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Cells and Surveys: Should Biological Measures Be Included in Social Science Research? (2001)

Chapter: Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)

« Previous: Collecting Biological Indicators in Household Surveys
Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
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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

Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
×

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.

Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
×

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

Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
×

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

Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
×

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.

Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
×

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

Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
×

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-

Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
×

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-

Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
×

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.

Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
×

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.

Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
×

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

Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
×

Box 2-1 Biological Measures that are Related to Demographic, Social, Psychological and Behavioral Influences and Health Outcomes

 

Source

Cardiovascular System

Systolic blood pressure

Exam

Diastolic blood pressure

Exam

Metabolic System

Body-mass index/waist-hip ratio

Self-report/exam

Total cholesterol

Blood

HDL/LDL cholesterol

Blood

Homocysteine

Blood

Glycosylated hemoglobin

Blood

Inflammation and coagulation Factors

IL-6, CRP, low cholesterol

Blood

Albumin

Blood

Fibrinogen

Blood

Antioxidant Profiles

Blood

Hypothalamic Pituitary Adrenal Axis

Cortisol

Urine

DHEAS

Blood

Sympathetic Nervous System

Neorepinephrine

Urine

Epinephrine

Urine

Renal Function

Creatinine clearance

Blood/urine

Lung Function

Peak flow rate

Exam

(LDL) —and their ratio. Measurement of the components of cholesterol could be productively included in population studies where blood samples are drawn. Comparison of total levels of cholesterol and levels of high density cholesterol in the MacArthur study indicates that using an indicator of total cholesterol or one of high-density cholesterol will stratify the population differently according to risk. Among those in the most risky quartile of the distribution of total cholesterol, only 22 percent are in the highest risk category of HDL cholesterol.

Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
×

Indicators of glucose metabolism could also be important in clarifying some of the population differences in health by both race and SES as well as the trend toward an increasing prevalence in diabetes now being experienced. Elevated glucose levels, which are common among minority group members and poorer people, have been related to heart disease risk (Reaven, 1988), to higher mortality (Fried et al., 1998), and to poorer cognitive function (Craft et al., 1993; Gradman et al., 1993; Manning et al., 1990; Reaven et al., 1990). Analyses using the MacArthur data have shown that functional decline in older persons is related to a history of diabetes and poorer glucose metabolism (Seeman et al., 1994b). Levels of glycosylated hemoglobin provide an indicator of glucose metabolism and can be determined using blood samples.

Homocysteine has garnered recent attention because of its importance in predicting many of the major health outcomes common in aging populations: cardiovascular, cerebrovascular, peripheral vascular disease, and poorer cognitive function (Arnesen et al., 1995; Jacques and Riggs, 1995; Riggs et al., 1996; Verhoef et al., 1996). Other cofactors involved in homocysteine metabolism, specifically vitamins B6, B12, and folic acid, have also been related to a lower incidence of such problems (Verhoef et al., 1996). These same cof actors are also required for synthesis of dopamine (Kruk and Pycock, 1983), suggesting the possibility that levels of these factors might be related to motor/physical functioning as well. Homocysteine is linked to age and likely to race and SES because homocysteine metabolism is strongly influenced by diet and deficiencies in some vitamins. Homocysteine levels are potentially more easily modified than those of other risk factors and are likely to change in the U.S. population in the near future with folic acid supplementation of flours and cereals. Therefore, changing homocysteine levels have the potential for explaining time change or cohort differences in population health.

Inflammation and Coagulation

Markers of the inflammation and coagulation processes have been linked to negative health outcomes and to some demographic, social, and psychological factors. Recent research using population-based studies has indicated that inflammation markers such as levels of C-reactive protein (CRP), interleukin 6 (IL-6), and albumin may be predictors of heart attack, stroke, functioning loss, and death among older persons (Cohen et al., 1997; Kannel et al., 1987; Kuller et al., 1991,1996; Mendall et al., 1996; Reuben et al., 2000a; Ridker et al., 1997; Tracy et al., 1995, 1997). Data from the MacArthur study have related levels of IL-6 and C-reactive protein to increased mortality risks and physical and cognitive decline (Reuben et al., 2000a; Weaver et al., 2000). Evidence for the influence of

Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
×

albumin levels and low cholesterol on functional decline and death has also been found in the Mac Arthur data (Reuben et al., 1999). Albumin has also been related to cognitive impairment (Cattin et al., 1997).

