American frail male veterans' networks, and to explore the relationship between lay health consultant network characteristics and health care utilization. Using a ...
BLENDING RESOURCES: INFORMAL NETWORKS AND HEALTH CARE UTILIZATION BY FRAIL MALE VETERANS
by KATHERINE HARRIS ABBOTT
Submitted in partial fulfillment of the requirements For the degree of Doctor of Philosophy
Dissertation Advisor: Dr. Eleanor Palo Stoller
Department of Sociology CASE WESTERN RESERVE UNIVERSITY
August, 2005
This is dedicated to my family.
TABLE OF CONTENTS PAGE CHAPTER 1: Overview and Statement of Purpose
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CHAPTER 2: Theoretical Perspectives
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CHAPTER 3: Literature Review, Conceptual Model, and Hypotheses
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CHAPTER 4: Research Design and Method
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CHAPTER 5: Results
78
CHAPTER 6: Discussion
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APPENDICES Appendix A:
Copy of Questionnaire and Medical Chart Review Form
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Appendix B: Informed Consent
162
Appendix C:
Main Effects Hypotheses
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Appendix D:
Exploratory Factor Solutions, External Correlate Tests,
Appendix E:
and Confirmatory Factor Solutions
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Assumptions of Regression Tests
187
REFERENCES
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LIST OF TABLES Table 1A: Income Thresholds for VA Health Care Benefits Table 4A: Demographic Characteristics of Respondents Table 4B: Physical and Psychological Health Characteristics of Respondents Table 4C: Demographic Characteristics by Race Table 4D: Health Characteristics by Race Table 4E: Health Care Utilization Univariate Descriptive Statistics Table 4F: Description of Measures Used, Coding, and Source Table 5A: Structural Characteristics of Networks: Univariate Distributions Table 5B: Relationship of Network Member to Veteran Table 5C: Network Function: Univariate Distributions Table 5D: Division of Labor Among Network Members Table 5E: Network Satisfaction: Univariate Distributions Table 5F: Correlations Between Formal Service Providers and Network Functions Table 5G: Veterans who mentioned only 1 person within a specific function Table 5H: Correlations Among Demographic Variables Table 5I: Correlations Among Physical and Psychological Health Variables Table 5J: Correlations Among Network Variables Table 5K: Correlations Among Health Care Utilization Variables Table 5L: Bivariate Relationships Among Network Variables and Physical and Psychological Health Table 5M: Race Differences in Network Composition Table 5N: Race Differences in Network Size, Function, and Satisfaction
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Table 5O: Race Differences in Hospitalization and Emergency Department Use Table 5P: Race Differences in Home Care Services Table 5Q: Predictors of Hospitalization ~ Logistic Regression Table 5R: Predictors of Hospitalization ~ Maximum Likelihood Regression and Standardized Indirect, Direct, and Total Effects Table 5S: Predictors of Emergency Department Use ~ Logistic Regression Table 5T: Predictors of Emergency Department Use ~ Maximum Likelihood Regression and Standardized Indirect, Direct, and Total Effects Table 5U: Predictors of Home Care ~ Logistic Regression
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LIST OF FIGURES Figure 3A: Proposed model to study the effects of the network structure, function, and satisfaction on frail veteran’s utilization of health care services Figure 4A: Sample Attrition for the Twelve Month Time point Figure 4B: Twelve-Month Sample Break Down and Response Rate Figure 5A: Total Number of Social Network Members Figure 5B: Conceptual Model Figure 5C: Path Diagram of Statistically Significant Regression Coefficients with Network Size Figure 5D: Path Diagram of Statistically Significant Regression Coefficients with Network Composition Figure 5E: Path Diagram of Statistically Significant Regression Coefficients with the Four Function Variables
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ACKNOWLEDGEMENTS
I am grateful to my committee members for the guidance they have given me throughout graduate school. The chair of my committee, Eleanor Stoller, wanted my voice to be heard and was instrumental in encouraging me to pursue my goals. Kyle Kercher helped me refine my ability to think through problems logically and provided me with the methodological expertise needed to succeed in academe. Julia Rose was gracious in providing me with the opportunity to be employed while collecting data for my dissertation through questions that were added to her longitudinal study. Gary Deimling provided me with valuable encouragement and support in the belief I would graduate. I would also like to thank Renee Lawrence for serving as a mentor and the frail veterans who gave their time and thoughtful answers to the study. I am fortunate to have a large network of supportive family, friends, and coworkers. I am particularly thankful to my parents, Phillip and Joan, who encourage me in all my endeavors. My grandparents, Felicia and Van were the inspiration for my dissertation topic. My classmates, Amy Wisnewski Dan, Jennifer Harris Kraley, Loren Lovegreen, Adam Perzynski, Josh Terchek, Heather Menne, Carolyn Lechner, and Karie Feldman were always available to discuss ideas, to provide advice, encouragement, and make me laugh. My work colleagues, Klara Papp, Tony D’Eramo, Denise Kresevic, Melissa Cappaert, and Tanetta Anderson were invaluable in their assistance in supporting my dissertation research. And Finally, I am grateful to my husband Aaron who has always supported the career path I have chosen and made many sacrifices so that I could complete this graduate school journey. I love you all.
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Blending Resources: Informal Networks and Health Care Utilization by Frail Male Veterans Abstract by Katherine Harris Abbott Social networks play an important role in monitoring symptoms and managing chronic conditions for frail elders. Elderly veteran populations are unique because they have an increased risk of chronic conditions and fewer barriers to health care treatment through the Veteran’s Affairs Medical Centers. This research focuses on the role of lay consultants in veterans’ management of their health using the concept of social networks. The purposes of this research are to; describe the characteristics of frail veterans’ informal networks, examine if there are differences between White and AfricanAmerican frail male veterans’ networks, and to explore the relationship between lay health consultant network characteristics and health care utilization. Using a cross-sectional design, data were collected at the twelve-month time point of an ongoing longitudinal study. Two-hundred frail male community dwelling veterans over the age of 55 with at least 2 activities of daily living impairments were interviewed by phone. Medical chart reviews were conducted to collect data on chronic disease conditions and utilization. Socio-demographic, physical, and psychological health variables were measured as well as network structure (size and composition), network function, (instrumental aid, emotional support, health appraisal, and health monitoring), and network satisfaction.
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Outcome measures include days hospitalized, emergency department visits, and the receipt of home health care. Based upon logistic and maximum likelihood regression analysis, veterans with more chronic conditions were more likely to be hospitalized, but those having a larger social network were less likely to be hospitalized. Veterans having family-only social networks were more likely to be hospitalized than those who have a mixed network (family, friends, and neighbors) controlling for veteran demographics, depression, and functional health. Being African American and having a larger instrumental support network were predictive of emergency department use. Being African American, having greater functional limitations, and being in the intervention group were predictive of home care use. No moderating relationships were found. Ways in which network members impact utilization are discussed. The opportunity to identify ‘at risk’ veterans with multiple chronic conditions and few network resources can enable formal care providers to assist with monitoring or appraisal support that may prevent hospitalization.
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Chapter 1: Overview and Statement of Purpose How people respond to illness is an important aspect of understanding health care utilization. The following section will describe developments in health that have changed the way people experience illness. In addition, elderly veteran populations are at increased risk of disease while at the same time having fewer barriers to health care treatment through the Veteran’s Affairs Medical Centers (VAMC) than the general population. This section will provide background information about the development of chronic diseases, unique health concerns of older veterans, and theoretical perspectives that can guide research on social networks. In addition, the specific aims and contributions of this study will be discussed as well as a brief discussion of the sample and major findings. Background In 1900 the five leading causes of mortality were from acute infections with death occurring soon after the onset of the illness. Due to medical and public health advances, acute infections have been replaced by chronic degenerative diseases as the major causes of disability and death (Olshansky & Ault, 1986). The transition from acute to chronic debilitating diseases has increased life expectancy, which also increases the chances for people to experience chronic illness. The theory of epidemiologic transition posits that developed countries have entered the age of delayed degenerative diseases. Various interpretations of this theory have led to two possible outcomes with regard to mortality and morbidity. Fries (1980) hypothesizes that a simultaneous compression of morbidity and mortality will occur. People will live longer healthier lives free from chronic degenerative diseases, become ill, and die relatively quickly after the onset of the disease.
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Other researchers disagree and believe that the age of onset for chronic degenerative diseases will extend into later ages, but the length of time spent with chronic degenerative diseases will be a considerable length of time. With the population of people age 65 and over more than doubling, growing to 20% of our population by 2030, most people agree that the need for chronic disease management will increase considerably (Difficult Dialogues, 2002). Studies show that the prevalence of chronic diseases is rising, due to increasing risk factors such as obesity, lack of physical activity, and smoking (National Academy on an Aging Society, 1999). The increased prevalence of chronic diseases has created the need for patients and caregivers to monitor and manage symptoms (Kleinman, 1988). As people age and visual and auditory senses decline the need for assistance with medications and symptom appraisal develops. Additionally, certain populations may develop more conditions than others, particularly those of lower socioeconomic and minority status. The population of interest in this study focuses on frail male veterans. In the 2000 census, 29 % of all veterans were disabled. The percentage of veterans reporting disability increases with age with one in two World War II veterans (45%) reporting disability (http://www.va.gov/vetdata/Census2000/c2kbr-22.pdf). Veterans are more likely to have chronic debilitating illnesses due to their military experiences and lifestyle. Veterans have higher incidences of smoking, especially those veterans who use VA medical centers (Klevens, Giovino, Peddicord, Nelson, Mowery, & Grummer-Strawn, 1995; McKinney, McIntire, Carmody, & Joseph, 1997). Twenty-one percent of inpatient veterans have substance use disorders (including alcohol, tobacco, and drug abuse).
