or resources is not explained by this model. The conceptual models briefly described in this section seek to understand how families respond to and manage child chronic illness and include resources and services

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37 as important for families. They emphasize the complexity of the relationships among family demands, capabilities, and adaptation. They do not, however, clarify the relationship among child and family characteristics, health and related services, and care coordination. Conceptual Framework The Behavioral Model of Health Service Utilization, also known as the Andersen Model, will be used in our study to organize the variables derived from the literature and clinical practice that are thought to be related to the use of care coordination by families (Andersen & Newman, 1973; Andersen, 1995). The Andersen Model reflects a social psychology / sociology perspective and has been applied to the study of health care services utilization over the past 30 years. An underlying assumption of this model is that individuals make decisions to seek medical care (the behavior) in order to decrease the risk of poor health. Another assumption is that increased use of health care services will improve health outcomes. This heuristic organizes variables according to predisposing, enabling, and need factors. The ability of these three factors to explain service utilization depends upon the type of service that is studied and how the determinants of use are conceptualized. Meanwhile, the outcome of the health care services is generally measured through satisfaction with care and improvement in health status. Although health care outcomes are not included in our study, it is important to note that they provide a feedback loop that may reinforce some predisposing characteristics and influence whether an individual continues to use a particular service (Andersen, 1995). Table 3-1 describes the variables of interest in our study organized by the Andersen (1995) model. Utilization of care coordination is the outcome of interest. Families responded, yes or no, if they received medical or nonmedical care coordination.

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38 Respondents also indicated who provided the coordination, whether a professional and/or the family themselves. The child and family predisposing, enabling, and need characteristics were derived from clinical practice and from the literature related to the use of medical services. The determinants of care coordination utilization have not been studied, particularly in relationship to health and related services. Table 3-1. Model for studying care coordination utilization Child & Family Characteristics Family Behavior Predisposing Enabling Need Child gender, age, race Family structure, size, education, and length of residence in same state Child health insurance, service use (type and variety) Family income, residence region, telephone access Child number of conditions, health status, unmet service need (type and variety) Family poverty, financial need, employment impact Use of care coordination Predisposing Factors Predisposing factors are the demographic, social, and psychological characteristics that influence an individual to use health services (Andersen, 1995). Child and family socio-demographic characteristics, together, may influence families to use care coordination. Child predisposing factors include age, gender, and race/ethnicity. For example, younger children may have a greater likelihood of receiving care coordination than adolescents as a result of their association with early intervention programs which commonly provide care coordination. Factors that influence families to use or carry out care coordination may be related to family structure, size, education, and residential stability. For example, one-parent families may have less time to arrange for services compared to two-parent families.

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39 Longterm residency in an area, meanwhile, may provide familiarity with community resources. Enabling Factors Characteristics that influence a predisposed person to use health services are called enabling factors (Andersen, 1995). Health insurance is a common enabling factor to medical services and may provide a pathway to care coordination. Exposure to different types or a variety of health and related services may also be enabling because they may increase child and family exposure to programs that provide care coordination. Family region of residence, including proximity to an urban area, may affect the distribution of care coordination by government programs. Availability of a telephone may enable coordination because it facilitates communication with providers. Family financial status is an enabling factor when it is applied to the use of medical services in that families with higher incomes are more likely to access health services. Income, especially below a certain level, is a customary criterion for eligibility determination in many government programs that provide health and related services. In programs with universal eligibility such as early intervention, income may not be a criterion for service whereas for Medicaid it is. Care coordination is more likely to be bundled with public health, education and/or developmental programs. Consequently, it is not universally available for purchase; therefore, a higher income may not enable access to care coordination as it does for health care. However, because many public programs have financial eligibility restrictions, lower family income may be a more realistic factor related to obtaining care coordination. In any event, the relationship between income and care coordination is not clear.

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40 Need Factors The third factor, the need for health care, must be present in order for health care utilization to occur (Andersen, 1995). Needs are either perceived by the individual or evaluated by a health care provider. Perceived needs are thought to better explain care-seeking behaviors while professionally evaluated need is related to the type and volume of services provided (Andersen, 1995). The need children and families have for care coordination may be associated with the family perception of the childs health status, child co-morbidities or number of health conditions, and family financial ability to pay for services. Children with a poor health status or co-morbid conditions may interact with health and related providers more frequently, thereby increasing opportunities for exposure to care coordination and/or have a need to arrange those services among multiple providers. Family poverty level (composite of income, family size, family structure) is also conceptualized as a need factor. These predisposing, enabling, and need factors and the use of care coordination were linked to variables in a national population data source. The study design, secondary data source, sampling, and collection methods are described in the following section. The identification of study measures related to child and family characteristics, the types and varieties of services used and/or needed by children, and care coordination are also discussed. Study Design and Data Source A descriptive/correlational design using national survey data examined the relationships among care coordination, the types and variety of services used and/or needed, and child and family characteristics. The 1994 National Health Interview Disability Follow-back Survey Phase II (NHIS-D Phase II) contains a representative

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41 sample of CSHCN and their families and the variables of interest for our study. This database is well-known, reputable and economical (Brown & Semradek, 1992; Young & Ryu, 2000). The NHIS, the primary data source for the NHIS-D Phase II, is an annual household survey of the non-institutionalized civilian population within the U. S. and has been conducted by the National Centers for Health Statistics since 1957 (Massey, Moore, Parsons & Tadros, 1989). The survey does not include individuals in long-term care facilities, persons in the military, U.S. nationals living abroad, or persons who are homeless. This cross-sectional survey collects information on the health status, health care use, and demographic characteristics of respondents. It is planned for a 10-year period by partitioning the overall sample into yearly sub-samples. A representative sample is assigned to four calendar quarters each year and then distributed into individual weeks. Each level of this distribution produces samples that are representative of the target population (Massey et al., 1989). NHIS data are collected on all living members of sampled households from adults ( 17 years old), in face-to-face interviews. Information on children and adults not at home during the time of the interview is gathered from a resident adult (19 years old). The interview takes an average of 50 minutes with a range of 20 to 90 minutes. The first part of the questionnaire called the core is repeated annually and contains basic health and demographic questions. A second section contains questions related to topics of current national interest that change annually. Approximately 49,000 households and 132,000 persons represent the sample population each year with over a 95% response rate of eligible households (Massey et al., 1989).

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42 NHIS Sampling The NHIS employs a stratified multistage probability design with continuous (weekly) sampling of a representative population which is additive over time (Massey et al., 1989). The sampling design has been revised each decade to coincide with the decennial census of the population. The first step in determining sampling units for the NHIS involves the selection of primary sampling units (PSUs) from over 1,900 geographical PSUs which cover the 50 states and the District of Columbia. A PSU is a county, a small group of contiguous counties, or a metropolitan statistical area (MSA) identified by the U.S. Census. The design of the 1994 NHIS selected 198 PSUs and over-sampled the Black population (Massey et al., 1989). The PSUs are grouped or stratified according to socioeconomic and demographic variables within a geographic stratum and then selected with a probability proportional to their population size within their stratum. The larger PSUs are considered self-representing (SR) and are included in the NHIS with certainty as they are equivalent to a stratum. The non-self-representing (NSR) PSUs are grouped based upon similarity and only 2 PSUs are selected from this group (Massey et al., 1989). PSUs are then broken into smaller segments and geographical clusters to form secondary sampling units (SSU) which are in turn partitioned into density strata based upon minority population concentration. These smaller segments and clusters contain housing units (households) that are either grouped or spread out over a geographical area. Data Source The primary data source used for our study was the supplement to the 1994 NHIS known as the National Health Interview Disability Follow-back Survey Phase II for adults and children. Eligibility for Phase II was determined during Phase I (National

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43 Center for Health Statistics, 1994). The Phase I questionnaire was conducted at the same time as the NHIS core for that year. It included three sections of questions directed towards CSHCN, special education, and child development for children under the age of five years. The survey used a used a variety of approaches (136 variables) to ask about the disability or condition of the adults and children in the household. The condition criteria included diagnoses, use of health and education services, activity limitations, developmental and behavioral indicators, adaptive and assistive device use, treatments and medications, physical functioning, assistance with activities of daily living, and family perception of disability. Thirty-three of these criteria were identified as single item exclusion meaning that they had to be combined with another variable to be considered a positive screen for inclusion of a child into the Phase II survey. Children deemed eligible (on the basis of Phase I criteria) for Phase II of the study numbered 4,724, of which 4,296 (90.9%) were located. The response rate for Phase I and Phase II of the NHIS-D was 84% (National Center for Health Statistics, 1994). The overall response rate including the NHIS core was 79%. It is estimated that only 2% of all disabled children are missed by household-based surveys like the NHIS-D (Hogan, Msall, Roger & Avery, 1997 as cited in Monahan, 1998). Data Collection Telephone interviews for the NHIS-D Phase II were conducted between August 1994 and 1997 with data issued in 1999 (National Center for Health Statistics, 1994). Information about CSHCN covered 14 sections of survey questions. Questions were related to: a) home care, b) child care, c) medical care, d) assistive devices/technology, e) allied health services, f) special education and early intervention programs, g) care coordination, h) physical activity, i) personal adjustment and role skills, j) impact on

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44 family, k) mental health, l) housing and transportation, m) insurance, and n) respondent information. The section on care coordination asked families about their experience with medical and nonmedical coordination. An important advantage of this database is that it included children who did not use care coordination. Creation of Dataset Approximately 210 variables of interest, including weights and identifiers, were exported into a spreadsheet file from a CD-ROM (National Center for Health Statistics, 1994) containing the NHIS-D Phase II data using the Statistical Export and Tabulation System (SETS) software. The spreadsheet was converted into a STATA for Windows Intercooled version 7.0 database file (StataCorp, 2001). All variables in the dataset were renamed from the NHIS-D alpha-numeric codes to descriptive names. Data were then cleaned, recoded, collapsed, and patterns of missing data were analyzed. Data Cleaning The dataset was analyzed for missing data using the tabmiss command in STATA and frequencies were run on each of the variables. Frequency tables were compared to the NHIS-D Phase II data dictionary which revealed that data coded by the NHIS and read by STATA as missing were actually not applicable responses and therefore were not truly missing. For example, 193 observations were read by STATA as missing for the variable, received special education services. However, these 193 observations were children under the age of three years who were not age eligible for special education, and therefore not applicable to being asked the question about special education. Meanwhile, the true missing data corresponded to codes such as not ascertained, or blank or not known. Data with a not applicable response were re

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45 coded to a or another unique code that distinguished them from other responses. Subsequently, these data were re-coded in STATA to missing. Missing Data Some of the survey sections were lengthy and not all respondents were asked all sections of the questions (National Center for Health Statistics, personal communication, January 20 2000). The average percent of non-missing data for 199 key variables was 99.45% meaning that most of the data was available. The largest frequency of missing data related to family income (414 observations), family poverty level (212 observations), and years lived in present residence (208 observations). Missing data for the remaining variables ranged between zero (or none) to 123 observations. These key variables were then collapsed into categories and reduced to 59 variables with an average percent of non-missing data equal to 99.15%. A second and final reduction of variables resulted in 30 independent variables (covariates) and two dependent variables with an average percent of non-missing data of 99.02%. Re-coding and generating new variables was performed by executing a STATA command file with documentation sent to a log file. A codebook was developed containing the NHIS-D original variable name, variable description, re-named variable, percent non-missing data, and un-weighted frequencies. An alpha-prefix for renamed and generated variables was assigned to correspond to the section of the Phase II from which the variable was drawn. Study Sample Selection Selection criteria for our study restricted the sample to the following: 1) children residing in a primary household, 2) respondent for the NHIS-D Phase I was a proxy for the child, 3) relationship of the respondent for the NHIS-D Phase II was the childs

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46 mother or father, and 4) adults living in the family identified at least one or both parents. A primary household contains only one family in contrast to multiple households. Proxy respondents originally included mother, father, grandparent, sibling and other relatives. However, grandparents, siblings, and other relatives had a 12-19% frequency of missing data for a number of the covariates, more than mothers and fathers, and were therefore excluded from the study. The application of these selection criteria produced a sample of 3,865 (89.96%) children and families from a total of 4,296 respondents. Complete data exists for 3,087 (79.87%) children for all of the study variables. Complete cases and incomplete (missing data) cases were compared on the basis of the dependent variable and the covariates to determine bias and potential of the study sample to represent the overall population. Comparison of Complete and Incomplete Cases No statistically significant differences on the basis of chi-square or t-tests were noted between the complete or incomplete cases for the dependent variable (care coordination). Likewise, no differences between incomplete and complete cases were noted for child age, gender, number of health conditions, family size, income, poverty status, years in the same residence, relationship of child to the family, and the use and/or need of health, equipment, home, or support services. Table 3-2 identifies the covariates for which statistically significant differences between complete and incomplete cases were noted (p<0.05). The significant differences average approximately 4% with a range of less than 1% to 8.3%. The greatest differences between complete and incomplete cases were for children with Medicaid (8.31%) and those with excellent to very good child health status (8.03%). This analysis suggests that children who represent incomplete cases may appear

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47 more needy than the selected study sample. However, given that the differences are relatively small and do not affect the dependent variables, it is determined that the study sample is nationally representative. Table 3-2. Significant differences between complete and incomplete cases Independent Variables Percent non-missing Complete Cases Incomplete Cases Percent difference Statistical significance Child has private insurance 99.1% 63.3% 56.5% 6.8% P<0.001 Child has Medicaid 99.1% 25.2% 33.5% 8.3% P<0.000 Child race White Black Other 100% 81.2% 16.2% 2.6% 74.4% 22.0% 3.6% 6.8% 5.8% 1.0% P<0.000 Child health status Excellent to very good Good Fair to poor 98.7% 62.5% 27.5% 10.0% 54.5% 32.1% 13.4% 8.0% 4.6% 3.4% P<0.000 Has severe economic need 99.4% 4.2% 7.2% 2.9% P<0.001 Family had severe impact on employment 99.5% 20.9% 24.3% 3.4% P<0.038 Adults in family Both parents Mother Father 99.7% 70.1% 28.1% 1.8% 65.4% 32.7% 1.9% 4.7% 4.6% 0.1% P<0.039 Education (adult) Elementary Some high school High school graduate Some college College graduate + 99.8% 3.6% 10.2% 36.0% 25.1% 25.1% 4.7% 15.0% 34.9% 24.2% 21.1% 1.1% 4.8% 1.1% 0.9% 4.0% P<0.001 Residence region Northeast Midwest South West 100% 18.8% 29.2% 32.4% 19.6% 19.4% 24.4% 33.5% 22.6% 0.6% 4.8% 1.1% 3.0% P<0.040 Urban (vs rural) 100% 74.4% 81.1% 6.7% P<0.000 No telephone 99.4% 7.1% 9.8% 2.7% P<0.013 Used therapy 99.8% 26.5% 31.3% 4.8% P<0.007 Used support services 99.8% 9.3% 12.3% 3.0% P<0.012 Used home care 99.6% 14.7% 18.7% 4.0% P<0.007 Unmet therapy need 99.8% 26.5% 31.3% 4.8% P<0.007 Unmet equipment need 99.4% 3.3% 5.1% 1.8% P<0.019 Variety used (0-5) 100% Mean 2.13 Mean 2.24 0.11 P<0.048 Unmet Variety (0-5) 100% Mean 0.50 Mean 0.58 0.08 P<0.023

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48 Measures Care Coordination Respondents were asked whether anyone provided medical and/or nonmedical care coordination and who performed this function. Medical coordination was defined as communication with other physicians or therapists and having an awareness of the childs treatment. Nonmedical coordination was defined as help arranging services such as social and personal care services. Persons identified as providing care coordination included health care professionals and families. The specific questions about care coordination in the NHIS-D Phase II asked if there was: 1) doctor who coordinates childs overall medical care, 2) anyone not a doctor who coordinates childs medical care; 3) physician or someone in a physicians office who helps with arranging childs nonmedical care (social services and personal care) and 4) anyone not in a physicians office who helps with arranging nonmedical services. Two measures of care coordination as the dependent variable were created in relationship to Aim 1 and 2. A dichotomous measure coded as yes or no represented whether anyone had coordinated care for the child in the prior 12 month period (Aim 1). This measure was constructed by pooling yes or no responses to four key NHIS variables (Table 3-3). The second care coordination dependent variable (Aim 2) distinguished the type (GTypeProv) of coordination provider: 1) no one, 2) family only, 3) professional only, and 4) family and professional. This variable was constructed in two stages. The first stage grouped yes and no responses for families (GfamCC) separately from professionals (GprofCC). These grouped variables were then used to generate the type of provider measure.

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49 Table 3-3. Coordination measures by NHIS-D variable and description Coordination Measures NHIS variable NHIS Description, yes or no responses Anyone coordinates care, Gany (yes or no) G_1521 G_1523 G_1532 G_1541 Doctor coordinates medical care Someone, not doctor coordinates medical Physician/staff arrange nonmedical care Anyone not in MD office arrange nonmedical care Family coordinates, GfamCC (yes or no) Professional coordinates, GprofCC (yes or no) G_1524 G_1525 G_1542 G_1543 G_1521 G_1526 G_1527 G_1528 G_1530 G_1531 G_1534 G_1535 G_1536 G_1537 G_1539 G_1540 G_1544 G_1545 G_1546 G_1547 G_1548 G_1549 Parent/guardian coordinates medical care Friend/family member coordinates medical care Parent/guardian-coordinates nonmedical care Friend/family member coordinates nonmedical Who coordinates medical care in physician office Physician Nurse Therapist Social worker Case manager Other professional Who coordinates nonmedical in physician office Physician Therapist Nurse Social worker Case manager Other professional Who coordinates nonmedical outside physician office Nurse Therapist Social worker Hospital discharge Case manager Other professional Who coordinates care, GTypeProv Based on GfamCC and GprofCC Generated = no one = family only = professional only = family and professional Child Child measures reflected the predisposing characteristics of gender, age, and race. The enabling measures were having a source of health insurance such as Medicaid or private insurance and receiving health and related services. Need measures were reflected by the number of child health conditions, health status, and unmet health or related service needs. Table 3-4 identifies the measures used for analyses.

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50 Table 3-4. Child measures by NHIS-D variable, description, and coding Child measures NHIS variable NHIS Description Variable coding Age CH_399 Age at follow-back Birth to 2 years 3-6 years 7-10 years 11-14 years 15-18 years Gender CH_25 Sex of child Male or female Race CH_43 Race recode 1 White, black, other Medicaid Private M_1831 M_1838 Covered by Medicaid Covered by private insurance Yes or no Yes or no Number health conditions CH_116 Number of health conditions None One Two Three or more Health status CH_70 Child health status Excellent or very good Good Fair/poor Child use of five different categories of health and related services was an enabling measure while the unmet need for these same services was conceptualized as a child need measure. The categories of services included: home care, health care, equipment, therapy, and support. Table 3-5 identifies the specific services included within each of the five categories. Between 2 and 18 NHIS variables were collapsed, based on yes or no response, to generate each of these categories of service use or umet need. Appendix A provides a detailed description of how these measures were constructed. A variety measure was generated to capture breadth of service utilization and unmet need among the five categories of services. The service variety measure counts the number of different types of services used or needed by the child. The range of responses is zero to five reflecting home, health, therapy, equipment, and support service types. Family Predisposing measures included family composition, size, years lived in state of present residence, and education of the responsible adult (Table 3-6). Enabling measures included region of residence, urban or rural location, availability of a telephone, and

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51 family income. Family need measures were financially driven (poverty status, severe financial need related to child, and impact on employment as a result of caring for child). The impact on family employment measure was generated by collapsing responses from six related variables. Respondents who answered yes to any one of the six questions were coded as yes, having some type of impact on employment opportunity. Table 3-5. Child service use and unmet service need categories Measure Type of service Home Care Personal care, respite, home care Health Care Office visits, hospital, mental health Equipment Assistive devices, home / motor vehicle modifications Therapy Physical, occupational, speech, audiology, communication, interpreter, recreation Support Center for Independent Living, social work, transportation Table 3-6. Family measures by NHIS-D variable, description, and coding Family measures NHIS variable NHIS Description Variable coding Adults in family CH_68 Parent/other adult(s) Both parents, >21 yr. relative Mother, >21 yr. relative Father, > 21 yr. relative Family size CH_67 Size of family recode Two to six or more persons Residence time CH_144 Years lived in state of present residence, US born Less than 5 years 5-10 years 10 + years Family education CH_56 Highest education of responsible adult, recode One to eight years Nine to eleven years Twelve years College, one to three years College graduate plus Residence region CH_182 Region of residence Northeast Midwest South West Urban or rural CH_186 MSA or non-MSA MSA, central or non-central Non-MSA, non-farm or farm Telephone CH_24 Has telephone Yes or No Income CH_60 Family income recode Under $5,000 to 50,000 plus Poverty status CH_61 NHIS poverty index At or above poverty threshold Below poverty threshold Financial need J_1662 Has financial problems Yes or no Impact on employment J_1651 J_1652 J_1653 J_1654 J_1655 J_1656 Not taken a job Quit working No job change Changed work hours Turned down job Worked fewer hours Yes or no to any questions J_1651 to J_1656

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52 Data Analyses Statistical analyses were conducted using STATA software (StataCorp., 2001) that takes into account sampling weights to accommodate for the complexity and levels (stratification, clustering, multistage sampling, over-sampling vs. simple random sampling) in the NHIS-D survey design (Massey et al., 1989). The two weights that must be considered in survey designs are person level and variance estimation weights (Kneipp & Yarandi, 2002). The person weights produce estimations that reflect the total number of persons in the population. Variance weights calculate sample variances (standard errors). Ignoring variance weights may result in incorrect standard errors, therefore, leading to Type I or Type II errors depending upon the design effect. In order to apply the variance weights the variable Pseudo PSU code was separated into two variables, CSTRATUM and CPSU, according to the method of variance estimation recommended by the NHIS (National Center for Health Statistics, 1994). The data were then set using STATAs survey set command. Prior to conducting analyses for Aims 1 and 2 statistics were generated to describe the sample (n=3087) demographic characteristics, the dependent variables, and the types and variety of services used and/or needed. The covariates were also analyzed in relationship to the dependent variables to assess cell size. As a result, multiple levels of selected variables (child age, family education, family composition, family size, number of child conditions, variety of services used, and variety of unmet needs) were collapsed to improve cell size. The reference group for the variety of services used by the child was constructed to include none or one service. Theoretically, one does not coordinate none or one service. The remaining two levels of this variable were coded to reflect children using two services and three to five services. Likewise, the variety of unmet

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53 service need variable was collapsed from six levels to two levels due to the small number of observations per cell. The reference category was coded as zero or no services needed and the second level of this variable reflects children needing one or more services. All tests for significance were set at the 0.05 alpha level. Aim 1 Analyses Two logistic regression models were used to estimate the likelihood of using or not using care coordination in relationship to the child and family covariates by 1) types of services (home, health, equipment, therapy, support) and 2) by variety (one to five). The type and variety of services were not included in the same regression due to known collinearity. The dependent variable (did anyone coordinate care, Gany) was dichotomously coded as no = 0 and yes = 1 consistent with STATAs interpretation of the value of as a negative outcome (failure) and all other values as positive outcomes (StataCorp, 2001). Analyses proceeded by simultaneously entering the explanatory variables into the logistic regression model to determine which variables significantly predicted the use of care coordination. Data were interpreted based on the Wald statistic and associated odds ratios. A reduced model was then estimated by using the significant covariates identified in the first analysis. Hosmer and Lemeshow (2000) note that model checking and goodness-of-fit procedures typically used in non-survey logistic regression are not currently available for complex survey data using software such as STATA (p. 218). Aim 2 Analyses Analyses were limited to respondents who used care coordination (n=2071). The dependent variable was stratified into three mutually exclusive groups based on who provided care coordination. This was done by dropping the responses to no one

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54 coordinates care from the GTypeProv variable. The modified variable was coded for family, for professional, and for family and professional. Multinomial logistic regression simultaneously estimated the association of the three provider types (family, professional, or family and professional) with the predisposing, enabling, and need covariates. This type of analysis is appropriate for nominal level outcome variables having more than two categories with no particular order (Kleinbaum & Klein, 2002). Multinomial logistic regression is also known as discrete choice model, polytomous and polychotomous (Hosmer & Lemeshow, 2000). Unlike a binary regression where the estimate is of the probability of an outcome being present, an outcome with three categorizes has two estimated probabilities (Hosmer & Lemeshow, 2000). One of the outcomes is designated as the reference category and in this analysis the family was chosen as the reference. The regression simultaneously modeled how professionals, as one group, and professionals and families, as a second group, vary on the basis of the covariates when compared to the family alone. The professional and family group represents a constructed variable from responses to a series of separate questions about who provides medical and nonmedical coordination. As in Aim 1, analyses proceeded by concurrently entering the explanatory variables into the logistic regression model to determine which covariates significantly predicted the two outcomes based on who provided the coordination; professional alone or professional and family. Data were interpreted based on the Wald statistic and associated relative risk ratios.