These markers have proven to be relatively strong risk factors for major health outcomes. In fact, high levels of CRP appear to increase the risk for cardiovascular disease as much as adverse levels of HDL cholesterol (de la Serna, 1994; Kannel et al., 1987; Ridker et al., 1997). Low albumin also has been found to be one of the stronger biological indicators, predicting mortality and coronary heart disease within a few years (Corti et al., 1995; Fried et al, 1998; Gillum, 1993; Gillum et al., 1994).

Many of the social and psychological mechanisms through which SES is expected to affect health outcomes also appear to show association with markers of inflammation. Thus, for example, levels of CRP and IL-6 have been linked to indicators of social integration such as participation in religious activities (Koenig et al., 1997). Higher SES has been found to correlate negatively with IL-6 (S.Cohen, unpublished data). There is also strong evidence from animal studies that IL-6 is related to stress (LeMay et al., 1990; Morrow et al., 1992; Takaki et al., 1994; Zhou et al., 1993), and similar relationships are expected in humans (Sternberg et al., 1992).

Fibrinogen levels, a marker of coagulation processes, have been included in analyses of SES differences in Finland and Great Britain, but such relationships have not been examined for a representative sample of the U.S. population (Markowe et al., 1985; Wilson et al., 1993). Fibrinogen has been shown to be strongly predictive of both mortality (Fried et al., 1998) and the onset of cardiovascular disease (Kannel et al., 1987; Patel et al., 1994,1995; Ridker et al., 1997; Tracy et al., 1995). Indeed, fibrinogen is as important as low levels of HDL cholesterol in predicting cardiovascular disease (de la Serna, 1994). The well-established relationship between SES and fibrinogen levels (in European data) has been suggested as the mechanism linking low social status and stress to cardiovascular disease (Brunner et al., 1996; De Boever et al., 1995; Markowe et al., 1985; Wilson et al., 1993).

Hypothalamic Pituitary Adrenal (HPA) Axis

The HPA axis plays a central role in the ongoing homeostatic regulatory processes of the body (McEwen and Stellar, 1993; Meites, 1982). Levels of cortisol and dehydroepiandrosterone sulfate (DHEAS) are indicators of axis activity. As an individual interacts with his/her environment, the stimuli encountered can serve as challenges or stressors that elicit responses from the HPA axis as well as other internal homeostatic regulatory systems. A number of the mechanisms through which race

Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
×

and SES are thought to affect health have also been shown to be related to cortisol levels (Seeman et al., 1994a; Seeman and McEwen, 1996). Recent data suggest that individuals from lower SES environments exhibit higher cortisol levels (Epel et al., 1998). Health consequences of exposure to elevated cortisol include increased cardiovascular risk (Henry, 1983), poorer cognitive functioning (Lupien et al., 1994; Seeman et al., 1997a), and increased risks for fractures (Greendale et al., 1999). Higher levels of urinary catecholamine excretion have also been shown to predict functional disability and mortality (Reuben et al., 2000b).

DHEAS has been hypothesized to serve as a functional antagonist to HPA axis activity and thus is an important indicator of overall activity in the HPA axis (Kimonides et al., 1998; Svec and Lopez, 1989). DHEAS is negatively related to a history of heart disease and mortality (Barrett-Connor and Goodman-Gruen, 1995; Beer et al., 1996; Feldman et al., 1998) as well as to physical functioning (Ravaglia et al., 1997). DHEAS is thought to be protective against heart disease because of its anticlotting and antiproliferative properties (Beer et al., 1996; Jesse et al., 1994).

Sympathetic Nervous System (SNS) Activity

SNS activity can be indicated by levels of norepinephrine and epinephrine. SNS activity is another mechanism through which racial, SES, and a variety of social/psychological mechanisms are assumed to affect population health outcomes. While these mechanisms play a major role in theories of why some race and SES groups have poor health outcomes, the relationship between SNS activity and life circumstances or health outcomes has not been clarified in the general population. However, levels of neuroendocrine functioning have been shown to relate to stress and challenge (Seeman and McEwen, 1996; Uchino et al., 1996), and endocrine patterns and cardiovascular reactivity have been related to selfefficacy and control (Gerin et al., 1995; Houston, 1972; Lundberg and Frankenhaeuser, 1978; Naditch, 1974; Rodin, 1986). Like the HPA axis, the SNS is one of the body’s central regulatory systems, assisting in maintaining the body’s overall physiological integrity in the face of changing environmental stimuli. Along with the HPA axis, the SNS is mobilized in response to challenge and serves a critical role in providing the body with the physiologic substrate needed for what have been termed “fight or flight” responses. Again, the problem is that activation of the HPA and SNS systems, while beneficial in the short-term, has been associated with increased risks for health problems such as hypertension and heart disease if activation is excessive or prolonged (Eliot et al., 1982; Krantz and Manuck, 1984; Matthews et al., 1986; Troxler et al., 1977; Williams, 1985).

Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
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Antioxidants

Antioxidants protect the body against the DNA damage that can be caused by free radicals (Marx, 1987). Low levels of antioxidants have been related to the likelihood of experiencing a heart attack or stroke (Gaziano, 1996; Gey, 1993), and have been shown to be related to hypertension (Kendall et al., 1994). Antioxidants may work to inhibit the oxidation of low density lipoproteins and thus reduce the onset of atherosclerosis (Kohlmeier et al., 1997). Higher levels of antioxidants have also been related to greater longevity (Gey, 1993), and they have been shown to protect against cognitive (Balanzs and Leon, 1994; Schmidt et al., 1996) and physical decline (Snowdon et al., 1996). Antioxidants are expected to be influenced by diet and may be related to the ingestion of vitamin supplements; through these mechanisms antioxidant levels would be hypothesized to show an association with SES and race and possibly age and gender.

Renal Function

The kidney performs several essential physiological functions, including regulation of the excretion of sodium and water, maintenance of extracellular fluid volume, regulation of acid-base homeostasis, and excretion of toxins and medications. In addition it has an important role as an endocrine organ. Although renal function, on average, declines with age, there are wide variations in the amount of decline for any individual (Lindeman et al., 1985). Lower SES has been found to predict greater renal dysfunction over time (Klahr, 1989; Rostand et al, 1989), and even mild decline in renal function has been associated with increased five-year mortality among community-dwelling older persons (Fried et al., 1998). There are a variety of possible indicators of renal function that can be gathered from either blood or urine samples, including creatinine clearance.

Lung Function

Lung function can be measured to provide an indicator of the functioning of the respiratory system. It has been measured in household surveys by the peak-flow rate. In the MacArthur sample it has been linked to both cognitive (Albert et al., 1995a) and physical decline (Seeman et al., 1994b) and has been shown to be related to both five-year mortality and SES within older populations (Cook et al., 1989, 1991). Reduced lung function is likely to be more common among those of lower SES and those with poor health behaviors.

Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
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Allostatic Load

In addition to the links between specific, individual biological parameters and health outcomes described above, analysts using the MacArthur data have been actively pursuing analyses deriving from a more integrative model of biological risk which focuses on the combined effects of multiple biological parameters as they impact on risks for various health outcomes. Much of this work is based on the concept of allostatic load, first proposed by McEwen and Stellar (1993) and further developed in McEwen (1998). Allostatic load represents the cumulative physiological toll of dysregulation across multiple systems over time, reflecting both a multisystem and life-span orientation, and is hypothesized to affect subsequent mortality, disease pathology, and aging. Recent research has established a link between allostatic load and socioeconomic status (Singer and Ryff, 1999).

Analyses based on the MacArthur data have shown a link between a summary measure of allostatic load and patterns of change in both physical and cognitive functioning, cardiovascular disease, and mortality (Seeman et al., 1997b). The first operational measure of allostatic load included information on 10 biological parameters that reflect functioning of four of the systems discussed above: the hypothalamic-pituitary-adrenal axis, sympathetic nervous system, cardiovascular system, and metabolic processes. Parameters included in initial measures of allostatic load included: systolic and diastolic blood pressure, waist-hip ratio, serum HDL and total cholesterol, blood plasma levels of glycosylated hemoglobin, DHEAS, 12-hour urinary cortisol excretion, and 12-hour urinary norepinephrine and epinephrine excretion levels. The summary measure of allostatic load was also shown to be a significantly better predictor of outcomes than the individual component factors, providing evidence that risk for these outcomes is related to the overall impact of dysregulation across the various regulatory systems. Comparison of the explanatory power of the index of allostatic load to an indicator of Syndrome X also demonstrated the value of the multisystem allostatic load approach to health outcomes.