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Within the VA outpatient population, almost 324,000 veterans treated had substance use disorders in 2000 (Finney, Willenbring, & Moos, 2000). Veterans are likely to have suffered an injury while on active duty that has developed into a chronic disease. “About 2.3 million veterans receive monthly VA disability compensation for medical problems related to their service in uniform (DVA State Summary, 2002, pg. 4).” Other veteran characteristics that contribute to disease risk include less education, lower socio-economic status, exposure to the cumulative effects of racism, and a higher prevalence of social isolation (Current Population Survey, March 1999, www.va.gov/vetdata/demographics/index.htm). Of the veterans that use the Veterans Health System (VHS) the majority are white males and 44% are 65 or older. Enrollees report an average of 3 chronic diseases and most (73%) have access to other types of health insurance, mostly (53%) Medicare (Shen, Hendricks, Zhang, Kazis, 2003). Veterans’ eligibility for health care benefits is based on active military service in the Army, Navy, Air Force, Marines, or Coast Guard (or Merchant Marines during WWII), and discharged under other than dishonorable conditions (http://www.va.gov/healtheligibility/home/hecmain.asp). Veterans must also fall into one of eight priority groups in order to be eligible to use the VHS due to budget constraints. The groups are based upon service-connected disability and income. A service-connected disability is one that was incurred while on active duty in the military and in the line of duty (http://www.va.gov/healtheligibility/eligibility/epg_all.asp). The financial assessment, or means test, required annually, may obligate veterans to pay some co-payments. For those that meet the income thresholds (see Table 1A), there are no fees associated with care. Veterans who refuse to complete the financial
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assessment must agree to pay the required co-payments in order to be eligible for VA health care services1 (http://www.va.gov/healtheligibility/costs/costs.asp). Table 1A. Income Thresholds for VA Health Care Benefits For a veteran with:
Free VA prescriptions and travel benefits: [All veterans]
Free VA health care: [0% service-connected (noncompensable) and Nonservice-connected veterans only]
0 dependents
$10,162 or less
$25,842 or less
1 dependent
$13,309 or less
$31,013 or less
2 dependents
$15,043 or less
$32,747 or less
3 dependents
$16,777 or less
$34,481 or less
4 dependents
$18,511 or less
$36,215 or less
For each dependent over 2 add:
$1,734
$1,734
Veterans Healthcare System (VHS) The VHS is one of the largest health care delivery systems in the world providing a range of medical, surgical, and rehabilitative care to over 4.8 million people (http://www1.va.gov/opa/fact/vafacts.html). The VHS operates 158 hospitals, 132 nursing homes, and 854 ambulatory care and community-based outpatient clinics (CBOC). In 2003, 49.8 million outpatient clinic visits were registered and 742,000 people were treated as inpatients, which is a 5.7 % increase from the previous year. The VHS also provides care to more than 100,000 homeless veterans every year. The VHS trains approximately 81,000 health professionals as part of the largest medical education
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Note that new veterans who apply for enrollment after January 16, 2003 and who decline to provide income information are not eligible for enrollment. 11
program in the United States. It is estimated that more than one-half of all practicing physicians received some professional education in the VHS. Almost 7.2 million veterans were enrolled to receive their health care through the VA in 2003. Their mission statement is listed below. “The mission of the Veterans Healthcare System is to serve the needs of America's veterans by providing primary care, specialized care, and related medical and social support services. To accomplish this mission, VHA needs to be a comprehensive, integrated healthcare system that provides excellence in health care value, excellence in service as defined by its customers, and excellence in education and research, and needs to be an organization characterized by exceptional accountability and by being an employer of choice.” http://www1.va.gov/Health_Benefits/page.cfm?pg=1 VHS in Ohio In 2003, more than 1 million veterans were served at a cost of nearly two-billion dollars. The VA Healthcare System operates Veterans Affairs Medical Centers in Cincinnati, Cleveland, Chillicothe, and Dayton (the recruitment site for this study). The VA has also established 23 community-based outpatient clinics (CBOCs) in order to improve access to services. More than 83,826 veterans are age 65 or older in Ohio (http://www1.va.gov/opa/fact/statesum/ohss.html). Very little is known about how frail male veterans in Ohio manage their chronic illnesses in the community. Therefore, this research turns to sociological theories for assistance in developing the conceptual model. Theoretical Background This dissertation is guided by four theoretical perspectives including; the social network perspective, lay consultation, the stress paradigm, and the health behavior framework. How people respond to symptoms they experience from chronic illnesses is an important area of study because of its ability to explain how people seek medical help. People do not experience illness in a vacuum, but within the contextual framework of
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their social networks; decisions about managing chronic diseases are often made in conjunction with others. Lay symptom management research uses three separate, but related concepts: self-care, lay consultation, and formal help seeking (George, 2001). Lay consultation is the advice people receive from family and friends about how they should proceed given the symptom(s) they are experiencing. Lay consultants serve many functions, some of which include; reassurance, appraisal, monitoring, adherence, education, emotional support, permission to seek care, referrals to formal and complementary or alternative providers, and a context for people to develop their illness narrative. Clark & Nothwehr (1997) found that lay consultants were particularly helpful in reminding people with asthma to take their medication. This type of monitoring support may help prevent exacerbation of chronic illnesses, or provide permission to seek care (Cameron, Leventhal, Leventhal, & Schaeffer, 1993). Management of chronic illness typically requires support and encouragement from lay consultants in order to continue health behaviors that maintain health (DeFriese & Woomert, 1992; Meillier, Lund & Kok, 1997). Some people develop ties with others experiencing similar chronic conditions, which can be helpful by sharing experiences of how they have learned to live with their illness (Anderson-Loftin & Moneyham, 2000; Charmaz, 2000). One reason complementary and alternative health care providers have become more available is because of a growing frustration with traditional medicine in treating people with chronic illnesses. Traditional medicine is unable to cure many chronic degenerative diseases, and often unable to provide relief from the pain and discomfort often related to such conditions as arthritis. Lay consultants can be extremely helpful in
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providing referrals to both formal and complementary or alternative health care providers (Kelner & Wellman, 1997). Complementary or alternative providers may offer temporary relief through a range of techniques from massage therapy to acupuncture. Older people may be reluctant to try complementary or alternative therapies unless referred by members of their network. Often people rely upon lay consultants for health appraisal and reassurance, either for a specific symptom or situation (Edwardson & Dean, 1999; Rakowski, Julius, Hickey, Verbrugge, & Halter, 1988; Strain, 1990). The emotional support received through discussing challenges in managing chronic illness can be especially important in legitimizing one’s illness (Schlesinger, 1993). Another avenue that legitimizes illness is the development of illness narratives. Illness narratives, or more broadly, ethnographic narrative accounts, are the telling of facts related to a personal illness experience. Illness narratives are thought to provide a therapeutic role in allowing the ‘patient’ to craft a story about the events, people involved, and the outcome of their experience that can be told and retold (Bury, 1997; Berg, 1998). As Kleinman (1988) states “the illness narrative is a story the patient tells, and significant others retell, to give coherence to the distinctive events and long-term course of suffering. …The personal narrative does not merely reflect illness experience, but rather it contributes to the experience of symptoms and suffering (pg. 49)”
Most illness episodes are managed outside the context of formal medical care, and many chronic conditions incorporate significant lay management, including both self-care and lay consultation (George, 2001). This research will focus on the role of lay consultants in veterans’ management of their health. One way of studying lay
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consultation is by using the concept of social networks. Pescosolido (2001) explains why this perspective should be used. “Too often, we have neglected to consider that what makes people’s experience in the community and in treatment systems “success” or “failure” are intimately tied to the kinds of relationships forged and maintained in those contexts…A social network perspective offers a way to break down these large, critical components into a set of ongoing ties that chart people’s experiences. The basic premise of social network theory,…is that individuals shape their everyday lives through consultation, suggestion, support, and nagging from others (pg. 468).”
In addition to the social network and lay consultation perspective, the stress paradigm guided this research by conceptualizing the social network as a buffer to poor physical and mental health. Finally, the health behavior framework provides guidance in adding appropriate background or control variables in order to properly specify the model. A more complete discussion of theoretical perspectives and their application to this dissertation can be found in chapter 2. Statement of the Problem To address gaps in the literature this study examines the effects of social networks and in particular the use of lay consultation by frail male veterans in assessing their health. The purposes of this study are to; describe the lay referral networks of frail male veterans, to examine if there are network differences between Whites and AfricanAmericans, and to explore the effects of networks on frail veterans’ health care utilization. Specific Aims Aim 1. To describe the characteristics of frail male veteran’s informal networks. This aim will provide a descriptive account of the network structure (size and composition), network function and division of labor (specific emotional, instrumental, appraisal, and
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monitoring assistance provided to frail veterans by specific members of the lay network), and satisfaction with the assistance provided by network members. Aim 2. To explore the relationship between demographic variables, network characteristics, physical and psychological health status, and health care utilization in frail male veterans. Aim 3. To examine the differences between White and African American frail male veterans’ networks in terms of size, composition, function, division of labor, and satisfaction with informal networks. Aim 4: To examine the relationship between lay health consultant network characteristics and health care utilization among frail male veterans, controlling for physical health status, functional limitations, and demographic variables. Sample and Methods The sample for this dissertation is a group of 200 frail male veterans age 55 and older who are nursing home eligible, but living in the community. The cross-sectional data were gathered during the 12-month follow-up of the longitudinal Care Coordination Study, which is discussed in depth in Chapter 4. Telephone interviews were conducted with additional name generating questions to elicit the names of network members. The response rate was 77 % and the sample of men included 24% African Americans with a mean age of 74 years. A complete description of the sample and results can be found in Chapters 4 and 5. Study Contributions This study contributes to the understanding of the broader social context through which health care decisions are made within one’s social network, expanding our
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understanding of lay consultation. It also assesses the degree to which network characteristics moderate the relationship between need and health care utilization. This study contributes to the literature about frail men’s social networks. Little is known about the composition or the division of labor within frail male networks or the differences between White and African American networks. Many community-based probability samples end up with too few African American men to support separate analyses of social network variables. Additionally, this research will attempt to clarify how lay consultation affects in home formal services and health care utilization.
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Chapter Two: Theoretical Perspectives To understand the relationship between personal characteristics of elderly individuals and health care utilization, a conceptual model was developed (see Figure 3A, page 39). This model synthesizes four major theoretical perspectives including, the social network perspective, the theory of lay referral, the health behavior model, and the stress paradigm. The first section of this chapter will discuss the social network perspective, focusing on lay consultation networks as one type of personal network. Second, the stress paradigm will be used to explain the relationship between health, networks, and utilization. Third, the health behavior model, which guided specification of hypothesized links among variables, will be discussed. After describing the contributions of the individual perspectives, a more detailed justification of the full model will be provided. Conceptualization of Social Networks Research on social networks has varied considerably in terms of conceptualization, measurement, and analysis. This variation reflects the overlap of several related concepts. The concepts of social networks, social support, and network analysis are often used inappropriately as synonyms for each other. In this research, social networks are conceptualized as links connecting individuals or groups that could be mobilized to provide a specific type of functional behavior, such as emotional support. Those in the social support tradition, usually frame questions without tying the support to specific people, but instead measuring support from generic categories of people. Both approaches are lodged in a Durkheimian perspective of social integration, but represent different strategies (Pescosolido and Levy, 2002).
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This dissertation will examine personal networks. Many of the analytic techniques developed by network analysts look at community or organization-based networks. When discussing individual social networks, theorists use the term ego-centric network to refer to the set of relationships surrounding a focal person. Network theory can also be conceptualized more broadly as relationships among members of any population, such as companies, other organizations or nation states. The individuals that make up a personal network are sometimes represented by nodes, and their relationships with the central person are called ties (Brissette, Choen, and Seeman, 2000). According to Berkman, Glass, Brissette, and Seeman (2000), networks have been hypothesized to affect health through four avenues: provision of social support, access to resources and material goods, social influence, and social engagement and attachment. Unfortunately, there is no standard way to gather the information to map the network. The use of network generating questions to map a person’s network is currently the preferred method. According to Pescosolido (2001), there are three broad approaches to mapping network relationships: network structure, network functions, and network content. Structure generally includes size (number of people in network), density (the extent to which members in a network know one another), frequency of contact, strength of tie, and composition (e.g., kin vs. non-kin) of a network. Function refers to the specific support functions network members provide (instrumental aid, emotional support, appraisal, and monitoring). Examples of instrumental aid would include transportation, lending money, and assistance with activities of daily living (ADL) and
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instrumental activities of daily living (IADL). The provision of emotional support would include someone to talk to when upset or concerned. Health appraisal is assistance with evaluating a problem or symptom whereas health monitoring includes specific tasks such as watching someone’s diet or making sure they exercise or take their medications in the correct amounts and at the correct times. Because one of the functions of social networks includes provision of social support, networks are often considered synonymous to social support. Most of the research on social networks stems from the desire to measure social support within a context. However, social networks can also be a source of stress when the demands on informal networks exceed available resources, a situation that is likely to occur in lowincome populations (Kessler & Wethington, 1986). Network content is the third overall characteristic of social networks that, according to Pescosolido, (2001) is important in determining the “quality and substance of social networks (pg 470).” Some examples of content include attitudes, beliefs, and cultural meanings held by network members. In order to determine the content of the network, it is necessary to interview all network members mentioned by the focal person, an aspect that was not feasible for this study. Therefore, this dissertation research will address two of the three approaches Pescosolido suggest: network structure and function. Another well known conceptualization of social networks is the life span developmental model of the convoy of social support by Kahn and Antonucci (1980). In this conceptualization an individual is surrounded by network members from birth. These contacts continue through life with relationships developing as the individual
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matures. Throughout the individual’s life, network members are lost and added in response to life events (e.g. marriage, moving away, death). While this conceptualization is effective in capturing the dynamic nature of networks over the life course, it is difficult to operationalize. Most often, respondents are asked to place network members in concentric circles, with the inner circle representing people closest to the respondent. This approach was designed to be used in face to face interviews, and researchers have modified this measurement strategy to be used in telephone interviews. Two recent studies use this modified strategy to conceptualize the ‘inner circle of support’. The inner circle is important because it represents strong relationships that have the greatest impact on health. Peek and Lin (1999) operationalized the inner circle placement in terms of people who were mentioned for all questions regarding who the respondent turns to for help or support. Stoller and Wisnewski (2003) adopted Peek and Lin’s strategy and operationalized the inner circle as consisting of people who were mentioned for all questions relating to self-care behaviors. Lay Consultation within the Social Network Perspective Freidson (1970) utilizes a symbolic interactionist perspective to explain how individuals use lay consultants (people who are not medical professionals) to make sense of symptoms, determine their seriousness, make self care decisions, seek medical care, and comply with medical regimes. As Furstenberg and Davis (1984) explain,
“interaction with others might be assumed to shape all aspects of how people take care of their health, not only decisions about seeking medical care (pg. 827).”
Typically lay consultants are family members but non-family members who have more expertise can also be involved. Typically, chronic conditions have a slow, subtle onset
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(and can also be asymptomatic e.g. hypertension) making it difficult to determine when medical care is needed. Therefore, the appraisal and monitoring support from lay consultants is an important partnership in recognizing the need for medical attention. Once conditions are diagnosed, lay consultant networks can be instrumental in managing the illness and in encouraging (or discouraging) medication compliance and lifestyle changes such as diet and exercise. While chronic illness management increases the need for lay consultation, the ability of the care recipient to maintain the size and range of their network is compromised. Retirement, disability, illness, and death of network members tend to shrink the size and range of the network, without creating opportunities to recruit new members. Network theorists argue that the structure of networks (over and above the characteristics of individuals comprising the networks or even the dyadic ties) influences health outcomes (Pescosolido & Levy, 2001). Illness, Disability and the Stress Paradigm The population being studied in this dissertation research is confronting the difficult task of managing multiple chronic illnesses leading to severe functional limitations. In addition to chronic health conditions, these older veterans must also cope with the cumulative effects of low socioeconomic and minority status. “Many stressful experiences, it should be recognized, don’t spring out of a vacuum but typically can be traced back to surrounding social structures and people’s location within them. The most encompassing of these structures are the various systems of stratification that cut across societies, such as those based on social and economic class, race and ethnicity, gender and age. To the extent that these systems embody the unequal distribution of resources, opportunities, and selfregard, a low status within them may itself be a source of stressful life conditions (Pearlin, 1989, pg. 242).” These types of chronic strains are major stressors to frail elders. Chronic strains are damaging to physical and mental health because they are everyday reminders of
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disability and impediments to living (Thoits, 1995). An important paradigm to help frame how frail elders cope with severe illness, disability, poverty, and racism is the stress paradigm. Using this approach aids the social network perspective in framing the network of people who can be relied upon for support. The stress paradigm conceptualizes the ways in which coping strategies and social support buffer or ameliorate the deleterious effects of stress. The focus of this research will be on social support (the functional behavior) and in particular the broader concept of social networks (the structure through which social support is provided) (Antonucci, 1985). Pearlin advocates for combining research investigating both social support and social networks because, “The identification of the connections between individuals’ social life and their inner well-being could be enhanced, I submit, by joining the study of social support more closely to the study of social networks (1989, pg 251).”
