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Integrating Biology into Demographic Research on Health and Aging

(With a Focus on the MacArthur Study of Successful Aging)

Eileen M.Crimmins and Teresa Seeman

Information on bioindicators can lead to a better understanding and clearer implications of the well-established relationships of demographic factors to health-related outcomes. The history of demographic analysis of health has been one of refining and widening the outcomes examined and clarifying the mechanisms through which demographic factors operate to affect health. Mechanisms now common in demographic analyses include social, economic, psychological, behavioral, and biological factors. The unique approach of demographic analysis is the use of large, representative samples of the population in order to understand and project trends and differences in health outcomes. Models of health outcomes currently used by demographers build heavily on a variety of interdisciplinary approaches. Recent epidemiological results indicate a clear role for the inclusion of additional bioindicators in demographic models of health outcomes at older ages. Such development would both specify the way that traditional demographic factors operate and point to places for intervention. Inclusion of the collection of biological data in representative surveys will be required to link population health outcomes to individual social, economic, and psychological characteristics and better understand and address the policy issues linked to questions about trends and differentials in population health.

This chapter outlines the current demographic approach to the study of health outcomes among older populations and concludes by arguing that the inclusion of bioindicators as proximate determinants of health outcomes is now appropriate. The second section of the chapter clarifies



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Cells and Surveys: Should Biological Measures be Included in Social Science Research? 2 Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging) Eileen M.Crimmins and Teresa Seeman Information on bioindicators can lead to a better understanding and clearer implications of the well-established relationships of demographic factors to health-related outcomes. The history of demographic analysis of health has been one of refining and widening the outcomes examined and clarifying the mechanisms through which demographic factors operate to affect health. Mechanisms now common in demographic analyses include social, economic, psychological, behavioral, and biological factors. The unique approach of demographic analysis is the use of large, representative samples of the population in order to understand and project trends and differences in health outcomes. Models of health outcomes currently used by demographers build heavily on a variety of interdisciplinary approaches. Recent epidemiological results indicate a clear role for the inclusion of additional bioindicators in demographic models of health outcomes at older ages. Such development would both specify the way that traditional demographic factors operate and point to places for intervention. Inclusion of the collection of biological data in representative surveys will be required to link population health outcomes to individual social, economic, and psychological characteristics and better understand and address the policy issues linked to questions about trends and differentials in population health. This chapter outlines the current demographic approach to the study of health outcomes among older populations and concludes by arguing that the inclusion of bioindicators as proximate determinants of health outcomes is now appropriate. The second section of the chapter clarifies

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Cells and Surveys: Should Biological Measures be Included in Social Science Research? the potential of specific bioindicators in answering demographic questions about health outcomes. Building on results from a number of epidemiological studies, but emphasizing results from the MacArthur Study of Successful Aging, we emphasize the links between major demographic variables of interest such as socioeconomic status and race, a set of bioindicators indicating physiological status, and important health outcomes in aging populations. The third section of the chapter introduces the details of biological data collection for the MacArthur Study. This study is an example of a completed, multiple-community study in the United States with extensive biological information of the type which can be useful in augmenting current demographic approaches toward health in aging populations. Details on the methods of data collection, sources of biological information, and use of this information from the MacArthur study, which are relevant to the collection of bioindicators in household surveys, are provided. BACKGROUND Incorporation of additional information on bioindicators will represent a continuation of a long-term trend toward widening the scope of outcomes and explanatory variables in demographic research on health outcomes. In the last three decades the scope of analysis has consistently been expanded because of theoretical, analytical, and data developments in demography and related fields (Crimmins 1993; Hummer, 1996; Mosley and Chen, 1984; Preston and Taubman, 1994; Rogers et al., 2000). The expansion of the scope of survey-based demographic analysis in the health area has occurred both in the outcomes examined and in the complexity and type of independent variables included in explanatory models. This proliferation of outcomes is related to improved understanding of the multidimensional aspects of health outcomes and of mechanisms through which health is affected (Verbrugge and Jette, 1994). This increased complexity of the conceptual model employed in demographic approaches to health has built on theoretical developments and empirical research in demography and related fields. For instance, the economic model of health has emphasized the role of economic pathways as well as the development of formal models (Grossman, 1972; Preston and Taubman, 1994). Epidemiological models have incorporated more emphasis on biological as well as social pathways (Howe, 1998; Mosley and Chen, 1984). Empirical results linking a large number of independent variables to health outcomes from targeted epidemiological studies such as the Framingham Heart Study (Kannel et al., 1987) or from nationally representative samples such as the National Health and Examination Survey (NHANES) have provided a solid epidemiological basis for further model development.