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CHAPTER 4 RESULTS Sample Characteristics Characteristics of the children and families are shown in Table 4-1. The weighted sample represents 7,870,264 children with more boys (59.5%) than girls (40.4%) and a mean age of 10 years. Racial distribution of the children was 81.7% white, 15.5% black, and the remainder was categorized as other or unknown. Children of Hispanic origin represent 11% of the sample and are included in all three categories. Compared to the 1990 U.S. Census, more boys, fewer pre-schoolers, more teens, and more children who are black (consistent with the over sampling by the NHIS) are represented in our study population. Family structure indicates that children resided mostly in two-parent families (71.4%) with 37.3% reporting a family size of four persons. Slightly more than one-half of responsible adults had some college education (51.1%). Comparison to the U.S. Census (1990) indicates that families in this sample are more highly educated with a higher frequency of college than elementary school education. Slightly more than one-half of the U.S. born families (54.5%) reported living in the state of their present residence for less than 10 years. Children who were foreign-born comprised 2.9% of the sample. Urban residents (75.8%) were more frequently represented than those in rural areas which is consistent with the U.S. Census. Regional distribution of children demonstrates a higher frequency from the South (33.4%), 55

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56 followed by the Midwest (28.2%), West (19.4%), and Northeast (18.9%). Most all of the families had access to a telephone (93.4%). Children were reported to have private health insurance (64.2%) more frequently than Medicaid (24.0%). Only 3.9% of the children had both private and public sources of insurance compared to 59.3% with private insurance only; 26.17% with public insurance only; and 10.5% that were uninsured (not shown in Table 4-1). Almost 40% of the children had no reported systems related medical condition; however, they may have had a functional limitation, behavioral, or developmental delay. The remaining children had at least one condition (36.7%), two conditions (15.5%) or three or more conditions (8.1%). Most respondents (90.4%) reported that children had excellent, very good, or good health status compared to 9.6% with fair to poor health status. As might be expected, children with fair or poor fair health status had two or more health conditions (47.8%) in contrast to children with no conditions (15%) (analyses not shown). Likewise, children who had excellent or very good health status had a higher frequency of no health condition (46.7%) compared to children with two or more conditions (16.7%). Children having only one condition had the same frequency (~36%) of either excellent or very good, good, or fair to poor health status suggesting that the respondent perception of child health status is affected by more than the condition. Approximately three-fourths (74.4%) of families lived in households with incomes at or above the 1993 federal poverty level with 57.4% reporting incomes of more than $25,000 per year. The household income distribution mirrors the U.S. Census for 1990; however, more study families were reported to be living below the poverty level compared to national figures. A small number of families, 4.5%, reported having severe

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57 financial problems due to their childs health and 21% reported an impact on their employment related to caring for their child. The characteristics of our study population appear to be consistent with studies of CSHCN. Epidemiologic estimates indicate that CSHCN are more likely to be boys, African-Americans, and older children with families at or below the poverty level (Newacheck, Strickland et al., 1998). Weller, Minkovitz, and Anderson (2003) studied health and related services utilization by school-aged children identified as having special health needs and describe a population with similar characteristics for gender, educational level, family size, family structure, poverty status, and health insurance. Table 4-1. Characteristics of the study sample % (weighted) n=7,870,264 n (unweighted) n=3087 Predisposing Child gender Male Female 59.5 40.4 1849 1238 Child age Birth to 2 years 3-6 years 7-10 years 11-14 years 15-18 years 4.8 17.9 24.8 27.4 24.9 143 556 788 882 718 Race White Black Other 81.7 15.5 2.7 2508 499 80 Adults in family Both parents Mother Father 71.4 26.8 1.7 2163 868 56 Family size Two persons Three persons Four persons Five persons Six or more persons 5.5 18.8 37.3 21.9 16.5 169 594 1132 679 513

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58 Table 4-1. Continued % (weighted) n=7,870,264 n (unweighted) n=3087 Predisposing, continued Highest education of responsible adult Elementary Some high school HS graduate Some college College Graduate 3.2 9.6 36.0 25.8 25.4 110 315 1113 774 775 Residence status (U.S. born) < 5 years 5 < 10 years > 10 years Foreign-born 24.0 30.5 42.5 2.9 720 980 1302 85 Enabling Urban or rural Urban Rural 75.8 24.2 2297 790 Region of U.S. residence Northeast Midwest South West 18.9 28.2 33.4 19.4 582 902 999 604 Medicaid 24.0 778 Private health insurance 64.2 1954 Telephone access 93.4 2867 Need Child health conditions None One Two Three or more 39.6 36.7 15.5 8.1 1216 1140 474 257 Child health status Excellent to very good Good Fair to poor 62.7 27.7 9.6 1929 848 310 Family income < $ 24,000 $ 25-49,000 > $ 50,000 42.5 34.3 23.1 1346 1040 701 Poverty level At or above Below poverty level 74.4 25.6 2278 809 Severe financial need 4.5 131 Impact on employment 21.0 645 Utilization of Care Coordination Almost 67% of families reported using any care coordination services during a 12 month period (Table 4-2). Professionals were more frequent providers of care

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59 coordination (44%) compared to families (19.3%). Approximately 37% of care coordination was provided by both, at least one professional and by the family. Of professionals, most of the providers of care coordination were physicians. Table 4-2. Utilization of care coordination n=3087 % (weighted) n (unweighted) Used care coordination 66.9 2071 Type of provider Family only Professional only Family with Professional 19.3 44.0 36.6 397 910 764 Utilization and Unmet Need for Health and Related Services Health care was the most frequently used service (84.6%) by children in the previous 12-months (Table 4-3). Equipment, assistive devices, or home and automobile modifications were used by 38.1% of the children. Therapeutic services used by 26.1% of the children reflect physical, occupational, speech, audiology, communication, respiratory, and special education recreation therapy services. Home care (14.7%) and support services (9.3%) were the least frequently used services. More than half of the children (55.2%) used a variety of two or more of the five service types. Child unmet need for home (7.0%) and health care (5.8%) were the most frequently mentioned types of services, followed by therapy (4.0%), equipment/assistive devices/home-care modifications (3.4%) and support services (1.6%). Slightly less than 15% of the children had an unmet need for one or more varieties of these services.

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60 Table 4-3. Use and unmet need for health and related services by type and variety n=3087 Percent Used (weighted) Percent Unmet Need (weighted) Types of Services 1. Health care 2. Equipment / assistive devices 3. Therapy 4. Home care 5. Support 84.6 38.1 26.1 14.7 9.3 5.8 3.4 4.0 7.0 1.6 Variety of Services None One Two Three Four Five Mean (SE) 2.8 42.0 34.2 13.1 5.5 2.4 1.8 (0.02) 84.9 10.2 3.3 1.0 0.44 0.03 0.22 (0.01) Aim 1: Determinants of Care Coordination Utilization Logistic regression analyses showed that a number of predisposing, enabling, and need factors were significantly associated with using care coordination. Table 4-4 reports the adjusted odds ratios for the multivariate regressions of the use of care coordination in relationship to the covariates. Separate regressions were run in conjunction with service type and service variety due to the collinearity of these variables. The odds ratios and p values for the child and family characteristics were similar in the type of services and variety of services models. Results are discussed for the variety of services model. Predisposing Children who were black, foreign-born, and who lived in families with more than five persons were significantly less likely to use care coordination services. Compared to children who were white, children who were black were half as likely (OR, 0.49) to use coordination. Likewise, families with five or more persons were 0.70 times as likely (compared to two or three person families) to use care coordination. Weller and

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61 colleagues (2003) reported a similar odds ratio for children who were black in relationship to medical and nonmedical coordination but no association with family size. Meanwhile, children who were foreign-born, compared to U.S. born children who maintained the same state residence for less than five years, were only 0.43 times as likely to use care coordination. Child gender, child age, family composition, and adult education were not associated with the use of care coordination. Enabling Children who had private health insurance and used different types of health and related services, especially two or more different services, and had an unmet need for more than one type of service were more likely to use care coordination. Children in the western region of the United States were only 0.67 times as likely to use care coordination compared to children living in the northeast. Children with private insurance were almost twice as likely (OR, 1.78) of having coordination compared to children who do not. The use of Medicaid was not associated with care coordination. Compared to children without health insurance, children who had a combination of private and public sources were more than twice as likely (OR, 2.21; p = 0.02) to use care coordination (not shown in Table 4-4). Children with only a public source of insurance, including Medicaid, were just as likely to use care coordination as children who were uninsured. This finding is consistent with Weller, Minkovitz, and Anderson (2003) in that uninsured children are less likely to have care coordination. However, they do report an increased likelihood that children with only a source of public insurance were more likely (OR, 1.38, p<0.05) to have nonmedical coordination.

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62 Table 4-4. Full model for use of care coordination by service type and variety Service Type Service Variety Odds 95% CI P Odds 95% CI p Predisposing Gender Male* 1.0 1.0 Female 0.95 (0.80, 1.13) 0.57 0.93 (0.79, 1.10) 0.43 Child age Birth 6 years* 1.0 1.0 7-10 years 1.24 (0.87, 1.77) 0.22 1.18 (0.84, 1.66) 0.34 11-14 years 1.09 (0.76, 1.56) 0.63 1.08 (0.76, 1.54) 0.67 15-18 years 1.04 (0.71, 1.52) 0.85 1.02 (0.69, 1.49) 0.93 Race/ethnicity White 1.0 1.0 Black 0.53 (0.38, 0.73) 0.000 0.49 (0.36, 0.68) 0.000 Other 0.80 (0.49, 1.28) 0.35 0.76 (0.47, 1.22) 0.25 Adults in family Two-parent* 1.0 1.0 Single parent 0.98 (0.73, 1.31) 0.90 0.98 (0.73, 1.32) 0.92 Family size Two or three person* 1.0 1.0 Four person 0.96 (0.74, 1.24) 0.75 0.94 (0.72, 1.21) 0.61 Five person 0.74 (0.53, 1.02) 0.07 0.70 (0.51, 0.97) 0.03 Six or more person 0.71 (0.51, 0.98) 0.03 0.71 (0.51, 0.98) 0.03 Education, adult < High school grad 1.0 1.0 High school grad 0.94 (0.69, 1.29) 0.71 0.95 (0.70, 1.28) 0.72 Some college 1.17 (0.82, 1.66) 0.37 1.19 (0.84, 1.68) 0.32 College grad + 1.11 (0.75, 1.63) 0.60 1.14 (0.78, 1.69) 0.48 Same residence (U.S. born) < 5 year* 1.0 1.0 5-10 years 0.86 (0.62, 1.19) 0.35 0.84 (0.61, 1.64) 0.30 10 + years 0.96 (0.64, 1.42) 0.83 0.91 (0.61, 1.36) 0.66 Foreign-born 0.44 (0.25, 0.78) 0.005 0.43 (0.24, 0.77) 0.005 Enabling Rural (vs urban) 0.98 (0.70, 1.39) 0.93 0.97 (0.68, 1.37) 0.85 Region Northeast* 1.0 1.0 Midwest 1.04 (0.72, 1.51) 0.82 1.01 (0.69, 1.47) 0.95 South 0.96 (0.70, 1.31) 0.78 0.92 (0.66, 1.27) 0.61 West 0.69 (0.51, 0.96) 0.02 0.67 (0.49, 0.92) 0.01 Medicaid ** 1.24 (0.88, 1.74) 0.22 1.38 (0.99, 1.92) 0.06 Private insurance ** 1.74 (1.25, 2.40) 0.001 1.78 (1.30, 2.45) 0.000 Telephone access ** 1.39 (0.96, 2.00) 0.08 1.42 (0.98, 2.06) 0.06 Family income < $ 24,000* 1.0 1.0 25-49,000 1.19 (0.86, 1.64) 0.28 1.20 (0.88, 1.65) 0.24 > $50,000 1.05 (0.73, 1.50) 0.81 1.09 (0.77, 1.56) 0.61

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63 Table 4-4. Continued Service Type Service Variety Odds 95% CI p Odds 95% CI p Enabling, continued Type of service used Home / respite 1.35 (0.91, 1.99) 0.13 Health 1.73 (1.35, 2.21) 0.000 Equipment 1.43 (1.13, 1.80) 0.003 Support 3.28 (2.07, 5.17) 0.000 Therapy 1.38 (1.10, 1.74) 0.006 Variety services used None or One* 1.0 Two 1.79 (1.43, 2.23) 0.000 Three to five 2.54 (1.82, 3.57) 0.000 Need Child health conditions None* 1.0 1.0 One 1.08 (0.86, 1.34) 0.51 1.08 (0.87, 1.34) 0.48 Two or more 1.28 (1.00, 1.65) 0.04 1.30 (1.01, 1.67) 0.04 Child health status Excellent/very good* 1.0 1.0 Good 0.86 (0.69, 1.07) 0.17 0.90 (0.73, 1.18) 0.35 Fair/poor 1.53 (1.12, 2.08) 0.007 1.63 (1.20, 2.21) 0.002 Poverty level At or above* 1.0 1.0 Below poverty level 1.23 (0.89, 1.69) 0.21 1.23 (0.89, 1.69) 0.20 Severe financial need ** 1.96 (1.03, 3.72) 0.04 2.20 (1.18, 4.12) 0.01 Impact on employment ** 1.51 (1.16, 1.97) 0.003 1.54 (1.17, 2.03) 0.002 Unmet service need Home/respite 1.34 (0.85, 2.12) 0.20 Health 1.06 (0.65, 1.72) 0.81 Equipment 1.40 (0.57, 3.41) 0.45 Support 0.90 (0.37, 2.19) 0.82 Therapy 0.94 (0.56, 1.57) 0.81 Unmet variety need None* 1.0 One to five 1.50 (1.08, 2.08) 0.01 Reference group. ** Reference group is no. P<.05 level of significance. Four of the five types of services were positively associated with care coordination. Children who used support services (social work, transportation, center for independent living) were three times as likely to use care coordination (OR, 3.28) compared to children not using them. Health services were also a positive predictor of care coordination use with children who had any health service being almost twice as likely

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64 (OR, 1.73) as those not using any health services to have coordination. Equipment and therapy service similarly predicted use of coordination (OR, 1.43, 1.38 respectively) while the use of home care services did not. Children who used more than one of these five services had an increasing likelihood of using care coordination. For example, children with two different types of services were almost twice as likely to have coordination (OR, 1.79) while children with three or more services were 2.54 times as likely. Residence in rural versus urban areas, access to a telephone, and family income were not associated with having coordination. Need Respondent perceived child health status, child co-morbidities, family financial strain, and an unmet need for one or more services were all positively associated with the use of care coordination. Children with fair or poor health status were at least one and one-half (OR, 1.63) times as likely to use coordination compared to children with excellent or very good health status. Weller and colleagues (2003) also report that children with fair or poor health status are twice as likely to use medical coordination compared to children with excellent health status. However, they did not find a significant relationship between child health status and nonmedical coordination. Similarly, children with two or more health conditions were 1.30 times as likely, compared to those with none, to use coordination. Families who reported having severe financial problems due to their childs health were more than twice as likely (OR, 2.2) to use coordination. In addition, families who reported that caring for their child limited their employment opportunities were also more likely (OR, 1.54) to use care coordination. Yet, family poverty level was not associated with coordination. Finally, families who reported having an unmet need for one or more

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65 (up to five) varieties of services were 1.50 times as likely to use care coordination compared to families whose needs were met. An unmet need for a particular type of service (home, health equipment, therapy, or support) did not significantly predict use of care coordination. Reduced Model The covariates that were statistically significant in the full model were entered into a reduced model (Table 4-5). Child age was included as a control variable due to the potential significance related to service eligibility, however, it was not significant in the full or in the reduced model. There are minor differences in odds ratios when comparing the types of services model to the variety of services model. Comparison between the full (Table 4-4) and the reduced models (Table 4-5) indicates that the same covariates are significant with minor changes in odds ratios and p values. The major difference is that the use of home care services, although not found to be significant in the full model (OR, 1.35; p=0.13), was a significant predictor of coordination in the reduced model (OR, 1.47; p=0.03). Further analyses found that more than one unidentified covariate was responsible for controlling differences among home care services in the full model. Aim 2: Care Coordination Utilization by Type of Provider Multinomial logistic regression simultaneously estimated the predictors for a professional, who coordinates care, and the professional and family who coordinate care, compared to the family alone. Covariates that emerged as statistically significant, whether analyzed in relationship to the type or to the variety of services identified similar predisposing, enabling, and need factors (Tables 4-6, 4-7). Relative risk ratios will be reported as a range for the types of service and the variety of service model for the significant covariates (RRR, services variety).

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66 Table 4-5. Reduced model for significant covariates of coordination use by service type and variety Service Type Service Variety Odds 95% CI p Odds 95% CI p Predisposing Race/ethnicity White 1.0 1.0 Black 0.55 (0.41, 0.75) 0.000 0.52 (0.39, 0.70) 0.000 Family size Two or three person* 1.0 1.0 Five persons 0.73 (0.55, 0.97) 0.03 Six or more person 0.74 (0.55, 0.99) 0.04 Same residence (U.S. born) < 5 year* 1.0 1.0 Foreign-born 0.43 (0.24, 0.77) 0.005 0.42 (0.23, 0.76) 0.004 Enabling Private insurance ** 1.65 (1.31, 2.07) 0.000 1.65 (1.31, 2.07) 0.000 Region Northeast* 1.0 1.0 West 0.71 (0.52, 0.97) 0.03 0.68 (0.50, 0.92) 0.01 Type services used Home ** 1.47 (1.03, 2.11) 0.03 Health ** 1.79 (1.40, 2.29) 0.000 Equipment ** 1.46 (1.16, 1.85) 0.002 Support ** 3.45 (2.16, 5.52) 0.000 Therapy ** 1.40 (1.11, 1.76) 0.004 Variety services used None or One* 1.0 Two 1.82 (1.46, 2.26) 0.000 Three to five 2.64 (1.89, 3.69) 0.000 Need Severe financial need ** 2.01 (1.07, 3.75) 0.02 2.11 (1.14, 3.93) 0.01 Employment impact ** 1.53 (1.19, 1.98) 0.001 1.56 (1.21, 2.04) 0.001 Child health conditions None* 1.0 1.0 Two or more 1.30 (1.01, 1.66) 0.04 1.31 (1.02, 1.68) 0.03 Health status Excellent/very good* 1.0 1.0 Fair/poor 1.45 (1.07, 1.97) 0.01 1.55 (1.15, 2.10) 0.004 Unmet variety need None* 1.0 One to five 1.49 (1.08, 2.06) 0.01 Reference group. ** Reference group is no. More highly educated and residentially stable families had a professional involved in care coordination while families with older children, children who were black, or who

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67 lived on the west coast did not. Families with a responsible adult having at least some college education were more likely to have professional (RRR, 1.37-1.43) or joint (2.32-2.36) coordination compared to those with less than a high school degree. Likewise, families who resided in the same state more than 10 years (compared to less than five years) were more than twice as likely to have a professional (RRR, 2.30-2.31) or a professional and family (RRR, 2.40-2.42) coordinate care. Table 4-6. Full model for service type: Comparison of coordination by type of provider for significant covariates Type of Service Model Professional vs Family Family & Professional vs Family RRR 95% CI p RRR 95% CI p Predisposing Child age Birth 6 year* 1.0 11-14 year 0.40 (0.22, 0.71) 0.002 0.52 (0.29, 0.94) 0.03 15-18 year 0.32 (0.17, 0.59) 0.000 0.44 (0.25, 0.79) 0.006 Race/ethnicity White* Black 0.56 (0.31, 0.98) 0.04 Same residence (U.S. born) < 5 year* 1.0 10 + years 2.31 (1.34, 3.99) 0.003 2.42 (1.45, 4.05) 0.001 Enabling Medicaid ** 2.24 (1.40, 3.57) 0.001 2.27 (1.30, 3.94) 0.004 Private insurance ** 1.75 (1.16, 2.62)_ 0.008 Used health services ** 1.62 (1.12, 2.34) 0.01 1.56 (1.05, 2.29) 0.02 Region Northeast* 1.0 West 0.53 (0.29, 0.97) 0.04 Need Child health status Excellent or very good* 1.0 Good 1.54 (1.09, 2.19) 0.01 Reference group. ** Reference group is no Children 11 years of age and older (compared to children under the age of 6 years), were less likely to have a professional (RRR, 0.32-0.40) or professional and family (RRR, 0.44-0.52) who coordinated their care. Compared to professional only or professional with family, the likelihood families alone coordinated care for children over

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68 the age of 11 years was two to three times that for families of younger children (analyses not shown). Children who were black were one-half as likely to have a professional and family (RR, 0.55-0.56) coordinate care and children on the west coast were similarly as likely (RRR, 0.52-0.59) to have professional only coordination. In fact, children living in the west were twice as likely (RRR, 1.8) to have family coordinate care compared to professional only and children who were black were similarly twice as likely (RRR, 1.8) to have family coordinate care alone compared to professional and family (not shown). Table 4-7. Full model for service variety: Comparison of coordination by type of provider for significant covariates Variety of Services Model Professional / Family Family & Professional / Family RRR 95% CI p RRR 95% CI p Predisposing Child age Birth 6 year* 1.0 11-14 year 0.38 (0.22, 0.68) 0.001 0.52 (0.29, 0.91) 0.02 15-18 year 0.32 (0.17, 0.59) 0.000 0.45 (0.26, 0.79) 0.006 Race/ethnicity White* Black 0.55 (0.32, 0.97) 0.03 Same residence (U.S. born) < 5 year* 1.0 10 + years 2.30 (1.32, 4.00) 0.003 2.36 (1.43, 3.90) 0.001 Education, adult High school grad or less* 1.0 Some college plus 1.37 (1.00, 1.86) 0.04 1.43 (1.01, 2.02) 0.04 Enabling Medicaid ** 2.30 (1.44, 3.66) 0.001 2.40 (1.41, 4.07) 0.001 Private insurance ** 1.80 (1.21, 2.70) 0.004 Region Northeast* 1.0 West 0.52 (0.28, 0.94) 0.03 0.59 (0.36, 0.96) 0.03 Need Child health status Excellent or very good* 1.0 Good 1.56 (1.10, 2.21) 0.01 Reference group. ** Reference group is no Private insurance, the use of any health services, and having Medicaid emerged as positive enabling predictors associated with professional or professional and family

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69 coordination. Private insurance was significant (RRR, 1.75-1.80) in relationship to professional coordination but not when performed by both a professional and family. Children with Medicaid, meanwhile, were more than twice as likely (RRR, 2.24-2.40) to have professional or professional and family involvement with coordinating care. Children who used health services, a logical consequence of having health insurance or Medicaid, were one and one-half times as likely of having coordination compared to children who used no health services. Only one need factor was associated with who provided the coordination. A child with a good (versus excellent to very good) health status was one and one-half times more likely (RRR, 1.54-1.56) to report having a professional provide care coordination. No significant relationship to health status was noted when a professional and family both coordinated care.

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CHAPTER 5 DISCUSSION This exploratory study found that a combination of child and family characteristics, and the use or unmet need for a variety of health and related services, independently predicted the utilization of care coordination. In addition, predictors of professional care coordination identified the protective effect of health insurance, residential stability, and adult education while at the same time revealing regional, racial, and child age-related disparities. One prior care coordination population-based study substantiates some of these findings (Weller, Minkovitz, & Anderson, 2003) and another supports that professional coordination enabled access to mental health services (Witt, Kasper, & Riley, 2003). Our study complements the existing research and adds that care coordination is associated with access to different types and a variety of services and that private health insurance and family characteristics are important predictors of utilization. Who Uses Care Coordination and Who Doesnt? Children who have access to private health insurance as well as home, health, therapy, equipment, and particularly support services, were more likely to receive care coordination. It is well known that having a source of health insurance enables access to health care (Newacheck et al., 2000) but that it also enables access to care coordination has not been previously reported. Furthermore, private insurance, as well as Medicaid, was associated with care coordination when a professional was involved in coordinating care. The fact that support services were the best predictor of care coordination utilization 70

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71 of the five difference service types is not surprising since these services included social workers who are providers of coordination. The positive association between having private health insurance and care coordination appears to contradict studies that report primary care physicians underestimate or do not meet family need for care coordination (Liptak & Revell, 1989; Perrin, Lewkowicz & Young, 2000, Scholle & Kelleher, 1995) and that coordination is more often provided in public programs (Krauss, Gulley, et al., 2000). One difference is that prior studies used convenience samples rather than population-based data, as our study did. A second difference is that changes have occurred in the health care system (managed-care and safety net program expansions; transition from specialty to primary providers) since the NHIS-D was conducted. Perhaps a family in the mid 1990s had continuity of a consistent medical provider which has been associated with better care coordination (Christakis, Wright, Zimmerman, Bassett, & Connell, 2003). A final difference may be related to how care coordination was defined. The NHIS-D defined care coordination primarily related to health care services, as medical or nonmedical, and not in terms of across delivery systems coordination. Consequently, the majority of the professionals who provided coordination were physicians and access to physicians was through health insurance. Children who used a variety of three to five different types of services or had an unmet need for more than one of these services were also more likely to use coordination. Using a variety of different services implies the involvement of more providers and consequently, the need for communication and coordination. Likewise, the availability of a person to coordinate care may mean greater access to additional services as has been

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72 described in prior studies (Gehl, 1993; Krauss, et al., 2000; Schwab & Pierce, 1986). Slightly more than one-half (55%) of the children used two or more different types of services with 20% using three to five services. Comparison is limited by lack of population-based studies using similar measures of health and related services use. One study found that a small proportion of children represented in the NHIS-D-Phase I received a combination of nonroutine medical care, special education services, and mental health care (Stein and Silver, 2003). The implication is that children who use a breadth of services across providers and service systems require someone to manage communication. Coordination was more likely among children who had two or more health conditions and a poor to fair health status. It is reasonable that children who have co-morbidities and whose parents perceived that they have poor health status would use health and related services. Weller, Minkovitz, and Anderson (2003) also reported a significant association between fair or poor health status and medical coordination. An interesting finding is that children with good health status (compared to excellent or very good) were more likely to utilize care coordination when it was provided by a professional in contrast to the family alone or a family and professional. A possible explanation is that perhaps this reflects children whose health status was initially poor but improved with intervention. Consequently, families formed a relationship with a professional and became predisposed to continue professional coordination. It may also be that families of children who had a good health status felt less of a need to coordinate care themselves. Jessop and Stein (1991) discovered that families with low coping resources and low burden benefited most from coordinated care (child function and

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73 psychological adjustment, family impact) compared to families with low coping and high burden (the neediest). They also point out that it was the social and not the medical factors that distinguished the population who most benefited. Families who were more highly educated were more likely to have a professional who coordinated care, either with or without family involvement, suggesting that college educated families know how to acquire what they need. In addition, families who reported residing in the same state of residence more than 10 years were twice as likely to have a professional involved with coordinating care. This stability of residence implies that families may become more knowledgeable about community resources and have long-term relationships with medical and allied health providers. Family characteristics associated with the financial impact of caring for the child were important predictors of using care coordination. Perception of severe financial problems and limited employment opportunities were positively associated with care coordination when controlling for income and poverty status. Prior studies suggest that increased caregiving is related to the severity of the childs condition and increased out-of-pocket expenses for families at all income levels (Breslau, 1983; Brust, Leonard, & Sielaff, 1992; Leonard, Brust, & Sapeinza, 1992; Krauss, Gulley, et al., 2000; Krauss, Wells, et al., 2001; Lukemeyer, Meyers, & Smeeding, 2000). Children who were less likely to use care coordination included families of five or more persons, children who were black or foreign-born, and those residing in the western region of the United States. The racial/ethnic and regional disparity persisted in that these children and families were also less likely to have a professional involved in coordination. Prior research has noted that as family size increases the use of routine

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74 preventive health care services declines which may be attributable to increased life demands (Slesinger, Tessler, and Mechanic, 1976). The racial disparity is consistent with the findings of Weller, Minkovitz, and Anderson (2003) who also note a significant difference for medical and nonmedical coordination among minoirities. Similarly, unmet health care needs were reported to be more likely among children between the ages of 11 to 17 years, black, living in large families, with the responsible adult having less than a high school education, and living in the west (Newacheck, et al., 2000). Using data from the new National Survey of CSHCN, Mayer, Skinner, & Slifkin (2004) report that black children were more likely to have an unmet health care need for routine (vs. specialty) care. In this same study, children whose mothers completed high school or more were less likely to report an unmet health need. The fact that the profile of children with unmet needs so closely resembles the profile of children less likely to use care coordination is highly troublesome. Researchers have found that after controlling for socioeconomic status, disparities remain for health care access and outcomes for minorities (Flores et al., 1999). The Institute of Medicine (2003) spotlight on racial and ethnic disparities theorizes that some of these may be iatrogenic, namely, occurring within the health care delivery system and not just the effect of socioeconomic differences or client personal and cultural preference. Finally, families with older children (11 to 18 years of age) were also less likely to have a professional involved with care coordination. One might consider that families with older children have more experience with systems of care and choose to coordinate their own care. However, given that families who had professional care coordination were more likely to be college educated indicates that families who are coordinating care

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75 themselves for pre-teens, teen, and young adults may be less well educated. Considering the barriers with access to care for young adults with special needs and the challenges with transitioning to adult health services (Reiss & Gibson, 2002; Scal, 2002; White, 2002) it is unlikely that as children get older their need for professional assistance to coordinate care diminishes. If anything, given the lack of adult health providers, treatment services, knowledge and experience with this population, young adults are at risk of not being able to access services (Reiss & Gibson, 2002). Care coordination at this transition period is intensive (Kelly, Kratz, Beilski & Rinehart, 2002) and families report that older children have an unmet need for specialty care (Mayer, Skinner, & Slifkin, 2004). Study Limitations Our cross-sectional, observational study is limited to measures of association and no inferences can be made about cause and effect. We dont know if children received coordination because they used a high variety of services or if as a result of having someone coordinate care they were referred to a variety of services. In the PACTS study, families in the experimental group acquired services (school, daycare, Medicaid) as a result of coordinated care but then subsequently lost them while the control group eventually acquired services (Jessop & Stein, 1994). The variability of service acquisition over time, the ebb and flow, is poorly understood and requires longitudinal research. Second, all data were generated from parent report which may be subject to recall bias. Although maternal recall of neonatal events eight to ten years later was found to be accurate (McCormick and Brooks-Gunn, 1999) the underreporting of hospital service use was found by this and a prior study (Pless and Pless, 1995). How well families whose children use a greater volume and variety of services can recall these occurrences within