In most studies, the broader framework of social networks is not taken into account in stress research; only one function, social support, is considered. The stress paradigm can be elaborated upon through using the social network perspective, particularly the unique impact of network structure and function. Health Behavior Framework Andersen’s Health Behavior Model was developed to understand why individuals2 use health services (could be used to predict or explain health service use) (Andersen, 1968; Andersen, 1995). This perspective reflects rational choice theory where individuals are assumed to engage in a cost-benefit analysis. The components that 2
Originally, families were the unit of analysis but this created measurement difficulties.
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make up the initial model include; predisposing characteristics, enabling resources, and need. Predisposing characteristics are made up of health beliefs (knowledge, attitudes, and values that influence perceptions of need and use), social structure (education, occupation, and ethnicity), and demographic characteristics (age, gender). Enabling resources include personal/family and community characteristics. Health facilities and trained personnel must be available in the community and family members must know how to access those resources. Finally, the need component considers how people perceive their health including both overall general health and interpretations of symptoms. The perception that a symptom is or might be serious must be present in order for a person to seek professional help. Research using the health behavior model has found that there is a lack of explanatory power from predisposing and enabling characteristics with most of the explained variance coming from the need characteristic (Haug, Wykle, & Namazi, 1989; Stoller & Forster, 1994). While the intent of this framework was to explain utilization, Haug, Wykle, and Namazi (1989) argue that this model could also be used to explain decisions not to seek formal care and reliance on lay consultation. One way to potentially increase the explanatory power of the health behavior model is through the addition of social networks to the category of enabling characteristics. Overall, this research is utilizing the basic framework of the health behavior model to be sure that key variables predicting health care utilization are included in the model. Theoretical Synthesis This research draws on Hagestad and Dannefer’s (2001) argument that the study of aging has had a strong focus on individuals but often neglects the impact of wider
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social contexts. This trend toward microfication, the authors argue, has prevented theoretical advances in social gerontology. The authors attribute this focus to several causes including the emphasis on individual agency (without investigating agency within structure) and the medicalization of old age (especially it’s problematic (i.e., costly) aspects). Due to the fact that most health care dollars are spent on older people, looking to the broader social context through which decisions about health care are made, as this dissertation research will do, is a timely subject.
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Chapter Three: Literature Review, Conceptual Model, and Hypotheses Social Networks and Demographic Characteristics Prior empirical evidence suggests that there are several factors that are important in the creation of social networks, and therefore should be included as variables in this study. Key factors include family characteristics, living arrangements, geographic proximity of adult children, income, education, and race (Mitchell & Trickett, 1980; Nelson, 1995). Family characteristics are central to the creation of social networks. For older people, who have most likely lost their parents, marital status and children are key factors that shape the network. Spouses, as the first source of help among married elders, are also more likely to have children and adult children make up a large percentage of older adult’s networks. Specifically, adult children living within a one hours drive are more likely to be part of an older persons’ social network than more geographically distant children (Wenger, 1995). Because someone can be married and living alone, living arrangements are a better indicator of social support (either living alone or with others) (Nelson, 1995). Income and education are linked because as education goes up, it is likely that income will also increase. Additionally, these variables can be conceptualized as resources. Older people may be able to remain integrated in their network if they have the money to pay for services such as transportation to activities (Unger, McAvay, Bruce, Berkman, & Seeman, 1999). The ability to reciprocate monetarily for services received is likely to reduce the demands on informal networks by purchasing market equivalents for some services.
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Generally, as age increases total network size decreases. Due in part to the death of age matched network ties, and an increase in functional limitations, which prevents continued participation in activities. Certain ties, may not stretch beyond shared activities. In addition, Carstensen’s selectivity theory suggests that as people age, they choose to maintain ties that provide emotional support over other types of support (Fung, Carstensen, & Lang, 2001). Race/Ethnic Differences in Social Networks The existing literature is mixed with regards to race differences in social networks. Some studies have found differences between White and African American social networks, while others have not. Most often, differences in the measurement of social networks and social support have been cited as reasons why findings have produced such inconsistencies. Additionally, Rosenthal (1986) encourages researchers to avoid hypothesizing that ethnic groups such as African Americans have more of a traditional family type than White Americans, reinforcing to the idea that African American families are more supportive of frail elders. Mendes de Leon, Gold, Glass, Kaplan, & George (2001) found that filial responsibilities are stronger among African Americans than White Americans leading to greater informal support. Johnson and Barer (1995) found that older African Americans have larger networks and received significantly more support from kin than older Whites. The authors also found that “Older blacks also give and receive more aid from siblings and other collateral relatives than white elderly (Johnson & Barer, 1995, pg. S369)”. On the other hand, Johnson (1999) argues that the notion that African Americans have greater family solidarity than whites has not been supported by the literature. She
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indicates that African American families interact less than white families because African American families “have fewer resources and hence have less to exchange with each other (Johnson, pg. S368, 1999).” Burton, Kasper, Shore, Cagney, LaVeist, Cubbin, & German (1995), found no differences between African American and Whites in the size of the network, instead finding that the level of functional disability was the strongest predictor of network size. Ajrouch, Antonucci and Janevic (2001) explored how network structure and composition differs between Whites and African Americans and found that African Americans had smaller networks than White Americans at all ages. The authors suggest that this could be due to cumulative disadvantages and reduced life chances experienced by African Americans leading to less diffuse networks. Having smaller networks is of concern because as the author states “network characteristics affect the probability of support, as well as the quality and type of support (Ajrouch et al., 2001, pg S113)”. Age may be a stronger determinant of network structure and composition due to the association between advanced age, disease, and disability. The authors found that the network structure of African American elderly and White elderly are more similar than dissimilar (Ajrouch, et al., 2001). Peek and O’Neill (2001) found some evidence of race differences when controlling for socioeconomic status among elderly people living in rural North Carolina. While there were no race differences in network size, the authors found a significantly higher percentage of kin in African American’s network composition than in White American’s networks. Additionally, the authors found that African Americans who were widowed and living alone had smaller networks and received less support. In other
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words, social networks for African American’s are shaped by household composition more than White American’s social networks. It is clear that research on ethnic differences in social networks has many inconsistencies. One goal of this study is to examine race/ethnic differences in network characteristics (size, composition, type of functional and satisfaction) between African American and White frail male veterans. Social Networks and Health Status Social networks are important, especially for older adults, because networks “not only provide opportunities for contact but also furnish the context through which both instrumental and emotional support are received (Peek & O'Neill, pg. 208, 2001)”. Social network ties have been shown to have a protective effect on health even when examining different types of outcomes, including less disease, lower mortality, reduced risk of institutionalization, better well-being, less disability and quicker recovery from illness episodes. In a cross-sectional study, Seeman and Syme (1987) investigated network structure and function of 119 people aged 30-70 years. They found that network size was not related to less disease, but network function was. Those who received greater support (instrumental and emotional) tended to have less atherosclerosis. The authors suggest that network size is often used as a proxy for measuring support (the larger the network the greater the support). Seeman and Syme (1987) state that the number of social ties seems to improve a person’s resistance to disease and encourage researchers to measure both network function as well as network structure.
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A longitudinal study of community dwelling older adults’ found that social network ties were associated with less disability ( Mendes de Leon, Glass, Beckett, Seeman, Evans, & Berkman, 1999). Respondents with more extensive social ties had less risk of developing disability. In the case where disability occurred, those with more social ties had an increased risk of recovering from disability, especially if they received emotional and instrumental support. Results from a longitudinal study of older adults aged 70-79 years (MacArthur Studies of Successful Aging) found that social ties had a stronger protective effect for men than women and also for those who experienced more physical impairments (Unger et al., 1999). Additionally, a study of fifty-four Hispanic elderly people found that a smaller social network was predictive of formal service utilization (Radina & Barber, 2004) Data from the Normative Aging Study investigated the source and type of support on physical and mental health and found that interaction with both friends and family promoted better mental health, as opposed to interaction with family only. In addition, perceived support from friends and family also leads to better mental health outcomes (less depressive symptoms) (DuPertuis, Aldwin, & Bosse, 2001). Support of social network ties protecting against dementia was found in a longitudinal study by Fratiglioni, Wang, Ericsson, Maytan, & Winblad (2000) who studied 1,203 community dwelling elderly in Sweden. The authors found a 60% increased risk of dementia in those individuals who had poor or limited social networks. Social Networks and Health Care Utilization The role of social networks in influencing help seeking behaviors is hypothesized to occur through a series of mechanisms. First, by conveying beliefs, attitudes, and
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norms about health seeking behavior, second, by influencing decisions about when and where to seek professional help (health monitoring and appraisal), and third, through providing instrumental support in obtaining formal health care services (i.e., transportation to the doctor’s office) (Mitchell and Trickett, 1980). While there have been several studies looking at the impact of social networks on a variety of behaviors, such as breast feeding, and in a variety of populations, such as HIV positive individuals, no studies were found that have looked into the impact of social networks on VAMC health care utilization in frail male veterans. In addition, this study proposes that the addition of network variables to enabling characteristics may increase the predictive ability of the Health Behavior Model. Measuring Health Care Utilization Generally, there are four aspects to measuring and reporting health care utilization: type, purpose, site, and time interval of use (Aday, 1993, Andersen & Aday, 1978). Whether the service is physician-based, community-based, or institution-based is referred to as the type of utilization. Purpose refers to the goals of treatment as either primary prevention, secondary prevention, or tertiary prevention. Site refers to whether the services are delivered as an inpatient, outpatient, or home-based. Finally, the time interval can refer to temporal aspects of health care utilization (e.g., six month period) or volume of utilization (e.g., number of emergency department visits) (Kronenfeld, 1997). This study will utilize two types of utilization (institutional and communitybased) and three forms of tertiary prevention: hospitalization, emergency department use, and home care use. The volume of days hospitalized, emergency department visits, along with home care use during a six month time frame will be used. To address concerns
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regarding inaccuracies in self-reported hospitalization and emergency department use, chart reviews will be performed using the Veteran’s Affairs computerized patient medical records. Health Status and Health Care Utilization Need factors such as perceived health and symptom interpretation are typically strong predictors of health care utilization. With increasing age, there is a greater likelihood of developing multiple chronic health conditions, functional limitations, and for some, depressive symptoms. Depending upon the severity and nature of the conditions, emergency department may be used and hospitalization may be needed. After an illness episode, or because of increasing functional limitations home health care may also be needed. All of these factors lead to frail older people to having higher rates of health care utilization than younger people. Having more chronic conditions is a well established determinant of health care utilization (Liu, Wall, & Wissoker, 1997). Functional limitations with ADL’s and IADL’s has been tied to greater health care utilization, through hospitalizations, home care use, and nursing home placements (Seeman, Bruce, & McAvay, 1996; Paddock & Hirdes, 2003). Having a lower body mass index and better physical functioning reduced the risk of hospitalization in older inner-city residents. But, chronic conditions, more prescribed medications and emergency department visits were predictors of hospitalization (Damush, Smith, Perkins, Dexter, & Smith, 2004). The impact of depression on health care utilization is striking with 580,000 emergency department visits associated with a primary diagnosis of depression (Harman, Scholle, & Edlund, 2004). The prevalence of depression in older people is high with 20-
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25% of geriatric patients experiencing depressive symptoms (Ferraro, 2000). Older men in particular have increased rates of depression approaching levels of depression found in women. One of the reasons thought to cause the increased rates of depression in older adults is the increase in physical illness and chronic disability (Turner & Noh, 1988). Symptoms of depression are similar to symptoms found in common acute illnesses such as fatigue, difficulty sleeping, and irritability. Because depressive symptoms are so similar to common illnesses it often goes undiagnosed. This becomes a costly problem because of the link between depression and health care utilization. Several studies have found increased use of both inpatient and outpatient services as well as increased risk of death, hospital readmission, and nursing home placement associated with depressive symptoms (Bula, Wietlisbach, Burnand, & Yersin, 2001; Koenig & Kuchibhatla, 1999; Lennox, Scott-Lennox, & Bohlig, 1993). Fischer, Wei, Rolnick, Jackson, Rush, Garrard, Nitz, & Luepke (2002) found that outpatient provider services increased by 19% for those people with depressive symptoms, while Koenig & Kuchibhatla (1999) found an increase in physician visits by depressed elderly people. Depression is the second most prevalent illness in VA healthcare settings with seven percent of patients meeting criteria for major depression. Over 14% of all VA healthcare costs can be attributed to depression (Rubenstein, Chaney & Smith, 2004; Yu W., Ravelo A., Wagner T.H., Phibbs, C.S., Bhandari, A., Chen, S., & Barnett, P. G. 2003). Druss, Rohrbaugh, & Rosenheck (1999) studied the effect of depressive symptoms on utilization using a sample of older veterans (60+ years) and found those with depressive symptoms had 50% greater medical costs. Additionally, the authors found that these costs were not associated with mental health treatment. Depression
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seems to affect health care utilization in a different way than other severe mental illnesses. Cradock-O’Leary, Young, Yano, Wang, and Lee (2002) found that VA patients with schizophrenia, PTSD, and bipolar disorder were more likely to go without medical care. In another study of mostly older male veteran’s, depression with coexisting dementia was though to lead to higher utilization rates, but Kales, Blow, Copeland, Bingham, Kammerer, and Mellow (1999) found that the group of patients who had depression alone, utilized significantly more outpatient services. Hospitalization Hospitalization is a slightly different type of utilization than either emergency department use or home care use. While decisions to go to the emergency department or to obtain home health care are often made by the care recipient and caregiver(s), decisions about hospitalization are typically made by physicians (Damron-Rodriguez, Wallace, & Kington, 1994). But, physicians are likely to take into account the availability of informal support in making decisions about hospitalization. A study of hospital use among middle-aged and older adults found that increasing age, male gender, and a variety of clinical indicators (e.g., high cholesterol & blood pressure) were significant predictors of hospitalization (Hanlon, Walsh, Whyte, Scott, Lightbody, & Gilhooly, 1998). In addition, the number of chronic conditions is also a significant predictor of hospitalization (Miller & McFall, 1989; Damush et al., 2004). Network size and social support have been fairly consistent predictors of hospitalization. Ritesh, Rosansky, McGuire, McDermott, and Jarvick (2001) found that physical and mental health, along with a small social network, were significant predictors of re-hospitalization. Additionally, Paddock and Hirdes (2003) found Canadians with
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poor social support and nutrition problems were more likely to use acute health care services. Bosworth & Schaie (1997) found that married individuals with low levels of social support had greater total health care costs. Larger social networks were associated with less hospitalization, but greater use of non-hospital services in patients with psychotic disorders (Becker, Thornicroft, Leese, McCrone, Johnson, Albert, & Turner, 1997). Emergency Department Use Emergency department use is probably the one type of health service that many would like to see reduced. Some patients try to avoid the emergency department because of long waiting times and providers that cannot offer patients their full attention. Additionally, the emergency department personnel are not trained to address the multiple chronic conditions of older people (White-Means, Thornton, Yeo, 1989; DamronRodriguez et al., 1994). The emergency department serves as the only access to health care for many without insurance. For this reason, emergency departments are sometimes use inappropriately for non-emergency health care. Frail elder use of emergency departments is of interest to this study. Several researchers have found that most emergency department visits by frail elders are appropriate, due to the severity of the illness (Rosenblatt, Wright, Baldwin, Chan, Clitherow, Chen, & Hart, 1999; Parboosingh & Larsen, 1987; Barnett, Harnett, & Bond, 1992). While the rate of emergency department use varied, those over 85 years of age had twice the emergency department use as patients aged 65 to 69. The authors also found that patients with a primary-care physician were less likely to use the emergency department than patients without such a relationship. Additionally, those with greater
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satisfaction with the primary care were less likely to use the emergency department (Schweitzer, French, Ullmann, & McCoy, 1998).