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Cells and Surveys: Should Biological Measures be Included in Social Science Research? As epidemiological knowledge has increased, the possibilities of clarifying the mechanisms through which traditional demographic differences in health outcomes arise and the interventions one might use to influence population health have also grown. The inclusion of bioindicators in demographic models of health outcomes adds an additional level of mechanisms through which traditional socio-demographic variables affect health outcomes. Inclusion of a set of biological proximate determinants in models of health outcomes among the older population represents a model expansion similar to that occurring over the last two decades in demographic models of fertility or of child health (Bongaarts, 1978; Mosley and Chen, 1984). Demographic analyses in these fields have moved toward specifying the biological mechanisms through which final outcomes are determined. Health Outcomes While demographic research once concentrated on mortality, as noted above, many additional indicators of health status are now included in demographic analyses. Clarification of the dimensions of health and their potential relationships to causal factors has been important in expanding demographic research to nonmortal outcomes (Verbrugge and Jette, 1994). Health outcomes now regularly investigated in population studies include disability, physical functioning loss, and the presence of specific diseases and conditions (Smith and Kington, 1997a, 1997b). The newest generation of national surveys has also included mental health and cognitive functioning among the domains investigated (Colsher and Wallace, 1991; Herzog and Wallace, 1997). These dimensions of health and examples of specific health outcomes investigated are shown in Figure 2–1. The number of analytical health outcomes has grown with the recognition that population health disparities at any point in time can vary across dimensions of health, and can change over time as well (Crimmins, 1996; Verbrugge and Jette, 1994). We have also clarified that trends in mortality and other health outcomes are not necessarily closely related in populations where death is dominated by chronic conditions (Crimmins et al., 1994). One reason for this is that among older populations nonfatal health outcomes like arthritis and cognitive loss are major causes of functioning loss and disability yet they are not important causes of mortality. Another reason is that mortality decline may occur because people with diseases and disabilities survive longer than in the past, resulting in an increase in people with disease and disability. Specification of health outcomes has been important in joining demographic research models to results derived from medicine and epidemiology. It has made clear the need to develop explanatory models tailored to

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Cells and Surveys: Should Biological Measures be Included in Social Science Research? FIGURE 2–1 Health outcomes included in contemporary demographic surveys. the health outcomes investigated. Some health outcomes are affected only by what happens “within the skin” of individuals; others are affected by the environment outside the skin as well as within the skin. For instance, the onset of heart disease may be affected primarily by an individual’s characteristics and behaviors. On the other hand, disability due to heart disease may be affected by the environment in which a person functions, i.e., the presence of steps, the characteristics of transportation, or the characteristics of the workplace may influence whether a person with heart disease can live alone or work. Other outcomes such as how long a person survives after a heart attack may be partially explained by the use and availability of medical care. A hallmark of the current demographic approach is the use of longitudinal data to investigate change in health status or onset of health problems rather than static health state. This approach is necessary for assessing the effect of causal mechanisms from an earlier point in time on subsequent health outcomes (Rogers et al., 2000). In an older population where health status has been achieved over a life span, the study of health change is particularly important in identifying current health processes and the relationship of independent variables to outcomes. Information on most of the health outcomes identified in Figure 2–1, other than mortality, is usually collected in population surveys through self or proxy report; however, performance testing of mental functioning has been incorporated in recent national surveys (Herzog and Wallace, 1997), and a number of more localized epidemiological studies have also