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76 a prescribed time frame is unknown. McCormick and Brooks-Gunn (1999) found that poor child health status slightly increased discrepancies in recall years later. Respondents in the NHIS-D study were asked about services within the past 12 months. Pless and Pless (1995) report that a one year recall was more accurate among their study population compared to remembering events over a childs lifetime. Third, no attempt was made to identify any particular group of CSHCN by using any of the popular non-categorical methods (Bethel et al., 2002; Stein, Westbrook, & Bauman, 1997; Stein & Silver, 1999) or by diagnoses. Restricting this sample may have eliminated families who reported using care coordination which was the primary interest of our study. The NHIS-D Phase II included multiple definitions of children with special needs, disabilities, and chronic illness and provides a broader representation. There may be differences among diagnostic groups in the use of care coordination and there may be variability between children in our study and those with special health needs. For example, Stein and Jessop (1989) report significant differences among diagnostic groups for children using multiple sources of care and nonmedical sources. Although their overall findings supported that psychosocial factors are not affected by diagnosis (popularizing the noncateogrical approach to identifying CSHCN) the fact that interaction with the health care system differed by diagnoses has implications for care coordination utilization. The problem is that even in such a large population sample as the NHIS-D comparison by diagnostic groups is limited due to heterogeneity of the diagnoses among children, hence, very small sub-population sample sizes. However, a similar study using the NHIS-D that applied selection criteria for CSHCN (Weller, Minkovitz & Anderson, 2003) suggests a great deal of similarity between their sample and the children in our

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77 study. It appears that children who use care coordination are also children who have special health care needs. Fourth, no implications can be made about the appropriateness, quality or intensity of care coordination or health and related service utilization. The decision to collapse medical and nonmedical coordination categories into one measure may have masked differences that might be attributable to one type of coordination but not the other. For example, Weller and colleagues (2003) reported that nonmedical coordination was significantly related to children with public insurance whereas in our study coordination was more likely among children with private insurance. The difference in our study was that it included comprehensive measures of covariates related to service use and unmet need. Also, no implications can be made about the degree, if any, of collaboration between professionals and families when the respondent reported that a professional coordinated care as well as a family member. Fifth, there were significant differences between the complete cases retained for study and the incomplete cases dropped from analyses. Characteristics of children among the incomplete cases represented more children who are black, have Medicaid, with a fair to poor health status, who used therapy, support, and home care or needed therapy and equipment services. More families among the incomplete cases, likewise, had a severe economic need, represented more single parents, adults with a lower educational level, less access to telephone, and lived in the west. Yet, oversampling by the NHIS-D of lower-income and minority groups may remedy any representation difficulties associated with missing data over time in the final analyses, particularly given the survey design and use of variance and sample estimation weights. Respondents among the incomplete cases

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78 appeared to be a more vulnerable population but the differences averaged only 4% overall. Also, with a few exceptions (family structure, telephone, need for services), many of these same variables were found to be significant in the present study. This suggests that eliminating the incomplete cases did not appreciably mask differences in care coordination predictors. In fact, some of the significant differences found for children not using care coordination may be underestimated. Sixth, even though adjustments were made by collapsing multiple levels of covariates to improve cell size that was not possible for variables containing only two levels. Consequently, some of the covariates had less than optimal observations per cell on the basis of the dependent variable, and may provide less than reliable weighted estimates (see Appendix B). There is little guidance in the literature related to what is an optimal cell size for survey weighted data specific to the NHIS-D. Considering the consistency in findings among the different regressions it is suspected that errors due to insufficient cell size are minimal. Finally, the data source for our study is between 7 to 10 years old and until recently, has been the only national source of information about this small and heterogeneous group of children. Utilization patterns may have changed with the proliferation of managed-care in the last decade, expansion of safety net programs such as SCHIP, and increased emphasis on including care coordination into a variety of health care financing mechanisms. Consequently, our study will provide a baseline for monitoring future utilization patterns. Also, the National Survey on Children with Special Health Care Needs, conducted by the National Center for Health Statistics may help our

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79 understanding of the variability of access to care coordination on a state-level (Van Dyck, et al., 2002) and provide a source of comparison in the future. Implications Our study suggests that children and families who might need care coordination may not be receiving it. Care coordination was utilized and/or performed by a wider variety of families than reported in prior studies (Kruger, 2002). Furthermore, children who have professionals involved in coordinating care are more highly educated compared to lower income and minority target populations described in the literature. The national focus on health disparities has highlighted the association between race and ethnicity in relationship to access, treatment outcomes, discrimination, health care system culture and structure, and professional-client relationships (Institute of Medicine, 2003). Care coordination has a positive mediating role associated with increased access to different types and a variety of services. Consequently, it may be a mechanism through which to explore and ameliorate either personal and cultural family preferences or the iatrogenic effects of the health care system. Studying care coordination may reveal and help explain inequities. An ethic of social justice (Drevdahl, Kneipp, Canales, & Dorcy, 2001) must frame future study. Society must consider how much of what families coordinate at their expense, to accommodate deficiencies in systems of care, is ethical. Disparities related to race/ethnicity and socioeconomic status is unconscionable. How social policy is constructed to support not only families of CSHCN but all families must be addressed. The association between care coordination and health insurance underscores that expanding insurance access to all children is likely to result in greater availability. The relationship between coordination and health care utilization means that health care providers have a particular opportunity to identify families who require or want care

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80 coordination. This has important implications for the medical home, the approach for pediatric primary care to assure compassionate, comprehensive, coordinated, continuous, culturally-effective, family-centered, and accessible care to CSHCN (American Academy of Pediatrics & Medical Home Initiatives for Children with Special Needs Project Advisory Committee, 2002). First, primary care medical homes must assess the childs use and need for all services (educational, developmental, and community) not just those within the medical system. Second, the focus in pediatric primary care for this population must be expanded to families (see American Academy of Pediatrics, 2003) who are the direct recipients (and providers) of care coordination. Assessment of family financial stress, impact on employment, and other social factors should be considered. Title V programs, generally based in a public health ideology, have an ecologic focus that considers the broad determinants of health (socio-cultural, political, environmental, economic). Therefore, in addition to the assessment of child medical needs and treatment, there is a complementary focus on the education of family, their access to and use of resources, financial concerns, problem-solving skills, support systems, and coping (Zimmerman et al., 2000). Assessment of family stressors (Burke, Kauffmann, Harrison, & Wiskin, 1999) using tested instruments that have a family-centered approach should also be considered. Collaboration between primary care medical homes and public health oriented Title V programs are supported in national demonstration programs (National Initiative for Childrens Healthcare Quality, 2004) and have the potential to combine the best of medical and public health care. The predisposing factors associated with care coordination such as family race/ethnicity, family size, education, and residence are considered immutable. However,

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81 outreach efforts in the medical home or health plan could specifically target these families for casefinding and/or application of predictive modeling. This preventive approach examines patterns of care that include comorbidities, clinical care, and social factors within ambulatory care (Predictive Modeling Care Management, 2001; Predictive Modeling, Integrated DM, 2002). This requires a comprehensive assessment by a care coordinator that considers information about client and family psycho-social issues as well as community dynamics. One important caveat is that the intent of care coordination should not be to supplant natural family supports and make families dependent upon professionals (Cooley, 1994). The emphasis on care coordination as one of two cross-cutting priority areas for transforming health care quality by the Institute of Medicine (2003) for all populations who have chronic illness is likely to intensify research efforts. Government policy-makers support a generic framework emphasizing the commonalties among the functions of screening, assessment, care planning, implementation, and reassessment across categorical populations (Falik, et al. 1993). In reality, there are different approaches to operationalizing care coordination in research (Christakis, et al., 2003; Cooley, McAllister, Sherrieb, & Clark, 2003) which will impede comparability for this population of children let alone across populations. A common understanding of what comprises care coordination is vital in order to compare family and health care system outcomes, test interventions, and identify appropriate staffing mix and reimbursement. From a nursing perspective, the process of care coordination with families of CSHCN is proactive, anticipatory, goal-directed, and individualized to meet child and family identified needs (Kruger, 2004). It is a holistic approach through which clinical

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82 services are delivered that is not restricted by office encounter, office setting, or financing mechanism (such as managed-care). The history of care coordination for CSHCN can traced back to the inception of the Childrens Bureau (Perrin, Shayne, & Bloom, 1993) and Title V of the 1935 Social Security Act (Hoeman & Repetto, 1992; Kruger, 2001). State programs frequently recognized the contribution of nurses, for example, "only the continuous assistance and cooperation of the public health nurses made it possible to have successfully working Crippled Children and Maternal and Child Health programs on a statewide basis" (Langer, 1967, p. 270). The contribution of nursing, as well as social work, to present-day care coordination within Title V programs persists (Zimmerman, et al., 2000). Nurses also maintain this legacy in hospitals; ambulatory care centers; schools; early intervention, developmental, mental health, community and independent practice (Kruger, 2003). Nursing has a legal mandate to provide health care services, in contrast to medical diagnosis or treatment (physicians). This framework is holistic, relationship-based, and is focused on prevention, education, and the maintenance of health. Nurses outnumber other health care providers, are distributed across all health and even non-traditional health care settings, have varying levels of generalist and specialist education and practice, and are well-suited to meet the preventive and illness needs of child and family. Although most health care settings incorporate a team approach to caring for CSHCN, a team does not perform the functions of care coordination; rather, it is one professional acting on behalf of the team in conjunction with the family. Nursing knowledge of disease processes, medical and genetic conditions, and physiology in conjunction with a holistic, preventive, and family focus gives nurses an advantage over other health care professionals. Nurses

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83 are also distributed across the health care system at various levels of educational preparation that correspond to the specialization required on the basis of population needs. More nurse coordinators with specialized graduate education are found in hospitals, for example, compared to other healthcare settings (Kruger, 2003). Primary care medical homes are also incorporating nurses, or changing staff roles, to include care coordination. From a theoretical perspective our study has implications for building nursing theory to guide care coordination interventions. It supports that nursing theory should be extended to incorporate a family perspective and policy perspective. Prior reviews of nursing case management literature acknowledge the absence of theory applied to nursing care coordination (Daiski, 2000; Lamb, 1992, 1995). Nurses have provided examples of how particular nursing theories such as Peplau (Forchuk, et al., 1989), Watsons Caring (Wadas, 1993), and Parses Human Becoming (Daiski, 2000; Milton & Buseman, 2002) can be applied to case management practice. Most of these examples focus on the care of the individual. Our study extends the practice of care coordination from the individual (child) to the family (group) and to the health care system (environment). Family predictors of care coordination include economic stresses of out-of-pocket expenses and interference with employment. Although the concept in the nursing metaparadigm of person (in addition to environment, health, and nursing) is generally accepted to include family and group, the application of nursing theory beyond the individual level of practice has not been well articulated. Similarly, the concept of environment in nursing theory is underdeveloped (Choporian, 1986; Kleffel, 1991, 1996) yet care coordination is specifically focused on

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84 family interaction with their environment, namely, health and human services systems. In our study children were found to use a range of services that were associated with care coordination. The application of nursing theories based on systems models such as Roy, Johnson, King and and Neuman (Williams, 1991)) are particularly relevant to the application of care coordination. Beyond the immediate environment of the family, the policy context is of critical importance to the distribution of care coordination services and the disparities noted among children who were black, are older in age, as well as the western region of the country. Nurses recognize that macro or policy context (the environment) is of specific interest to nursing however, this is even less well applied in nursing theory. Drevdahl (1999) asserts that nursing theories which consider social justice need to be developed to consider societal issues such as race disparities. Milio (1986) also has long supported that the policy context is a distal determinant of health, has implications for disparities in health, and should be considered as a locus of change. This think tall (policy) rather than small (individual) perspective in nursing theory has been described by Butterfield (1990) as looking upstream. Nurses can do this by taking action to change the system and to empower families through care coordination. Incorporation and development of existing nursing theory to guide care coordination interventions at family and policy levels is strongly supported in lieu of creating new theory. The research challenge is to bridge the gap in knowledge about how families interact with the health care system, a current deficit in family and pediatric chronic illness (Gilliss & Knafl, 1999) and health services research (Perrin, 2002). A public health population focus would aid our understanding of systems that enhance and impede

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85 coordination while we simultaneously focus on providing care at the family or individual level. Future research would also benefit by building on the rich conceptual literature about children and families (Knafl & Gilliss, 2002) and advanced practice nursing case management (Brooten, Youngblut, Deatrick, Naylor, & York, 2003). Our understanding of how families and providers across medical, allied health, mental health, education, and developmental services systems coordinate care may shed important light on why disparities in patterns of utilization exist and how they can be ameliorated. Nurses natural role, among these systems of care, as interpreters and translators of medical jargon into the everyday language of families and nonmedical providers affords a particular opportunity to shift the health care delivery paradigm to universal family-centered care. Nurses must take this opportunity to not only demonstrate their unique contribution to over 100 years of care coordination to children and families but to also mediate the language barriers between families and the health care disciplines and potentially among the disciplines to reveal new knowledge about the puzzle of disparities among this population of children and families.

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APPENDIX A CONSTRUCTION OF TYPE OF SERVICE VARIABLES Ten variables were generated to measure different types of service used (five variables) and an unmet need for these services (five variables) by children. These measures represent home care, health care, equipment, therapy, and support services. Survey questions about services used and unmet service needs were distributed across seven sections of the NHIS-D Phase II questionnaire as well as the first interview (NHIS core and Phase I). The sections of services in the NHIS-D Phase II were: home care helpers and respite care (section A), medical care (section C), use of assistive devises or equipment (section D), allied health services (section E), special education and/or early intervention programs (section F), mental health (section K) and use of home or motor vehicle modifications (section L). Variables from across sections of the survey were collapsed into summary measures in one or two stages. If a respondent answered yes to any individual question they were coded as yes for that subcategory or category of service. No attempt was made to quantify services. Measures for an unmet need for services reflect family perceived need. Respondents were asked if their child 1) had a need for a service they were not receiving, 2) required more services of a particular type than they were receiving, 3) were on a waiting list for services, or 4) reported trouble getting services. Used Home Care Home care services were reported in section A of the NHIS-D Phase II in relation to having a personal care attendant and respite care. Section E (allied health) reported 86

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87 visiting nurse and personal care attendant, and section F (education) asked about home training visits for infants and toddlers. Families who responded that their child received one of these services in the past 12 months were counted once and coded yes in the summary variable (SHome). The variable asking about the number of helpers in the past 2 weeks (versus in the past 12 months as in the other variables) was converted to a categorical variable (yes if any helpers) before collapsing into the summary variable (Table A-1). Table A-1. Home care service measure NHIS Variable Name & Description Used Home Care Received any home care services, yes or no, in past 12 months A_405 Number personal care helpers at home in past 2 weeks recoded to yes or no A_550 Used respite care E_951 Received visiting nurse E_1001 Received non-family personal care attendant F_1456 Received family training home visits, early intervention SHome Used Health Care Health care service use was reported in the NHIS Phase I and the NHIS-D Phase II section C (health care), section E (allied health), section F (education services), and section K (mental health). One variable related to hospitalization from the Phase I data is included in the summary variable. The Phase I question asked if the child ever was hospitalized overnight for an ongoing condition. The remaining variables included in the summary health use measure (SHealth) are drawn from the Phase II data. Health care services include any visits for health care, mental health, nursing, nutrition, including hospital in-patient and community services during the past 12 months. Collapsing of variables was completed in two stages. The first stage pooled variables by medical services (7 variables), hospital in-patient (2 variables), and out-patient mental health (4 variables). The second stage collapsed responses from medical, hospital, and mental health into one overall health measure (Table A-2).

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88 Table A-2. Health care service measure Generated Variables NHIS Variable Name & Description Type Health Care Use Health Care C_641 Any visits to MD office, clinic, hospital, etc. for heath care E_1101 Received home visits from doctor F_1386 Received medical diagnosis via special education F_1388 Received nursing services via special education F_1467 Received medical diagnosis via early intervention F_1457 Received nursing or health services via early intervention F_1460 Received nutrition services via early intervention CH_356 Ever had overnight hospitalization for ongoing condition K_1671 Stayed overnight in hospital for mental health care F_1375 Received mental health/ counseling via special education F_1462 Received psychological services via early intervention K_1708 Received out-patient mental health services K_1751 Received community support mental health services SMedical SHospin SMental SHealth Used Equipment Sections D (equipment), F (special education), and L (home modifications) contained questions about child use of equipment or car modifications. Variables were initially collapsed within each section before the overall equipment variable was generated (Table A-3). Used Therapy Therapy services included physical, occupational, speech, respiratory, communication, interpreter, and recreation. The first stage of creating the therapy measure involved collapsing individual variables drawn from section E (allied health) and section F (special education) by type of therapy. The overall therapy variable collapsed the different sub-categories (Table A-4).

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89 Table A-3. Equipment service measure NHIS Variable Name & Description Type Equipment Used Equipment D_681, used any medical devices or supplies F_1382 Received eyeglasses, special education F_1383 Received hearing aids, special education F_1384 Received wheelchair, special education F_1385 Received other assistive devices, special education F_1466 Received other assistive devices, early intervention L_1788 Widened doorways or hallways L_1789 Ramps L_1790 Railings L_1791 Automatic or easy to open doors L_1792 Accessible parking or drop-off L_1793 Bathroom modifications L_1794 Kitchen modifications L_1795 Elevator, chair lift or stair glide L_1796 Alerting devices L_1797 Other special features L_1809 Motor vehicle special equipment Dequip FSequip Lsequip SEquip Table A-4. Therapy service measure NHIS Variable Name & Description Type Therapy Used Therapy E_701 Received physical therapy F_1377 Received physical therapy, special education F_1458 Received physical therapy, early intervention E_751 Received occupational therapy F_1378 Received occupational therapy, special education F_1459 Received occupational therapy, early intervention E_851 Received speech therapy F_1373 Received speech therapy, special education F_1454 Received speech therapy, early intervention E_801 Received audiology services E_1051 Received reader/ interpreter services F_1374 Received audiology services, special education F_1387 Received communication services, special education F_1455 Received audiology services, early intervention E_1201 Received respiratory therapy services F_1380 Received respiratory therapy services, special education E_901 Received recreation therapy services F_1379 Received recreation therapy services, special education Spt Sot Sst Saudio Spulmon Srec Stherapy Used Support Support services were defined as social work, transportation, and center for independent living services. These variables were drawn from sections E (allied health)

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90 and F (special education) and initially collapsed according to type of service. The overall support variable was then created by collapsing the sub-categories. Table A-5. Support service measure NHIS Variable Name & Description Type Support Used Support E_1151 Received center for independent living services E_1251 Received social work services F_1381 Received social work services, special education F_1461 Received social work services, early intervention E_1301 Received transportation services F_1372 Received transportation services, special education F_1453 Received transportation services, early intervention Esctrlvg Ssw Stransp Ssupport Unmet Home Care Need Section A had one question asking about family need for additional respite care and eleven questions about family difficulty in obtaining home care in past 12 months. Four questions in section E (allied health) asked families who were not receiving visiting nurse or personal care attendant services if they needed these services in the past 12 months and if they were on a waiting list. Section F contained one question asking families if they needed early intervention home services. These were collapsed into a sub-category variable and then into the overall home care need variable (NHome) shown in Table A-6. Unmet Health Care Need Needs for health care services were asked in 11 questions among sections E (allied health), F (education services), and K (mental health) but not section C (health care). Although section C asked about the types of health care services used, no questions were asked about the need for health care. Variables were collapsed into two sub-categories representing education and mental health before further collapsing into one health care need (NHealth) summary variable (Table A-7).

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91 Table A-6. Home care unmet need measure NHIS Variable Name & Description Type Home Need Home A_551 Needed additional respite care A_603 Trouble getting help at home, not available A_604 Trouble getting, find right service A_605 Trouble getting, Medicaid not accepted A_606 Trouble getting, no insurance coverage A_607 Trouble getting, cant afford A_608 Trouble getting, help arranging it A_609 Trouble getting, help not reliable A_610 Trouble getting, help not trained A_611 Trouble getting, hours not convenient A_612 Trouble getting, could not take time from work to arrange A_613 Trouble getting help at home, other reason E_952 Needed visiting nurse E_1357 On waiting list for visiting nurse E_1002 Needed personal care attendant E_1358 On waiting list for personal care attendant F_1494 Tried to get family home visits, special education ANhome EFNhom NHome Table A-7. Health care unmet need measure NHIS Variable Name & Description Type Health Need Health Care E_1102 Needed home visits from doctor in past 12 months E_1360 On waiting list for home visits from doctor F_1438 Tried to get medical diagnostic services, special education F_1440 Tried to get nursing services, special education F_1505 Tried to get diagnostic services, early intervention F_1495 Tired to get nursing or health, early intervention F_1498 Tried to get nutrition services, early intervention F_1427 Tried to get mental health services, special education F_1500 Tried to get psychological services, early intervention K_1752 On wait list for out-patient mental health K_1765 Needed mental health, substance abuse, counseling EFN-medical Nmental NHealth Unmet Equipment Need Questions about the child need for equipment was asked in section F (special education) and L (home / motor vehicle modifications) but not section D (equipment). The equipment need summary variable (NEquip) was created based on responses to five questions about the need for eyeglasses, hearing aids, wheelchair, or other (non-specified) equipment (EFNequip) and 11 questions about the need for home modifications (LNequip). Refer to Table A-8.

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92 Table A-8. Equipment unmet need measure NHIS Variable Name & Description Type Equipment Need Equipment F_1434 Tried to get eyeglasses, special education F_1435 Tried to get hearing aids, special education F_1436 Tried to get wheelchair, special education F_1437 Tried to get other assistive devices, special education F_1504 Tried to get other assistive devices, early intervention L_1798 Needs widened doorways or hallways L_1799 Needs ramps or street level entrance L_1800 Needs railings L_1801 Needs automatic or easy to open doors L_1802 Needs accessible parking or drop-off site L_1803 Needs bathroom modifications L_1804 Needs kitchen modifications L_1805 Needs elevator, chair lift, or stair glide L_1806 Needs alerting devices L_1807 Needs other special features for home L_1818 Needs special equipment or features on motor vehicle EFNequip LNequip NEquip Unmet Therapy Need Questions in section E (allied health) asked if children who were not receiving therapy services required them and if they were on a wait list. Section F (special education) asked families who either were receiving or not receiving education services if they tired to get additional or any therapy services. These services included physical, occupational, speech, respiratory, communication, interpreter, and recreation therapies. The summary variable (Ntherapy) was generated by combining 25 of these variables by sub-categories (Table A-9). Unmet Support Need Variables related to an unmet need for social work, transportation, and center for independent living were drawn from sections E (allied health) and F (special education). Families were either not receiving these services, tried to get additional services and/or were on a waiting list. Ten variables corresponding to these questions from sections were collapsed to produce the summary variable (Nsupport) in two stages (Table A-10).

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93 Table A-9. Therapy unmet need measure NHIS Variable Name & Description Type Therapy Need Therapy E_702 Needed physical therapy services E_1352 On wait list for physical therapy F_1429 Tried to get physical therapy, special education F_1496 Tried to get physical therapy, early intervention E_752 Needed occupational therapy E_1353 On wait list for occupational therapy F_1430 Tried to get occupational therapy, special education F_1497 Tried to get occupational therapy, early intervention E_852 Needed speech therapy in past 12 months E_1355 On wait list for speech therapy F_1425 Tried to get speech therapy, special education F_1492 Tried to get speech therapy, early intervention E_802 Needed audiology services E_1052 Needed reader / interpreter services E_1354 On wait list for audiology services E_1359 On wait list for reader or interpreter services F_1426 Tried to get audiology services, special education F_1439 Tried to get communication services, special education F_1493 Tried to get audiology services, early intervention E_1202 Needed respiratory services E_1362 On wait list for respiratory therapy F_1432 Tried to get respiratory therapy, special education E_902 Needed recreation therapy E_1356 On wait list for recreation therapy F_1379 Tried to get recreation therapy, special education Npt Not Nst Naudio Npulmon Nrec Ntherapy Table A-10. Support unmet need measure NHIS Variable Name & Description Type Support Need Support E_1152 Needed services from center for independent living E_1361 On wait list for center for independent living E_1252 Needed social work services E_1363 On wait list for social work services F_1433 Tried to get social work services, special education F_1499 Tried to get social work services, early intervention E_1302 Needed transportation services E_1364 On wait list for transportation services F_1424 Tried to get transportation services, special education F_1491 Tried to get transportation services, early intervention Enctrlvg ENW-ctrlvg Nsw Ntransp Nsupport

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APPENDIX B COVARIATE CELL SIZE The covariates having less than optimal observations per cell on the basis of the dependent variable, thereby having the potential to provide less than reliable weighted estimates, are identified in this appendix. In the first regression Aim 1) covariates containing 50 to 100 observations that were significantly associated with care coordination included: child health status poor or fair, home care service use and the need for one or more variety of services. Still other covariates had 49 or fewer observations per cell. These variables found to be significant were children who were foreign-born, families with severe financial need and children who used support. Covariates which were not significant and had fewer than 49 observations per cell were children of an other race, and those with an unmet need for home, support, equipment, therapy, and support services. Likewise, cell sizes were reduced in the multinomial regression in Aim 2. Access to telephone, other race, foreign-born, severe financial need, poor to fair child health status, home care used, and need for home, health, support, equipment, and therapy services all represent covariates with at least one cell size less than 70 observations. Covariates with observation of 70-99 per cell were related to more than two child health conditions, use of health services, variety of services used or needed, family income over $50,000, residence in the west, and urban versus rural location. 94

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107 Whall, A. L., & Fawcett, J. (1991). Family theory development in nursing: State of the science and art. Philadelphia: F.A. Davis Company. White, P. H. (2002). Access to health care: Health insurance considerations for young adults with special health care needs/disabilities. Pediatrics, 110, 1328-1335. Williams, B. S. (1991). The utility of nursing theory in nursing case management practice. Nursing Administration Quarterly, 15, 60-65. Witt, W. P. (2001). Family stressors, psychosocial functioning, and mental health care utilization among disabled children: Results from the 1994-1995 national health interview survey, disability supplement. Dissertation Abstracts International, AI, 62, no. 02B. (UMI 3006369). Witt, W. P., Kasper, J. D., & Riley A. W. (2003). Mental health services use among school-aged children with disabilities: The role of sociodemographics, functional limitations, family burdens, and care coordination. Health Services Research, 38, 1441-1466. Wright, L. M., & Leahey, M. (1987). Families & chronic illness. Springhouse, PA: Springhouse Corporation. Young, W. B., & Ryu, H. (2000). Secondary data for policy studies: Benefits and challenges. Policy, Politics, & Nursing Practice, 1, 302-307. Zimmerman, B., Schwalberg, R., Gallagher, J., Harkins, M., & Sines, E. (2000). Title V roles in coordinating care for children with special health care needs. Washington, DC: Health Systems Research, Inc.