Similar findings were reported with
those 75 years of age and older using the emergency department twice as much as groups aged 45-64 and 65-74 (Carmel, Anson, Levin 1990). In studies using the health behavior framework there are inconsistencies in the predictive strength of the predisposing, enabling, and need characteristics. Need has been identified as the strongest predictor of emergency department use by some researchers (Walter-Ginzburg, Chetrit, Medina, Blumstein, Gindin, & Modan, 2001; Padgett, Struening, Andrews, & Pittman, 1995). Other researchers have found predisposing and enabling characteristics such as income and insurance status to be the strongest predictors. Because insurance status is often linked to income, insurance status is another predictor of emergency department use with those that do not have insurance or access to primary care often resorting to emergency departments as a substitute for regular sources of medical care (Brokaw & Zaraa, 1991; White-Means, 1995). Additionally, Padgett et al., (1995) found enabling and predisposing factors such as ethnicity, education, and marital status to be predictors of emergency department use. Barzargan, Bazargan, & Baker (1998) found that demographic variables such as age, gender, education, and living arrangements were not predictors of emergency department use. In their study of 1,114 African Americans aged 62 years and older several other predictors of emergency department use were found. Predictors included viewing their health and/or sickness as a function of external forces such as luck, believing that the health care professionals were responsible for their health and/or
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illness, reporting poorer health, heart & eye conditions, and those reporting a higher level of instrumental support (Bazargan et al., 1998). Finally, some studies of emergency department use focus on homelessness. Because many veterans are also homeless these results are reported, although none of the veterans in this dissertation research were homeless. Masson, Sorensen, Phibbs, and Okin (2004) found that homelessness and alcohol use were associated with greater emergency department use. Depressive symptoms and physical disability were found to be the strongest predictors of homeless men’s use of the emergency department. Home Care Use Data from the 2000 census indicate that almost one-quarter of all adults are providing some form of informal care to a person age 65 or older (Weitz, 2004). Home care has quickly become an important service because of an aging population living with several chronic illness and functional limitations (Houde, 1998). Smith & Longino (1994) reported a tenfold increase in home health care spending between 1975 and 1989. Predictors of formal home care services include age, living arrangements, educational level of 12 years or less, functional limitations, number of informal helpers, fair to poor self-rated health, prior home care use, income, and having a prior hospitalization or nursing home stay (Tennstedt, Crawford, & McKinlay 1993; Logan & Spitze, 1994; Coughlin, McBride, Perozek, & Liu, 1992; Kemper, 1992; Solomon, Wagner, Merenberg, Acampora, & Cooney, 1993; Shapiro, 1986). With regards to social networks, Stoddart, Whitely, Harvey, and Sharp (2002) found that social networks and social support were not generally associated with the use of home care after controlling for demographic variables.
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Frail elders living with others were less likely to use formal home care services than those who lived alone. Men are also more likely to receive formal home care services than women (Houde, 1998; Katz, Kabeto, & Langa, 2000). The effects of race on home care use are mixed. Kemper (1992) found that African Americans and Hispanics are less likely to receive formal home care services than Whites. But, Wolinsky and Johnson (1991) found no race differences in the receipt of formal home care services. Overall, need, or greater functional limitations explains the majority of the 19% of variance in home care use, with predisposing and enabling variables adding relatively little explanation to the total variance (Stum, Bauer, & Delaney, 1996). Conceptual Model Specification of the conceptual model for this dissertation draws on the existing literature and integrates social network perspective, lay referral, the health behavior model, and the stress paradigm. The social network perspective is important to combine with the health behavior model because the health behavior model reflects a rational choice perspective. Rational choice perspective views people as engaging in individual cost-benefit analysis but neglects what Pescosolido characterizes as the “critical role that others play in social life…It is not enough to see the influence of social networks as one more factor in the individual’s weighing of the costs and benefits of action. Rather, social interaction often determines whether individuals have a choice to make, whether and how they are pushed in a particular direction, and whether they recognize problems and see certain actions as within the range of acceptable possibilities (Pescosolido, 2001, pg. 475).”
Additionally, the stress paradigm informs both of these perspectives by acknowledging that chronic disease and disability are stressors that can be buffered through the provision
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of social support. A conceptual model of these four perspectives is summarized below (See Figure 3A).
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Brief Overview/Orientation to the Conceptual Model Figure 3A. Proposed model to study the effects of the network structure, function, and satisfaction on frail veteran’s utilization of health care services
D. Utilization A. Socio-Demographic Variables
C. Network Variables
Structure
Race Education Age Living Arrangement
(Size, composition)
1). Non-Acute Care (Physician Visits)
Function (Emotional Support, Instrumental Support, Health Monitoring & Appraisal)
Resource Variables B. Physical & Psychological Health Variables
Income
Satisfaction
Geographic Proximity of Adult Children
2). Acute Care (Emergency Room Visits & Days Hospitalized)
(Quality of Care Amount of Care
Chronic Disease Conditions
RCT Assignment
3). Home Care Services
Functional Health (ADL/IADL)
Intervention vs. Control
= Chart Review Depression
= Self Report
Hypotheses The conceptual model presented above is a visual representation of this dissertation research. Data has been collected through two mechanisms: self report via telephone interview and medical chart abstraction. Interview questions that are asked of frail veterans include variables in Box A (except for randomized control trial (RCT) assignment), functional health and depression in Box B, and social network variables in Box C. Additionally, veterans were asked if they received home care services (Box, D).
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Chronic disease conditions, days hospitalized, and emergency department visits were abstracted from respondent’s medical charts with a geriatric nurse practitioner reviewing the chart and data abstraction for accuracy. Hypotheses The primary goal of this study is to examine the main effects of health status variables, as indicators of need, on medical care utilization while examining the extent to which social network variables moderate those main effects. Main effects hypotheses are included in Appendix C. The thicker lines in Figure 3A represent key areas of interest regarding interaction terms with specific hypotheses defined below. As can be seen in Figure 3A antecedents of health status and network variables are also included (i.e. sociodemographic, resource variables and group assignment). It is important to note that because of the cross sectional design of this study, the causal flow cannot be established. The possibility of reciprocal effects exists, which may bias parameter estimates (e.g., more conservative estimates). The positive relationship between poor health and health care utilization is reduced by the negative reciprocal path from health care utilization to poor health because one would assume that health care utilization would result in improving health. The causal ordering in this model however, has been carefully designated based on theory, prior empirical work, and logic. Potential reciprocal relationships, while they can’t be tested in this study, will be kept in mind and noted when interpreting findings. This cross-sectional study seeks to lay the foundation for future work with longitudinal data.
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Predictors of Network Structure Hypothesis 1.1: Individuals who live with others are more likely to have larger networks than individuals who live alone. Hypothesis 1.2: Individuals with adult children living within a one hour drive are more likely to have larger networks than individuals without children or individuals with children living more than an hour away. Hypothesis 1.3: African-American men are more likely than white men to have more family members mentioned in their network. Predictors of Network Function and Satisfaction Due to the cross sectional design of this study network variables and health status variables will be treated as correlated. A longitudinal design would be needed to tease out the causal ordering of these variables. For instance, network variables could affect health status through network members making sure that frail veterans take their medications in the correct amounts and at the correct times (e.g., maintaining blood glucose levels, which prevents declines in physical health). In turn, health status variables could affect network variables. For example, frail veterans who can no longer drive would require the assistance of a network member to shop for groceries or clothes or take them to their doctor visits. Predictors of Health Status Prior empirical evidence has found socio-demographic variables to have an impact on health therefore socio-demographic variables are included to properly specify the model. The correlated relationship between health status variables and network variables is hypothesized to be a reciprocal relationship, with those in poorer health
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requiring more assistance with instrumental support, health appraisal, and health monitoring. Because of the cross-sectional nature of this study, feedback loops cannot be tested, therefore the effect sizes may be underestimated. This support (or lack of support), in turn would affect health status. Predictors of Health Care Utilization The poorer the physical and mental health of male veterans, the greater their overall health care utilization but this relationship is hypothesized to be a function of the network (defined as structure, function, and satisfaction) after adjusting for sociodemographic variables, resource variables, and randomized group assignment. In other words, the relationship between health and utilization measures are anticipated to be moderated by network variables. Therefore the following moderating hypotheses will be tested. Predictors of Non-Acute Care Utilization 3 Hypothesis 2.1. Poor health status is positively related to the receipt of non-acute medical care. This relationship will be stronger among people who have network member(s) who provide health appraisal (by recognizing the need for medical care) and weaker among people who do not have a network member assisting with health appraisal. For example, a respondent who has a network member(s) that is able to recognize slight health declines in the focal person, may be able to prevent an exacerbation of illness through contacting providers sooner than someone who does not have a network member providing health appraisal support.
3
While originally hypothesized, non acute care data was not able to be obtained from the VAMC. These hypotheses were left as a part of the originally conceptualized model.
