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. 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.

FIGURE 2-1

Health outcomes included in contemporary demographic surveys.

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 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 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 additional 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.

FIGURE 2-2

Model of health outcomes employed with contemporary demographic surveys.

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 Ben-Shlomo, 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.

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.

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. 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 hypothalamic-pituitary-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.

BOX 2-1

Biological Measures that are Related to Demographic, Social, Psychological and Behavioral Influences and Health Outcomes.

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-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 home-based 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 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.

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 information 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, Boyce T, Chesney M, Cohen S, Folkman S, Kahn R, Syme L. Socioeconomic status and health. American Psychologist. 1994;49:15–24. [PubMed: 8122813]
• Adler N, Boyce T, Chesney M, Folkman S, Syme L. Socioeconomic inequalities in health. Journal of American Medical Association. 1993;269(24):3140–3145. [PubMed: 8505817]
• Albert M, Graffagnino C, McClenny C, DeLong D, Strittmatter W, Saunders A, Roses A. ApoE genotype and survival from intracerebral hemorrhage. Lancet. 1995;346:575. [PubMed: 7658797]
• Albert M, Jones K, Savage C, Berkman L, Seeman T, Blazer D, Rowe J. Predictors of cognitive change in older persons: MacArthur Studies of Successful Aging. Psychology and Aging. 1995;10:578–589. [PubMed: 8749585]
• Arnesen E, et al. Serum total homocysteine and coronary heart disease. International Journal of Epidemiology. 1995;23:704–709. [PubMed: 8550266]
• Balanzs L, Leon M. Evidence of an oxidative challenge in the Alzheimer's brain. Neurochemical Research. 1994;19:1131. [PubMed: 7824065]
• Barker D. Mothers, Babies, and Health in Later Life. 2nd Edition. London: Churchill Livingstone; 1998.
• Barrett-Connor E, Goodman-Gruen D. The epidemiology of DHEAS and cardiovascular disease. Annals of the New York Academy of Sciences. 1995;774:259–270. [PubMed: 8597464]
• Beer N, Jakubowicz D, Matt D, Beer R, Nestler J. Dehydroepiandrosterone reduces plasma plasminogen activator inhibitor type I and tissue plasminogen activator antigen in men. American Journal of the Medical Sciences. 1996;311:205–210. [PubMed: 8615394]
• Benfante R, Reed D, Brody J. Biological and social predictors of health in an aging cohort. Journal of Chronic Diseases. 1985;28:385–395. [PubMed: 3998053]
• Berkman L, Seeman T, Albert M, Blazer D, Robert R. High, usual and impaired functioning in community-dwelling older men and women: Findings from the MacArthur Foundation Research Network on Successful Aging. Journal of Clinical Epidemiology. 1993;46:1129–1140. [PubMed: 8410098]
• Blackwell D, Hayward M, Crimmins E. Does childhood health affect chronic morbidity in later life? Social Science and Medicine. in press. [PubMed: 11281409]
• Bongaarts J. A framework for analyzing the proximate determinants of fertility. Population and Development Review. 1978;4:105–132.
• Brancati FL, Kao WHL, Folsom AR, Watson RL, Szklo M. Incident type 2 diabetes mellitus in African American and white adults: The Atherosclerosis Risk in Communities Study. Journal of American Medical Association. 2000;283:2253–2259. [PubMed: 10807384]
• Brownson RC, Jackson-Thompson J, Wilkerson JC, Kiani F. Reliability of information on chronic disease risk factors collected in the Missouri behavioral risk factor surveillance system. Epidemiology. 1994;5:545–549. [PubMed: 7986871]
• Brunner E, Smith G, Marmot M, Canner R, Beksinska M, O'Brien J. Childhood social circumstances and psychosocial and behavioural factors as determinants of plasma fibrinogen. Lancet. 1996;347:1008–1013. [PubMed: 8606563]
• Bucher H, Ragland D. Socioeconomic indicators and mortality from coronary heart disease and cancer: A 22 year follow-up of middle-aged men. American Journal of Public Health. 1995;85:1231–1236. [PMC free article: PMC1615577] [PubMed: 7661230]
• Bush T, Miller S, Golden A, Hale W. Self-report and medical record agreement of selected medical conditions in the elderly. American Journal of Public Health. 1989;85:1231–1236. [PMC free article: PMC1349815] [PubMed: 2817172]
• Caldrella A, Reardon G, Canalis E. Analysis of cortisol in serum by liquid chromatography. Clinical Chemistry. 1982;28:538–543. [PubMed: 7067101]
• Canalis E, Reardon G, Caldrella A. A more specific, liquid chromatography for free cortisol in urine. Clinical Chemistry. 1982;28:2418–2420. [PubMed: 7139923]