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BIOGRAPHICAL SKETCH Barbara J. Kruger is a faculty member at the University of North Florida (UNF), School of Nursing, in Jacksonville, where she teaches community/public health nursing. She received her undergraduate degree in nursing from St. Anselms College (1975) in New Hampshire where she was first exposed to pubic health nursing and children with special needs. Shortly after graduation, she joined the NH Bureau of Public Health Nursing where she practiced district nursing and coordinated care for children and families in a number of community-based multidisciplinary specialty programs. She continued her education at Boston College where she obtained a masters degree (1979) in community health nursing. She held numerous positions at the NH Division of Public Health Services (including Nursing Consultant and Clinical Administrator for Title V-CSHCN) while continuing to provide care coordination. Her responsibilities included administration, program planning, quality assurance and evaluation, grant-writing, contracts management, conference planning, and interagency liaison. She facilitated the development of statewide programs for children with special health care needs and their families (such as Child Development, Children with Neuromotor Disabilities, Deaf/Hearing Impaired Home-based Intervention for Infants and Toddlers, Parent to Parent Support Network, and Home-based Nutrition). She also developed Care Coordination standards in conjunction with the nursing staff and parents. 108

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109 After leaving NH, Ms. Kruger was contracted through the Massachusetts Health Research Institute (now Third Sector New England) to continue consultation in areas of program development and evaluation. She obtained a Master of Public Health (1999) from the University of South Florida in Tampa, entered the doctoral program at the University of Florida (1999) in Gainesville, and transitioned to an academic position at the University of North Florida (2000). She published a policy article related to Title V and a chapter in a book on care coordination while in the doctoral program. She is currently helping UNF School of Nursing to implement a community-based and community-focused undergraduate curriculum; and has developed community-campus partnerships in northeast Florida. Her primary teaching goal is to stimulate student interest in community/public health nursing, health promotion, family nursing, and children with special needs. Her practice interests incorporate students and include 1) development and evaluation of an obesity and cardiovascular-risk prevention program for elementary school children in partnership with Northeast Florida Area Health Education Center (AHEC); 2) development of a community-oriented primary care model, including coordination, in a Volunteers in Medicine clinic for employed persons who are uninsured; and 3) working with the Jacksonville Commission on Services for Children with Special Needs to develop systems of care in northeast Florida. Her primary research interest is to demonstrate the contribution of community-based nursing care coordination to family and health care system outcomes among a variety of population groups.


Permanent Link: http://ufdc.ufl.edu/UFE0004266/00001

Material Information

Title: Utilization of care coordination among children with special ceeds in the 1994 National Health Interview Survey on Disability Phase II
Physical Description: xii, 109 p. ; ill.
Language: English
Creator: Kruger, Barbara J. ( Dissertant )
Kneipp, Shawn ( Thesis advisor )
Duncan, Paul ( Reviewer )
Edler, Dr. ( Reviewer )
Nealis, Dr. ( Reviewer )
Reiss, John ( Reviewer )
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2004
Copyright Date: 2004

Subjects

Subjects / Keywords: Department of Nursing thesis, Ph. D.
Research   ( lcsh )
United States Public Health Service   ( lcsh )
Dissertations, Academic -- UF -- College of Nursing -- Department of Nursing
Delivery of Health Care -- Child   ( lcsh )
Disabled Persons -- Child   ( lcsh )

Notes

Abstract: UTILIZATION OF CARE COORDINATION AMONG CHILDREN WITH SPECIAL NEEDS IN THE 1994 NATIONAL HEALTH INTERVIEW SURVEY ON DISABILITY PHASE II Care coordination helps families of children with special needs obtain a variety of services and manage communication among providers. Some families coordinate care themselves, while others receive assistance from professionals. Health care system changes are making these children less visible, and therefore at-risk for not receiving care coordination. A key question is how to identify families who need care coordination. Research about use of care coordination is sparse, and no generalizable method exists to identify which families require coordination. One aim of our study was to explore the differences among child and family factors and the use and need for health and related services between families who do and do not use care coordination. A second aim was to identify the determinants of professional care coordination. Secondary analyses of children, birth to 18 years of age, produced a weighted sample of 7,870,264 children of which 67% used care coordination. Professionals were more frequent providers of coordination compared to families and professionals or families alone. Multivariate logistic regression showed that children who had private insurance, those who used health, support, equipment, or therapy services, and those who used a greater variety of these services were more likely to have coordination. Fair or poor child health status, co-morbidities, family financial stress, and need for services also predicted use of coordination. Children least likely to receive coordination were black, were foreign-born, lived in large families, or lived on the west coast. Multinomial logistic regression showed that highly educated, residentially stable families whose child had private or public insurance, used health services, and had good health status were more likely to have professional coordination. Families with older children, children who were black, or who lived on the west coast were less likely to use professional coordination. Our study suggests that children and families who might need care coordination may not be receiving it, and that racial/ethnic disparities exist. It supports that a focus on the family and social context, as well as the child and medical context, is necessary. Nurses are well-positioned across health and human services systems to influence policy, practice, and research.
Subject: children, coordination, nhis, special, utilization
General Note: Title from title page of source document.
General Note: Document formatted into pages; contains 121 pages.
General Note: Includes vita.
Thesis: Thesis (Ph.D.)--University of Florida, 2004.
Bibliography: Includes bibliographical references.
Original Version: Text (Electronic thesis) in PDF format.

Record Information

Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
System ID: UFE0004266:00001

Permanent Link: http://ufdc.ufl.edu/UFE0004266/00001

Material Information

Title: Utilization of care coordination among children with special ceeds in the 1994 National Health Interview Survey on Disability Phase II
Physical Description: xii, 109 p. ; ill.
Language: English
Creator: Kruger, Barbara J. ( Dissertant )
Kneipp, Shawn ( Thesis advisor )
Duncan, Paul ( Reviewer )
Edler, Dr. ( Reviewer )
Nealis, Dr. ( Reviewer )
Reiss, John ( Reviewer )
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2004
Copyright Date: 2004

Subjects

Subjects / Keywords: Department of Nursing thesis, Ph. D.
Research   ( lcsh )
United States Public Health Service   ( lcsh )
Dissertations, Academic -- UF -- College of Nursing -- Department of Nursing
Delivery of Health Care -- Child   ( lcsh )
Disabled Persons -- Child   ( lcsh )

Notes

Abstract: UTILIZATION OF CARE COORDINATION AMONG CHILDREN WITH SPECIAL NEEDS IN THE 1994 NATIONAL HEALTH INTERVIEW SURVEY ON DISABILITY PHASE II Care coordination helps families of children with special needs obtain a variety of services and manage communication among providers. Some families coordinate care themselves, while others receive assistance from professionals. Health care system changes are making these children less visible, and therefore at-risk for not receiving care coordination. A key question is how to identify families who need care coordination. Research about use of care coordination is sparse, and no generalizable method exists to identify which families require coordination. One aim of our study was to explore the differences among child and family factors and the use and need for health and related services between families who do and do not use care coordination. A second aim was to identify the determinants of professional care coordination. Secondary analyses of children, birth to 18 years of age, produced a weighted sample of 7,870,264 children of which 67% used care coordination. Professionals were more frequent providers of coordination compared to families and professionals or families alone. Multivariate logistic regression showed that children who had private insurance, those who used health, support, equipment, or therapy services, and those who used a greater variety of these services were more likely to have coordination. Fair or poor child health status, co-morbidities, family financial stress, and need for services also predicted use of coordination. Children least likely to receive coordination were black, were foreign-born, lived in large families, or lived on the west coast. Multinomial logistic regression showed that highly educated, residentially stable families whose child had private or public insurance, used health services, and had good health status were more likely to have professional coordination. Families with older children, children who were black, or who lived on the west coast were less likely to use professional coordination. Our study suggests that children and families who might need care coordination may not be receiving it, and that racial/ethnic disparities exist. It supports that a focus on the family and social context, as well as the child and medical context, is necessary. Nurses are well-positioned across health and human services systems to influence policy, practice, and research.
Subject: children, coordination, nhis, special, utilization
General Note: Title from title page of source document.
General Note: Document formatted into pages; contains 121 pages.
General Note: Includes vita.
Thesis: Thesis (Ph.D.)--University of Florida, 2004.
Bibliography: Includes bibliographical references.
Original Version: Text (Electronic thesis) in PDF format.

Record Information

Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
System ID: UFE0004266:00001


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UTILIZATION OF CARE COORDINATION AMONG CHILDREN WITH SPECIAL
NEEDS IN THE 1994 NATIONAL HEALTH INTERVIEW SURVEY ON
DISABILITY PHASE II
















By

BARBARA J. KRUGER


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


2004


































Copyright 2004

by

Barbara J. Kruger


































To the families of children with special health needs and to the professionals who help
them coordinate care.















ACKNOWLEDGMENTS

Any accomplishment is accompanied by significant contributions from many

individuals. Foremost, my parents, Edwin and Apolonia, had the vision to value higher

education for their children-an opportunity not available to them. Ken has particularly

been a source of encouragement and support through my four academic degrees during

our years together. Meanwhile, Lindsay and Michael have provided amusement,

diversion, and their own style of support. Patricia, Joe, and their families have

accommodated the intrusion of my laptop during family retreats and made sure I

balanced work with play. The support of my family is deeply appreciated.

I am grateful to my dissertation committee whose marvelous blend of expertise and

collegiality provided an affirming and stimulating learning environment. I particularly

acknowledge Dr. Shawn Kneipp, my dissertation chair, whose gentle guidance sustained

my self-efficacy, whose challenging questions stretched me to respond critically from

multiple perspectives, and whose concept of rigorous doctoral study has provided a

strong foundation for future research. Each of my committee members provided valuable

insights. Dr. Paul Duncan provided clarity and simplicity for the purpose of this research

and its relevance. Drs. Edler and Nealis kept me faithful to my nursing heritage and its

rich theoretical and research legacy. Dr. John Reiss, meanwhile, generously shared his

expertise as he guided me through policy study. Everyone's generosity is greatly

appreciated. I also thank the University of Florida School of Nursing for fellowship

support during the first 2 years of study.









I acknowledge the discussions, support, encouragement, and feedback from my

New England colleagues: Diane M. McCann, RN, MSN; Jane M. Hybsch, RN, BSN

MHA; and Judith A. Bumbalo, RN, PhD (with NH Title V-CSHCN); and Susan G.

Epstein, MSW (with New England SERVE). Their willingness to provide feedback at

various stages of this doctoral program has been immensely valuable. I particularly

acknowledge the editorial assistance of Diane McCann for review of numerous

manuscripts. I especially thank these individuals for providing me with a practical

research question and an enriching collegial practice environment.

Finally, I extend gratitude to the faculty of the University of North Florida, College

of Health, and School of Nursing. The leadership of Dr. Pam Chally and Dr. Lucy Trice

(College of Health) and Dr. Li Loriz (School of Nursing) provided a stimulating and

supportive environment for faculty development and scholarship. I also acknowledge

their continuous personal support. I thank Dr. Kathleen Bloom and Dr. Kathy Robinson

for their mentoring and coaching; and Dr. Barbara Olinzock for her steadfast emotional

and instrumental support. I recognize all School of Nursing faculty and staff for their

encouragement and humor; and for providing a professionally and personally enriching

work environment.
















TABLE OF CONTENTS

page

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

LIST OF TABLES .............................................................. ........ ix

ABSTRACT .............. ......................................... xi

CHAPTER

1 STU D Y C O N TEX T ....................... .............................. ... ...... ..............

Background and Significance ......... .......................... ............... ...............
P o licy C o n tex t .............................................................................................................. 4
Title V .................................. ..... .................. ...............
Children with Special Health Care Needs....... .............................. ...............7
C are C coordination ................... .... ........ ..................... ... ...... ......... .. ....
Service System Fragmentation ..... ................. .. ...............10
Community-based Care ........... ...... ........ .............................. 13
Sum m ary of Study C ontext ........................................... ....................................... 15

2 LITERATURE REVIEW ............................ ............................... ............... 18

N u rsin g R e search ........ .... .. .... .. .... .. .......... ........ ...................................... .. 1
U se of Services by C SH CN ........... ................. ........................... ............... 21
Barriers to Service Use ............. ................ .............. .. ...... .. ............ .. 24
E conom ic B barriers ....... .... ................................................ ... ....... .. ......24
N on-econom ic B barriers ............................................... ............................ 26
C are C oordin ation ........ ........................................................................ ...... .......... .. 2 7
P process ............... ................................... .......... ............. 28
O utcom es ..................... .. .................................................... ....... 29
Families Who Use Care Coordination............................. ... ............... 32
Sum m ary of Literature Review ............. ........................... .............. ............ ...33

3 M E T H O D .............................................................................3 5

T theoretical R review ....... ...... ......................... ................................ .. ... 35
C onceptual F ram ew ork ...................................................................... ...................37
P redisp o sin g F actors........... ..... .................................................. .. .... .... .. ....3 8
Enabling Factors ............. ... ..... ........... .............. .. .. .......... .... 39









N eed F actors ................................................................... ............ 40
Study Design and Data Source ...........................................................................40
N H IS Sam pling .......................................... .. .. .... ........ ......... 42
D ata S o u rc e ................................................................................................... 4 2
D ata Collection ............. .... ............... ........ .. ...................... 43
Creation of D ataset .......... .. .... ..... .............. .......... .......... .. ............. 44
D ata C lean in g ...............................................................4 4
M issin g D ata .................................................................................................. 4 5
Study Sam ple Selection .......... .............. ................ ........... .......... .......... .. ..45
Comparison of Complete and Incomplete Cases ..................................................46
M measures ......... ... ...... ........... ............................ 48
C are C oordin action .............................. ......................... ... ...... .. .... ............4 8
C h ild ................................................................4 9
F am ily .............. ................. ................................................................... 5 0
Data Analyses ..... ....................... ................... 52
A im 1 A n aly se s ............................................................................................. 5 3
A im 2 A n aly se s ............................................................................................. 5 3

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

Sample Characteristics.................................. 55
U utilization of Care Coordination .................................................................... .... 58
Utilization and Unmet Need for Health and Related Services ................................59
Aim 1: Determinants of Care Coordination Utilization ......................................60
P re d isp o sin g ........................................................................ .......................... 6 0
E n ab lin g ...................................................................................................6 1
N eed................. ................................ ....... ............... 64
Reduced Model ......................... .. ....... ............. 65
Aim 2: Care Coordination Utilization by Type of Provider................ ...............65

5 DISCUSSION ............. .. ................. ........... ... .............. 70

Who Uses Care Coordination and Who Doesn't? ..................... ........................... 70
Study Lim itations........ ......... .............................. .. .. .. ...... ........ 75
Im p licatio n s ................................................................7 9

APPENDIX

A CONSTRUCTION OF TYPE OF SERVICE VARIABLES ...................................86

U sed H o m e C are ..................... ..... .............. .. .............................. .......... .. .. .. 8 6
Used Health Care ...................................... ..................87
U sed Equipm ent.............................................. 88
U sed T h erapy .......................... ..................................................................... 88
U sed Su p p ort ................................................................89
U nm et H om e Care N eed .............. ......................... ...........................................90
U nm et H health C are N eed .................................................................. .. ............. 90










U nm et E quipm ent N eed ..................................................................... ..................9 1
U nm et Therapy N eed ......... ................ .... .............. ............. .......... 92
U nm et Support N eed ......................................................................... ................... 92

B COV ARIA TE CELL SIZE ........... .................................. ................. ............... 94

LIST OF REFEREN CES ..................................................................... ............... 95

BIO GRAPH ICAL SK ETCH ......... ......................................................... ............. 108
















LIST OF TABLES


Table Page

3-1 Model for studying care coordination utilization.......................................38

3-2 Significant differences between complete and incomplete cases ........................47

3-3 Coordination measures by NHIS-D variable and description..............................49

3-4 Child measures by NHIS-D variable, description, and coding...........................50

3-5 Child service use and unmet service need categories........................... .........51

3-6 Family measures by NHIS-D variable, description, and coding .........................51

4-1 Characteristics of the study sam ple.................................. ........................ 57

4-2 U tilization of care coordination ........................................ ......................... 59

4-3 Use and unmet need for health and related services by type and variety ..............60

4-4 Full model for use of care coordination by service type and variety...................62

4-5 Reduced model for significant covariates of coordination use by service type
an d v variety ................................. .......................................................... .... 6 6

4-6 Full m odel for service type ............................................................................. 67

4-7 Full model for service variety .......................... ......... .. ...... ......... 68

A-i H om e care service m measure .............................................................................87

A -2 H health care service m easure........................................................ ............... 88

A -3 Equipm ent service m measure .................................. ............... .....................89

A -4 Therapy service m easure............................................... ............................. 89

A -5 Support service m measure ............................................... ............................. 90

A-6 H om e care unm et need m measure ........................................ ........................ 91









A -7 H health care unm et need m measure ........................................ ........ ............... 91

A-8 Equipment unmet need measure ................................................................. 92

A -9 Therapy unm et need m measure ........................................ .......................... 93

A -10 Support unm et need m easure........................................................................ .. ...93















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

UTILIZATION OF CARE COORDINATION AMONG CHILDREN WITH SPECIAL
NEEDS IN THE 1994 NATIONAL HEALTH INTERVIEW SURVEY ON
DISABILITY PHASE II

By

Barbara J. Kruger

May, 2004

Chair: Shawn M. Kneipp
Major Department: Nursing

Care coordination helps families of children with special needs obtain a variety of

services and manage communication among providers. Some families coordinate care

themselves, while others receive assistance from professionals. Health care system

changes are making these children less visible, and therefore at-risk for not receiving care

coordination. A key question is how to identify families who need care coordination.

Research about use of care coordination is sparse, and no generalizable method

exists to identify which families require coordination. One aim of our study was to

explore the differences among child and family factors and the use and need for health

and related services between families who do and do not use care coordination. A second

aim was to identify the determinants of professional care coordination.

Secondary analyses of children, birth to 18 years of age, produced a weighted

sample of 7,870,264 children of which 67% used care coordination. Professionals were

more frequent providers of coordination compared to families and professionals or









families alone. Multivariate logistic regression showed that children who had private

insurance, those who used health, support, equipment, or therapy services, and those who

used a greater variety of these services were more likely to have coordination. Fair or

poor child health status, co-morbidities, family financial stress, and need for services also

predicted use of coordination. Children least likely to receive coordination were black,

were foreign-born, lived in large families, or lived on the west coast. Multinomial logistic

regression showed that highly educated, residentially stable families whose child had

private or public insurance, used health services, and had good health status were more

likely to have professional coordination. Families with older children, children who were

black, or who lived on the west coast were less likely to use professional coordination.

Our study suggests that children and families who might need care coordination

may not be receiving it, and that racial/ethnic disparities exist. It supports that a focus on

the family and social context, as well as the child and medical context, is necessary.

Nurses are well-positioned across health and human services systems to influence policy,

practice, and research.














CHAPTER 1
STUDY CONTEXT

Background and Significance

Chronic illness and disability among children may generate a combination of

physical, social, financial, psychological, and educational impacts on the child and family

(Hobbs, Perrin, & Ireys, 1985). Consequently, children with special health care needs

(CSHCN) require a greater volume and variety of health and related services than do

children generally. These services include primary and specialty medical care, allied

health, education, developmental, social, and financial services. Families continuously

manage the care of their child into adulthood and over the chronic illness trajectory. They

also manage communication among professionals across health and human services

systems. Some families coordinate this care themselves, some report difficulty navigating

the various delivery systems, and others may receive assistance from professionals. It is

unknown, however, which families require care coordination from a professional.

Studies suggest that care coordination connects families to information and to a

range of services; and facilitates communication across those services. The process of

care coordination is goal-directed and individualized for each family to meet family-

identified needs. Its purpose may be to improve quality of life, child health, continuity of

care, and to facilitate family capacity for self-care. Health-system outcomes related to

care coordination include satisfaction with care, access to health services, and cost

savings. A variety of programs and direct services for CSHCN (such as hospital

discharge and transition programs, specialty medical care, early intervention, and









Medicaid managed-care carve-out programs) include family care coordination as an

approach to the delivery of heath and related clinical services (Kruger, 2002). Care

coordination, however, is not a universally available or billable service and family

acquisition of care coordination may be fortuitous. Furthermore, families who coordinate

their own child's care do so even though they have a source of health care (namely,

access to a health care provider).

Currently, there are no generalizable methods that help distinguish which families

need care coordination. One approach to identifying families who may require

professional assistance is to determine how families who receive this service differ from

those who do not. Although clinical research supports that families who receive care

coordination are more likely to be referred to a variety of health and related services,

there is no study that associates the use of health and related services with care

coordination. Based on the literature, it is reasonable to expect that a combination of

factors (that relate to the child, family, and their use and need for health and related

services) can be used to differentiate among families.

The primary purpose of our study is to explore the characteristics that distinguish

children and families who use care coordination from those who do not. Determining

how these families differ from each other could provide information that can be used for

casefinding and program planning. The research questions are as follows:

Aim 1: What are the differences in child and family socio-demographic factors,

use of health and related services, and unmet need for health and related services,

between families who use care coordination and families who do not?









Aim 2: What are the determinants (child, family, use of health and related

services, or unmet service need) of the use of care coordination depending on who

provides it (family only, professional only, family with professional).

Our study would contribute to answering a significant policy question. Federal

policy (in conjunction with popular support by provider and family advocacy groups)

values the development of community-based systems of care for these children that

includes care coordination. However, these systems are sporadically available and there

is no customary method for identifying families who require care coordination.

Determining which families are more likely to use care coordination could assist

agencies, who are mandated to assure the availability of these services, to better plan

resource allocation.

Our study would also contribute to answering a significant clinical practice

question: how and where might we identify families? Studies suggest that families who

use care coordination are those whose children are enrolled in categorical or specialty

programs. Research, however, does not address how families who require care

coordination are identified within or outside of these programs. The literature describes

the care coordination process in terms of providing information, initiating referrals, and

communication across service systems. Yet, the pediatric chronic illness and family

research emphasis has been on how families cope with the illness, not on their interaction

with health and related systems. It is well known that CSHCN use a variety of medical

and nonmedical services. What is unexplored is the relationship among these services and

care coordination. Nurses are employed throughout the health, education, and









developmental service systems and are well-positioned to identify families for care

coordination services.

The proliferation and emphasis on care coordination by policymakers, families, and

health care professionals intensifies the demand for research. Yet, studies regarding care

coordination for CSHCN are limited in volume and are largely based on convenience

samples of families who receive these services in clinical programs. Non-users of care

coordination are not included in this research.

The 1994 National Health Interview Survey on Disability (NHIS-D) Phase II for

children was used as the data source for answering the questions posed in our study. This

nationally representative household survey contains a sample of families of children who

use and who do not use care coordination. This is important because non-users of care

coordination have not been included in prior research. The NHIS-D Phase II also contains

information about the child and family and their utilization of a variety of health and

related services. This exploratory study proposes to identify child and family

characteristics associated with the use of care coordination and with a wide variety of

medical and nonmedical services. This is an initial step in a program of research that

seeks to develop methods to identify families who require care coordination and

understand how families use external resources.

Policy Context

This section discusses factors that influence the development of systems of care,

including care coordination, for CSHCN. It will also define the population of CSHCN

and care coordination. Finally, family-expressed needs and changes in the health care

system are discussed to provide a rationale for why care coordination has been and will

continue to be an integral component of service delivery for these families.









Title V

Care coordination for CSHCN in the United States has been supported under the

policy context of Title V of the Social Security Act of 1935. Title V, known as the

Maternal and Child Health Block Grant, authorizes federal funds to states for maternal

and child health services, including CSHCN. A revision to this federal mandate in 1989,

in conjunction with ongoing changes in health policy, has continued to influence the

delivery of services to this population (Kruger, 2001). A significant change was the

emphasis on creating systems of care for all CSHCN, not just those enrolled in state

programs. States are required to provide and promote family-centered, culturally

competent, community-based, coordinated care (including care coordination) and to

facilitate the development of systems of care in communities where families and children

live (USDHHS, 1998). Healthy People 2010 (the nation's prevention agenda) supports

this federal mandate by calling for an increase in the proportion of states that have

community-based systems of care for CSHCN (USDHHS, 2000).

Care coordination for CSHCN has a long history that has been linked to the work

of the Children's Bureau in the early part of the century (Perrin, Shayne, & Bloom,

1993). In 1984, most State Title V-CSHCN programs reported that coordinating patient

care services was very important to their mission and an activity they did not spend

enough time doing (Ireys & Eichler, 1988). A recent survey of state Title V-CSHCN

programs reported that 30 of 46 states have increased the intensity of provision of care

coordination for CSHCN, expanded eligibility for these services, and/or are providing a

comprehensive model of services (Zimmerman, Schwalberg, Gallagher, Harkins, &

Sines, 2000). Advocacy groups are echoing the importance of defining goals and

principles for care coordination to guide Title V- CSHCN programs (Association of









Maternal and Child Health Programs, 2000, 2002). Family needs assessments, as required

by the Maternal and Child Health Block Grant, also validate that care coordination is a

priority for families (Reiss, 2000).

The most recent development in the evolution of federal policy has been the been

the dissemination of a 10-year action plan (McPherson & Honberg, 2002). This plan

identifies six core goals to be achieved by the year 2010, and numerous action steps

related to improving coordination of care. These strategies include developing models of

coordination between primary and specialty health care providers, developing medical

home models, coordinating services with community professionals, improving pediatric

to adult medical transition, supporting tele-health initiatives, determining the cost of care

coordination, developing financing models, and establishing adequate reimbursement for

care coordination (USDHHS, 2001).

A requirement of the Maternal and Child Health Block Grant is that all state Title

V-CSHCN programs must annually report the percent of CSHCN who have a medical

home. The medical home was recommended as an approach for pediatricians to assure

coordinated care for CSHCN in response to the OBRA 89 impetus to move the care of

CSHCN to community-based primary care (Brewer, McPherson, Magrab, & Hutchins,

1989). However, it has only recently received heightened national attention due to the

combined efforts of the Maternal and Child Health Bureau and the American Academy of

Pediatrics. A medical home is described as an approach to providing quality and cost-

effective health care services at the primary care level (AAP Medical Home Initiatives

for Children with Special Needs Project Advisory Committee, 2002). Professionals and









parents are envisioned as working together to identify and access medical and

nonmedical services required by CSHCN.

The sustained and escalating emphasis on care coordination for CSHCN and their

families is unmistakable. The interest in assuring that these services are provided at the

community level, particularly in primary care settings, is a more recent development. For

families to receive care coordination, they must first be identified. Currently no approach

exists that helps to identify which families require care coordination. This is different

than the approaches that attempt to identify which children are CSHCN. Care

coordination is targeted to families of children (not to the child alone). Comparing

characteristics of children and families who use and don't use care coordination may

provide information about potential predictors of need for care coordination. To date, this

type of study has not been reported in the literature.

Children with Special Health Care Needs

The definition of CSHCN includes children "... who have or are at increased risk

for a chronic physical, developmental, behavioral, or emotional conditions and who also

require health and related services of a type or amount beyond that required by children

generally" (McPherson et al., 1998, p. 138). The revision to the Title V-CSHCN mandate

in OBRA 89 created a change in how children were defined. CSHCN are children first.

The label of "crippled child" and use of diagnoses were replaced in favor of a non-

categorical approach because children share similar needs across medical diagnoses.

CSHCN are a heterogeneous group with more than 200 conditions ranging from

prevalent to very rare and from mild to severe conditions. These children are included in

three overlapping groups: 1) children with developmental delays or disabilities; 2)

children with ongoing medical disorders and chronic illness; and 3) children with









emotional and behavioral problems. Approximately 18% of children (12.6 million) under

18 years of age in the United States in 1994 were reported to have a chronic condition

(physical, developmental, behavioral or emotional) and service use or presumed need for

greater health or related services beyond that generally required by children (Newacheck,

Strickland et al., 1998). The prevalence of a special health care need increases with age,

with boys more likely than girls, and African-American children more likely than other

minority children. Additionally, the occurrence of co-morbidity affects about 5% of

children who have one chronic condition; and places them at risk for developmental,

learning, and behavioral problems, along with a progressive increase in physician and

hospital services (Newacheck & Stoddard, 1994).

There has been much emphasis in the literature on the development of approaches

to operationalize a consistent definition of CSHCN. This is necessary in order to collect

population-based data for the purposes of epidemiology, program planning, quality

assurance, and risk adjustment. None of these approaches for identifying CSHCN were

intended to identify families who require care coordination (Epstein & Walker, 2002).