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Hypothesis 2.2. Poor health status is positively related to the receipt of non-acute medical care however, this relationship will be stronger among veterans who do not have a network member who monitors their health and weaker among veterans who do have a network member monitoring their health. For example, a respondent who has a network member who monitors the focal person’s health is more likely to assist the focal person in activities that will prevent health declines such as, medication compliance, appropriate diet (e.g. diabetics), exercise, and avoidance of smoking and alcohol. Hypothesis 2.3. Poor health status is positively related to the receipt of non-acute medical care however, this relationship will be stronger among those who have lower network satisfaction and weaker among those who have higher network satisfaction (satisfaction is measured as quality of care received from family, friends, and neighbors). For example, a respondent who is less satisfied with the care they receive from their lay health consultant network is more likely to have more frequent visits to their primary care physician than a respondent who is more satisfied with their informal care. Predictors of Acute Care Utilization Veterans with poorer physical and mental health will have greater use of acute care medical services. Prior empirical evidence has found that need is the greatest predictor of emergency department visits, along with the lack of having a primary care physician and a lack of insurance. In this study, all respondents have a primary care physician because they were recruited from primary care clinics in the VAMC. Additionally, all veteran’s have the same access to VAMC primary care and emergency department care eliminating the barrier that a lack of insurance causes. Therefore, health
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status is expected to be the only predictor of acute care utilization (although the full model will be tested). Predictors of Home Care Services Hypothesis 3.1. Poor health status is positively related to the receipt of supportive care services, however, this relationship will be stronger among people with smaller networks and weaker among those with larger networks. For example, respondents with larger networks may receive needed supportive care from members of their network while respondents with smaller networks may need to request this service from formal care providers. Larger networks generally translate into a greater array of support that can be provided to an individual. Hypothesis 3.2. Poor health status is positively related to the receipt of home care services however, this relationship will be stronger among people with a smaller proportion of non-family members in their network and weaker among those with a higher proportion of family members in their network. For example, respondents with more family vs. non family members will not need to rely upon formal supportive services because family members are more likely to provide the care than non family members (e.g., Non-family tend to be more reluctant to provide assistance in ADLs such as bathing and dressing). Hypothesis 3.3. Poor health status is positively related to the receipt of home care services however, this relationship will be stronger among those living alone and weaker among those living with a caregiver. For example, frail veterans are at a greater risk of having difficulties with meal preparation and bathing. If they live alone, they are more likely to receive formal
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supportive care services than those who live with a caregiver who provides this type of care. Hypothesis 3.4. Poor health status is positively related to the receipt of home care services however, this relationship will be stronger among those who have lower network satisfaction and weaker among those who have higher network satisfaction. For example, respondents who are satisfied with the assistance they receive from their lay health consultant network are less likely to request these services be provided from formal care services.
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CHAPTER FOUR: Research Design and Method Overview of Research Design and Procedures This study of the informal networks of frail veterans is part of an ongoing study of long-term care utilization conducted by the Dayton, Ohio Veterans Administration Medical Center. The larger study, the Care Coordination Study, developed in response to a federal mandate to evaluate the effectiveness of long-term care utilization planning. The data for this dissertation were gathered during the 12-month follow-up of the longitudinal Care Coordination Study. The data analysis for this dissertation research is considered a secondary data analysis because the network sub-study was not the original purpose of the Care Coordination Study. Data collection was performed over a two-year time period, during which, data were edited, cleaned, and entered on an ongoing basis. The data entry software Teleform was used. A brief description of the larger study will be followed with the specific research design of the Network Substudy, including the data collection protocol, a description of the sample, and operationalization of both predictor and outcome variables. Care Coordination Study The Department of Veterans Affairs was required by public Law 106-117 section 102 to implement three pilot programs to study the effectiveness of all-inclusive longterm care in reducing hospital and nursing home utilization by frail elderly veterans. In one of these programs (Model 1, VA as Sole Provider) the Dayton, Ohio VAMC provided all of the long-term care services for nursing home eligible veterans, including transportation to medical appointments, homemaker/home healthcare, nursing home,
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assisted living, and adult day health care. Eligible veterans were identified in outpatient clinics as being age 55 or older and nursing home eligible by the State of Ohio (having 2 or more functional limitations based upon their Activities of Daily Living (ADLs), or needing help taking their medications (an Instrumental Activity of Daily Living, IADL)). Over a two-year time period, from July 1, 2001 through June 30, 2003, 438 frail elderly veterans were enrolled and randomized into one of three groups: a usual care control group, a ‘placebo’ control group, and the intervention group. The ‘placebo’ control received usual care in addition to annual mailings about how to access the range of services available to them, but services were not coordinated for them. The intervention was a nurse practitioner functioning as a care coordinator of long-term care services whose main goal was to identify services needed by the veterans. The interventionist made an initial home visit to assess the frail veteran’s health status in their home and identify needed services. The veteran and caregiver (if identified) were able to contact the interventionist at any time (24 hours a day/ 7 days a week) with health concerns and problems with medication refills. At a minimum, the interventionist would contact the veteran once a month to discuss any concerns, usually this was in the form of a home visit, but it could have also been via a phone call. Veterans were followed by the interventionist until their death or the end of the study in September of 2004. Respondents were contacted for follow-up interviews at 5 time points (3, 6, 12, 18, 24 months). Veterans age 55 years or older with upcoming appointments to primary care clinics were sent a letter explaining that they may be contacted to determine if they were eligible for this study. When possible, potential respondents received a phone call
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the day before their appointment and were screened for ADL and IADL impairment. If they were screened as eligible, the interviewer made arrangements to speak with them on the day of their appointment. If they could not be reached via phone the day before their appointment, potential respondents were approached in the waiting room prior to their appointment and screened for eligibility. Once eligibility was determined, a member of the research staff explained the study, obtained informed consent, and administered the instrument to collect baseline measures. The baseline interview was an in person interview, while follow-up interviews were done via telephone. Primary caregivers identified by the veteran were also interviewed at the same time points. Network Sub-Study Design Data for this dissertation comes from the twelve-month time point interview of the Care Coordination Study, which was collected between July 1, 2002 and June 30, 2004. Social network questions were added to the interview guide for the 12-month interviews. The Care Coordination study specified that all follow-up interviews would be conducted via telephone, instead of mailed questionnaires, in order to ensure accuracy and quality of the data. In a few cases, face to face interviews were conducted at the Dayton VAMC for veterans who had hearing or speech impairments. Ninety-five percent of the twelve-month interviews were conducted by this author via telephone. Five percent of interviews were conducted face to face for people who were unable to complete a phone interview; these interviews were conducted by a member of the research team in Dayton, Ohio.
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In some cases, respondents could not be reached for either phone or face to face interviews. These respondents were sent a mailed version of the 12-month questionnaire. Due to the complexity of the added network questions, they were not included in the mailed version so individuals receiving the mailed version could not be included in the network sub-study. While the care coordination study interviewed respondents regardless of cognitive impairment, the network sub-study did not include cognitively impaired subjects (defined as scoring a 5 or higher on the mental status instrument, which Pfeiffer (1975) has identified as the starting point for cognitive impairment. This decision was made due to problems with reliability and increased respondent burden. Responses to all interviews were entered into the Care Coordination Microsoft Access database, and separately into a Microsoft Access database specifically for the twelve-month time point. A separate data base for respondents in the network sub-study was created, since some respondents in the larger study were deemed ineligible for the sub-study, either because of mental status impairment or having completed the mailed questionnaire that did not include network questions. Once interviews were completed and logged into the database, they were reviewed for completeness, and out of range values. They were then coded, and scanned using Cardiff Teleform data entry software. Editing the questionnaires and verifying responses in Teleform prior to sending them into SPSS helped ensure the accuracy of the data. Teleform software has the capacity to export data into a variety of programs including SPSS.
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Sample Description The Care Coordination study enrolled 438 frail elderly veterans. Almost all of these respondents were men (97%, N=425). Women were not included in the network sub-study, because there was not a sufficient sample size in order to perform analysis by gender. Of the 425 men, 79% were white (N=336), 21% were African American (N=88), and one was Hispanic (.2%). By the twelve-month time point when the network substudy was conducted, 18 % had died, 14 % had disenrolled from the study (either refused to continue participation or became ineligible due to a move to a sheltered living environment), and 7 % were no longer eligible (due to mental status scores of 5 or higher) (See Figure 4A). Figure 4A: Sample Attrition for the Twelve Month Time point B a s e lin e C a r e C o o r d in a t io n S tu d y N = 4 3 9
T o t a l M a le s N=426
W h it e N = 3 3 6
H is p a n ic N = 1
A f r ic a n A m e r ic a n N =89
D eceased by 12M N=63
D eceased by 12M N=1
D eceased by 12M N =12
D is e n r o lle d p r io r t o 1 2 M N=48
D is e n r o lle d p r io r t o 1 2 M N =12
N o t E lig ib le D u e to M e n ta l S ta tu s N=25
N o t E lig ib le D u e to M e n ta l S ta tu s N=6
1 2 M o n t h T im e P o in t S a m p le A t t r it io n
T o t a l E lig ib le fo r 1 2 M N=259
Of the 259 veterans who were eligible to be interviewed at the 12 month time point, 10 % could not be reached via telephone at their home, 2 % refused, 3 % were too
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ill, and 2% were permanently living in either a VA or community nursing home. Partial data were collected through a mailed questionnaire sent to the five percent of the sample that could not be reached via telephone. The final sample size for the Network SubStudy is 200 (77 % response rate). See Figure 4B below for sample break down. Figure 4B: Twelve-Month Sample Break Down and Response Rate
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Power and the Ability to Test Figure 1 Power is the probability of detecting a particular effect size given a specific alpha level and sample size. Power is determined by the number of control variables (or covariates), the amount of explained variance expected by these variables, sample size, alpha, and effect size of predictors. A power of .80 or greater is desirable (Cohen, 1988, Murphy & Myors, 2004). A priori power analyses, calculated using SPSS Sample Power, indicated that a sample size of 170 would be needed to detect an effect size of .03 for an alpha of .05 with 19 control variables that account for 40% of the explained variance. The sample size for the 12-month Network Substudy sample was 200; therefore, a power of 87% was reached. The Network Sub-Study includes a sub-sample of African American Veterans (21% n=89) and 48 were interviewed for the network sub study. The power analysis listed above accounts for all variables in the model. Therefore, in order to detect effects of race, this study has the power to detect a race effect of .03 or greater. Additionally, because race/ethnicity has a somewhat skewed distribution (the sample has a split of 79% White and 21% African Americans) there is the possibility that effect sizes will be attenuated due to the restricted variance. Measures Establishing Validity and Reliability of Measures Testing the assumptions of regression, and assessing reliability, and validity of measures are important first steps before assessing study hypotheses. Please see Appendix E for a complete discussion of the tests for the primary assumptions of regression. With regards to primary assumptions of regression skewness and kurtosis
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values were examined to determine if the variables were normally distributed. In general, values were relatively normal (Skew not greater than an absolute value of three and Kurtosis not greater than an absolute value of eight (Klein, 1998)), but two outcome variables were transformed and are discussed later in this chapter. Where possible, multiple-indicator measures, which had been used in previous research, were selected for use in the Care Coordination study. Multiple indicators, combined into composite scales, are generally more reliable than single items (Kercher, 2000). Two multiple indicator measures were used in the network sub study; the Geriatric Depression Scale, and the Quality of Care Scale. In order to establish construct validity (Chronbach & Meehl, 1955), these measures were examined using exploratory factor analysis (EFA) in SPSS. Factor analysis is a data reduction technique used to help determine the number and nature of factors by identifying clusters of items that are highly correlated. Principal Axis Factoring and oblique rotation (direct oblim) were used to determine the number of latent constructs. When possible, multiple factor solutions were tested, including one more factor than anticipated and one less. The scree plot and residual correlation matrix were used to help identify the best fitting factor solution. The scree plot is a visual indicator of the approximate number of factors underlying a set of indicators while the residual correlation matrix was used to help determine the correct number of factors and the items to be retained for each factor. McDonald (1999) suggests that this approach is helpful in determining the fit of factor solutions. The residual correlations reflect the difference between the reproduced/implied and the observed correlations. As the average
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reproduced residuals decrease in size, the fit improves. Finally, logic, theory, and prior empirical evidence were used to identify the cleanest factor solution. Fabrigar, Wegener, MacCallum, and Straham (1999) argue that a cut point of 0.4 or greater for primary factor loadings can be interpreted as acceptable loadings. Secondary loadings were also examined. Items with secondary loadings of .3 or higher were not used, even if the item had a primary loading of .4 or higher. When the factor structure indicated more than one latent construct existed, an external correlates test was performed to be sure that the latent constructs were in fact unique constructs and not alternative measures of the same thing (Carmines & Zeller, 1979). The reliability of scales used was examined with Chronbach’s alpha. An alpha of .80 or greater is generally considered indicative of a reliable scale (Carmines & Zeller, 1979), and those criteria were applied in evaluating measures in this research. EFA results are summarized below under descriptions of particular measures and in more detail in Appendix D. Descriptive Information This section describes strategies for operationalizing the variables in the Network Substudy. Univariate statistics for these variables will also be reported. In addition to describing the respondents in the Network Sub-study sample, univariate statistics provide preliminary checks for distributional requirements for the multivariate analyses. A description of all measures used, coding, and source can be found in Table 4F.