Other related analyses using summary biological risks scores similar to allostatic load have focused on risks associated with low albumin and cholesterol and on a combined index of inflammation based on high IL-6 and C-reactive protein in combination with low albumin and cholesterol. Results of these analyses have indicated that higher scores on these summary indices are associated with increased risks for functional disability and mortality (Reuben et al., 1999, 2000a).

In the preceding section, we have listed a number of bioindicators that we feel are appropriate for inclusion in population surveys because

Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
×

of evidence of their role in causing the health differences that are the focus in demographic approaches to health. In addition, some of these factors are likely to be important in explaining both current differences and future trends in health. We believe, however, that a comprehensive approach to indicators of the health of bodily systems related to demographic factors, major health outcomes, and social, behavioral, and psychological factors remains to be completed. Most epidemiologic and demographic analysis has been limited to the inclusion of bioindicators representing cardiovas-cular and metabolic states. Epidemiological and medical results regularly add to the group of potential indicators linked to both demographic variables and health outcomes. We also believe that while a myriad of research results indicate the potential of such an approach, it has yet to be comprehensively documented with empirical data for the United States.

The issue of measurement error is relevant to the biological data as well as the self-report data. Measurement error is one factor that prevents the collection of some data as part of home-based protocols where collection and processing methods make it difficult, if not impossible, to collect certain data with any reasonable accuracy. Thus, for example, data on additional bioindicators such as plasma adrenocorticotropic hormone (ACTH) or catecholamines would be extremely difficult to collect due to the need for rapid processing and cold storage of these samples. Likewise, data that require large and complex instrumentation are likely to be difficult to collect in home-based protocols both because of the difficulty in transporting such instruments as well as the standardization of the instruments after moving them. Examples of such limitations would include current instrumentation needed to collect heart rate variability data, which is not easily transportable, and MRI instrumentation, which is large, extremely complex, and not at all transportable at this time. With respect to those protocols that are possible (e.g., saliva sampling, blood samples for certain parameters, urine collection), assays performed based on these samples should always include standard laboratory tests for reliability and validity of the assays (e.g., intra-assay coefficient of variation, sensitivity of the assay).

COLLECTION OF BIOLOGICAL INFORMATION IN THE MacARTHUR STUDY

Many of the findings cited above have been derived from or replicated in the MacArthur Study of Successful Aging with which Seeman has long been associated (see Rowe and Kahn, 1998, for an overview). We use the MacArthur Study as an illustrative example of the type of bioindicator approach that is possible with existing data. The data collection effort for this study was designed in the mid-1980s and used then-

Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
×

available technology and epidemiological knowledge. This study was among the first to explicitly design protocols for collection of biological data in subjects’ homes, including not only blood collections but overnight urine sampling as well. The home-based approach to collection of biological material used in MacArthur, however, is similar to that used in other studies including the National Institute on Aging’s Established Populations for Epidemiologic Studies of the Elderly (EPESE) studies and the Taiwan Study described by Weinstein and Willis (2000). The homebased nature of the data collection has important implications for the use of this approach with larger, more geographically dispersed samples.

There are numerous other community-based surveys that have collected biological data using a clinic setting for collection of bioindicators. These include the long-running Framingham Study, a study that began in the 1950s as a representative sample of adults living in the town of Framingham, Massachusetts. Data collection for the original cohort as well as their offspring has continued to this day and has largely, if not solely, been done through clinic-based protocols. Similar clinic-based data collection has been used for multisite, community-based studies such as the Cardiovascular Health Study (Newman et al., 1999), the Atherosclerosis Risk in Communities study (Brancati et al., 2000), CARDIA (Iribarren et al., 2000), the Study of Women’s Health Across the Nation, as well as the nationally representative NHANES surveys (Wu et al., 1999). In each case, data on biological characteristics have been collected for large cohorts of individuals through clinic-based data collection.

While all of the studies listed above provide important background relevant to the inclusion of bioindicators in demographic surveys, we describe the home-based data collection procedures used in the MacArthur study. The MacArthur Study of Successful Aging is a three-site longitudinal study of 1,189 persons aged 70–79 years at baseline. MacArthur subjects were interviewed three times: 1988, 1991, and 1996. Respondents were originally selected on the basis of age and both high physical and cognitive functioning from three community-based cohorts of the EPESE in Durham, NC, East Boston, MA, and New Haven, CT (Cornoni-Huntley et al., 1986). While this sample has better cognitive and physical functioning than a national sample of the same age, comparison of the MacArthur sample and the Longitudinal Study of Aging (LSOA) respondents from 1984 indicates that this sample is actually fairly similar to the total U.S. population aged 70–79. While it is an initially healthier sample, the sample has similar social and economic variation to the national LSOA sample. In addition, there is a large (19 percent black) minority component of the sample.