Care Coordination

The use of the term care coordination (versus case management) is consistent with

the family-centered care values of the Title V legislation. Care coordination has been

defined specifically in relationship to CSHCN by the Title V legislation and the

American Academy of Pediatrics (AAP); and referred to as case management within

nursing (by the American Nurses Association) and within the case management industry

(by the Case Management Society of America).

Care coordination services, as defined in OBRA 1989 "... promote the effective

and efficient organization and utilization of resources to assure access to necessary









comprehensive services for children with special health care needs and their families"

(USDHHS, 1998, Section 2, p. 3). This definition is focused on the health care system

and access to services. In comparison, the AAP defines care coordination as a process

". .. that links children with special health care needs and their families to services and

resources in a coordinated effort to maximize the potential of the children and provide

them with optimal health care" (Committee on Children with Disabilities, 1999, p. 978).

This definition considers the context of the family and the long-term effects on the child.

Nursing and allied health case management focus their definitions on all

populations. The purpose of nursing case management is to ". .. integrate, coordinate,

and advocate for individuals, families and groups requiring extensive services. The

ultimate goal is to achieve planned care outcomes by brokering services across the health

care continuum" (Bower, 1992, p. 3). This focus of case management is on high users of

services and it suggests that brokering is the mechanism of coordinating care. The

international allied health case management industry identifies the components of a

"... collaborative process which assesses, plans, implements, coordinates, monitors and

evaluates the options and services to meet an individual's health needs through

communication and available resources to promote quality, cost-effective outcomes"

(Case Management Society of America, 2002, p. 5). All of these definitions are focused

on health care services delivery and explicitly or implicitly identify the functions of

assessment, service planning, implementation, monitoring, and evaluation.

The Vanderbilt Study, the first comprehensive assessment of this population of

children and families in the U.S., emphasized the value of coordination as a role for

nursing (Hymovich, 1985) and as a method to improve child and family functioning and









reduce unnecessary use of medical care (Perrin, Ireys, Hobbs, Shayne, & Moynihan,

1985). Nurses trace the origins of care coordination to the settlement-house movement in

the 1800's and turn-of-the century community health services coordination performed by

public health nurses (Kersbergen, 1996; Tahan, 1998) as the forerunner to contemporary

nursing case management in primary, hospital and managed-care settings (Lyon, 1993).

The nursing literature vacillates between the terms care coordination and case

management. The term, coordinator of care, is a core competency for baccalaureate

prepared nurses as identified by the American Association of Colleges of Nursing (1998).

A distinction between the terms care coordination and case management was made

in a study of state Medicaid managed-care programs. Rosenbach and Young (2000)

described care coordination as a social service model whose goal was to facilitate access

to quality care across a broad range of programs in the community for vulnerable

populations. Case management, in contrast, was described as containing costs within a

medical model of service delivery for high users of costly services. Whether these

distinctions are universal is moot for families who prefer the term care coordination.

Service System Fragmentation

Hundreds of categorical programs that fund services for children and families have

developed over 30 years of legislation (Grason & Guyer, 1995). Categorical programs are

developed to meet a particular human service need, are affiliated with a particular lead

agency; and have specific eligibility criteria and scope of services, and designated

providers (including coordinators). A family who has a young child may complete an

application for early intervention, Title V-CSHCN, Medicaid or a children's health

insurance program, nutrition services through WIC, medical/health services through a

primary care office or clinic, and a family support program through mental health (all in









different agencies, in different places across town). This is a relatively mild example of

the extent of service requirements by some families. Even if families have been deemed

eligible for any or all of these services, some may require annual re-application. Knowing

what to ask for and where to find it (and the associated extra work and hassles) is a

challenge for families who seek a variety of services to support themselves and their

children. Furthermore, some of these services may actually duplicate each other, yet

paradoxically leave gaps where families "don't fit" the eligibility criteria for age,

condition, or financial status. For example, grandparents who are primary caregivers of

children have a particularly difficult time "fitting" into eligibility criteria and may be

denied benefits such as social security-disability, Medicaid, or special education for their

grandchildren. Case managers who accompanied grandparents to appeal denials for

services referred 25% of the cases to legal assistance before a benefit could be received

(McCallion, Janicki, Grant-Griffin, & Kolomer, 2000).

A decade of studies that have identified the needs of families with CSHCN validate

that families require help locating and obtaining medical and nonmedical services, and

communicating across multiple systems (Davis & Steele, 1991; Diehl, Moffitt, & Wade,

1991; Gabor & Farnham, 1996; Garwick, Patterson, Bennett, & Blum, 1998; Horner,

Rawlins, & Giles, 1987; Krauss, Wells, Gulley, & Anderson, 2001; New England

SERVE, 1997; Saywell, Zollinger, Schafer, Schmit, & Ladd, 1993; Walker, Epstein,

Taylor, Crocker, & Tuttle, 1989). As an aggregate, these studies, based on convenience

samples, represent thousands of single, two-parent, and minority families of children with

a variety of conditions, living in urban and rural areas across the country. They identify









that some families want and/or use care coordination, but they do not identify what

distinguishes families who use care coordination from those who do not.

Coordinating care for CSHCN is not an exclusively U.S. phenomenon. A "link"

person who assists families to access services is common in Canada and the United

Kingdom where the lack of health insurance is a less significant barrier to care (Baine,

Rosenbaum, & King, 1995; Ray, 1997; Sloper & Turner, 1992). Navigating the health

care system is described by Ray (1997) as "working the system" and was identified by

Canadian parents as the worst aspect of having a CSHCN and a large part of parental

caregiving. Parents spent a considerable amount of time learning, phoning, following-up,

searching, and adjusting their employment hours to assume the role of their child's

coordinator of care. The work of parents included researching medical treatments,

monitoring their child's quality of care, participating in advocacy groups, learning the

politics of the health care system, and ultimately learning how to work around the system

to get what they required. Parent work and expertise, however, was reported to remain

invisible to many health care providers (Ray, 1997).

Care coordination is an individual response to service system fragmentation in that

it works at the level of the family to assure access to a broad range of medical and

nonmedical services. This is distinct from a service systems response to fragmentation

that seeks to integrate services by pooling funding streams and creating single points of

entry into service delivery systems (Hughes, Halfon, Brindis, & Newacheck, 1996).

Systems coordination is focused on agency networks and the structure within which care

coordination takes place. System outcomes are concerned with cost-effective use of

services and encouraging service integration. Care coordination and systems responses









are interdependent, and both approaches are necessary to assist families to access

services.

Community-based Care

The expansion of managed-care during the 1990s has precipitated a shift from

specialty care to primary care for many populations. Historically, CSHCN have been

higher users of specialty medical services in contrast to primary care (Perrin & Ireys,

1984). Furthermore, children attending specialty clinics did not always have a source of

primary care (Palfrey, Levy, & Gilbert, 1980). The movement to community-based

services, close to where families live, is consistent with the Title V-CHSCN mission;

however, this has been a change for families and providers.

Managed-care offers families the opportunity to access comprehensive pediatric

primary care with minimal out-of-pocket expense. However, concerns about Medicaid

managed-care were found to include the disruption of long-term provider relationships,

restrictions placed on referrals, and disincentives to refer children to the services they

may require (Mele & Flowers, 2000). For example, 3 years after disbanding a 25-year-old

team clinic for children with myelomeningocele, one-half of the clients reported no

orthopedic or urologic follow-up and two-third's reported no neurological or pediatric

follow-up (Kaufman et al., 1994). One third of the children reported no primary pediatric

provider. This means that they lost their connection to specialty care and did not make a

connection to primary care. Clients from the disbanded clinic also were observed to

undergo fewer proactive surgical procedures and more preventable and serious surgical

procedures compared to a comparison site. The role of the coordinator, central to a

multidisciplinary team, was not assumed by the local physician or the family.









Federal expansions of Medicaid and State Children's Health Insurance Programs

have enabled CSHCN to access primary care. Consequently, more CSHCN are enrolled

in Medicaid versus employer-insured plans than in the past (Shatin, Levin, Ireys, &

Haller, 1998). Furthermore, CSHCN in Medicaid now receive most of their care from

generalist providers at the primary care level (Kuhlthau, Ferris, Beal, Gortmaker, &

Perrin, 2001). Although care coordination has been incorporated into some of these

financing initiatives, and within private managed-care plans for high-risk populations, for

CSHCN it is more often provided in public programs (Krauss, Gulley, Leiter, Minihan, &

Sciegaj, 2000).

Specialists, in comparison to generalists, appear to have more experience in

providing care coordination. For example, specialists more than primary care providers

met family needs related to care coordination (Scholle & Kelleher, 1995), connected

them with other parents for support (Ireys & Perry, 1999), and were better informed

about community resources (Liptak & Revell, 1989). Furthermore, families report that

primary care physicians underestimate family desire for care coordination (Liptak &

Revell, 1989; Perrin, Lewkowicz, & Young, 2000). Pediatricians identify constraints

related to coordinating care. Primary care providers who were highly committed to

providing services to CSHCN reported very low satisfaction with the time available to

care for CSCHN, to assist with coordinating school services, and to transition older

children to adult care (Davidson, Silva, Sofis, Gantz, & Palfrey, 2002). These

pediatricians identified training about public programs, time, and financial constraints as

barriers to coordinating care for CSHCN.









In a primary care setting, CSHCN are dispersed among many providers (in contrast

to being grouped within diagnostic-related specialty teams). This means that providers at

the primary care level deliver services to children with a wider diversity of conditions and

are expected to link families to generic as well as specialty services. This is compounded

by the potential lack of provider awareness about the value of care coordination to

families, the time to do this, and the lack of knowledge of community resources. The shift

to primary care may make CSHCN and families less visible, and intensifies the need to

develop methods to identify families.

Summary of Study Context

CSHCN, who represent hundreds of heterogeneous conditions, use a greater

volume and variety of medical and nonmedical services than do children generally.

Families describe a common and prevailing desire for information and services from

educational, developmental, and social service systems; not just medical care. Families

report difficulty finding, getting, and navigating the maze of available programs which

occurs in spite of having an identified primary care provider. Some families report

coordinating care for themselves. Federal policymakers, advocacy groups, providers, and

families agree on the value of care coordination to assure that families and children are

linked to a broad range of services, across a variety of delivery systems; and to assure

that services are easy to use. Care coordination, however, is not universally available.

The shift to community-based primary care is a change for families who were

accustomed to receiving health care from specialists. System development initiatives

such as the medical home may improve access to care coordination in the future, but

current evidence shows that families perceive that primary care providers are less

knowledgeable about resources than specialty care providers. Also, the barrier of









reimbursement for the time and cost of providing care coordination at the primary care

level has not been addressed. As CSHCN become integrated into primary care systems

they compete with the larger population of children who do not have special needs for

provider time. Families who need care coordination may be at-risk for becoming invisible

to inexperienced or uninterested providers.

All of these factors (the high use of services, family need to find resources, the

changes in health care financing, the shift to community-based care, and the emphasis on

care coordination by policymakers) intensify the urgency to assure that families receive

care coordination. A key policy question is, which families require professional care

coordination and how will they be identified? The answer to this question would provide

information to agencies responsible for assuring service delivery and for allocating

resources for care coordination. The answer to this question would also assist providers to

casefind families from within any practice setting. Nurses are particularly well positioned

across the health and education systems to identify families and to provide care

coordination.

Currently, there is no universal method for identifying families who require

professional care coordination; nor is there research that associates family use of care

coordination with a variety of health and related services. Yet that is what care

coordination has been purported to do (link families to services). Our study proposes to

explore the relationship among child and family characteristics and child and family use

of medical and nonmedical services to distinguish between families who use and don't

use care coordination. The NHIS-D, a national household survey, provides the advantage






17


of a randomized sample of users and non-users of care coordination and contains

information about child and family use of health and related services.














CHAPTER 2
LITERATURE REVIEW

This literature review examines three areas of research related to families and

CSHCN; and identifies gaps in the literature related to care coordination and family use

of a variety of services. The first section discusses nursing research related to families

and children with chronic illness (which has largely focused on the impact of the child's

condition on family life). The second section identifies the variety and volume of health

services used by this population, and identifies barriers that may be mediated by care

coordination. The third section discusses the process and outcome of care coordination,

specifically as it relates to family use of services, and which families are reported to use

care coordination.

Nursing Research

A review of nursing literature between 1966 and 1981 discovered much descriptive

research of how nurses were providing care to families and children with chronic illness

(Hymovich, 1985). Nurses were also developing assessment and intervention frameworks

to guide their practice with these children and families (Wright & Leahey, 1987). A

research review during the 1980s reported that nursing was still in the early stages of

knowledge development and although there was evidence of theory building and

reflection on practice, the evidence was insufficient to guide nursing interventions (Burke

& Roberts, 1990). The decade of the 1990s witnessed an intensification of interest in

carving out nursing's contribution to care of families in theory development and research

(Broome, Knafl, Pridham, & Feetham, 1998; Feetham, Meister, Bell, & Gilliss, 1993;









Whall & Fawcett, 1991). Nurses were broadening their focus from the individual child to

the family as a unit of care.

The challenge to theory development and research that guides practice with

families as a unit of analysis vis-a-vis individuals within a family has been discussed

extensively in the literature. A 50-year review highlighted that nursing has followed

CSHCN and their families from institutional to community settings; and has modified the

view of families from needful to competent, and from a deficit model of how families

cope with stress to a family-strengths-and-transformation perspective (Faux, 1998).

Exploration of how families respond to the chronic illness experience included examining

and re-visiting concepts such as chronic sorrow (Clubb, 1991), adaptation (Austin, 1991),

hardiness and stress (Huang, 1995), normalization (Deatrick, Knafl, & Murphy-Moore,

1999), family empowerment (Hulme, 1999), and uncertainty (Stewart & Mishel, 2000).

The program of research related to Family Management Styles provides a framework for

assessing family response to chronic illness; and a future basis for the development and

testing of nursing interventions (Knafl, Breitmayer, Gallo, & Zoeller, 1996; Knafl, Gallo,

Zoeller, Breitmayer, & Ayres, 1993).

Stress and coping are the predominant underlying concepts within nursing research

related to families and children with chronic illness. Family stress theories suggest that

resources help families cope and adapt to stresses (Hill and McCubbin as cited in

Sterling, 1990). Family resources were derived in Sterling's study from interviews with

families who provided home care to infants with severe respiratory diseases requiring

professional monitoring. Interview questions asked families about home caregiving

experience. Themes related to parental resource needs were identified as knowledge and









skills, time, coping strategies, support from family and friends, and professional and

material resources. Frequency or intensity of resources was not measured, and

professional resources were identified simply as health care professionals. A follow-up

study tested psychometric properties of the Home Care Resources Inventory and assessed

family resource availability as indicators of the ability to care for the child at home

(Sterling, Jones, Johnson, & Bowen, 1996). Family resources were conceptualized as

psychological, social, interpersonal, and material (finances, equipment, transportation)

means by which individuals gain control over their lives. The two main resource

categories were support and assets (internal to the family unit) and health or community

services. The studies were not related to care coordination, they did not address a broad

range of medical and nonmedical services, and they were focused on infant home care.

Researchers recognize that the absence of community resources may interfere with

family resilience and promotion of strengths (Patterson, 2002).

The family and children's chronic illness literature has contributed to clarifying

concepts, has provided direction for theory development, and has given nurses a

perspective into the life of the family as the primary caregiver. The themes of service

system barriers, need for community resources or services, and information is often

reported in the "results or discussion" sections of qualitative studies that explore the

impact of chronic illness on family life. However, nursing conceptualization of resources

has been focused on internal family dynamics. Family researchers acknowledge that the

interaction between family and the health care system is of interest to nurses (Gilliss &

Knafl, 1999). However, child and family use of health and related services is

understudied in the nursing literature (Knafl & Gilliss, 2002).









Use of Services by CSHCN

CSHCN use a greater volume and wider variety of health and related services than

do children generally. Consequently, families desire information about these services and

assistance to access them. The following section examines what is known about the use

of health services by CSHCN and the barriers which may affect service utilization. This

provides a rationale for why care coordination may be necessary for some families.

Studies which examine the use of health services by CSHCN indicate that no

matter how the definition of the condition is conceptualized (diagnosis, severity, non-

categorical) these children use a variety and greater volume of services than do children

generally. Medical care, especially hospitalization, may vary widely by condition and can

be 2.5 to 20 times more costly than for children in general (Ireys, Anderson, Shaffer, &

Neff, 1997). Furthermore, a small proportion of CSHCN (10%) accounted for 80% of

Medicaid payments, with the largest expenditures attributable to hospitalization.

A 12-month clinical study examined unscheduled hospital ICU admissions by

children with chronic conditions and children with no chronic conditions (Dosa, Boeing,

Ms, & Kanter, 2001). Children with chronic illness had a 3-fold risk of unscheduled

admission compared to children in general. The risk was higher for children who were

technology dependent but one-third of the preventable admissions occurred among

children with chronic illness who were not technology-dependent. Factors associated

with the preventable admission included family stress and delay in seeking care and

inadequate coordination of care.

CSHCN have typically been higher users of specialty medical services in contrast

to primary care (Perrin & Ireys, 1984). One specialty clinic estimated that 31% of

enrolled children considered the clinic as their source of primary care; particularly if they









lived close to the medical center (Palfrey, Levy, & Gilbert, 1980). National household

studies verify that children with chronic conditions made 15 visits per year to a physician

compared to 4 visits per year for children with no chronic condition (Newacheck &

Stoddard, 1994). CSHCN make twice as many physician visits and have five times as

many hospitalization compared to children in general (Newacheck, Stoddard et al., 1998).

Similarly, children with limitations in activity (disabilities) were reported to use 8.8

physician contacts compared to 3 contacts for children who are not disabled, and were

more likely to be hospitalized (Newacheck & Halfon, 1998). Children with a chronic

condition enrolled in a state children health insurance program were also found to use a

greater volume of out-patient services over a 12-month period; 6 outpatient visits in

comparison to 2 outpatient visits by children without chronic illness (Lin & Lave, 2000).

The predominant theme among these studies is that higher utilization of health care is

significantly associated with chronic illness / special need / disability and exceeds use by

children generally.

Fewer studies report the use of allied health or nonmedical services. One clinical

study examined the use of rehabilitation and support services by a convenience sample of

children with cystic fibrosis, myelodysplasia, cerebral palsy and multiple physical

handicaps (Smyth-Staruch, Breslau, Weitzman, & Gortmaker, 1984). Comparison to a

control group of children without special needs found that CSHCN used a greater volume

of hospital, specialty medical, therapy, mental health, and social services that varied

widely by type of condition. A distinction among diagnoses found that children with

cerebral palsy and myelodysplasia were higher users of services compared to children

with cystic fibrosis and multiple handicaps. CSHCN also had a greater volume of contact









with the school nurse and contact with office nurses, nurse practitioners, and public

health nurses was 5-times greater compared to children in general.

Children with impaired mobility and severe functional limitations were also

identified as having extensive use of equipment, primary and specialty providers; and

moderate use of therapy and counseling (Walker, Palfrey, Butler, & Singer, 1988). This

study pointed out that children used a variety of public and private agencies in the

community to secure services; while family support, respite, homemaker, summer camp

and after-school programs were less often used and more often not available. The need

for care coordination as a strategy to help families locate community services is

emphasized in this study.

A recent study identified predictors of health care use among 93 children with

cerebral palsy enrolled in a state Medicaid program (Balkrishnan, Naughton, Smith,

Manuel, & Koman, 2002). A caregiver survey found that children with cerebral palsy

used a higher volume of inpatient, out-patient, home health, and orthopedic services. The

caregiver behavioral factors that were associated with higher service use were the

willingness by the family to use home health care and respite care and family perception

that their child was receiving adequate care. Families who used fewer services were

associated with having more years of caregiving experience (older child), having

financial difficulties, and a caregiver who was employed. One conclusion drawn from

this study was that families who used allied health services may be better informed about

the availability of services. Clinical experience supports this observation. Once families

gain entry into the "system" of providers who specialize in this population and are

familiar with the resources; the door opens to a wider range of services.









These studies underscore that CSHCN use a greater volume of health and medical

services than do children generally. Only a few studies reported utilization of allied

health or community supports. In some of these studies, care coordination is

acknowledged as a necessary accessory to assure linkage to services, yet none of these

studies investigated the relationship between use of health or community services and

care coordination.

Barriers to Service Use

Epidemiologic study indicates that CSHCN are more likely to include those with

incomes below the poverty level and living in single-parent families where the education

level of the head of household is lower compared to families where children do not have

a special health care need (Newacheck, Strickland et al., 1998). This may place some

families at particular risk of not being able to access health or community services. The

following section identifies some of the common economic and non-economic barriers to

accessing services.

Economic Barriers

The economic impact of caring for CSHCN is well established. Family access to

financial assistance to pay for care is an enabling factor discussed in the health service

utilization literature. Having health insurance, and a usual source of health care, has been

associated with increased access to health care, however, it is estimated that 11.2% or 1.3

million CSHCN did not have any form of insurance coverage during 1994-1995

(Newacheck, McManus, Fox, Hung, & Halfon, 2000). The major barrier to access to

medical care was the cost of insurance followed by employment issues. Children who

were uninsured were less likely to have a regular source of primary care and children

with public insurance were twice as likely to be without a regular clinician. Furthermore,









children with public insurance did not have access to after-hours medical care from their

usual source compared to children with private insurance. Likewise, children who were

either uninsured or publicly insured were more likely to have an unmet need such as

dental care, prescription medications, eyeglasses and medical care. Under-insurance,

therefore, creates gaps in coverage for CSHCN.

Regardless of insurance status, children who are minorities or poor have reduced

access to primary care (Newacheck, Stoddard, Hughes, & Pearl, 1998). Children enrolled

in Medicaid managed-care may be particularly vulnerable since they may not receive the

broad range of services they may require due to ". .. referral barriers, financial

disincentives that discourage the use of appropriate specialty care and medications, and

disruption of long-standing providers relationship" (Mele & Flowers, 2000, p. 70).

Meanwhile, a higher percentage of children enrolled in Medicaid versus employer-

insured plans were reported to have a special need and consequently use more services,

which varied by diagnosis (Shatin, Levin, Ireys, & Haller, 1998). The shift in where

health care access now occurs, primary versus specialty level, is affecting CSHCN.

Unlike in the past, when services were predominately delivered by specialty care

providers, children with chronic illness enrolled in Medicaid were reported to receive a

majority of their care from generalist physicians (Kuhlthau, Ferris, Beal, Gortmaker, &

Perrin, 2001). The availability of health insurance, the predominance of Medicaid use for

this population, and the shift to primary care are important factors to consider when

determining where to target families for care coordination.

Economic impact also includes out-of-pocket costs and decreased employment

opportunities related to the time spent caring for the child. Out of pocket expenses among









families of children who were severely disabled increased with the time spent caring for

the child and consumed 12.2 % of family income (Leonard, Brust, & Sapienza, 1992).

Caregiving time in the home, escorting the child to appointments, and coordinating

medical care has been shown to be associated with the child's medical condition and to

occur at the expense of women's leisure or career time (Breslau, 1983; Brust, Leonard, &

Sielaff, 1992; Leonard, Brust, & Sapienza, 1992). Mothers (at or below poverty level

income) who were caring for a child with a severe disability had a 15% lower probability

of employment, worked an average of 15 fewer hours per month, and a had higher

likelihood of incurring out-of pocket expenses (Lukemeyer, Meyers, & Smeeding, 2000).

Higher income families also report financial hardship with out-of-pocket expenses,

increased hours providing home care, and interference with employment (Krauss et al.;

2000; Krauss et al., 2001). This triple effect (home care, reduced employment, out-of-

pocket costs) particularly subjects low-income families to deeper poverty and is an

important consideration when distinguishing among families who use and do not use care

coordination.

Non-economic Barriers

Factors such as minority status, language, poverty, lack of support, and low

educational level of parents are recognized as increasing the risk that vulnerable

populations will not be able to access health and related services (Halfon, Inkelas, &

Wood, 1995). This may apply to the use of care coordination as well. Families may be

aware of their eligible for assistance from public programs that include care coordination,

such as Medicaid, State Children's Health Insurance Programs (SCHIP), Title V-CSHCN,

or Supplemental Security Income Benefits (SSI). However, families may have difficulty









negotiating the eligibility determination process, gaining entry, and/or learning how to

use the system to meet their needs.

The current national concern with disparities in heath care has highlighted the role

of ethnicity and culture in mediating access to care. Variables such as socio-economic

status, education, and race were found to not explain the differences in health care service

use by Native American, Black, and Hispanic children who had fewer physician visits

(Flores, Bauchner, Feinstein, & Nguyen, 1999). The researchers suggest that cultural

differences such as language, health beliefs, and provider practices may account for the

disparities in service use. Language has been identified as a significant factor

differentiating access to primary care for children (Weinick & Krauss, 2000). Interviews

conducted in English were 2.6-times more likely to be associated with a child who had a

usual source of health care compared to interviews for children that were conducted in

Spanish. The interaction of family economic and socio-cultural factors may exacerbate

barriers for families who try to access services for their child. The literature supports that

care coordination has been targeted to lower-income and ethnic populations thereby

acknowledging that these families may particularly require assistance.

Care Coordination

This section synthesizes the literature in relation to the process and outcomes of

care coordination as it relates specifically to families of CSHCN. It emphasizes what is

known and unknown about the relationship between care coordination and the use of

resources; and which families are reported to use care coordination. It concludes by

identifying the gaps in the current literature related to the identification of families who

may require care coordination.









Process

Care coordination for CSHCN is an individualized and goal-directed process of

how care is delivered in conjunction with other service providers (Kruger, 2002).

Coordination generally accompanies the transition of a child from hospital to home,

specialty team diagnosis and treatment; as well as early intervention or developmental

services. The process of care coordination typically includes child and family assessment,

individualized service planning, implementation, monitoring and evaluation. For

example, assessment in the community-based Florida REACH program revealed that

nurses who made home visits performed physical (52%), nutritional (49%), educational

(44%), psychosocial (44%), environmental (25%), and developmental (18%) assessments

of children and/or families (Schwab & Pierce, 1986). The nurses identified that 55% of

their family encounters included providing direct care to the child while 63% of the

encounters were related to educating the family (such as how to access services).

A study conducted in 20 states reported that 50% of families with CSHCN have a

care coordinator (Krauss et al., 2000). The coordinator helped families to identify

community-based programs and services (63%), coordinate care among different

providers and services (56%), find ways to pay for services/equipment (47%), access

public programs (43%), and help understand child's health insurance plan benefits (39%).

The low frequency response to these structured questions raises questions about how else

coordinators assisted families.

Referral is a common mechanism by which coordinators link families to services.

An insurance-based coordinator reported that 40% of her time was spent identifying a

wide spectrum of available resources for families within a 250 mile area (Gehl, 1993).

The nurse made 363 referrals for unmet needs among 67 families (who had a primary









care provider) to 91 different public and private programs. The average referral per

family was 5.5 which covered 13 different service categories (counseling, dental,

equipment, health education, financial, legal, medical, social/recreation, school, support,

developmental /therapy, prescription assistance and vision). The highest frequency

referrals were made for financial assistance (30%), followed by dental services (11%),

and health education (11%). Furthermore, 55 % of the families accessed additional

referrals upon subsequent contact by the nurse. This underscores that families who had a

primary care health provider still required assistance to obtain additional medical and

nonmedical services and that the need for care coordination may be continuous. This is

the only study that measured the variety and volume of referrals made for families as a

result of care coordination (Gehl, 1993). No studies have measured the relationship

between care coordination and health or related services using a randomized sample.