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Predictor Variables Demographics Demographic variables, available from the Care Coordination Study, include respondent age (measured in years), education level (coded as a categorical variable (0-8 years, 9-11 years, 12 years, 13-15 years, 16+ years)) and race/ethnicity (1=African American, 2=Asian, 3=Hispanic, 4=White, 5=Native American, 6=Other). The mean age for the sample (measured at baseline) is 74 years of age with a range of 55 to 88 (SD 7.1). Eighteen percent of the sample reported completing 0-8 years of school, 19% completed 9-11 years, 37% graduated from high school, 19% completed some college (13-15 years), and 7% graduated from college (16+). Twenty-three percent of respondents are AfricanAmerican, 1% are Hispanic, and 76% are White (See Table 4A). Living arrangements were assessed through a clinical instrument called the VA Choice, which asks patients with whom they live. This instrument was being pilot tested by the VAMC, and response options were changed three times throughout the study enrollment period. Despite these changes, it was possible to consistently classify respondents for the Network Sub-study into two categories: lives alone (22%) or lives with others (78%). Lives with others is the reference category in the multivariate analyses. Income was not asked directly of the respondent. The primary caregiver was asked to select a category that best fits their total household income from all sources last year before taxes, (e.g., $0-$4,999, $5,000-9,999, $10,000-14,999). This proxy data will be used as an alternative measure of veteran income. For the majority of respondents, (81%) household income was obtained from caregivers living with the respondent
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(mostly wives and adult children). Household income, as reported by the family caregiver, was distributed as follows: 1% between 0 and $4,999, 7% between $5,000 and $9,999, 13% between $10,000 and 14,999, 25% between $15, 000 and 19,999, 29% between $20,000 and $29,999, 8% between $30,000 and $39,999, 6% between $40,000 and $49,999, and 11% at or above $50,000 (See Table 4A).
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Table 4A. Demographic Characteristics of Respondents
Percent (N) Age Marital Status Married Widowed Separated or Divorced Single Adult Children Total children No Children 1 Child 2 Children 3-4 Children 5-8 Children 9-14 Children Education 0-8 yrs 9-11 yrs 12 Years 13-15 Years 16+ Years Caregiver Income 0-4,9999 5,000-9,9999 10,0000-14,999 15,000-19,999 20,000-29,999 30,000-39,999 40,000-49,999 50,000 and higher Race White African American Hispanic Living Arrangements lives alone lives with others
Mean (Standard Deviation) 74 (7.2)
Skewness (Kurtosis) -0.7 (0.1)
Range 55-88
% Valid Data (N) 100 (200) 100 (200)
62.0 (124) 17.5 (35) 14.0 (28) 6.5 (13) 3.1 (2.3)
1.4 (3.4)
0-14
100 (200)
0-4
100 (200)
0-8
63 (126)
0-2
100 (200)
0-1
100 (200)
9 (18) 11 (22) 25.5 (51) 34.5 (69) 17 (34) 3.0 (6)
17.5 (35) 19.0 (38) 37.0 (74) 19.5 (39) 7.0 (14)
1.0 (1) 7.0 (9) 13.0 (16) 25.0 (32) 29.0 (36) 8.0 (10) 6.0 (8) 11.0 (14)
76 (151) 24 (48) .5 (1) -1.4 (-.15) 22 (44) 78 (156)
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For the Network Sub-study, group assignment is used as a control variable. As described above, respondents were randomly assigned to the experimental and control groups of the Care Coordination Study, with fifty-two percent of the sample in the intervention group (care coordination provided by a nurse practitioner), 24% in the placebo control group (services available but no care coordination provided), and 24% in the usual care control group. The two control groups were combined into one control group for purposed of the Network Sub-study. Additional questions were added to the twelve-month interview time point to gather information about the respondent’s family and characteristics of their network. The question on marital status was supplemented with additional items asking how long respondents had been married, separated, divorced, or widowed. These questions were included to determine if there had been a recent change in marital status, which would be likely to affect informal networks. The majority of respondents are married (63%), seven percent are single, 13% are separated or divorced and 18% are widowed. Respondents were asked if they have any children and if yes, how many sons and how many daughters. To determine geographic proximity, a follow up question asking ‘how many of your sons live within an hour’s drive from your house’ was asked and the same question is asked for daughters. Respondents have an average of 3 children (S.D. = 2.3) (includes step children). The modal number of children is 2 (26%). Nine percent of respondents have no children, while 11% have one child, 35% have 3-4 children, 17% have 5-9 children, and 2% have 10-14 children. Respondents have an average of 1.8 children living within an hours drive (S.D. = 1.6). The modal number of children living within a one hour drive is one (28%).
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The mean number of daughters for the sample is 1.5 (S.D. = 1.4) and the mean number of daughters who live within an hours drive is .9 (S.D. = 1.0). The mean number of sons for the sample is 1.5 (S.D. = 1.5) and the mean number of daughters who live within an hours drive is .9 (S.D. = 1.0). History of military service was not included as part of the interviews nor was this information documented in a standardized way in the computerized medical charts. Therefore, no data are available regarding the dates of military service, prior combat experience, or participation in specific military conflicts (e.g., WWII, Korean War, Vietnam War). Mental Status The short portable mental status questionnaire was used to assess cognitive impairment (Pfeiffer, 1975). The ten item scale was designed as a brief test of cognitive function in community-dwelling elderly individuals. The items test memory (both short and long term), mathematical tasks, current events, and orientation to surroundings (McDowell & Newell, 1996). The number of errors are counted and categorized as no cognitive impairment (0-4 errors), mild cognitive impairment (5-7 errors) and severe cognitive impairment (8-10 errors). As described above, individuals scoring a five or higher, indicating cognitive impairment (Pfeiffer, 1975), at the twelve-month time point were excluded from the network sub-study. This decision was made due to the questionable reliability of respondents with cognitive impairment and increased respondent burden of the social network questions.
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Physical Health Physical health was measured by abstracting data from the respondent’s VAMC computerized medical chart or Computerized Patient Record System (CPRS). Two measures were used: The Charlson Comorbidity Index (Charlson, Pompei, Ales, & MacKenzie, 1987), and a modified OARS symptom checklist (Fillenbaum, 1988). Patient medical charts were examined and this author recorded the existence of medical diagnoses that were part of either the Charlson or OARS indices. Results were reviewed for accuracy by a geriatric nurse practitioner. The Charlson is a weighted checklist of diagnosed medical conditions that are predictive of mortality. This checklist takes into account both the number and seriousness of multiple diseases, but does not take into account diseases that may contribute to functional limitations such as degenerative eye diseases (i.e. macular degeneration, glaucoma), osteoarthritis, and Parkinson’s disease. Therefore, the network sub-study supplemented the Charlson Index with a modified OARS condition checklist. The OARS includes diagnoses such as coronary artery disease, anemia, and glaucoma that were recorded and used as a count of conditions. It is not appropriate to conduct an exploratory factor analysis (EFA) on these measures, because these are cause or producer indicators where the items drive the underlying construct (Bollen & Lennox, 1991). Health condition items are not expected to be correlated, (such as cancer and congestive heart failure), so an EFA analysis would not provide meaningful results.
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The Charlson comorbidity scores for this sample range from 0-12 with a mean of 3.2 (S.D. = 2.2) and a modal value of 2. The modified OARS Symptom checklist (22items) identified additional chronic health conditions not part of the Charlson. The weighted Charlson comorbidity index and the modified OARS condition count were summed for an overall measure of chronic health conditions. These two measures were combined into total chronic diseases for analysis. The combined index indicated an average of 6.7 health conditions, with a standard deviation of 2.5 and a range of 0 to 63. Functional Health Veteran functional health was measured through two commonly used scales; The Index of Independence in Activities of Daily Living (Index of ADL) (Katz, Ford, Moskowitz, Jackson, & Jaffee, 1963) and The Older Americans Resources and Services Multidimensional Functional Assessment Questionnaire – IADL (OARS-IADL) (Fillenbaum, 1988). The Index of ADL is a six-item scale with yes/no response categories to determine if the respondent could perform the following functions: eating, bathing, dressing, transferring, toileting (2-items). This measure has a range from 0 (no limitations) to 6 (completely limited). This scale has been used extensively with frail elders and has an alpha of .7. The OARS-IADL scale is a seven-item instrument developed to assess personal functioning. Scores ranged from 0 (not help needed) to 14 (unable to perform any activity) (Kane & Kane, 2000). Individual items include using the telephone, shopping, meal preparation, housework, transportation, taking medications, and handling money.
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There is some sensitivity to the IADL scale differentiating between people who are unable to perform a task (scored 2), need some help (scored 1), and are able to complete the task on their own (scored 0). This scale has also been used extensively with frail elders and has an alpha of .8. For analysis the ADL and IADL composite scores were summed for a measure of total functional limitations. This decision is based upon prior work indicating ADLs and IADLs make up a single underlying construct of disability (Saliba, Orlando, Wenger, Hays, and Rubenstein, 2000). The mean number of ADL limitations for this sample is 1.5 (S.D. = 1.6). The range is from 0 to 6. The mean number of IADL limitations was 5.7 with a standard deviation of 3.7 and a range of 0 to 14. These two measures were combined into one variable for analysis. Total functional limitations ranges from 0 to 20 with a mean number of functional limitations of 7.2 with a standard deviation of 4.7 (See Table 4B). Table 4B. Physical and Psychological Health Characteristics of Respondents
Percent (N) RCT Group Control Intervention
Mean (Standard Deviation)
Skewness (Kurtosis)
Range
-.6 (-2.0)
0-1
% Valid Data (N) 100 (200)
48.5(97) 51.5 (103)
Charlson Comorbidity Modified OARS Symptom Checklist
3.2 (2.2)
1.0 (1.5)
0-12
3.5 (1.4)
.2 (-.5)
0-22
ADL Limitations
1.5 (1.6)
1.0 (.3)
0-6
IADL Limitations
5.7 (3.7)
.2 (-.9)
0-14
Depression (GDS)
4.4 (3.3)
.6 (-.4)
0-15
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100 (200) 99 (198) 100 (200) 100 (200) 100 (200)
Depression Depression was measured using the Geriatric Depression Scale-Short Form (Sheikh & Yesavage, 1986). This measure consists of 15 items with yes/no response categories that were taken from the original 30 item long form. The 15 items were selected because they had the highest correlation with depression symptoms, and preformed as well as the long form in indicating depressed individuals (Sheikh & Yesavage, 1986). It is easy for respondents to answer because of the dichotomous response categories making it quick to administer, especially over the phone. Five of the items are reverse coded to indicate the presence of depression. Scores of four or less are within the normal range, scores between 5 and 9 indicate mild depression, and scores 10 and over indicate moderate to severe depression (McDowell & Newell, 1996). Construct validity was difficult to obtain because this measure has dichotomous response categories, which violates one of the assumptions of linear exploratory factor analysis, such as the program used in SPSS. The programs available for non-linear variables (M+, Latent Gold) require larger sample sizes than are available in this dissertation. Therefore, an EFA forcing a one, two, and three factor solution were performed, but the results interpreted cautiously. Exploratory factor analysis forcing one factor indicated that the 15 items do load on a single factor with factor loadings ranging from .311 to .683 and has a Chronbach’s alpha of 0.82 (see appendix D for detailed information). The two and three factor solution did not produce meaningful factors and the average reproduce residual did not improve. Additionally, a confirmatory factor analysis supported this decision with a CFI of .867, Tucker Lewis Index of .823, Root Mean Square Error of Approximation of .065 Chi square was 166 with 90 degrees of
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freedom. Therefore, this measure will be treated as a higher order single factor measure of depression as typically used in the literature. A composite score was created for respondents with eight or more valid responses. For those individuals missing items (up to 7), their mean score was substituted for the item(s) and a composite created (Sum.8 coding in SPSS). The mean Geriatric Depression Score for this sample is 4.6 (S.D. = 3.3) with a range from 0 to 14 (See Table 4B). Sample Characteristics by Race As part of the description of the sample, this next section will explore race/ethnic differences in demographic and physical and psychological health variables. Determining if there are race/ethnic differences among the social network variables is one specific aim of this study and will be reported in Chapter 5. Cross-tabs and Chi-Square were used to look for race differences with education, and living arrangements. There were no significant differences between race and education. However, there were significant differences between race and living arrangements with 18% of White veterans and 35% of African American veterans living alone. In other words, whites were more likely to be living with someone and African Americans were more likely to be living alone (p=.011) (see Table 4C).