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Cells and Surveys: Should Biological Measures be Included in Social Science Research? incorporated performance measures for physical functioning (Berkman et al., 1993; Seeman et al., 1994b). In addition to self-reports, links to administrative data such as Medicare files can provide more detailed information on inpatient and outpatient medical diagnoses and treatments. Information on causes of death from the National Death Index can also be used to supplement knowledge of health experiences reported in surveys. Models of Health Outcomes It is not only the health outcomes or dependent variables that have expanded in demographic approaches to health in recent years. While the demographic approach to health outcomes for most of the last century has continued to emphasize differences by age, sex, race/ethnicity, and socioeconomic status (SES) (Figure 2–2), in recent years demographers have increasingly employed a more model-based approach to explain health outcomes, with greater emphasis on developing fully specified models of health outcomes. This approach has led to incorporating addi FIGURE 2–2 Model of health outcomes employed with contemporary demographic surveys.

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Cells and Surveys: Should Biological Measures be Included in Social Science Research? tional independent influences as well as a clarification of the mechanisms through which demographic variables work. Following developments in other demographic areas of research, there is increasing interest in understanding the social, economic, psychological, and behavioral mechanisms causing health change and health differences. Developments in the field of economics have emphasized the role of individual choice in determining health outcomes (Preston and Taubman, 1994). Social psychological approaches have emphasized the role of social networks and personality characteristics in determining health outcomes (Taylor et al., 1997). Epidemiological research has emphasized the role of personal health practices, while medical research has focused on the role of differential care. These developments have led to the incorporation in demographic models of indicators of health behaviors (e.g., diet, weight, drinking, smoking, exercise), social-psychological characteristics (e.g., social support and demands, personality characteristics), life circumstances (e.g., stress, control, and job characteristics), and health care usage and availability (e.g., source of payment, use of preventive care, use of prescription and nonprescription drugs) (Figure 2–2). In addition to including a wider range of psychosocial and behavioral factors as explanatory factors of health outcomes, current research has incorporated a lifecycle approach and recognized the value of including circumstances surrounding birth and childhood as well as a time component to later lifecycle influences (Barker, 1998). Childhood and early life exposure to infectious disease or toxic substances has also been related to health outcomes in old age (Blackwell et al., in press; Kuh and BenShlomo, 1997). Finally, the bidirectionality of many relationships in Figure 2–2 has been recognized (Ettner, 1996). Particularly with regard to the role of SES, the potential for reverse causation has been investigated. It is also possible that psychological states can be affected by health outcomes, or that functional loss could affect social support, or that even marital status could be linked to health status. The potential for reverse causation is another reason for the emphasis on longitudinal study, which allows the timing of health events and changes in independent variables to be noted. Bioindicators in the Demographic Approach The demographic approach to health analysis currently includes some indicators of “biology” which can be thought of as either biological risk factors for the onset of disease, precursors of disease outcomes, or additional biological outcomes. The development of models clearly specifying the role of biological factors in determining health outcomes in old age has been less formal than in other areas of demographic research. In