Outcomes

Service delivery programs for CSHCN consistently identify that the purpose of care

coordination is to improve overall quality of life, child health, continuity of care, and

family capacity for self-care (Kruger, 2002). Yet, the outcomes related to these indicators

have not been studied. Research related to care coordination outcomes have reported

positive change in relation to parent perceived child health/mental health status, health

and/or child development, increased service use, satisfaction with care or quality, and

savings from higher cost inpatient care by displacement to lower cost of community

services (Kruger, 2002). A limitation of these studies is that most did not clearly identify

the components of the care coordination intervention or they did not use comparison

groups. Interpretation of coordination outcomes, therefore, is problematic.









Research related to family outcomes is sparse. The CHOICES national

demonstration project (Shriners Hospital) asked families how care coordination benefited

them and their child (Presler, 1998). An evaluation of 1,094 respondents found that

children benefited through fewer doctor visits (43%), improved function (36%), more

community-based services (35%), and fewer complications (29%) and hospitalizations

(22%). The family benefited from care coordination through ease in obtaining equipment

and supplies (49%), they better understood services and agencies (48%), received help

from people who cared (45%), and saved time finding health care and worried less about

their child (41%). Gehl (1993) reported that her families received information about

services (50%), referrals (43%); and benefited from a caring provider attitude (38%),

having telephone calls returned and by home visits (21%). These descriptive studies used

convenience samples and measured respondent perception at one point in time.

A more recent evaluation of the Rare and Expensive Case Management (REM),

Maryland's Medicaid managed-care program for individuals participating in Medical

Assistance, reports that care coordination lowered overall health care costs (Pandey,

Mussman, Moore, Folkemer, & Kaelin, 2000). Acute care hospital in-patient costs

decreased while non-acute care special services and pharmacy costs increased. The

displacement of the more expensive hospital to less expensive community services

resulted in the overall net savings. Special services were delivered in the school such as

transportation, therapies, and school health nursing as well as durable medical equipment

and disposable medical supplies. The estimated reduction in medical costs, per-member-

per-month, was calculated to be $607 and adjusted by the cost of case management ($253

per member per month) to produce a net lowering attributable to case management of









$354 per-member-per-month. This study quantified the costs but not the actual variety or

volume of services attributable to case management.

The Pediatric Ambulatory Care Treatment Study (PACTS), a randomized control

trial, suggested that there was variability in how families acquired and retained care

coordination and related services over time (Stein & Jessop, 1984). Care coordination

was integrated into the PACTS delivery approach that included direct primary and

specialty care, health education, and psychosocial services for families across all care

settings, including the home (Stein, 1978). A majority of families in the PACTS

intervention and control groups reported acquiring the following services over the 18-

month study period: a usual source of health care, acute care, health maintenance,

coordination with sub-specialists, as well as, someone who discussed family risk, gave

advice, and listened to family concerns (Jessop & Stein, 1994). A statistically significant

difference was reported between intervention and control groups when coordination with

other agencies (school, daycare or Medicaid) was provided at enrollment and retained at

6-months but was not sustained at 12-months. Furthermore, coordination had decreased

for the intervention group and increased for the control group. The reason why the

treatment group "lost" their source of care coordination was not provided. This

longitudinal study points out the potential for variability in the use of care coordination.

To date, one nationally representative study has examined the impact of care

coordination on the use of mental health care by school-aged CSHCN (Witt, 2001).

Coordination of medical care by a physician or a physician in conjunction with the family

significantly increased the likelihood that children received outpatient mental health care

and decreased the likelihood that children used inpatient care. Families who coordinated









their own care or who did not have anyone coordinating care did not show these positive

results. This study is an important contribution to the literature about the mediating effect

of care coordination on the use of mental health services within a population at-risk for

psychosocial morbidity.

Families Who Use Care Coordination

We do not know, at a population level, which families receive care coordination

services. We do know that if children are enrolled in specific publicly funded programs

then the probability that they are receiving care coordination increases. Studies of care

coordination for CSHCN infrequently and incompletely described the socio-

demographics of children and families included in the study sample (Kruger, 2002).

When reported, the ages of children in the samples ranged from birth through young

adulthood while the diagnoses covered a range of heterogeneous conditions from

medically fragile to more prevalent conditions such as asthma. Only a couple of studies

in this literature reported a high representation from Hispanic and Latino populations.

Socio-economically, these families demonstrated a consistently low income population.

Characteristics of children and families who had a care coordinator (50% of all

families) were reported by the Family Partners survey (Krauss et al., 2000; Krauss et al.,

2001). The children were more likely to be younger, a minority, have a more severe

and/or less stable condition, and a poorer overall health rating. Parents of the children

were less likely to be employed or have a bachelor's degree and more likely to be lower

income. No statistically significant differences were noted for sex of child, age of parent,

health status of parent, family structure, or number of CSHCN in the family.

A sub-population of CSHCN, those who were technology dependent, used care

coordination more frequently (70%) than children who were not technology dependent









(Krauss et al., 2000). Children with technology dependence were more likely to be

younger with a more compromised and severe health status and less likely to be a

minority. These children also used more primary care, outpatient specialties, emergency

room, hospitalization, disposable medical supplies, durable medical equipment, special

diets, nutrition counseling, and physical, occupational, speech therapies and consumed

more parent time providing in-home health care. Almost half (49%) of the parents of

children with technology dependence provided 20 or more hours per week of home care

compared to 12% of parents who children had special needs but were not technology

dependent. Consequently, the former parents reported a greater impact on their finances

and employment; and also spent five or more hours per week coordinating care.

Most all of these studies used convenience samples and/or did not use comparison

or control groups of families who did not use care coordination. They emphasized that

care coordination was important to families and was targeted to populations enrolled in

the demonstration programs. Missing from the population demographics was

representation from families with varied socio-economic status, a broader range of

minority groups, and children who were uninsured. Also, children who appear absent

from these studies are those who might have been medically stable.

Summary of Literature Review

Four decades of nursing research have largely focused on the impact of the child's

condition on family life or family functioning. Nurses have acknowledged that resources

play a role in assisting families; however, the focus has been on resources within the

family unit. Nurses have not studied how families use health and related services.

Health care utilization studies identify that CSHCN use primary, specialty, and

hospital care, namely medical services, in greater volume than do children generally.









Studies of child use of health related or community services are less frequent.

Meanwhile, care coordination process studies report that families are referred to many

different services while separate outcome studies suggest that this positively affects the

child (developmental and mental health status, increased use of services), family

(satisfaction with care), and health care system (cost savings). Research also supports that

care coordination has been targeted to lower-income minority populations and is also

received by higher income families.

A major limitation is that most of the research about care coordination is based on

convenience samples and not population or household survey data which limits

generalizability. This research also does not examine the relationship among care

coordination, health and related service use, and child and family characteristics.

Consequently, it does not provide sufficient information that could help identify families

for care coordination. Our exploratory study proposes to address this gap.














CHAPTER 3
METHOD

This chapter provides a brief overview of conceptual models applied to care

coordination and confirms that none of them sufficiently describe, predict, or explain the

relationship between the use of care coordination and health and related services.

However, the Behavioral Model of Health Service Utilization (Andersen & Newman,

1973; Andersen, 1995) that is frequently applied to health care utilization studies does

provide a heuristic for the study of care coordination. The application of this model, study

design, data source, sampling, and data collection procedures, development of measures,

and data analysis are discussed in this section.

Theoretical Review

A theory or conceptual model which explains or predicts relationships among care

coordination, the use of and need for health and related services, and child and family

characteristics, has not been reported in the literature. In fact, few studies of care

coordination identified a theoretical framework (Kruger, 2002). Models used in care

coordination studies included Bronfenbrenner's ecological human development (Gillette,

Hansen, Robinson, Kirkpatrick, & Grywalski, 1991), the enabling and empowering

framework ofDunst, Trivette and Deal (Farel & Rounds, 1998), the family partnership

model of Tunbull and Turnbull (Jackson, Finkler, & Robinson, 1992) and Orem's self-

care deficit theory (Pierce & Giovinco, 1983). These conceptual frameworks were

predominately applied to the delivery of clinical interventions rather than care

coordination services.









Meanwhile, models that identify health and related services as being important to

families do not relate acquisition or the need for these services with care coordination.

For example, the Family Adjustment and Adaptation Response Model (FAAR) associates

community resources with family adaptation (Patterson & Garwick, 1994). Resources

help families to balance demands and promote adaptation while a lack of resources

undermines family capacity to be resilient (Patterson, 2002). Similarly, research with

vulnerable populations relates resource availability with health status risk (Flaskerud &

Winslow, 1998). Populations that do not receive services or resources have an increased

risk of poor health. How families acquire resources or services was not discussed.

Only one model was found that relates care coordination with family use of

services. Dunst and colleagues conceptualize case management as "... a particular set of

functions for linking what is needed with what resources are provided" (Dunst, Trivette,

& Deal, 1994, p. 187). Principles and beliefs about families that guide the model include

the family as the unit of intervention, family empowerment and self-identification of

needs, family as having strengths and capabilities, and social support. Resources are

conceptualized as concentric circles surrounding the family beginning from the center

with informal supports and extending outward to include professional services. The intent

is not to supplant informal supports with professional services (Dunst, Trivette, & Deal,

1988). The informal supports and professional services are thought to interact with each

other to strengthen family functioning and help families gain mastery. Identification of

which families use or need which services or resources is not explained by this model.

The conceptual models briefly described in this section seek to understand how

families respond to and manage child chronic illness and include resources and services









as important for families. They emphasize the complexity of the relationships among

family demands, capabilities, and adaptation. They do not, however, clarify the

relationship among child and family characteristics, health and related services, and care

coordination.

Conceptual Framework

The Behavioral Model of Health Service Utilization, also known as the Andersen

Model, will be used in our study to organize the variables derived from the literature and

clinical practice that are thought to be related to the use of care coordination by families

(Andersen & Newman, 1973; Andersen, 1995). The Andersen Model reflects a social

psychology / sociology perspective and has been applied to the study of health care

services utilization over the past 30 years. An underlying assumption of this model is that

individuals make decisions to seek medical care (the behavior) in order to decrease the

risk of poor health. Another assumption is that increased use of health care services will

improve health outcomes. This heuristic organizes variables according to predisposing,

enabling, and need factors. The ability of these three factors to explain service utilization

depends upon the type of service that is studied and how the determinants of use are

conceptualized. Meanwhile, the outcome of the health care services is generally

measured through satisfaction with care and improvement in health status. Although

health care outcomes are not included in our study, it is important to note that they

provide a feedback loop that may reinforce some predisposing characteristics and

influence whether an individual continues to use a particular service (Andersen, 1995).

Table 3-1 describes the variables of interest in our study organized by the Andersen

(1995) model. Utilization of care coordination is the outcome of interest. Families

responded, yes or no, if they received medical or nonmedical care coordination.










Respondents also indicated who provided the coordination, whether a professional and/or

the family themselves. The child and family predisposing, enabling, and need

characteristics were derived from clinical practice and from the literature related to the

use of medical services. The determinants of care coordination utilization have not been

studied, particularly in relationship to health and related services.

Table 3-1. Model for studying care coordination utilization


Family Behavior


Use of care
coordination


Predisposing Factors

Predisposing factors are the demographic, social, and psychological characteristics

that influence an individual to use health services (Andersen, 1995). Child and family

socio-demographic characteristics, together, may influence families to use care

coordination. Child predisposing factors include age, gender, and race/ethnicity. For

example, younger children may have a greater likelihood of receiving care coordination

than adolescents as a result of their association with early intervention programs which

commonly provide care coordination.

Factors that influence families to use or carry out care coordination may be related

to family structure, size, education, and residential stability. For example, one-parent

families may have less time to arrange for services compared to two-parent families.


Child & Family Characteristics
Predisposing Enabling Need
Child gender, age, race Child health insurance, Child number of
service use (type and conditions, health
variety) status, unmet service
need (type and variety)
Family structure, size, Family income,
education, and length residence region, Family poverty,
of residence in same telephone access financial need,
state employment impact









Long- term residency in an area, meanwhile, may provide familiarity with community

resources.

Enabling Factors

Characteristics that influence a predisposed person to use health services are called

enabling factors (Andersen, 1995). Health insurance is a common enabling factor to

medical services and may provide a pathway to care coordination. Exposure to different

types or a variety of health and related services may also be enabling because they may

increase child and family exposure to programs that provide care coordination. Family

region of residence, including proximity to an urban area, may affect the distribution of

care coordination by government programs. Availability of a telephone may enable

coordination because it facilitates communication with providers.

Family financial status is an enabling factor when it is applied to the use of medical

services in that families with higher incomes are more likely to access health services.

Income, especially below a certain level, is a customary criterion for eligibility

determination in many government programs that provide health and related services. In

programs with universal eligibility such as early intervention, income may not be a

criterion for service whereas for Medicaid it is. Care coordination is more likely to be

bundled with public health, education and/or developmental programs. Consequently, it

is not universally available for purchase; therefore, a higher income may not enable

access to care coordination as it does for health care. However, because many public

programs have financial eligibility restrictions, lower family income may be a more

realistic factor related to obtaining care coordination. In any event, the relationship

between income and care coordination is not clear.









Need Factors

The third factor, the need for health care, must be present in order for health care

utilization to occur (Andersen, 1995). Needs are either perceived by the individual or

evaluated by a health care provider. Perceived needs are thought to better explain care-

seeking behaviors while professionally evaluated need is related to the type and volume

of services provided (Andersen, 1995). The need children and families have for care

coordination may be associated with the family perception of the child's' health status,

child co-morbidities or number of health conditions, and family financial ability to pay

for services. Children with a poor health status or co-morbid conditions may interact with

health and related providers more frequently, thereby increasing opportunities for

exposure to care coordination and/or have a need to arrange those services among

multiple providers. Family poverty level (composite of income, family size, family

structure) is also conceptualized as a need factor.

These predisposing, enabling, and need factors and the use of care coordination

were linked to variables in a national population data source. The study design, secondary

data source, sampling, and collection methods are described in the following section. The

identification of study measures related to child and family characteristics, the types and

varieties of services used and/or needed by children, and care coordination are also

discussed.

Study Design and Data Source

A descriptive/correlational design using national survey data examined the

relationships among care coordination, the types and variety of services used and/or

needed, and child and family characteristics. The 1994 National Health Interview

Disability Follow-back Survey Phase II (NHIS-D Phase II) contains a representative









sample of CSHCN and their families and the variables of interest for our study. This

database is well-known, reputable and economical (Brown & Semradek, 1992; Young &

Ryu, 2000).

The NHIS, the primary data source for the NHIS-D Phase II, is an annual

household survey of the non-institutionalized civilian population within the U. S. and has

been conducted by the National Centers for Health Statistics since 1957 (Massey, Moore,

Parsons & Tadros, 1989). The survey does not include individuals in long-term care

facilities, persons in the military, U.S. nationals living abroad, or persons who are

homeless. This cross-sectional survey collects information on the health status, health

care use, and demographic characteristics of respondents. It is planned for a 10-year

period by partitioning the overall sample into yearly sub-samples. A representative

sample is assigned to four calendar quarters each year and then distributed into individual

weeks. Each level of this distribution produces samples that are representative of the

target population (Massey et al., 1989).

NHIS data are collected on all living members of sampled households from adults

(> 17 years old), in face-to-face interviews. Information on children and adults not at

home during the time of the interview is gathered from a resident adult (>19 years old).

The interview takes an average of 50 minutes with a range of 20 to 90 minutes. The first

part of the questionnaire called the "core" is repeated annually and contains basic health

and demographic questions. A second section contains questions related to topics of

current national interest that change annually. Approximately 49,000 households and

132,000 persons represent the sample population each year with over a 95% response rate

of eligible households (Massey et al., 1989).









NHIS Sampling

The NHIS employs a stratified multistage probability design with continuous

(weekly) sampling of a representative population which is additive over time (Massey et

al., 1989). The sampling design has been revised each decade to coincide with the

decennial census of the population. The first step in determining sampling units for the

NHIS involves the selection of primary sampling units (PSU's) from over 1,900

geographical PSU's which cover the 50 states and the District of Columbia. A PSU is a

county, a small group of contiguous counties, or a metropolitan statistical area (MSA)

identified by the U.S. Census. The design of the 1994 NHIS selected 198 PSU's and

over-sampled the Black population (Massey et al., 1989).

The PSU's are grouped or stratified according to socioeconomic and demographic

variables within a geographic stratum and then selected with a probability proportional to

their population size within their stratum. The larger PSU's are considered self-

representing (SR) and are included in the NHIS with certainty as they are equivalent to a

stratum. The non-self-representing (NSR) PSU's are grouped based upon similarity and

only 2 PSU's are selected from this group (Massey et al., 1989). PSU's are then broken

into smaller segments and geographical clusters to form secondary sampling units (SSU)

which are in turn partitioned into density strata based upon minority population

concentration. These smaller segments and clusters contain housing units (households)

that are either grouped or spread out over a geographical area.

Data Source

The primary data source used for our study was the supplement to the 1994 NHIS

known as the National Health Interview Disability Follow-back Survey Phase II for

adults and children. Eligibility for Phase II was determined during Phase I (National









Center for Health Statistics, 1994). The Phase I questionnaire was conducted at the same

time as the NHIS core for that year. It included three sections of questions directed

towards CSHCN, special education, and child development for children under the age of

five years. The survey used a used a variety of approaches (136 variables) to ask about

the disability or condition of the adults and children in the household. The condition

criteria included diagnoses, use of health and education services, activity limitations,

developmental and behavioral indicators, adaptive and assistive device use, treatments

and medications, physical functioning, assistance with activities of daily living, and

family perception of disability. Thirty-three of these criteria were identified as "single

item exclusion" meaning that they had to be combined with another variable to be

considered a positive screen for inclusion of a child into the Phase II survey. Children

deemed eligible (on the basis of Phase I criteria) for Phase II of the study numbered

4,724, of which 4,296 (90.9%) were located. The response rate for Phase I and Phase II

of the NHIS-D was 84% (National Center for Health Statistics, 1994). The overall

response rate including the NHIS core was 79%. It is estimated that only 2% of all

disabled children are missed by household-based surveys like the NHIS-D (Hogan,

Msall, Roger & Avery, 1997 as cited in Monahan, 1998).

Data Collection

Telephone interviews for the NHIS-D Phase II were conducted between August

1994 and 1997 with data issued in 1999 (National Center for Health Statistics, 1994).

Information about CSHCN covered 14 sections of survey questions. Questions were

related to: a) home care, b) child care, c) medical care, d) assistive devices/technology,

e) allied health services, f) special education and early intervention programs, g) care

coordination, h) physical activity, i) personal adjustment and role skills, j) impact on









family, k) mental health, 1) housing and transportation, m) insurance, and n) respondent

information. The section on care coordination asked families about their experience with

medical and nonmedical coordination. An important advantage of this database is that it

included children who did not use care coordination.

Creation of Dataset

Approximately 210 variables of interest, including weights and identifiers, were

exported into a spreadsheet file from a CD-ROM (National Center for Health Statistics,

1994) containing the NHIS-D Phase II data using the Statistical Export and Tabulation

System (SETS) software. The spreadsheet was converted into a STATA for Windows

Intercooled version 7.0 database file (StataCorp, 2001). All variables in the dataset were

renamed from the NHIS-D alpha-numeric codes to descriptive names. Data were then

cleaned, recorded, collapsed, and patterns of missing data were analyzed.

Data Cleaning

The dataset was analyzed for missing data using the "tabmiss" command in

STATA and frequencies were run on each of the variables. Frequency tables were

compared to the NHIS-D Phase II data dictionary which revealed that data coded by the

NHIS and read by STATA as missing were actually "not applicable" responses and

therefore were not truly missing. For example, 193 observations were read by STATA as

missing for the variable, "received special education services." However, these 193

observations were children under the age of three years who were not age eligible for

special education, and therefore not applicable to being asked the question about special

education. Meanwhile, the true missing data corresponded to codes such as "8" not

ascertained, or "9" blank or not known. Data with a "not applicable" response were re-









coded to a "6" or another unique code that distinguished them from other responses.

Subsequently, these data were re-coded in STATA to missing.

Missing Data

Some of the survey sections were lengthy and not all respondents were asked all

sections of the questions (National Center for Health Statistics, personal communication,

January 20 2000). The average percent of non-missing data for 199 key variables was

99.45% meaning that most of the data was available. The largest frequency of missing

data related to family income (414 observations), family poverty level (212

observations), and years lived in present residence (208 observations). Missing data for

the remaining variables ranged between zero (or none) to 123 observations. These key

variables were then collapsed into categories and reduced to 59 variables with an average

percent of non-missing data equal to 99.15%. A second and final reduction of variables

resulted in 30 independent variables (covariates) and two dependent variables with an

average percent of non-missing data of 99.02%.

Re-coding and generating new variables was performed by executing a STATA

command file with documentation sent to a log file. A codebook was developed

containing the NHIS-D original variable name, variable description, re-named variable,

percent non-missing data, and un-weighted frequencies. An alpha-prefix for renamed and

generated variables was assigned to correspond to the section of the Phase II from which

the variable was drawn.

Study Sample Selection

Selection criteria for our study restricted the sample to the following: 1) children

residing in a primary household, 2) respondent for the NHIS-D Phase I was a proxy for

the child, 3) relationship of the respondent for the NHIS-D Phase II was the child's









mother or father, and 4) adults living in the family identified at least one or both parents.

A primary household contains only one family in contrast to multiple households. Proxy

respondents originally included mother, father, grandparent, sibling and other relatives.

However, grandparents, siblings, and other relatives had a 12-19% frequency of missing

data for a number of the covariates, more than mothers and fathers, and were therefore

excluded from the study.

The application of these selection criteria produced a sample of 3,865 (89.96%)

children and families from a total of 4,296 respondents. Complete data exists for 3,087

(79.87%) children for all of the study variables. Complete cases and incomplete (missing

data) cases were compared on the basis of the dependent variable and the covariates to

determine bias and potential of the study sample to represent the overall population.

Comparison of Complete and Incomplete Cases

No statistically significant differences on the basis of chi-square or t-tests were

noted between the complete or incomplete cases for the dependent variable (care

coordination). Likewise, no differences between incomplete and complete cases were

noted for child age, gender, number of health conditions, family size, income, poverty

status, years in the same residence, relationship of child to the family, and the use and/or

need of health, equipment, home, or support services. Table 3-2 identifies the covariates

for which statistically significant differences between complete and incomplete cases

were noted (p<0.05). The significant differences average approximately 4% with a range

of less than 1% to 8.3%.

The greatest differences between complete and incomplete cases were for children

with Medicaid (8.31%) and those with "excellent to very good" child health status

(8.03%). This analysis suggests that children who represent incomplete cases may appear










more needy than the selected study sample. However, given that the differences are

relatively small and do not affect the dependent variables, it is determined that the study

sample is nationally representative.

Table 3-2. Significant differences between complete and incomplete cases
Independent Variables Percent non- Complete Incomplete Percent Statistical
missing Cases Cases difference significance
Child has private insurance 99.1% 63.3% 56.5% 6.8% P<0.001
Child has Medicaid 99.1% 25.2% 33.5% 8.3% P<0.000
Child race 100%
White 81.2% 74.4% 6.8%
Black 16.2% 22.0% 5.8% P<0.000
Other 2.6% 3.6% 1.0%
Child health status 98.7%
Excellent to very good 62.5% 54.5% 8.0%
Good 27.5% 32.1% 4.6% P<0.000
Fair to poor 10.0% 13.4% 3.4%
Has severe economic need 99.4% 4.2% 7.2% 2.9% P<0.001
Family had severe impact 99.5% 20.9% 24.3% 3.4% P<0.038
on employment
Adults in family 99.7%
Both parents 70.1% 65.4% 4.7%
Mother 28.1% 32.7% 4.6% P<0.039
Father 1.8% 1.9% 0.1%
Education (adult) 99.8%
Elementary 3.6% 4.7% 1.1%
Some high school 10.2% 15.0% 4.8% P<0.001
High school graduate 36.0% 34.9% 1.1%
Some college 25.1% 24.2% 0.9%
College graduate + 25.1% 21.1% 4.0%
Residence region 100%
Northeast 18.8% 19.4% 0.6%
Midwest 29.2% 24.4% 4.8% P<0.040
South 32.4% 33.5% 1.1%
West 19.6% 22.6% 3.0%
Urban (vs rural) 100% 74.4% 81.1% 6.7% P<0.000
No telephone 99.4% 7.1% 9.8% 2.7% P<0.013
Used therapy 99.8% 26.5% 31.3% 4.8% P<0.007
Used support services 99.8% 9.3% 12.3% 3.0% P<0.012
Used home care 99.6% 14.7% 18.7% 4.0% P<0.007
Unmet therapy need 99.8% 26.5% 31.3% 4.8% P<0.007
Unmet equipment need 99.4% 3.3% 5.1% 1.8% P<0.019
Variety used (0-5) 100% Mean 2.13 Mean 2.24 0.11 P<0.048
Unmet Variety (0-5) 100% Mean 0.50 Mean 0.58 0.08 P<0.023









Measures

Care Coordination

Respondents were asked whether anyone provided medical and/or nonmedical care

coordination and who performed this function. Medical coordination was defined as

communication with other physicians or therapists and having an awareness of the child's

treatment. Nonmedical coordination was defined as help arranging services such as social

and personal care services. Persons identified as providing care coordination included

health care professionals and families. The specific questions about care coordination in

the NHIS-D Phase II asked if there was: 1) doctor who coordinates child's overall

medical care, 2) anyone not a doctor who coordinates child's medical care; 3) physician

or someone in a physicians office who helps with arranging child's nonmedical care

(social services and personal care) and 4) anyone not in a physicians office who helps

with arranging nonmedical services.

Two measures of care coordination as the dependent variable were created in

relationship to Aim 1 and 2. A dichotomous measure coded as "yes" or "no" represented

whether anyone had coordinated care for the child in the prior 12 month period (Aim 1).

This measure was constructed by pooling yes or no responses to four key NHIS variables

(Table 3-3). The second care coordination dependent variable (Aim 2) distinguished the

type (GTypeProv) of coordination provider: 1) no one, 2) family only, 3) professional

only, and 4) family and professional. This variable was constructed in two stages. The

first stage grouped yes and no responses for families (GfamCC) separately from

professionals (GprofCC). These grouped variables were then used to generate the type of

provider measure.










Table 3-3. Coordination measures by NHIS-D variable and description
Coordination NHIS variable NHIS Description, yes or no responses
Measures
Anyone coordinates G_1521 Doctor coordinates medical care
care, Gany G_1523 Someone, not doctor coordinates medical
(yes or no) G_1532 Physician/staff arrange nonmedical care
G_1541 Anyone not in MD office arrange nonmedical care


Family coordinates,
GfamCC
(yes or no)


Professional
coordinates, GprofCC
(yes or no)




















Who coordinates care,
GTypeProv
Based on GfamCC
and GprofCC


G 1524
G 1525
G 1542
G 1543


G_1521
G 1526
G 1527
G 1528
G 1530
G 1531

G 1534
G 1535
G 1536
G 1537
G 1539
G 1540

G 1544
G 1545
G 1546
G 1547
G 1548
G 1549

Generated


Parent/guardian coordinates medical care
Friend/family member coordinates medical care
Parent/guardian-coordinates nonmedical care
Friend/family member coordinates nonmedical

Who coordinates medical care in physician office
Physician
Nurse
Therapist
Social worker
Case manager
Other professional
Who coordinates nonmedical in physician office
Physician
Therapist
Nurse
Social worker
Case manager
Other professional
Who coordinates nonmedical outside physician office
Nurse
Therapist
Social worker
Hospital discharge
Case manager
Other professional

"0" = no one
"1" = family only
"2" = professional only
"3" = family and professional


Child


Child measures reflected the predisposing characteristics of gender, age, and race.

The enabling measures were having a source of health insurance such as Medicaid or

private insurance and receiving health and related services. Need measures were reflected

by the number of child health conditions, health status, and unmet health or related


service needs. Table 3-4 identifies the measures used for analyses.