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Table 4C. Demographic Characteristics by Race White African American % (N) % (N) Education 0-8 years 9-12 years High School Graduate Some College College Graduate Living Arrangements Lives with Others Lives Alone Income $0-4,999 $5,000-9,999 $10,000-14,999 $15,000-19,999 $20,000-29,999 $30,000-39,999 $40,000-49,999 $50,000 + RCT Group Intervention Control
16 (24) 18 (27) 42 (63) 17 (26) 7 (11)
23 (11) 23 (11) 21 (10) 27 (13) 6 (3)
82 (124) 18 (27)
65 (31) 35 (17)
1 (1) 4 (4) 13 (13) 25 (25) 29 (29) 9 (9) 7 (7) 12 (12)
0 20 (5) 12 (3) 24 (6) 28 (7) 4 (1) 4 (1) 8 (2)
49 (74) 51 (77)
58 (28) 42 (20)
T-Tests were performed with race as the grouping variable (0=White, 1=African American) to look for mean differences in the continuous variables of age and total children living within one hours drive. No differences were found. Whites have a mean age of 74 years and African Americans with a mean age of 73 years. Similarly, there were no race differences by the total number of children living within one hour drive (Whites 1.8 and African Americans 1.9) (see Table 4D). Pearson correlation indicated that there was no statistically significant relationship between income and race.
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Table 4D. Health Characteristics by Race African White American Mean Mean (SD) (SD)
PValue
F
Age
74.0 (7.5)
73.0 (6.2)
.298
2.30
Total Children Living within an hours drive
1.8 (1.6)
1.9 (1.7)
.648
1.72
Total Chronic Conditions
6.5 (2.4)
7.0 (2.8)
.250
.450
Total Functional Limitations
7.3 (4.7)
6.6 (4.7)
.358
.019
Depression
4.7 (3.2)
4.3 (3.7)
.431
.567
Physical and Psychological Health and Race T-Tests again were used to look for mean differences in the continuous variables of total chronic conditions, total functional limitations, and depression. No differences were found by race with Whites having an average of 6.5 chronic conditions and African Americans have 7.0 chronic conditions. Whites had on average 7.3 functional limitations and African Americans had 6.6. Finally, both White and African Americans were mildly depressed with average depression scores of 4.7 and 4.3 respectively (see Table 4D above). The next section continues to discuss the remainder of the measures used. Characteristics of Informal Networks Describing the characteristics of respondents’ informal networks is a central focus of this dissertation (See Specific Aim 1). In this section, strategies for operationalizing dimensions of social networks, including network function, network composition, and network satisfaction are described. Univariate descriptors of informal networks are included in Chapter 5, Results.
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Network Function Social network function was measured through a series of name generating questions related to emotional support (3 questions), instrumental aid (3 questions), health monitoring (4 questions) and health appraisal (3 questions). The three name generating questions for emotional support are from the Sarason Social Support Scale (Sarason, Levin, Basham RB, & Sarason, 1983): (1) Whom can you really count on to listen to you when you need to talk? (2) Whom can you count on to console you when you are very upset?, and (3) Whom could you really count on to help you out in a crisis situation, even though they would have to go out of their way to do so? The three name generating questions for instrumental aid include who helps with ADL’s, IADL’s and transportation to medical appointments. Four name generating questions for health appraisal were modified from a study by Stoller and Wisnewski (2003): (1) Whom do you talk to when you want some information about a particular disease or symptom, what might be causing it or how you might treat it?, (2) Whom do you talk to when you are worried about your health?, (3) Whom do you talk to about what the doctor has told you?, and (4) Whom do you ask for advice about whether or not you should go see your doctor about a particular symptom or problem that is bothering you? The four health monitoring questions were developed specifically for this study: (1) Does anyone remind you to take your medications? (2) Does anyone remind you to try to keep yourself moving and get some exercise? (3) Does anyone watch your diet?, and (4) Does anyone advise you to avoid unhealthy habits like smoking or drinking alcohol?
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This approach to generating names of network members related to specific tasks will provide information that will be generalizable to other frail populations (Antonucci, 1985). A potential drawback to this approach is that support sources are limited to the four functions of personal networks included in this study (Pescosolido, 2001): emotional support, instrumental aid, health monitoring, and health appraisal. Network Composition This research employs several indicators of network composition. To generate this information, respondents were asked to report the first name and last initial of each person mentioned in response to the name generating questions. Initials of the last name were asked in order to keep people with the same first name separate. Names were recorded on a network roster (See Appendix A), which tracked the question(s) that elicited the name. The respondent was asked a series of questions about each person they mentioned. These questions include: relationship to respondent, age, sex, geographic proximity to respondent (measured in number of minutes or number of hours it takes them to get to respondents residence), number of years respondent has known the individual (not asked if individual is a son or daughter), if the person was ever employed in a health care occupation (if yes, a follow up question is asked to establish exactly what the occupation is), how they met the person if it is not a relative (with categories of work related, church related, neighbor, formal service provider or other), and an overall rating of how helpful they have found this person to be with response categories ranging from not at all helpful to very helpful. The information provided from the questions listed above was used to examine a range of network characteristics, including: (1) Network size; (2) Network composition
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(based on relationships to respondent); (3) Duration of relationships; (4) Network homogeneity; and (5) Division of labor. Network size is the count of network members, including all family, friends, and neighbors mentioned in response to the name generating questions. Network composition is categorized as family only networks, friends and neighbor only networks, and mixed networks (including family, friends, and neighbors). Duration of relationships with friends and neighbors is reported as the mean number of years respondents knew their friends and neighbors. Network homogeneity is defined as the extent to which the focal person shares characteristics with network members. Network homogeneity was assessed with respect to gender and to geographic location. Gender homogeneity is the percent of members that are the same gender as the respondent. In this study, all of the respondents were male, so this measure will be the percent of male network members mentioned. Geographic homogeneity will be assessed using a modification of a typology developed by Wenger, Burholt, and Thissen (2001). The three categories include neighborhood networks (all network members live within a 30 minute drive of respondent), wider local networks (all network members live within a 60 minute drive of the respondent), and dispersed networks (some members living within one hour’s drive and others living more than one hours drive from the respondent. Division of labor of the network encompasses two dimensions: the inner circle and specialization. Inner circle members are members mentioned for at least one question across the four functions (e.g., respondent mentions their wife for at least one emotional support question, one instrumental support question, one health monitoring and one health appraisal question). This conceptualization stems from Kahn & Antonucci’s
70
(1980) Convoy Model where focal members were asked to place network members in concentric circles with members in the inner circle representing the most significant. This operationalization was modified by Peek & Lin (1999) for use with name generating questions. Specialization occurs when a person with special expertise may be mentioned for only one particular function. For example, the respondent mentions a neighbor who is also a nurse for the questions in health appraisal, but this person is not mentioned for emotional support, instrumental support, or health monitoring. Network Satisfaction Network satisfaction was assessed with the modified Appraisal of Client's Network Quality/Adequacy scale, developed at the Benjamin Rose Institute, to measure caregiver satisfaction with help received from formal care services. The original ten-item scale was reduced to 6 items by Bass, Noelker, & McCarthy (1999) based upon exploratory factor analysis results. In the Care Coordination study, this scale was used to measure veteran satisfaction with the quality of care received from family, friends, and neighbors. In the network sub study, it is also used to measure of satisfaction with network members. Items asked of respondents are summarized in Appendix D response categories included strongly agree, agree, disagree, and strongly disagree. There are three positively worded and three negatively worded items, with all items are coded so the more positive response receives the higher score (ranging from 0 to 3). Because this scale had not been used as a measure of satisfaction with sources of informal support, the structure of the revised instrument was examined. This analysis used Exploratory Factor Analysis (EFA), first forcing one factor, as suggested by
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previous psychometric analysis of the original version. The scree plot indicated a single factor and all items loaded fairly highly (factor loadings ranged from .556 to .717). The Chronbach’s alpha was 0.81 but the average reproduced residual correlation was .07, indicating that there was possibly another factor (McDonald, 1999). The EFA forcing two factors separated out four items on one factor and two items on the second factor (see Appendix D). The average reproduced residual correlation dropped substantially to .01 indicating a better fit. The Chronbach’s alpha for the 4 item scale was .76 and .77 for the two-item scale. In order to determine if the two factors were substantively tapping different dimensions, composite scales were created and correlated with the major predictor and outcome variables of interest. If two scales correlate similarly, they are likely alternative measures of the same thing. The external correlates test (see Appendix D) indicated that the two scales were different due to correlations with six variables that were substantially different. Therefore, the scales will both be used in analysis. The first four-item scale will be conceptualized as satisfaction with the quality of care received and the second two-item measure will be conceptualized as satisfaction with the amount of care received. Outcomes: Health Care Utilization Receipt of Home Care Services Receipt of home care services was measured by asking respondents if they had received home care services in the past six months. The response categories were a (yes=1, no=0) format. Almost half (49%) of the respondents had received home care services in the past six months while 51% had not.
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Health Care Utilization4 Health Care utilization data were obtained from respondent medical charts for a six month time frame measured between the 6 and 12-month interview date. The VAMC uses a sophisticated computerized patient medical chart making data abstraction between date ranges possible without the need to look though the entire patient chart. The number of emergency department visits and the number of days hospitalized within the Veteran’s Affairs Medical Center were collected and added to the interview data. In order to collect utilization data, it was critical to know if the respondent has used services in the community that would not be accounted for in the VA system. Therefore, respondents were asked if in the past 12 months they have seen VA providers only, both VA providers and community providers or only community providers. This question was used to determine whether respondents used the VAMC exclusively. For those that do not use the VAMC exclusively, there is the possibility that their health care utilization will be underreported. Most veterans (63%) had only used VA providers since the beginning of the study (last 12 months), while 37% had used VA providers as well as non-VA providers (doctors in their communities). For these veterans, their health care utilization will be underestimated. Emergency Department Use. Sixty-two percent of veterans did not use the VAMC Urgent Care center, while 38% did. 25% had one visit, five percent had two visits, and eight percent had three or more visits between the six and twelve month data collection 4
Primary care and rehabilitation visits were originally going to be used as a
measure of non-acute care. Difficulty in obtaining this data led to this outcome being dropped.
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time points. The distribution of this variable indicated it was fairly kurtotic, therefore, it was transformed in order to normalize the distribution. First a one was added to the original scores and then a square root transformation was performed. Square root transformations of count variables are theoretically driven transformations (Hair, Anderson, Tatham, & Black, 1998; Mosteller & Tukey, 1977). The distribution results for both the original and transformed variables can be found in Table 4E. Days Hospitalized. Thirty-three percent of veterans were hospitalized at the VAMC between one and 180 days (maximum number of days for six months).
The majority of
those hospitalized at the VAMC ranged between 1 and 44 days. There were two outliers with hospitalizations lasting 94 and 180 days respectively. Including the outliers the mean days hospitalized was 5.0 with a standard deviation of 16.2. In order to attempt to normalize the distribution for this variable, first the two outliers were recoded to 62 and 63 days respectively and the skewness and kurtosis improve dramatically to 3.8 and 17.0 respectively. Because these are still high values for skewness and kurtosis, according to Klein (1998), the variable was also transformed. A one was added to all scores (the log of 0 would be undefined) as a constant and the log was computed. Then the log was computed for both the original variable and the recoded variable. Skewness and kurtosis values improved to (log of original data) 1.5 and 1.3 and (log with outliers recoded) 1.4 and .7 respectively (see Table 4E). Second, the square root was taken of the original variable to see if this transformation would improve the skewness or kurtosis. The square root transformation was the transformation that was used in analysis due to the improvement in distribution and because it is a theoretically
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driven transformation for count variables (Hair, Anderson, Tatham, & Black, 1998; Mosteller & Tukey, 1977). Table 4E. Health Care Utilization Univariate Descriptive Statistics
Percent (N)
Mean (Standard Deviation)
Skewness (Kurtosis)
Range
% Valid Data (N)
Dichotomous Hospitalizations
33 (65)
0.75 (-1.5)
0-1
Emergency Department Use
39 (77)
0.46 (-1.8)
0-1
Home Care Use Continuous
49 (98)
0.04 (-2.0)
0-1
100 (200) 100 (200) 100 (200)
0.7 (1.1)
2.5 (8.0)
0-7
100 (200)
1.2 (.4)
1.7 (3.0)
1-2.83
5.0 (16.2)
7.8 (73.8)
0-180*
0.7 (1.2)
1.5 (1.3)
.00-5.20
1.8 (1.6)
3.4 (16.5)
1-13.5
Emergency Department Use –raw data Emergency Department Use – raw +1 and square root transformation
Hospitalizations- raw data Hospitalizations – raw +1 log transformation Hospitalizations – raw +1 square root transformation
100 (200)
Hospitalizations – outliers recoded to 62 & 63 4.1 (10.1) 3.8 (17.0) 0-63 Hospitalization – outliers recoded, +1, log transformation .70 (1.1) 1.4 (.67) 0-4.2 Hospitalization – outliers recoded, +1, square root transformation 1.7 (1.4) 2.3 (5.6) 1-8 * Two outliers that had been in the hospital for 94 and 180 days respectively, otherwise range is 0-61 days.