Initial data collection included a 90-minute, face-to-face interview covering detailed self and performance-based assessments of physical and

Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
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cognitive performance, health status, social and psychological characteristics, and other lifestyle characteristics. This included standard sociodemographic characteristics plus extensive information from the following domains: psychological characteristics (e.g., self-efficacy and personal mastery beliefs, depression, happiness, life satisfaction), social ties and support, and health behaviors (e.g., smoking and alcohol consumption as well as extensive data on physical activities). In addition, subjects were asked to provide blood samples and 12-hour, overnight urines. Wave 2 data collection included reassessment of all measures included in the baseline interview and collection of blood samples and 12-hour urines. In the third wave only interview data were collected; no blood or urine samples were gathered.

Biomedical Assessments

While most population surveys get self-reports on some biological indicators, the MacArthur study included measurement of seated and postural blood pressure, pulmonary function, and waist/hip ratio as part of the biomedical protocol. Systolic and diastolic pressures were calculated as the average of three readings (Hypertension Detection and Follow-up Program Cooperative Group, 1978). Waist/hip ratio was assessed by measuring the waist at its narrowest point between the ribs and iliac crest, and the hips at the iliac crest (Lohman et al., 1988). A second hip circumference was also taken at the maximal buttocks level. Pulmonary function was assessed by the peak flow rate of subjects using a mini-Wright meter. Subjects were asked to exhale at maximal effort through the meter. Measures are based on the average of three attempts (Cook et al., 1991). Physical functioning was assessed using a series of performance tests as well as being self-reported by subjects; cognitive functioning was also directly assessed with a series of tasks covering major aspects of cognition.

Physiological Measures from Blood and Urine Specimens

As noted above, both blood and urine samples were collected from MacArthur subjects. These were collected by phlebotomists and laboratory personnel who were sent to subjects’ homes the morning after the home interview to collect a blood sample and the overnight urine. The development of standardized urine collection procedures for almost 1,200 elderly across three geographic sites represented a major effort. Twelve-hour samples (from 8 PM until 8 AM) rather than 24-hour samples were used in this study to optimize participation and complete collection. Pilot data for this study indicated that a 24-hour schedule would result in

Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
×

higher refusal and incidence of incomplete collection. Pilot tests also showed that 12-hour specimens and 24-hour specimens were highly correlated for epinephrine, norepinephrine, and cortisol. The overnight collection protocol has a further advantage in that it serves to minimize the potential confounding effects of physical activity as subjects generally spent this time at home (and much of that time in bed). As a result, individual differences in overnight excretion of cortisol as well as norepinephrine and epinephrine may well reflect differences in “steady state” operating levels of their HPA axis and SNS (i.e., estimates of “basal,” non-stimulated levels of activity).

Insulated cooler packs were designed in order to permit optimal urine temperature to be maintained to preserve the specimens in the respondents’ homes. A bottle within each cooler pack contained 12 ml of 6 N HCL to acidify and preserve the urine during collection. Upon collection, the mean temperature of the specimens was 14.6°C with a pH of 2–3. A 60-ml aliquot of urine was then sent to Nichols Laboratories for assays of cortisol, epinephrine, norepinephrine, dopamine, and creatinine content. Results for each of the three outcomes are reported as “micrograms (NE, E, or C) per gram creatinine” in order to adjust for body size. Urinary free cortisol was assayed by high-performance liquid chromatography (HPLC) (Caldrella et al., 1982; Canalis et al., 1982). Urine catecholamines were extracted via column chromatography and also determined by HPLC (Krsulovic, 1983).