Table 3-4. Child measures by NHIS-D variable, description, and coding
Child measures NHIS variable NHIS Description Variable coding
Age CH_399 Age at follow-back Birth to 2 years
3-6 years
7-10 years
11-14 years
15-18 years
Gender CH 25 Sex of child Male or female
Race CH 43 Race recode 1 White, black, other
Medicaid M_1831 Covered by Medicaid Yes or no
Private M_1838 Covered by private insurance Yes or no
Number health CH 116 Number of health conditions None
conditions One
Two
Three or more
Health status CH_70 Child health status Excellent or very good
Good
Fair/poor


Child use of five different categories of health and related services was an enabling

measure while the unmet need for these same services was conceptualized as a child need

measure. The categories of services included: home care, health care, equipment,

therapy, and support. Table 3-5 identifies the specific services included within each of the

five categories. Between 2 and 18 NHIS variables were collapsed, based on "yes" or "no"

response, to generate each of these categories of service use or umet need. Appendix A

provides a detailed description of how these measures were constructed.

A variety measure was generated to capture breadth of service utilization and

unmet need among the five categories of services. The service variety measure counts the

number of different types of services used or needed by the child. The range of responses

is zero to five reflecting home, health, therapy, equipment, and support service types.

Family

Predisposing measures included family composition, size, years lived in state of

present residence, and education of the responsible adult (Table 3-6). Enabling measures

included region of residence, urban or rural location, availability of a telephone, and










family income. Family need measures were financially driven (poverty status, severe

financial need related to child, and impact on employment as a result of caring for child).

The impact on family employment measure was generated by collapsing responses from

six related variables. Respondents who answered "yes" to any one of the six questions

were coded as "yes," having some type of impact on employment opportunity.

Table 3-5. Child service use and unmet service need categories
Measure Type of service
Home Care Personal care, respite, home care
Health Care Office visits, hospital, mental health
Equipment Assistive devices, home / motor vehicle modifications
Therapy Physical, occupational, speech, audiology, communication, interpreter, recreation
Support Center for Independent Living, social work, transportation


Table 3-6. Family measures by NHIS-D variable, description, and coding
Family measures NHIS variable NHIS Description Variable coding
Adults in family CH_68 Parent/other adults) Both parents, >21 yr. relative
Mother, >21 yr. relative
Father, > 21 yr. relative
Family size CH_67 Size of family recode Two to six or more persons
Residence time CH_144 Years lived in state of Less than 5 years
present residence, US born 5-10 years
10 + years
Family education CH_56 Highest education of One to eight years
responsible adult, recode Nine to eleven years
Twelve years
College, one to three years
College graduate plus
Residence region CH_182 Region of residence Northeast
Midwest
South
West
Urban or rural CH 186 MSA or non-MSA MSA, central or non-central
Non-MSA, non-farm or farm
Telephone CH_24 Has telephone Yes or No
Income CH_60 Family income recode Under $5,000 to 50,000 plus
Poverty status CH_61 NHIS poverty index At or above poverty threshold
Below poverty threshold
Financial need J_1662 Has financial problems Yes or no
Impact on J_1651 Not taken a job Yes or no to any questions
employment J_1652 Quit working J_1651 to J1656
J_1653 No job change
J_1654 Changed work hours
J_1655 Turned down job
J 1656 Worked fewer hours









Data Analyses

Statistical analyses were conducted using STATA software (StataCorp., 2001) that

takes into account sampling weights to accommodate for the complexity and levels

(stratification, clustering, multistage sampling, over-sampling vs. simple random

sampling) in the NHIS-D survey design (Massey et al., 1989). The two weights that must

be considered in survey designs are person level and variance estimation weights (Kneipp

& Yarandi, 2002). The person weights produce estimations that reflect the total number

of persons in the population. Variance weights calculate sample variances (standard

errors). Ignoring variance weights may result in incorrect standard errors, therefore,

leading to Type I or Type II errors depending upon the design effect. In order to apply the

variance weights the variable "Pseudo PSU code" was separated into two variables,

CSTRATUM and CPSU, according to the method of variance estimation recommended

by the NHIS (National Center for Health Statistics, 1994). The data were then "set" using

STATA's survey set command.

Prior to conducting analyses for Aims 1 and 2 statistics were generated to describe

the sample (n=3087) demographic characteristics, the dependent variables, and the types

and variety of services used and/or needed. The covariates were also analyzed in

relationship to the dependent variables to assess cell size. As a result, multiple levels of

selected variables (child age, family education, family composition, family size, number

of child conditions, variety of services used, and variety of unmet needs) were collapsed

to improve cell size. The reference group for the variety of services used by the child was

constructed to include "none" or "one" service. Theoretically, one does not coordinate

none or one service. The remaining two levels of this variable were coded to reflect

children using two services and three to five services. Likewise, the variety of unmet









service need variable was collapsed from six levels to two levels due to the small number

of observations per cell. The reference category was coded as zero or no services needed

and the second level of this variable reflects children needing one or more services. All

tests for significance were set at the 0.05 alpha level.

Aim 1 Analyses

Two logistic regression models were used to estimate the likelihood of using or not

using care coordination in relationship to the child and family covariates by 1) types of

services (home, health, equipment, therapy, support) and 2) by variety (one to five). The

type and variety of services were not included in the same regression due to known

collinearity. The dependent variable (did anyone coordinate care, Gany) was

dichotomously coded as no = 0 and yes = 1 consistent with STATA's interpretation of the

value of "0" as a negative outcome (failure) and all other values as positive outcomes

(StataCorp, 2001).

Analyses proceeded by simultaneously entering the explanatory variables into the

logistic regression model to determine which variables significantly predicted the use of

care coordination. Data were interpreted based on the Wald statistic and associated odds

ratios. A reduced model was then estimated by using the significant covariates identified

in the first analysis. Hosmer and Lemeshow (2000) note that model checking and

goodness-of-fit procedures typically used in non-survey logistic regression are not

currently available for complex survey data using software such as STATA (p. 218).

Aim 2 Analyses

Analyses were limited to respondents who used care coordination (n=2071). The

dependent variable was stratified into three mutually exclusive groups based on who

provided care coordination. This was done by dropping the responses to "no one









coordinates care" from the "GTypeProv" variable. The modified variable was coded "0"

for family, "1" for professional, and "2" for family and professional.

Multinomial logistic regression simultaneously estimated the association of the

three provider types (family, professional, or family and professional) with the

predisposing, enabling, and need covariates. This type of analysis is appropriate for

nominal level outcome variables having more than two categories with no particular

order (Kleinbaum & Klein, 2002). Multinomial logistic regression is also known as

discrete choice model, polytomous and polychotomous (Hosmer & Lemeshow, 2000).

Unlike a binary regression where the estimate is of the probability of an outcome being

present, an outcome with three categorizes has two estimated probabilities (Hosmer &

Lemeshow, 2000). One of the outcomes is designated as the reference category and in

this analysis the family was chosen as the reference. The regression simultaneously

modeled how professionals, as one group, and professionals and families, as a second

group, vary on the basis of the covariates when compared to the family alone. The

professional and family group represents a constructed variable from responses to a series

of separate questions about who provides medical and nonmedical coordination.

As in Aim 1, analyses proceeded by concurrently entering the explanatory variables

into the logistic regression model to determine which covariates significantly predicted

the two outcomes based on who provided the coordination; professional alone or

professional and family. Data were interpreted based on the Wald statistic and associated

relative risk ratios.














CHAPTER 4
RESULTS

Sample Characteristics

Characteristics of the children and families are shown in Table 4-1. The weighted

sample represents 7,870,264 children with more boys (59.5%) than girls (40.4%) and a

mean age of 10 years. Racial distribution of the children was 81.7% white, 15.5% black,

and the remainder was categorized as "other" or unknown. Children of Hispanic origin

represent 11% of the sample and are included in all three categories. Compared to the

1990 U.S. Census, more boys, fewer pre-schoolers, more teens, and more children who

are black (consistent with the over sampling by the NHIS) are represented in our study

population.

Family structure indicates that children resided mostly in two-parent families

(71.4%) with 37.3% reporting a family size of four persons. Slightly more than one-half

of responsible adults had some college education (51.1%). Comparison to the U.S.

Census (1990) indicates that families in this sample are more highly educated with a

higher frequency of college than elementary school education.

Slightly more than one-half of the U.S. born families (54.5%) reported living in the

state of their present residence for less than 10 years. Children who were foreign-born

comprised 2.9% of the sample. Urban residents (75.8%) were more frequently

represented than those in rural areas which is consistent with the U.S. Census. Regional

distribution of children demonstrates a higher frequency from the South (33.4%),









followed by the Midwest (28.2%), West (19.4%), and Northeast (18.9%). Most all of the

families had access to a telephone (93.4%).

Children were reported to have private health insurance (64.2%) more frequently

than Medicaid (24.0%). Only 3.9% of the children had both private and public sources of

insurance compared to 59.3% with private insurance only; 26.17% with public insurance

only; and 10.5% that were uninsured (not shown in Table 4-1).

Almost 40% of the children had no reported systems related medical condition;

however, they may have had a functional limitation, behavioral, or developmental delay.

The remaining children had at least one condition (36.7%), two conditions (15.5%) or

three or more conditions (8.1%). Most respondents (90.4%) reported that children had

excellent, very good, or good health status compared to 9.6% with fair to poor health

status. As might be expected, children with fair or poor fair health status had two or more

health conditions (47.8%) in contrast to children with no conditions (15%) (analyses not

shown). Likewise, children who had excellent or very good health status had a higher

frequency of no health condition (46.7%) compared to children with two or more

conditions (16.7%). Children having only one condition had the same frequency (-36%)

of either excellent or very good, good, or fair to poor health status suggesting that the

respondent perception of child health status is affected by more than the condition.

Approximately three-fourths (74.4%) of families lived in households with incomes

at or above the 1993 federal poverty level with 57.4% reporting incomes of more than

$25,000 per year. The household income distribution mirrors the U.S. Census for 1990;

however, more study families were reported to be living below the poverty level

compared to national figures. A small number of families, 4.5%, reported having severe










financial problems due to their child's health and 21% reported an impact on their

employment related to caring for their child.

The characteristics of our study population appear to be consistent with studies of

CSHCN. Epidemiologic estimates indicate that CSHCN are more likely to be boys,

African-Americans, and older children with families at or below the poverty level

(Newacheck, Strickland et al., 1998). Weller, Minkovitz, and Anderson (2003) studied

health and related services utilization by school-aged children identified as having special

health needs and describe a population with similar characteristics for gender, educational

level, family size, family structure, poverty status, and health insurance.

Table 4-1. Characteristics of the study sample
% (weighted) n unweightedd)
n=7,870,264 n=3087
Predisposing
Child gender
Male 59.5 1849
Female 40.4 1238
Child age
Birth to 2 years 4.8 143
3-6 years 17.9 556
7-10 years 24.8 788
11-14 years 27.4 882
15-18 years 24.9 718
Race
White 81.7 2508
Black 15.5 499
Other 2.7 80
Adults in family
Both parents 71.4 2163
Mother 26.8 868
Father 1.7 56
Family size
Two persons 5.5 169
Three persons 18.8 594
Four persons 37.3 1132
Five persons 21.9 679
Six or more persons 16.5 513










Table 4-1. Continued
% (weighted) n unweightedd)
n=7,870,264 n=3087
Predisposing, continued
Highest education of responsible adult
Elementary 3.2 110
Some high school 9.6 315
HS graduate 36.0 1113
Some college 25.8 774
College Graduate 25.4 775
Residence status (U.S. born)
< 5 years 24.0 720
5 < 10 years 30.5 980
> 10 years 42.5 1302
Foreign-born 2.9 85
Enabling
Urban or rural
Urban 75.8 2297
Rural 24.2 790
Region of U.S. residence
Northeast 18.9 582
Midwest 28.2 902
South 33.4 999
West 19.4 604
Medicaid 24.0 778
Private health insurance 64.2 1954
Telephone access 93.4 2867
Need
Child health conditions
None 39.6 1216
One 36.7 1140
Two 15.5 474
Three or more 8.1 257
Child health status
Excellent to very good 62.7 1929
Good 27.7 848
Fair to poor 9.6 310
Family income
< $ 24,000 42.5 1346
$ 25-49,000 34.3 1040
> $ 50,000 23.1 701
Poverty level
At or above 74.4 2278
Below poverty level 25.6 809
Severe financial need 4.5 131
Impact on employment 21.0 645

Utilization of Care Coordination

Almost 67% of families reported using any care coordination services during a 12

month period (Table 4-2). Professionals were more frequent providers of care









coordination (44%) compared to families (19.3%). Approximately 37% of care

coordination was provided by both, at least one professional and by the family. Of

professionals, most of the providers of care coordination were physicians.

Table 4-2. Utilization of care coordination
n=3087 % (weighted) n unweightedd)

Used care coordination 66.9 2071

Type of provider
Family only 19.3 397
Professional only 44.0 910
Family with Professional 36.6 764


Utilization and Unmet Need for Health and Related Services

Health care was the most frequently used service (84.6%) by children in the

previous 12-months (Table 4-3). Equipment, assistive devices, or home and automobile

modifications were used by 38.1% of the children. Therapeutic services used by 26.1% of

the children reflect physical, occupational, speech, audiology, communication,

respiratory, and special education recreation therapy services. Home care (14.7%) and

support services (9.3%) were the least frequently used services. More than half of the

children (55.2%) used a variety of two or more of the five service types.

Child unmet need for home (7.0%) and health care (5.8%) were the most frequently

mentioned types of services, followed by therapy (4.0%), equipment/assistive

devices/home-care modifications (3.4%) and support services (1.6%). Slightly less than

15% of the children had an unmet need for one or more varieties of these services.










Table 4-3. Use and unmet need for health and related services by ype and variety
n=3087 Percent Used Percent Unmet Need
(weighted) (weighted)

Types of Services
1. Health care 84.6 5.8
2. Equipment / assistive devices 38.1 3.4
3. Therapy 26.1 4.0
4. Home care 14.7 7.0
5. Support 9.3 1.6

Variety of Services
None 2.8 84.9
One 42.0 10.2
Two 34.2 3.3
Three 13.1 1.0
Four 5.5 0.44
Five 2.4 0.03

Mean (SE) 1.8 (0.02) 0.22 (0.01)


Aim 1: Determinants of Care Coordination Utilization

Logistic regression analyses showed that a number of predisposing, enabling, and

need factors were significantly associated with using care coordination. Table 4-4 reports

the adjusted odds ratios for the multivariate regressions of the use of care coordination in

relationship to the covariates. Separate regressions were run in conjunction with service

type and service variety due to the collinearity of these variables. The odds ratios andp

values for the child and family characteristics were similar in the type of services and

variety of services models. Results are discussed for the variety of services model.

Predisposing

Children who were black, foreign-born, and who lived in families with more than

five persons were significantly less likely to use care coordination services. Compared to

children who were white, children who were black were half as likely (OR, 0.49) to use

coordination. Likewise, families with five or more persons were 0.70 times as likely

(compared to two or three person families) to use care coordination. Weller and









colleagues (2003) reported a similar odds ratio for children who were black in

relationship to medical and nonmedical coordination but no association with family size.

Meanwhile, children who were foreign-born, compared to U.S. born children who

maintained the same state residence for less than five years, were only 0.43 times as

likely to use care coordination. Child gender, child age, family composition, and adult

education were not associated with the use of care coordination.

Enabling

Children who had private health insurance and used different types of health and

related services, especially two or more different services, and had an unmet need for

more than one type of service were more likely to use care coordination. Children in the

western region of the United States were only 0.67 times as likely to use care

coordination compared to children living in the northeast.

Children with private insurance were almost twice as likely (OR, 1.78) of having

coordination compared to children who do not. The use of Medicaid was not associated

with care coordination. Compared to children without health insurance, children who had

a combination of private and public sources were more than twice as likely (OR, 2.21; p

= 0.02) to use care coordination (not shown in Table 4-4). Children with only a public

source of insurance, including Medicaid, were just as likely to use care coordination as

children who were uninsured. This finding is consistent with Weller, Minkovitz, and

Anderson (2003) in that uninsured children are less likely to have care coordination.

However, they do report an increased likelihood that children with only a source of

public insurance were more likely (OR, 1.38, p<0.05) to have nonmedical coordination.











Table 4-4. Full model for use of care coordination by service type and variety
Service Type Service Variety
Odds 95% CI P Odds 95% CI p
Predisposing


Gender
Male*
Female
Child age
Birth 6 years*
7-10 years
11-14 years
15-18 years
Race/ethnicity
White*
Black
Other
Adults in family
Two-parent*
Single parent
Family size
Two or three person*
Four person
Five person
Six or more person
Education, adult
< High school grad
High school grad
Some college
College grad +
Same residence (U.S. born)
< 5 year*
5-10 years
10 + years
Foreign-born


Enabling


Rural (vs urban)
Region
Northeast*
Midwest
South
West
Medicaid **
Private insurance **
Telephone access **
Family income
< $ 24,000*
25-49,000
> $50,000


1.0
0.95 (0.80, 1.13) 0.57

1.0
1.24 (0.87, 1.77) 0.22
1.09 (0.76, 1.56) 0.63
1.04 (0.71, 1.52) 0.85

1.0
0.53 (0.38, 0.73) 0.000 ?
0.80 (0.49, 1.28) 0.35

1.0
0.98 (0.73, 1.31) 0.90

1.0
0.96 (0.74, 1.24) 0.75
0.74 (0.53, 1.02) 0.07
0.71 (0.51, 0.98) 0.03f

1.0
0.94 (0.69, 1.29) 0.71
1.17 (0.82, 1.66) 0.37
1.11 (0.75, 1.63) 0.60

1.0
0.86 (0.62, 1.19) 0.35
0.96 (0.64, 1.42) 0.83
0.44 (0.25, 0.78) 0.005 f



0.98 (0.70, 1.39) 0.93

1.0
1.04 (0.72, 1.51) 0.82
0.96 (0.70, 1.31) 0.78
0.69 (0.51, 0.96) 0.02
1.24 (0.88, 1.74) 0.22
1.74 (1.25, 2.40) 0.001 f
1.39 (0.96, 2.00) 0.08

1.0
1.19 (0.86, 1.64) 0.28
1.05 (0.73, 1.50) 0.81


(0.79, 1.10) 0.43


(0.84, 1.66) 0.34
(0.76, 1.54) 0.67
(0.69, 1.49) 0.93


(0.36, 0.68) 0.000 ?
(0.47, 1.22) 0.25


(0.73, 1.32) 0.92


(0.72, 1.21) 0.61
(0.51, 0.97) 0.03f
(0.51, 0.98) 0.03 f


(0.70, 1.28) 0.72
(0.84, 1.68) 0.32
(0.78, 1.69) 0.48


(0.61, 1.64) 0.30
(0.61, 1.36) 0.66
(0.24, 0.77) 0.005 1


0.97 (0.68, 1.37) 0.85

1.0
1.01 (0.69, 1.47) 0.95
0.92 (0.66, 1.27) 0.61
0.67 (0.49, 0.92) 0.01 1
1.38 (0.99, 1.92) 0.06
1.78 (1.30, 2.45) 0.000
1.42 (0.98, 2.06) 0.06


(0.88, 1.65) 0.24
(0.77, 1.56) 0.61











Table 4-4. Continued
Service Type Service Variety
Odds 95% CI p Odds 95% CI p


Enabling, continued
Type of service used
Home / respite
Health
Equipment
Support
Therapy
Variety services used
None or One*
Two
Three to five

Need


Child health conditions
None*
One
Two or more
Child health status
Excellent/very good*
Good
Fair/poor
Poverty level
At or above*
Below poverty level
Severe financial need **
Impact on employment **
Unmet service need
Home/respite
Health
Equipment
Support
Therapy
Unmet variety need
None*
One to five
Reference group.
** Reference group is "no."
t P<.05 level of significance.


(0.91, 1.99)
(1.35, 2.21)
(1.13, 1.80)
(2.07, 5.17)
(1.10, 1.74)


0.13
0.000 -
0.003 f
0.000 -
0.006 f


(0.86, 1.34) 0.51
(1.00, 1.65) 0.04


(0.69, 1.07) 0.17
(1.12, 2.08) 0.007


(0.89, 1.69)
(1.03, 3.72)
(1.16, 1.97)

(0.85, 2.12)
(0.65, 1.72)
(0.57, 3.41)
(0.37, 2.19)
(0.56, 1.57)


0.21
0.04 f
0.003 f

0.20
0.81
0.45
0.82
0.81


1.0
1.79
2.54




1.0
1.08
1.30

1.0
0.90
1.63

1.0
1.23
2.20
1.54


(1.43, 2.23) 0.000 f
(1.82, 3.57) 0.000 f





(0.87, 1.34) 0.48
(1.01, 1.67) 0.04 f


(0.73, 1.18) 0.35
(1.20, 2.21) 0.002


(0.89, 1.69)
(1.18, 4.12)
(1.17, 2.03)


0.20
0.01 ?
0.002 t


1.0
1.50 (1.08. 2.08) 0.01 t


Four of the five types of services were positively associated with care coordination.

Children who used support services (social work, transportation, center for independent

living) were three times as likely to use care coordination (OR, 3.28) compared to

children not using them. Health services were also a positive predictor of care

coordination use with children who had any health service being almost twice as likely









(OR, 1.73) as those not using any health services to have coordination. Equipment and

therapy service similarly predicted use of coordination (OR, 1.43, 1.38 respectively)

while the use of home care services did not.

Children who used more than one of these five services had an increasing

likelihood of using care coordination. For example, children with two different types of

services were almost twice as likely to have coordination (OR, 1.79) while children with

three or more services were 2.54 times as likely. Residence in rural versus urban areas,

access to a telephone, and family income were not associated with having coordination.

Need

Respondent perceived child health status, child co-morbidities, family financial

strain, and an unmet need for one or more services were all positively associated with the

use of care coordination. Children with fair or poor health status were at least one and

one-half (OR, 1.63) times as likely to use coordination compared to children with

excellent or very good health status. Weller and colleagues (2003) also report that

children with fair or poor health status are twice as likely to use medical coordination

compared to children with excellent health status. However, they did not find a

significant relationship between child health status and nonmedical coordination.

Similarly, children with two or more health conditions were 1.30 times as likely,

compared to those with none, to use coordination.

Families who reported having severe financial problems due to their child's health

were more than twice as likely (OR, 2.2) to use coordination. In addition, families who

reported that caring for their child limited their employment opportunities were also more

likely (OR, 1.54) to use care coordination. Yet, family poverty level was not associated

with coordination. Finally, families who reported having an unmet need for one or more









(up to five) varieties of services were 1.50 times as likely to use care coordination

compared to families whose needs were met. An unmet need for a particular type of

service (home, health equipment, therapy, or support) did not significantly predict use of

care coordination.

Reduced Model

The covariates that were statistically significant in the full model were entered into

a reduced model (Table 4-5). Child age was included as a control variable due to the

potential significance related to service eligibility, however, it was not significant in the

full or in the reduced model. There are minor differences in odds ratios when comparing

the types of services model to the variety of services model. Comparison between the full

(Table 4-4) and the reduced models (Table 4-5) indicates that the same covariates are

significant with minor changes in odds ratios andp values. The major difference is that

the use of home care services, although not found to be significant in the full model (OR,

1.35; p=0.13), was a significant predictor of coordination in the reduced model (OR,

1.47; p=0.03). Further analyses found that more than one unidentified covariate was

responsible for controlling differences among home care services in the full model.

Aim 2: Care Coordination Utilization by Type of Provider

Multinomial logistic regression simultaneously estimated the predictors for a

professional, who coordinates care, and the professional and family who coordinate care,

compared to the family alone. Covariates that emerged as statistically significant,

whether analyzed in relationship to the type or to the variety of services identified similar

predisposing, enabling, and need factors (Tables 4-6, 4-7). Relative risk ratios will be

reported as a range for the types of service and the variety of service model for the

significant covariates (RRR, services variety).











Table 4-5. Reduced model for significant covariates of coordination use by service type
and variety
Service Type Service Variety
Odds 95% CI p Odds 95% CI p


Race/ethnicity
White*
Black
Family size
Two or three person*
Five persons
Six or more person
Same residence (U.S. born)
< 5 year*
Foreign-born

Enabling

Private insurance **
Region
Northeast*
West
Type services used
Home **
Health **
Equipment **
Support **
Therapy **
Variety services used
None or One*
Two
Three to five

Need

Severe financial need **
Employment impact **
Child health conditions
None*
Two or more
Health status
Excellent/very good*
Fair/poor
Unmet variety need
None*
One to five


(0.41, 0.75) 0.000


1.0

0.74 (0.55, 0.99) 0.04


(0.24, 0.77) 0.005


1.65 (1.31, 2.07) 0.000


(0.52, 0.97) 0.03


(1.03, 2.11)
(1.40, 2.29)
(1.16, 1.85)
(2.16, 5.52)
(1.11, 1.76)


0.03
0.000
0.002
0.000
0.004


2.01 (1.07, 3.75) 0.02
1.53 (1.19, 1.98) 0.001


(1.01, 1.66) 0.04


(1.07, 1.97) 0.01


(0.39, 0.70) 0.000


(0.55, 0.97) 0.03



(0.23, 0.76) 0.004


1.65 (1.31, 2.07) 0.000


(0.50, 0.92) 0.01









(1.46, 2.26) 0.000
(1.89, 3.69) 0.000


2.11 (1.14, 3.93) 0.01
1.56 (1.21, 2.04) 0.001


(1.02, 1.68) 0.03


(1.15,2.10) 0.004


(1.08. 2.06) 0.01


Reference group.
** Reference group is "no."

More highly educated and residentially stable families had a professional involved

in care coordination while families with older children, children who were black, or who


Predisposing










lived on the west coast did not. Families with a responsible adult having at least some

college education were more likely to have professional (RRR, 1.37-1.43) or joint (2.32-

2.36) coordination compared to those with less than a high school degree. Likewise,

families who resided in the same state more than 10 years (compared to less than five

years) were more than twice as likely to have a professional (RRR, 2.30-2.31) or a

professional and family (RRR, 2.40-2.42) coordinate care.

Table 4-6. Full model for service type: Comparison of coordination by type of provider
for significant covariates
Type of Service Model Professional vs Family Family & Professional vs Family
RRR 95% CI p RRR 95% CI p
Predisposing
Child age
Birth 6 year* 1.0
11-14 year 0.40 (0.22,0.71) 0.002 0.52 (0.29, 0.94) 0.03
15-18 year 0.32 (0.17,0.59) 0.000 0.44 (0.25, 0.79) 0.006
Race/ethnicity
White*
Black 0.56 (0.31, 0.98) 0.04
Same residence (U.S. born)
< 5 year* 1.0
10 + years 2.31 (1.34, 3.99) 0.003 2.42 (1.45, 4.05) 0.001

Enabling
Medicaid** 2.24 (1.40, 3.57) 0.001 2.27 (1.30, 3.94) 0.004
Private insurance ** 1.75 (1.16, 2.62)_ 0.008
Used health services** 1.62 (1.12, 2.34) 0.01 1.56 (1.05, 2.29) 0.02
Region
Northeast* 1.0
West 0.53 (0.29, 0.97) 0.04

Need
Child health status
Excellent or very good* 1.0
Good 1.54 (1.09, 2.19) 0.01
Reference group.
** Reference group is "no"

Children 11 years of age and older (compared to children under the age of 6 years),

were less likely to have a professional (RRR, 0.32-0.40) or professional and family

(RRR, 0.44-0.52) who coordinated their care. Compared to professional only or

professional with family, the likelihood families alone coordinated care for children over










the age of 11 years was two to three times that for families of younger children (analyses

not shown). Children who were black were one-half as likely to have a professional and

family (RR, 0.55-0.56) coordinate care and children on the west coast were similarly as

likely (RRR, 0.52-0.59) to have professional only coordination. In fact, children living in

the west were twice as likely (RRR, 1.8) to have family coordinate care compared to

professional only and children who were black were similarly twice as likely (RRR, 1.8)

to have family coordinate care alone compared to professional and family (not shown).