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Table 4F. Description of Measures Used, Coding, and Source Concept
Measure and Coding
Part of Care Coordination Study
Added for Network SubStudy
Socio- Demographic Variables Race
Marital Status Education Age
1=African American; 2= Asian; 3=Hispanic; 4=White; 5=Native American; 6=Other 0= Single; 1=Married; 2=Separated or Divorced; 3= Widowed 0=0-8 years; 1= 9-11 years; 2=12 Years; 3= 13-15 years; 4= 16 or more years Number of years
Living Arrangement With whom do you live? 1=alone; 2= spouse only; 3= child; 4= others; 5= group setting with non relative; 6= spouse with others; 7= other Recoded into 0=lives alone and 1=lives with others Resource Variables Income Household income before taxes of caregiver Number of Children Total number of children (biological, adopted, and step)
X X X X
X
X X
Number of Daughters Total number of daughters (biological, adopted, and step)
X
Geographic proximity Total number of daughters living within a of daughters 1 hours drive
X
Geographic proximity Total number of sons living within a 1 of sons hours drive
X
Number of Children Total number of children (biological, within 1 hours Drive adopted, and step) living within one hours drive from respondent RCT Assignment Intervention & 03 = All Inclusive Care, no Elder Care Control Coordinator; 4= All Inclusive Care with Elder Care Coordinator; 6 = Usual Care
X
Mental Status
Ten items coded Correct =0 or Error =1. Items were summed for a total number of errors. Errors of 5 or higher indicated the presence of cognitive impairment.
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X
X
Concept
Measure and Coding
Part of Care Coordination Study
Physical and Psychological Health Variables
X
Total Chronic Disease Conditions Charlson
X
OARS-Conditions Functional Health Limitations Activities of Daily Living Instrumental Activities of Daily Living Depression Depression Network Variables Network Structure Size Composition
Charlson Comorbidity Index and the OARS-Symptom Checklist Condition List
X
Katz
X
Modified OARS
X
Geriatric Depression Scale- Short Form (15 items Yes/No responses)
X
Total number of network members mentioned 0=Formal Care only 1= Family only; 2= Friend and Family; 3= Friend Only
X X
Network Function Emotional Support
Total number of network members mentioned providing emotional support Instrumental Support Total number of network members mentioned providing instrumental support Health Monitoring Total number of network members Support mentioned providing health monitoring Health Appraisal Total number of network members Support mentioned providing health appraisal Network Satisfaction Satisfaction with the Bass, Noelker, and McCarthy 4 item scale; Quality of Care from response options from strongly agree to Informal Support strongly disagree Satisfaction with the Bass, Noelker, and McCarthy 2 item scale; Amount of Care from response options from strongly agree to Informal Support strongly disagree Health Care Utilization Home Care Single Item: Have you received home care services in the last six months?
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Added for Network SubStudy
X X X X
X
X
X
Concept
Measure and Coding
Emergency Department
Single Item: # of ER visits in the last six months
Number of days hospitalized
Single Item: # of days hospitalized in last six months
Part of Care Coordination Study X
X
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Added for Network SubStudy
Chapter 5: Results In this chapter, bivariate and multivariate data analyses are used to address the project’s specific aims. Results will be organized around the four specific aims presented in chapter 1. Aims one, two, and three are primarily descriptive, and aim four addresses the causal model presented in Chapter 3. Tests for the underlying primary assumptions of regression (linearity, multicollinearity, and tests for influence) are presented in Appendix E. Specific Aim 1 To describe the characteristics of frail male veterans informal care networks. This section will provide a descriptive account of the network structure, network function, and satisfaction with the assistance provided by network members. Network structure includes the size, composition, and duration of relationships with friends and neighbors. In addition, network homogeneity by gender and geographic proximity will be discussed. Network function includes emotional and instrumental support, health appraisal, and health monitoring. Within this category, the division of labor and specialized consultants will also be discussed. The last characteristic is network satisfaction. Finally, two unanticipated findings will be discussed; at risk veterans and the reliance on formal care providers. Network Structure Figure 5A is a graphic representation of the total number of network members frail veterans reported. Three percent of respondents have empty networks, 32% have one to two members, 43% have three to four members, and 23% have five to nine members.
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Figure 5A: Total Number of Social Network Members
60
50
Frequency
40
30
20
10 Mean = 3.3687 Std. Dev. = 1.8443 N = 198 0 0
1
2
3
4
5
6
7
8
9
10
Total number of network members
Table 5A displays information about the structural aspects of the network. The mean size of the informal network for this sample is 3.4 people (S.D. = 1.9) with a range from zero to nine network members and a modal number of three network members. As expected, the number of children living within a one hours drive was positively related to network size (.275, p=.01). The majority of networks are comprised exclusively of family (63%); 33% have a mix of network members (family and friends), 3% have friend or neighbor only networks, and 1% have networks comprised of formal care providers only. The average length of relationships with friends was 24 years with a range of 1 to 68 years and a standard deviation of 18 years. Relationships with neighbors averaged 19 years with a range of 1 to 70 years and a standard deviation of 18.
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Table 5A. Structural Characteristics of Networks: Univariate Distributions Percent (N) Network Size Empty Networks One Member Two Members Three Members Four Members Five Members Six to Nine Member Network Composition Formal Only Family Only Friend/neighbor only Mixed (family & Friend/neighbor) Duration of Relationship (in years) Friends Neighbors Network Homogeneity Gender % Male Zero 1 to 49 50 51-99 100 Geographic location Neighborhood Wider local Dispersed
Mean (Standard Deviation) 3.4 (1.8)
Skewness (Kurtosis) .9 (.7)
% Valid Data (N) 99 (198)
Range 0-9
2 (3) 13 (25) 20 (39) 28 (55) 15 (30) 13 (25) 10 (21) 98.5 (197) 1.0 (2) 63.0 (124) 3.0 (6) 33.0 (65) 99 (198) 24 (18) 19 (18) .35 (.3)
1-68 1-70 .4 (-.6)
0-100
98 (196)
30 (58) 30 (58) 18 (36) 16.8 (33) 6 (11) 98 (195) 66 (129) 9 (17) 25 (49)
Roughly two-thirds of respondents had network members that included other men. Twenty-nine percent had zero male network members, 29% had between 1 and 49 percent male network members, 36% had between 50 and 99 percent male network members and 6% had male only networks. Sixty-six percent of respondents have neighborhood networks, defined as networks consisting of people either living with the respondent or living within a 30 minute drive. Nine percent of respondents had wider local networks that consisted of people living within a 1-60 minute drive. Finally, 25%
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of respondents had dispersed networks, consisting of people who lived both within one hour and more than one hour drive away (see Table 5A above). Table 5B displays information regarding the relationship of the network member to the frail veteran. Of frail veterans who were married, 97% mentioned their spouse. Of veterans who had adult daughters, 66% mentioned their daughter(s), and in those respondents with adult sons, 60% mentioned their sons in response to the name generating questions. Table 5B. Relationship of Network Member to Veteran % 97 66 60 27 22 23 18
Wife* (N=123) Adult Daughter* (N=151) Adult Son* (N=146) Other Female Relative (N=197) Other Male Relative (N=197) Male Friend or Neighbor (N=197) Female Friend of Neighbor (N=197)
* calculated based upon number of people who had the potential to mention this relationship, e.g., 123 respondents were married. Adult children include step children.
Network Function Table 5C displays information regarding network function. When network size is broken down by function, frail veterans have an average of three people they rely upon for emotional support with a range of zero to nine people. Instrumental support is provided by an average of one person with a range from zero to six. Respondents have an average of two lay health appraisal consultants they talk to about their health and about what their doctor told them, with a range of zero to seven. Health monitoring is the smallest consultant group with respondents turning to, on average, one person for help managing their medications, diet, and activity with a range of zero to eight.
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Table 5C. Network Function: Univariate Distributions Mean Skewness % Valid (Standard Deviation) (Kurtosis) Range Data (N) Network Function Size of Emotional Support
2.75 (1.9)
.85 (.99)
0-9
Size of Instrumental Support
1.46 (1.1)
1.2 (2.3)
0-6
Size of Health Appraisal
1.9 (1.5)
1.0 (2.3)
0-7
Size of Health Monitoring
1.2 (1.2)
2.1 (7.1)
0-8
100 (200) 100 (200) 100 (200) 100 (200)
When looking at frail veterans who mention someone for each function versus those who do not, most respondents receive emotional (89%) and instrumental support (83%) followed by Health Appraisal (78%) and Health Monitoring (65%) support. Table 5D displays information regarding the division of labor among network members. The inner circle member is defined as a member that is mentioned for each of the four functions. Fifty-four percent did not have an inner circle member. Of the 46% of respondents who did mention someone in each category, indicating inner circle placement, 41% had one inner circle member, 4.5% had two inner circle members and .5% had three inner circle members. The specialized network consultant is defined as a person to whom the respondent turns for only one specific function. Specialized consultants occur most frequently for emotional support (53%). Of those with a specialized emotional consultant, 21% have one consultant, 15% have two, 11% have three, and 5% have between four and seven people whom they can turn to for emotional support. For Instrumental support 18% have a specialized consultant. Of those with a specialized instrumental consultant, 14% have one consultant, and 4% have between two and four instrumental support consultants. For
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Health Appraisal, 39% have a specialized person they turn to for questions about their health. Of those with a specialized consultant for health appraisal, 26% have one specialized consultant, 8% have 2 people, and 5% have between 3 and 5 people they turn to exclusively for questions about their health. For health monitoring, 11% have a specialized person they turn to for monitoring their health. Of those with a specialized health monitoring consultant, 10.5% have one specialized consultant and 1% has between two and three people they turn to exclusively to monitor their health (see Table 5D). Table 5D. Division of Labor Among Network Members Percent (N)
% Valid Data (N)
99.0 (198)
Division of Labor Inner Circle Member one member two members three members Specialized Consultant For Emotional Support For Instrumental Support For Health Appraisal For Health Monitoring
46 (91) 41 (81) 4.5 (9) 0.5 (1) 52 (103) 18 (35) 39 (78) 11 (23)
Network Satisfaction Table 5E displays information regarding the satisfaction frail veterans reported with the quality and amount of care they received. Satisfaction with networks encompasses both the quality of care and the amount of care. Satisfaction with the quality of care from family friends and neighbors (4 item-scale) has a mean of 7.8 (SD 1.4), indicating respondents were slightly higher than the midpoint on satisfaction. A zero score represents those who always strongly disagreed that they were satisfied. The highest score (12) indicates those who always strongly agreed that they were satisfied.
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The two-item scale measuring satisfaction with the amount of care has a mean of 3.4 (SD 1.0). Respondents were only slightly above the midpoint of being satisfied with the amount of care with a range of zero to six (See Table 5E). Table 5E. Network Satisfaction: Univariate Distributions Mean (Standard Deviation)
Skewness (Kurtosis)
Range
% Valid Data (N)
99.0 (198)
Network Satisfaction Satisfaction with Quality of Care (4-item)
7.8 (1.4)
-.8 (4.8)
0-12
99.0 (198)
Satisfaction with Amount of Care (2-item)
3.4 (1.0)
-1.2 (1.2)
0-6
98.5 (197)
Additional Insights: Formal Care Providers While the fourteen name generating questions were designed to elicit names of lay network members who provided emotional and instrumental support, health appraisal and health monitoring, some respondents mentioned formal care providers such as their doctor, nurse, or home health aid in response to the questions. In fact two respondents mentioned only formal care providers. Thirty-eight percent of respondents did not mention a formal care provider. Of the 62 % that did mention a formal care provider, 38 % mentioned one, 16 % mentioned two, five percent mentioned three, and 2.5% mentioned between four and six formal care providers. Among veterans who mentioned a formal care provider, 68 % mentioned their doctor (either VA or community based), 51% mentioned a nurse, and 23% mentioned their home health aid. Additional formal providers identified as network members included meals on wheels drivers, psychologists, dentists, optometrists, physical therapists, and pharmacists; these other categories accounted for five percent of the formal care providers identified.
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A statistically significant difference was found between the control and intervention groups and the number of formal care providers. Respondents in the control group mentioned an average of .8 (1.0 SD) formal care providers and respondents in the intervention mentioned an average of 1.2 (1.1 SD) formal care providers (p=.01). Breaking down the number of formal care providers mentioned by function provides additional descriptive information. The majority of formal care providers were mentioned for instrumental support and health appraisal. Ten respondents mentioned formal care providers for emotional support. One-hundred and forty-five respondents mentioned a formal care provider for instrumental support. Seventy-nine formal care providers were mentioned in response to the health appraisal questions and thirty-two formal care providers were mentioned for health monitoring. T-tests between the number of formal care providers in each function by the randomization variable indicated there were no statistically significant differences between the intervention and control groups. Interestingly, as the size of respondent’s emotional support networks increased, so too did the number of formal service providers mentioned. This pattern was also found with the size of the health appraisal network (see Table 5F) but not for instrumental support or health monitoring. Table 5F: Correlations Between Formal Service Providers and Network Functions N=198 Number of formal service providers mentioned Number of lay providers for Emotional Support .155* Number of lay providers for Instrumental Support .004 Number of lay providers for Health Appraisal .346** Number of lay providers for Health Monitoring -.036 p