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Cells and Surveys: Should Biological Measures be Included in Social Science Research? the areas of fertility (Bongaarts, 1978) and child health (Mosley and Chen, 1984), the role of biological indicators as proximate determinants of fertility and child health have been well integrated both theoretically and empirically. In both of these areas, biological factors have been introduced into models to clarify the mechanisms through which differentials and trends arise and the paths through which other variables work. Better specification of the biological paths relevant to health outcomes in older age will be an important addition to future demographic work. Currently employed biological indicators have usually been indicators of increased risk for poor health outcomes. For instance, the presence of high cholesterol and hypertension are generally asked in current surveys, reflecting the increase in both professional and informant knowledge of these health indicators as risk factors for poor health outcomes. Height and weight combined into a body mass index is often included as an additional risk factor. Information on symptoms and precursors to disease is sometimes collected through questions on symptoms such as stiffness in joints, balance problems, and pain. Information is also solicited on childhood diseases and occupational exposure to toxic substances. It is important to realize that some traditional demographic variables are often interpreted as representing a mixture of biology and other influences. For instance, age often has been seen as having largely biological effects. The demographic emphasis on the similarity of age curves of mortality across societies and across species relates to the underlying biological mechanisms of aging (Ricklefs and Finch, 1995). While chronological age may be related to the biological “aging” of an organism, social and other lifestyle factors are known to influence the age at which health outcomes occur. With social and behavioral factors controlled, however, age is often assumed to represent a biological effect. Sex or gender also combines both biology and other influences. Over the life cycle, sex differences in life expectancy are thought to be approximately evenly divided between behavior and biology (Verbrugge, 1983; Waldron, 1986). The biological basis of sex differences in populationbased studies has generally not been directly measured; rather, it has been assumed to be indicated by residual effects after gender, behavioral differences, and other lifestyle factors are controlled. Observed differences in health by SES are generally assumed to have little to do with innate biological differences, instead being due to a difference in resources, knowledge, and opportunity resulting in differences in behavior and life situations (Adler et al., 1993, 1994; Kaplan and Keil, 1993; Link and Phelan, 1995; Marmot et al., 1997). Race and ethnic differences are also regarded as arising from a combination of SES differences and behavioral differences, which are, in turn, socioeconomically determined. Some biological differences between racial groups are acknowl-

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Cells and Surveys: Should Biological Measures be Included in Social Science Research? edged, however, especially with regard to specific health outcomes like stroke or diabetes. The emphasis of much demographic research has been an attempt to eliminate effects of race and SES through the statistical control of behavioral and social variables; evidence to date, however, suggests that these factors do not wholly account for race or SES differences in health. Recent analyses have indicated that the inclusion of biological variables is an important addition in explaining health outcomes in these models (Luoto et al., 1994). We argue that more insight could be gathered into the paths by which demographic, social, economic, psychological, and behavioral variables affect health outcomes by clarifying the biological pathways through which variables of interest produce varying health outcomes. A better understanding of biological pathways will allow us to develop effective policies and interventions to improve health outcomes and reduce health inequalities within populations. Ultimately, social, psychological, and behavioral factors must work through biological mechanisms; that is, they must get “under the skin” in order to affect health outcomes. We need to clarify how this occurs. There is a significant body of research linking demographic variables to biological indicators and bioindicators to health outcomes important in aging populations that will allow the development of a set of biological proximate determinants of health outcomes relevant to older populations. Of course, while all health outcomes have biological components, development of models relevant to specified health outcomes is appropriate. For instance, models specific to physical functioning, mental functioning, and mortality from specified causes may differ in the biological components emphasized. Collection of Data in Household Surveys Our aim in this chapter is to clarify the potential for developing demographic approaches incorporating bioindicators that can be collected from large nationally representative samples of the population. The content of these surveys has grown in recent years in conjunction with the developments described above. Before proceeding to discuss specific biological indicators that would require the collection of biological materials in addition to questionnaire responses, we briefly discuss data quality relevant to health measurement using survey-based responses. Most surveys have relied heavily on the ability of people to accurately selfreport information to an interviewer in a limited amount of time. This has contributed to the nature of biologically relevant measures included and excluded from population surveys. Demographic surveys have generally used self-reports of disease presence, functioning, and disability as analytic outcomes. Some infor-