A phlebotomist was sent to each respondent’s home to draw blood. Although subjects were not required to fast, most blood samples were taken early in the morning before subjects had eaten. Samples were processed and frozen within eight hours of blood drawing. Nine cm3 of blood were drawn in serum-separator tubes and another 10 cm3 of blood were drawn into heparinized tubes. Blood in the serum-separator tubes was allowed to clot and the tube was centrifuged at 1,500 RCF for ten minutes in a refrigerated (4°C) centrifuge. The sera were then sent to Nichols Laboratories for standard assessments including a CBC and an SMAC and measurements of HDL and total cholesterol, dehydroepiandrosterone sulfate (DHEAS), and serum glutamic-oxaloacetic transaminase (SCOT). Two cm3 of blood from the heparinized tubes were removed after mixing for assays of glycosylated hemoglobin (HbA). This measure provides an assessment of integrated long-term blood glucose concentrations and was assayed using affinity chromatography methods (Little et al., 1983). The remainder was centrifuged and the plasma and cell pellet were frozen for future analyses. From each of the first two interviews, three vials of plasma (1 cm3 each) and a cell pellet for DNA were obtained and stored for future use. The stored samples have proven to be a valuable resource for examining biological factors whose importance was not

Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
×

yet recognized when the samples were first collected. From the stored samples assays for homocysteine (and cofactors involved in its metabolism—folic acid, B12), IL-6, and C-reactive protein have been completed using plasma samples, and APOE genotyping is in process using DNA from the cell pellets; assays for fibrinogen and antioxidants are currently being obtained.

Most of the subjects in this study agreed to provide blood and urine samples. Eighty percent (80.3%) agreed to provide blood samples and 85.8 percent consented to provide urine samples at the first interview. This resulted in baseline biological data for over 900 subjects, of whom 765 provided complete blood and urine data. Comparison of the refusers to the complete cohort suggests that they do not have distinctive characteristics (Seeman et al., 1997a). The collection of specimens does not appear to have made the sample prone to attrition. At the second interview only 4 percent of the sample interviewed at the first wave refused to participate; 5 percent refused at the third. Among those who gave blood and urine at the first interview and who were interviewed at the second wave, 91 and 93 percent, respectively, provided a second sample. About half of those who refused to provide blood and urine samples at the first interview agreed to do so at the second. This experience indicates that the request for blood and urine samples had little effect on survey response.

SUMMARY AND CONCLUSIONS

We argue that the current demographic approach to health incorporates a limited set of biological explanatory mechanisms into models containing a variety of social, economic, psychological, behavioral, and environmental indicators that must eventually affect biology. The variables included to date have been limited to those that can be self-reported in either a personal interview or telephone interview rather than those that will best clarify the mechanisms through which health differences and health changes arise. In order to clarify the mechanisms through which health trends arise and to reduce health differentials in the U.S. population, we should develop sets of proximate biological factors related to health outcomes based on our knowledge of biology and the relationship between bioindicators, demographic variables, and health outcomes. The value of population surveys addressing health issues will increase markedly when additional biological information is collected which can be used to clarify how demographic factors get under the skin to produce health outcomes.

As outlined above, studies such as the MacArthur Study of Successful Aging, which have included demographic information along with infor-

Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
×

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.

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Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
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Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
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Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
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Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
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Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
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Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
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Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
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Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
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Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
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Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
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Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
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Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
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Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
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Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
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Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
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Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
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Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
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Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
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Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
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Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
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Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
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Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
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Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
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Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
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Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
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Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
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Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
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Page 29
Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
×
Page 30
Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
×
Page 31
Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
×
Page 32
Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
×
Page 33
Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
×
Page 34
Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
×
Page 35
Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
×
Page 36
Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
×
Page 37
Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
×
Page 38
Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
×
Page 39
Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
×
Page 40
Suggested Citation:"Integrating Biology into Demographic Research on Health and Aging (With a Focus on the MacArthur Study of Successful Aging)." National Research Council. 2001. Cells and Surveys: Should Biological Measures Be Included in Social Science Research?. Washington, DC: The National Academies Press. doi: 10.17226/9995.
×
Page 41
Next: Biological Material in Household Surveys: The Interface Between Epidemiology and Genetics »
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What can social science, and demography in particular, reasonably expect to learn from biological information? There is increasing pressure for multipurpose household surveys to collect biological data along with the more familiar interviewer-respondent information. Given that recent technical developments have made it more feasible to collect biological information in non-clinical settings, those who fund, design, and analyze survey data need to think through the rationale and potential consequences. This is a concern that transcends national boundaries. Cells and Surveys addresses issues such as which biologic/genetic data should be collected in order to be most useful to a range of social scientists and whether amassing biological data has unintended side effects. The book also takes a look at the various ethical and legal concerns that such data collection entails.

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