Table 4-7. Full model for service variety: Comparison of coordination by type of
provider for significant covariates
Variety of Services Model Professional / Family Family & Professional / Family
RRR 95% CI p RRR 95% CI p
Predisposing

Child age
Birth 6 year* 1.0
11-14 year 0.38 (0.22,0.68) 0.001 0.52 (0.29,0.91) 0.02
15-18 year 0.32 (0.17,0.59) 0.000 0.45 (0.26, 0.79) 0.006
Race/ethnicity
White*
Black 0.55 (0.32, 0.97) 0.03
Same residence (U.S. born)
< 5 year* 1.0
10 + years 2.30 (1.32, 4.00) 0.003 2.36 (1.43, 3.90) 0.001
Education, adult
High school grad or less* 1.0
Some college plus 1.37 (1.00, 1.86) 0.04 1.43 (1.01, 2.02) 0.04

Enabling
Medicaid** 2.30 (1.44, 3.66) 0.001 2.40 (1.41, 4.07) 0.001
Private insurance ** 1.80 (1.21, 2.70) 0.004
Region
Northeast* 1.0
West 0.52 (0.28, 0.94) 0.03 0.59 (0.36, 0.96) 0.03

Need
Child health status
Excellent or very good* 1.0
Good 1.56 (1.10,2.21) 0.01
Reference group.
** Reference group is "no"

Private insurance, the use of any health services, and having Medicaid emerged as

positive enabling predictors associated with professional or professional and family









coordination. Private insurance was significant (RRR, 1.75-1.80) in relationship to

professional coordination but not when performed by both a professional and family.

Children with Medicaid, meanwhile, were more than twice as likely (RRR, 2.24-2.40) to

have professional or professional and family involvement with coordinating care.

Children who used health services, a logical consequence of having health insurance or

Medicaid, were one and one-half times as likely of having coordination compared to

children who used no health services.

Only one need factor was associated with who provided the coordination. A child

with a good (versus excellent to very good) health status was one and one-half times

more likely (RRR, 1.54-1.56) to report having a professional provide care coordination.

No significant relationship to health status was noted when a professional and family

both coordinated care.














CHAPTER 5
DISCUSSION

This exploratory study found that a combination of child and family characteristics,

and the use or unmet need for a variety of health and related services, independently

predicted the utilization of care coordination. In addition, predictors of professional care

coordination identified the protective effect of health insurance, residential stability, and

adult education while at the same time revealing regional, racial, and child age-related

disparities. One prior care coordination population-based study substantiates some of

these findings (Weller, Minkovitz, & Anderson, 2003) and another supports that

professional coordination enabled access to mental health services (Witt, Kasper, &

Riley, 2003). Our study complements the existing research and adds that care

coordination is associated with access to different types and a variety of services and that

private health insurance and family characteristics are important predictors of utilization.

Who Uses Care Coordination and Who Doesn't?

Children who have access to private health insurance as well as home, health,

therapy, equipment, and particularly support services, were more likely to receive care

coordination. It is well known that having a source of health insurance enables access to

health care (Newacheck et al., 2000) but that it also enables access to care coordination

has not been previously reported. Furthermore, private insurance, as well as Medicaid,

was associated with care coordination when a professional was involved in coordinating

care. The fact that support services were the best predictor of care coordination utilization









of the five difference service types is not surprising since these services included social

workers who are providers of coordination.

The positive association between having private health insurance and care

coordination appears to contradict studies that report primary care physicians

underestimate or do not meet family need for care coordination (Liptak & Revell, 1989;

Perrin, Lewkowicz & Young, 2000, Scholle & Kelleher, 1995) and that coordination is

more often provided in public programs (Krauss, Gulley, et al., 2000). One difference is

that prior studies used convenience samples rather than population-based data, as our

study did. A second difference is that changes have occurred in the health care system

(managed-care and safety net program expansions; transition from specialty to primary

providers) since the NHIS-D was conducted. Perhaps a family in the mid 1990's had

continuity of a consistent medical provider which has been associated with better care

coordination (Christakis, Wright, Zimmerman, Bassett, & Connell, 2003). A final

difference may be related to how care coordination was defined. The NHIS-D defined

care coordination primarily related to health care services, as "medical" or "nonmedical,"

and not in terms of across delivery systems coordination. Consequently, the majority of

the professionals who provided coordination were physicians and access to physicians

was through health insurance.

Children who used a variety of three to five different types of services or had an

unmet need for more than one of these services were also more likely to use coordination.

Using a variety of different services implies the involvement of more providers and

consequently, the need for communication and coordination. Likewise, the availability of

a person to coordinate care may mean greater access to additional services as has been









described in prior studies (Gehl, 1993; Krauss, et al., 2000; Schwab & Pierce, 1986).

Slightly more than one-half (55%) of the children used two or more different types of

services with 20% using three to five services. Comparison is limited by lack of

population-based studies using similar measures of health and related services use. One

study found that a small proportion of children represented in the NHIS-D-Phase I

received a combination of nonroutine medical care, special education services, and

mental health care (Stein and Silver, 2003). The implication is that children who use a

breadth of services across providers and service systems require someone to manage

communication.

Coordination was more likely among children who had two or more health

conditions and a poor to fair health status. It is reasonable that children who have co-

morbidities and whose parents perceived that they have poor health status would use

health and related services. Weller, Minkovitz, and Anderson (2003) also reported a

significant association between fair or poor health status and medical coordination.

An interesting finding is that children with good health status (compared to

excellent or very good) were more likely to utilize care coordination when it was

provided by a professional in contrast to the family alone or a family and professional. A

possible explanation is that perhaps this reflects children whose health status was initially

poor but improved with intervention. Consequently, families formed a relationship with a

professional and became predisposed to continue professional coordination. It may also

be that families of children who had a good health status felt less of a need to coordinate

care themselves. Jessop and Stein (1991) discovered that families with low coping

resources and low burden benefited most from coordinated care (child function and









psychological adjustment, family impact) compared to families with low coping and high

burden (the neediest). They also point out that it was the social and not the medical

factors that distinguished the population who most benefited.

Families who were more highly educated were more likely to have a professional

who coordinated care, either with or without family involvement, suggesting that college

educated families know how to acquire what they need. In addition, families who

reported residing in the same state of residence more than 10 years were twice as likely to

have a professional involved with coordinating care. This stability of residence implies

that families may become more knowledgeable about community resources and have

long-term relationships with medical and allied health providers.

Family characteristics associated with the financial impact of caring for the child

were important predictors of using care coordination. Perception of severe financial

problems and limited employment opportunities were positively associated with care

coordination when controlling for income and poverty status. Prior studies suggest that

increased caregiving is related to the severity of the child's condition and increased out-

of-pocket expenses for families at all income levels (Breslau, 1983; Brust, Leonard, &

Sielaff, 1992; Leonard, Brust, & Sapeinza, 1992; Krauss, Gulley, et al., 2000; Krauss,

Wells, et al., 2001; Lukemeyer, Meyers, & Smeeding, 2000).

Children who were less likely to use care coordination included families of five or

more persons, children who were black or foreign-born, and those residing in the western

region of the United States. The racial/ethnic and regional disparity persisted in that these

children and families were also less likely to have a professional involved in

coordination. Prior research has noted that as family size increases the use of routine









preventive health care services declines which may be attributable to increased life

demands (Slesinger, Tessler, and Mechanic, 1976). The racial disparity is consistent with

the findings of Weller, Minkovitz, and Anderson (2003) who also note a significant

difference for medical and nonmedical coordination among minorities. Similarly, unmet

health care needs were reported to be more likely among children between the ages of 11

to 17 years, black, living in large families, with the responsible adult having less than a

high school education, and living in the west (Newacheck, et al., 2000). Using data from

the new National Survey of CSHCN, Mayer, Skinner, & Slifkin (2004) report that black

children were more likely to have an unmet health care need for routine (vs. specialty)

care. In this same study, children whose mothers completed high school or more were

less likely to report an unmet health need. The fact that the profile of children with unmet

needs so closely resembles the profile of children less likely to use care coordination is

highly troublesome. Researchers have found that after controlling for socioeconomic

status, disparities remain for health care access and outcomes for minorities (Flores et al.,

1999). The Institute of Medicine (2003) spotlight on racial and ethnic disparities

theorizes that some of these may be iatrogenic, namely, occurring within the health care

delivery system and not just the effect of socioeconomic differences or client personal

and cultural preference.

Finally, families with older children (11 to 18 years of age) were also less likely to

have a professional involved with care coordination. One might consider that families

with older children have more experience with systems of care and choose to coordinate

their own care. However, given that families who had professional care coordination

were more likely to be college educated indicates that families who are coordinating care









themselves for pre-teens, teen, and young adults may be less well educated. Considering

the barriers with access to care for young adults with special needs and the challenges

with transitioning to adult health services (Reiss & Gibson, 2002; Scal, 2002; White,

2002) it is unlikely that as children get older their need for professional assistance to

coordinate care diminishes. If anything, given the lack of adult health providers,

treatment services, knowledge and experience with this population, young adults are at

risk of not being able to access services (Reiss & Gibson, 2002). Care coordination at this

transition period is intensive (Kelly, Kratz, Beilski & Rinehart, 2002) and families report

that older children have an unmet need for specialty care (Mayer, Skinner, & Slifkin,

2004).

Study Limitations

Our cross-sectional, observational study is limited to measures of association and

no inferences can be made about cause and effect. We don't know if children received

coordination because they used a high variety of services or if as a result of having

someone coordinate care they were referred to a variety of services. In the PACTS study,

families in the experimental group acquired services (school, daycare, Medicaid) as a

result of coordinated care but then subsequently "lost" them while the control group

eventually acquired services (Jessop & Stein, 1994). The variability of service acquisition

over time, the ebb and flow, is poorly understood and requires longitudinal research.

Second, all data were generated from parent report which may be subject to recall

bias. Although maternal recall of neonatal events eight to ten years later was found to be

accurate (McCormick and Brooks-Gunn, 1999) the underreporting of hospital service use

was found by this and a prior study (Pless and Pless, 1995). How well families whose

children use a greater volume and variety of services can recall these occurrences within









a prescribed time frame is unknown. McCormick and Brooks-Gunn (1999) found that

poor child health status slightly increased discrepancies in recall years later. Respondents

in the NHIS-D study were asked about services within the past 12 months. Pless and

Pless (1995) report that a one year recall was more accurate among their study population

compared to remembering events over a child's lifetime.

Third, no attempt was made to identify any particular group of CSHCN by using

any of the popular non-categorical methods (Bethel et al., 2002; Stein, Westbrook, &

Bauman, 1997; Stein & Silver, 1999) or by diagnoses. Restricting this sample may have

eliminated families who reported using care coordination which was the primary interest

of our study. The NHIS-D Phase II included multiple definitions of children with special

needs, disabilities, and chronic illness and provides a broader representation. There may

be differences among diagnostic groups in the use of care coordination and there may be

variability between children in our study and those with special health needs. For

example, Stein and Jessop (1989) report significant differences among diagnostic groups

for children using multiple sources of care and nonmedical sources. Although their

overall findings supported that psychosocial factors are not affected by diagnosis

(popularizing the noncateogrical approach to identifying CSHCN) the fact that interaction

with the health care system differed by diagnoses has implications for care coordination

utilization. The problem is that even in such a large population sample as the NHIS-D

comparison by diagnostic groups is limited due to heterogeneity of the diagnoses among

children, hence, very small sub-population sample sizes. However, a similar study using

the NHIS-D that applied selection criteria for CSHCN (Weller, Minkovitz & Anderson,

2003) suggests a great deal of similarity between their sample and the children in our









study. It appears that children who use care coordination are also children who have

special health care needs.

Fourth, no implications can be made about the appropriateness, quality or intensity

of care coordination or health and related service utilization. The decision to collapse

medical and nonmedical coordination categories into one measure may have masked

differences that might be attributable to one type of coordination but not the other. For

example, Weller and colleagues (2003) reported that nonmedical coordination was

significantly related to children with public insurance whereas in our study coordination

was more likely among children with private insurance. The difference in our study was

that it included comprehensive measures of covariates related to service use and unmet

need. Also, no implications can be made about the degree, if any, of collaboration

between professionals and families when the respondent reported that a professional

coordinated care as well as a family member.

Fifth, there were significant differences between the complete cases retained for

study and the incomplete cases dropped from analyses. Characteristics of children among

the incomplete cases represented more children who are black, have Medicaid, with a fair

to poor health status, who used therapy, support, and home care or needed therapy and

equipment services. More families among the incomplete cases, likewise, had a severe

economic need, represented more single parents, adults with a lower educational level,

less access to telephone, and lived in the west. Yet, oversampling by the NHIS-D of

lower-income and minority groups may remedy any representation difficulties associated

with missing data over time in the final analyses, particularly given the survey design and

use of variance and sample estimation weights. Respondents among the incomplete cases









appeared to be a more vulnerable population but the differences averaged only 4%

overall. Also, with a few exceptions (family structure, telephone, need for services),

many of these same variables were found to be significant in the present study. This

suggests that eliminating the incomplete cases did not appreciably mask differences in

care coordination predictors. In fact, some of the significant differences found for

children not using care coordination may be underestimated.

Sixth, even though adjustments were made by collapsing multiple levels of

covariates to improve cell size that was not possible for variables containing only two

levels. Consequently, some of the covariates had less than optimal observations per cell

on the basis of the dependent variable, and may provide less than reliable weighted

estimates (see Appendix B). There is little guidance in the literature related to what is an

optimal cell size for survey weighted data specific to the NHIS-D. Considering the

consistency in findings among the different regressions it is suspected that errors due to

insufficient cell size are minimal.

Finally, the data source for our study is between 7 to 10 years old and until

recently, has been the only national source of information about this small and

heterogeneous group of children. Utilization patterns may have changed with the

proliferation of managed-care in the last decade, expansion of safety net programs such

as SCHIP, and increased emphasis on including care coordination into a variety of health

care financing mechanisms. Consequently, our study will provide a baseline for

monitoring future utilization patterns. Also, the National Survey on Children with Special

Health Care Needs, conducted by the National Center for Health Statistics may help our









understanding of the variability of access to care coordination on a state-level (Van Dyck,

et al., 2002) and provide a source of comparison in the future.

Implications

Our study suggests that children and families who might need care coordination

may not be receiving it. Care coordination was utilized and/or performed by a wider

variety of families than reported in prior studies (Kruger, 2002). Furthermore, children

who have professionals involved in coordinating care are more highly educated compared

to lower income and minority target populations described in the literature. The national

focus on health disparities has highlighted the association between race and ethnicity in

relationship to access, treatment outcomes, discrimination, health care system culture and

structure, and professional-client relationships (Institute of Medicine, 2003). Care

coordination has a positive mediating role associated with increased access to different

types and a variety of services. Consequently, it may be a mechanism through which to

explore and ameliorate either personal and cultural family preferences or the iatrogenic

effects of the health care system. Studying care coordination may reveal and help explain

inequities. An ethic of social justice (Drevdahl, Kneipp, Canales, & Dorcy, 2001) must

frame future study. Society must consider how much of what families coordinate at their

expense, to accommodate deficiencies in systems of care, is ethical. Disparities related to

race/ethnicity and socioeconomic status is unconscionable. How social policy is

constructed to support not only families of CSHCN but all families must be addressed.

The association between care coordination and health insurance underscores that

expanding insurance access to all children is likely to result in greater availability. The

relationship between coordination and health care utilization means that health care

providers have a particular opportunity to identify families who require or want care









coordination. This has important implications for the medical home, the approach for

pediatric primary care to assure compassionate, comprehensive, coordinated, continuous,

culturally-effective, family-centered, and accessible care to CSHCN (American Academy

of Pediatrics & Medical Home Initiatives for Children with Special Needs Project

Advisory Committee, 2002). First, primary care medical homes must assess the child's

use and need for all services (educational, developmental, and community) not just those

within the medical system. Second, the focus in pediatric primary care for this population

must be expanded to families (see American Academy of Pediatrics, 2003) who are the

direct recipients (and providers) of care coordination.

Assessment of family financial stress, impact on employment, and other social

factors should be considered. Title V programs, generally based in a public health

ideology, have an ecologic focus that considers the broad determinants of health (socio-

cultural, political, environmental, economic). Therefore, in addition to the assessment of

child medical needs and treatment, there is a complementary focus on the education of

family, their access to and use of resources, financial concerns, problem-solving skills,

support systems, and coping (Zimmerman et al., 2000). Assessment of family stressors

(Burke, Kauffmann, Harrison, & Wiskin, 1999) using tested instruments that have a

family-centered approach should also be considered. Collaboration between primary care

medical homes and public health oriented Title V programs are supported in national

demonstration programs (National Initiative for Children's Healthcare Quality, 2004) and

have the potential to combine the best of medical and public health care.

The predisposing factors associated with care coordination such as family

race/ethnicity, family size, education, and residence are considered immutable. However,









outreach efforts in the medical home or health plan could specifically target these

families for casefinding and/or application of predictive modeling. This preventive

approach examines patterns of care that include comorbidities, clinical care, and social

factors within ambulatory care ("Predictive Modeling Care Management," 2001;

"Predictive Modeling, Integrated DM," 2002). This requires a comprehensive assessment

by a care coordinator that considers information about client and family psycho-social

issues as well as community dynamics. One important caveat is that the intent of care

coordination should not be to supplant natural family supports and make families

dependent upon professionals (Cooley, 1994).

The emphasis on care coordination as one of two cross-cutting priority areas for

transforming health care quality by the Institute of Medicine (2003) for all populations

who have chronic illness is likely to intensify research efforts. Government policy-

makers support a generic framework emphasizing the commonalties among the functions

of screening, assessment, care planning, implementation, and reassessment across

categorical populations (Falik, et al. 1993). In reality, there are different approaches to

operationalizing care coordination in research (Christakis, et al., 2003; Cooley,

McAllister, Sherrieb, & Clark, 2003) which will impede comparability for this population

of children let alone across populations. A common understanding of what comprises

care coordination is vital in order to compare family and health care system outcomes,

test interventions, and identify appropriate staffing mix and reimbursement.

From a nursing perspective, the process of care coordination with families of

CSHCN is proactive, anticipatory, goal-directed, and individualized to meet child and

family identified needs (Kruger, 2004). It is a holistic approach through which clinical









services are delivered that is not restricted by office encounter, office setting, or financing

mechanism (such as managed-care). The history of care coordination for CSHCN can

traced back to the inception of the Children's Bureau (Perrin, Shayne, & Bloom, 1993)

and Title V of the 1935 Social Security Act (Hoeman & Repetto, 1992; Kruger, 2001).

State programs frequently recognized the contribution of nurses, for example, "...only the

continuous assistance and cooperation of the public health nurses made it possible to

have successfully working Crippled Children and Maternal and Child Health programs

on a statewide basis" (Langer, 1967, p. 270). The contribution of nursing, as well as

social work, to present-day care coordination within Title V programs persists

(Zimmerman, et al., 2000). Nurses also maintain this legacy in hospitals; ambulatory care

centers; schools; early intervention, developmental, mental health, community and

independent practice (Kruger, 2003).

Nursing has a legal mandate to provide health care services, in contrast to medical

diagnosis or treatment (physicians). This framework is holistic, relationship-based, and is

focused on prevention, education, and the maintenance of health. Nurses outnumber other

health care providers, are distributed across all health and even non-traditional health care

settings, have varying levels of generalist and specialist education and practice, and are

well-suited to meet the preventive and illness needs of child and family. Although most

health care settings incorporate a team approach to caring for CSHCN, a team does not

perform the functions of care coordination; rather, it is one professional acting on behalf

of the team in conjunction with the family. Nursing knowledge of disease processes,

medical and genetic conditions, and physiology in conjunction with a holistic, preventive,

and family focus gives nurses an advantage over other health care professionals. Nurses









are also distributed across the health care system at various levels of educational

preparation that correspond to the specialization required on the basis of population

needs. More nurse coordinators with specialized graduate education are found in

hospitals, for example, compared to other healthcare settings (Kruger, 2003). Primary

care medical homes are also incorporating nurses, or changing staff roles, to include care

coordination.

From a theoretical perspective our study has implications for building nursing

theory to guide care coordination interventions. It supports that nursing theory should be

extended to incorporate a family perspective and policy perspective. Prior reviews of

nursing case management literature acknowledge the absence of theory applied to nursing

care coordination (Daiski, 2000; Lamb, 1992, 1995). Nurses have provided examples of

how particular nursing theories such as Peplau (Forchuk, et al., 1989), Watson's Caring

(Wadas, 1993), and Parse's Human Becoming (Daiski, 2000; Milton & Buseman, 2002)

can be applied to case management practice. Most of these examples focus on the care of

the individual. Our study extends the practice of care coordination from the individual

(child) to the family (group) and to the health care system (environment). Family

predictors of care coordination include economic stresses of out-of-pocket expenses and

interference with employment. Although the concept in the nursing metaparadigm of

person (in addition to environment, health, and nursing) is generally accepted to include

family and group, the application of nursing theory beyond the individual level of

practice has not been well articulated.

Similarly, the concept of environment in nursing theory is underdeveloped

(Choporian, 1986; Kleffel, 1991, 1996) yet care coordination is specifically focused on









family interaction with their environment, namely, health and human services systems. In

our study children were found to use a range of services that were associated with care

coordination. The application of nursing theories based on systems models such as Roy,

Johnson, King and and Neuman (Williams, 1991)) are particularly relevant to the

application of care coordination.

Beyond the immediate environment of the family, the policy context is of critical

importance to the distribution of care coordination services and the disparities noted

among children who were black, are older in age, as well as the western region of the

country. Nurses recognize that macro or policy context (the environment) is of specific

interest to nursing however, this is even less well applied in nursing theory. Drevdahl

(1999) asserts that nursing theories which consider social justice need to be developed to

consider societal issues such as race disparities. Milio (1986) also has long supported that

the policy context is a distal determinant of health, has implications for disparities in

health, and should be considered as a locus of change. This think "tall" (policy) rather

than "small" (individual) perspective in nursing theory has been described by Butterfield

(1990) as looking "upstream." Nurses can do this by taking action to change the system

and to empower families through care coordination. Incorporation and development of

existing nursing theory to guide care coordination interventions at family and policy

levels is strongly supported in lieu of creating new theory.

The research challenge is to bridge the gap in knowledge about how families

interact with the health care system, a current deficit in family and pediatric chronic

illness (Gilliss & Knafl, 1999) and health services research (Perrin, 2002). A public

health population focus would aid our understanding of systems that enhance and impede









coordination while we simultaneously focus on providing care at the family or individual

level. Future research would also benefit by building on the rich conceptual literature

about children and families (Knafl & Gilliss, 2002) and advanced practice nursing case

management (Brooten, Youngblut, Deatrick, Naylor, & York, 2003). Our understanding

of how families and providers across medical, allied health, mental health, education, and

developmental services systems coordinate care may shed important light on why

disparities in patterns of utilization exist and how they can be ameliorated. Nurse's

natural role, among these systems of care, as interpreters and translators of medical

jargon into the everyday language of families and non- medical providers affords a

particular opportunity to shift the health care delivery paradigm to universal family-

centered care. Nurses must take this opportunity to not only demonstrate their unique

contribution to over 100 years of care coordination to children and families but to also

mediate the language barriers between families and the health care disciplines and

potentially among the disciplines to reveal new knowledge about the puzzle of disparities

among this population of children and families.














APPENDIX A
CONSTRUCTION OF TYPE OF SERVICE VARIABLES

Ten variables were generated to measure different types of service used (five

variables) and an unmet need for these services (five variables) by children. These

measures represent home care, health care, equipment, therapy, and support services.

Survey questions about services used and unmet service needs were distributed across

seven sections of the NHIS-D Phase II questionnaire as well as the first interview (NHIS

core and Phase I). The sections of services in the NHIS-D Phase II were: home care

helpers and respite care (section A), medical care (section C), use of assistive devises or

equipment (section D), allied health services (section E), special education and/or early

intervention programs (section F), mental health (section K) and use of home or motor

vehicle modifications (section L).

Variables from across sections of the survey were collapsed into summary

measures in one or two stages. If a respondent answered "yes" to any individual question

they were coded as "yes" for that subcategory or category of service. No attempt was

made to quantify services. Measures for an unmet need for services reflect family

perceived need. Respondents were asked if their child 1) had a need for a service they

were not receiving, 2) required more services of a particular type than they were

receiving, 3) were on a waiting list for services, or 4) reported trouble getting services.

Used Home Care

Home care services were reported in section A of the NHIS-D Phase II in relation

to having a personal care attendant and respite care. Section E (allied health) reported









visiting nurse and personal care attendant, and section F (education) asked about home

training visits for infants and toddlers. Families who responded that their child received

one of these services in the past 12 months were counted once and coded "yes" in the

summary variable (SHome). The variable asking about the number of helpers in the past

2 weeks (versus in the past 12 months as in the other variables) was converted to a

categorical variable (yes if any helpers) before collapsing into the summary variable

(Table A-1).

Table A-1. Home care service measure
NHIS Variable Name & Description Used Home Care
Received any home care services, yes or no, in past 12 months SHome
A_405 Number personal care helpers at home in past 2 weeks recorded to yes or no
A_550 Used respite care
E_951 Received visiting nurse
E_1001 Received non-family personal care attendant
F_1456 Received family training home visits, early intervention

Used Health Care

Health care service use was reported in the NHIS Phase I and the NHIS-D Phase II

section C (health care), section E (allied health), section F (education services), and

section K (mental health). One variable related to hospitalization from the Phase I data is

included in the summary variable. The Phase I question asked if the child ever was

hospitalized overnight for an ongoing condition. The remaining variables included in the

summary health use measure (SHealth) are drawn from the Phase II data. Health care

services include any visits for health care, mental health, nursing, nutrition, including

hospital in-patient and community services during the past 12 months.

Collapsing of variables was completed in two stages. The first stage pooled

variables by medical services (7 variables), hospital in-patient (2 variables), and out-

patient mental health (4 variables). The second stage collapsed responses from medical,

hospital, and mental health into one overall health measure (Table A-2).










Table A-2. Health care service measure
NHIS Variable Name & Description Generated Variables
Type Health Care Use Health Care
C_641 Any visits to MD office, clinic, hospital, etc. for heath care SMedical SHealth
E 1101 Received home visits from doctor
F_1386 Received medical diagnosis via special education
F_1388 Received nursing services via special education
F_1467 Received medical diagnosis via early intervention
F_1457 Received nursing or health services via early intervention
F_1460 Received nutrition services via early intervention

CH_356 Ever had overnight hospitalization for ongoing condition SHospin
K_1671 Stayed overnight in hospital for mental health care

F_1375 Received mental health/ counseling via special education SMental
F_1462 Received psychological services via early intervention
K_1708 Received out-patient mental health services
K_1751 Received community support mental health services

Used Equipment

Sections D (equipment), F (special education), and L (home modifications)

contained questions about child use of equipment or car modifications. Variables were

initially collapsed within each section before the overall equipment variable was

generated (Table A-3).

Used Therapy

Therapy services included physical, occupational, speech, respiratory,

communication, interpreter, and recreation. The first stage of creating the therapy

measure involved collapsing individual variables drawn from section E (allied health)

and section F (special education) by type of therapy. The overall therapy variable

collapsed the different sub-categories (Table A-4).