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Cells and Surveys: Should Biological Measures be Included in Social Science Research? mation has been shown to be relatively reliably reported among older persons, although certainly other sources of information would result in different assessments of health status for some individuals. For instance, self-reports of having had a heart attack have 75 percent agreement with medical records (Bush et al., 1989), and the sensitivity of self-reports of hypertension has been shown to be about 80 percent reliable (Brownson et al., 1994; Giles et al., 1995). Self-reports of diabetes also appear to be highly reliable while those of arthritis are somewhat less so (Kehoe et al., 1994), and the reliability of cancer reporting varies by site (Schrijvers et al., 1994). Health behaviors and health care usage have also been shown to be relatively reliably reported (Brownson et al., 1994). Demographic researchers must realize that even with accurate reporting to questions on the part of respondents, it can be difficult to use survey responses to set up groups with similar biological profiles. For instance, while respondents are able to self-report whether they have ever been told that they have hypertension by a medical professional, this information often groups people who are currently very heterogeneous with respect to health risks. Comparing self-reports and measured blood pressure and having knowledge of medication usage in the MacArthur sample indicates that among those who report they are not hypertensive, only approximately half are truly nonhypertensive. The other half includes people who do not know they have measured high blood pressure as well as people who are taking medication that lowers blood pressure, even though they report they were never told that they were hypertensive (Lu et al., 1998). There is also variability in measured blood pressure and current drug usage among those who have been told they are hypertensive. For instance, many persons who are currently using medication still have elevated measured blood pressure, while some who have been hypertensive in the past measure as normotensive even though they are not under treatment. THE INCORPORATION OF ADDITIONAL BIOLOGICAL INDICATORS Biological indicators can appropriately be included in demographic analysis if they represent biological states that are reasonably prevalent in the general population and states that have been linked both to major population health outcomes and to demographic, social, or psychological mechanisms affecting health outcomes. Biomarkers have been increasingly introduced into epidemiologic studies (Howe, 1998), and there has been a long history of the study of biomarkers of aging (Sprott, 1999). Both of these areas provide a foundation on which to further develop demographic approaches.

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Cells and Surveys: Should Biological Measures be Included in Social Science Research? Development of specific models including biological indicators as proximate determinants of health outcomes needs to be tailored to the outcomes studied. As an example, some biological indicators known as Syndrome X are relevant for cardiovascular conditions. Other indicators like balance and strength may be more important factors in determining functioning ability. Some biological measures indicate initial biological capacity, resiliency, or resistance of an organism, while others indicate later physiological status resulting from an organism’s initial capacity and consequent lifecycle influences. In epidemiological terms, biomarkers represent susceptibility to health outcomes, exposure to health-affecting events, or the presence of adverse health outcomes (Schulte and Rothman, 1998). Biological characteristics indicating initial resiliency or susceptibility of an organism include genetic profiles. As noted above, genetic markers need to have a high prevalence in the population and have a reasonably strong effect on common population health outcomes, or have an interaction effect with other health-affecting mechanisms, to be candidates for inclusion in population studies. At the moment, the only known genetic marker of clear value in a population survey is the apolipoprotein E gene (APOE), although this is likely to change in the very near future. APOE allele status is clearly related to a number of major health outcomes in older populations which are reasonably well measured in population surveys: mortality, heart disease, and cognitive functioning (Albert et al., 1995b; Corder et al., 1993; Evans et al., 1997; Ewbank, 1997; Hofman et al., 1997; Hyman et al., 1996; Luc et al., 1994; Saunders et al., 1993). Both the prevalence of alleles indicating higher risk and the size of the effect are large enough to be of importance in explaining variability in currently studied health outcomes. APOE allele status has been shown to have independent effects on health outcomes and to interact with other life circumstances such as sex and race in its effect on health outcomes (Jarvik et al., 1995; Maestre et al., 1995; Payami et al., 1992). Incorporation of information on this genetic indicator could lead to increased knowledge of the interactive mechanisms of this genetic marker and other social and behavioral variables and thus clarify some of the mechanisms leading to population differentials in cognition, heart disease, and mortality. We suggest that other bioindicators appropriate for current demographic surveys include those that represent the physiological status of major regulatory systems and processes through which demographic, social, psychological, and behavioral variables work to affect health. These measures would indicate current biological status, which would be determined by some combination of genetic and lifetime environmental factors. In this discussion we limit ourselves to indicators that can be collected in a household survey with current technology and trained staff.

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Cells and Surveys: Should Biological Measures be Included in Social Science Research? These indicators have all been shown to have relationships to demographic variables and to health outcomes in older populations. Currently, some of these indicators are obtained from blood samples, some from urine samples, and some from examinations of the survey respondent. Biological parameters which meet the criteria for inclusion in population health surveys include measures of cardiovascular health, metabolic processes, markers of inflammation and coagulation, indicators of musculoskeletal health, and respiratory function (Seeman et al., 1997b). We also believe that inclusion of markers of activity in the hypothalamicpituitary-adrenal (HPA) axis and the sympathetic nervous system (SNS) represent important biological indicators that are related to demographic, social, and psychological factors, and their inclusion will clarify how population health differences and trends arise. These systems, potential biological measures, and sources of information are included in Box 2–1. Cardiovascular System Blood pressure is an indicator of the health of the cardiovascular system, which has been linked in numerous studies to age, race, sex, and SES and a range of poorer health outcomes including higher death rates, the onset of cardiovascular disease, and the loss of both physical and cognitive functioning (Kaplan and Keil, 1993; Seeman et al., 1994b; Zelinski et al., 1999). Blood pressure is also related to health behaviors, social-psychological factors, life circumstances, and the use of health care. Among the older population systolic blood pressure appears to be the best predictor of these outcomes, and measuring both systolic and diastolic blood pressure would be an important addition to current surveys. As noted above, questions on hypertension are asked in population surveys, but measurement would provide valuable supplemental information on actual blood pressure and its components. Metabolic Processes Metabolic processes have usually been indicated by levels of obesity and presence of reported high cholesterol. Higher total serum cholesterol and higher relative weight have been shown to be risk factors for poor health outcomes including mortality, cardiovascular disease, and functioning loss and to be related to race, sex, age, and SES (Adler et al., 1994; Benfante et al., 1985; Bucher and Ragland, 1995; Kaplan and Keil, 1993; Lynch et al., 1996; Marmot et al., 1997; Winkleby et al., 1992). While information has been gathered in surveys on the presence of high total cholesterol, recent medical practice emphasizes the importance of knowing the components of cholesterol—high density (HDL) and low density

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Cells and Surveys: Should Biological Measures be Included in Social Science Research? mation on a range of social, psychological, behavioral, and biological parameters, have demonstrated the value of this information in linking traditional demographic variables and health outcomes. There are strong relationships between all of the biological parameters described and what are usually considered demographic variables. There are also relationships between the major health outcomes of interest in aging populations and these biological indicators. Evidence such as this clearly points to the potential value of developing more comprehensive data collection strategies in our efforts to better understand inequalities in health outcomes in populations. It is only through efforts to build more comprehensive models of health and aging that we will gain the requisite knowledge to develop effective policies and interventions to promote health and reduce health disparities in populations. The value of stored specimens for continuing to augment sample information as science progresses is also evident in the MacArthur data. Planning ahead for appropriate storage of samples can allow the data from a survey that is well underway to be used to address new scientific questions. Of course, scientific advances in the methods of collecting biological data are imminent. Less invasive and cumbersome means of scanning are being developed that will allow for noninvasive collection of biological information on the health of the cardiovascular system, the skeletal system, and other organs. Potentially, information once gathered from blood and urine samples can be gathered in some less invasive manner in the future; however, at the moment the technology for household collection of urine and blood is manageable. We have not discussed the ethical issues that arise when a survey adds the collection of biological information to its protocols; other chapters in this volume comprehensively address these issues. We should recognize that asking respondents to provide biological samples does place additional burden on respondents. Both respondent cost as well as potential gain need to be considered in making this request. While respondent burden is greater, collecting biological information creates an opportunity for respondent gain by potentially providing participants with information on a set of individual bioindicators that could personally be useful. REFERENCES Adler, N., T.Boyce, M.Chesney, S.Cohen, S.Folkman, R.Kahn, and L.Syme 1994 Socioeconomic status and health. American Psychologist 49:15–24. Adler, N., T.Boyce, M.Chesney, S.Folkman, and L.Syme 1993 Socioeconomic inequalities in health. Journal of American Medical Association 269(24):3140–3145.

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