• Cattin L, Bordin P, Fonda M, Adamo C, Barbone F, Bovenzi M, Manto A, Pedone C, Panor M. Factors associated with cognitive impairment among older Italian inpatients. Journal of American Geriatrics Society. 1997;45:1124–1130. [PubMed: 9361657]
• Cohen H, Pieper C, Harris T, Rao KM, Currie M. Plasma IL-6: An indicator of functional disability in community dwelling elderly. Journal of Gerontology: Medical Sciences. 1997;52A:M201–M208. [PubMed: 9224431]
• Colsher P, Wallace R. Longitudinal application of cognitive function measures in a defined population of community-dwelling elders. Annals of Epidemiology. 1991;1:215–230. [PubMed: 1669503]
• Cook N, Evans D, Scherr P, Speizer F, et al. Peak expiratory flow rate in an elderly population. American Journal of Epidemiology. 1989;130:66–78. [PubMed: 2787111]
• Cook NR, Evans DA, Scherr PA, Speizer FE, Taylor JO, Hennekens CH. Peak expiratory flow rate and 5-year mortality in an elderly population. American Journal of Epidemiology. 1991;133:784–794. [PubMed: 2021145]
• Corder EH, Saunders A, Strittmatter W, Schmechel D, Gaskell P, Small G, Roses A, Haines J, Pericak-Vance M. Gene dose of apolipoprotein-E type 4 allele and the risk of Alzheimer's disease in late-onset families. Science. 1993;261:921–923. [PubMed: 8346443]
• Cornoni-Huntley J, Brock DM, Ostfeld A, Taylor JO, Wallace RB, editors. Established Populations for Epidemiological Studies of the Elderly, Resource Data Book. Bethesda, MD: National Institutes of Health; 1986. NIH Pub. No. 86-2443.
• Corti M, Guralnik JM, Salive ME, Harris T, Field TS, Wallace RB, Berkman LF, Seeman TE, Glynn RJ, Hennekens CH, Havlik RJ. HDL cholesterol predicts coronary heart disease mortality in older persons. Journal of American Medical Association. 1995;274(7):539–544. [PubMed: 7629981]
• Craft S, Dagogo-Jack S, Wiethop B, Murphy C, Nevins R, Fleischman S, Rice V, Newcomer J, Cryer P. Effects of hyperglycemia on memory and hormone levels in dementia of the Alzheimer type: A longitudinal study. Behavioral Neuroscience. 1993;107:926–940. [PubMed: 8136068]
• Crimmins E. Demography: The past 30 years, the present, and the future. Demography. 1993;30(4):579–591. [PubMed: 8262281]
• Crimmins E. Mixed trends in population health among older adults. Journal of Gerontology: Social Sciences. 1996;51B(5):S223–S225. [PubMed: 8809006]
• Crimmins E, Hayward M, Saito Y. Changing mortality and morbidity rates and the health status and life expectancy of the older U.S. population. Demography. 1994;31:159–175. [PubMed: 8005340]
• De Boever E, De Bacquer D, Braeckman L, Baele G, Rosseneu M, De Backer G. Relation of fibrinogen to lifestyles and to cardiovascular risk factors in a working population. International Journal of Epidemiology. 1995;24(5):915–921. [PubMed: 8557447]
• de la Serna G. Fibrinogen: A new major risk factor for cardiovascular disease: A review of the literature. Journal of Family Practice. 1994;39:468–477. [PubMed: 7964545]
• Eliot RS, Buell JC, Dembroski TM. Bio-behavioral perspectives on coronary heart disease, hypertension, and sudden cardiac death. Acta Medica Scandinavica. 1982;13(suppl. 660):203–219. [PubMed: 6958189]
• Epel E, McEwen B, Ickovics J. Embodying psychological thriving: Physical thriving in response to stress. Journal of Social Issues. 1998;54:301–322.
• Ettner S. New evidence in the relationship between income and health. Journal of Health Economics. 1996;15:67–86. [PubMed: 10157429]
• Evans D, Beckett L, Field T, Albert M, Benneett D, Tycko B, Mayeux R. Apolipoprotein E epsilon 4 and incidence of Alzheimer disease in a community population of older persons. Journal of American Medical Association. 1997;277:822–824. [PubMed: 9052713]
• Ewbank D. The genetic make-up of population and its implications for mortality by cause of death: Links between Alzheimer's and ischemic heart disease. Symposium on Health and Mortality; Brussels, Belgium. 1997.
• Feldman H, Johannes C, McKinlay J, Longcope C. Low dehydroepiandrosterone sulfate and heart disease in middle-aged men: Cross-sectional results from the Massachusetts Male Aging Study. Annals of Epidemiology. 1998;8:217–228. [PubMed: 9590600]
• Fried L, Kronmal R, Newman A, Bild D, Mittelmark M, Polak J, Robbins J, Gardin J. Risk factors for 5-year mortality in older adults: The Cardiovascular Health Study. Journal of American Medical Association. 1998;279:585–592. [PubMed: 9486752]
• Gaziano M. Antioxidants in cardiovascular disease: Randomized trials. Nutrition. 1996;12:584–588. [PubMed: 8878166]
• Gerin W, Litt M, Deich J, Pickering T. Self-efficacy as a moderator of perceived control effects on cardiovascular reactivity: Is enhanced control always beneficial? Psychosomatic Medicine. 1995;57:390–397. [PubMed: 7480569]
• Gey K. Prospects for the prevention of free radical disease, regarding cancer and cardiovascular disease. British Medical Bulletin. 1993;49:679–699. [PubMed: 8221032]
• Giles W, Croft J, Keenan N, Lane M, Wheeler F. Validity of self-reported hypertension and correlates of hypertension awareness among blacks and whites within the stroke belt. American Journal of Preventive Medicine. 1995;11:163–169. [PubMed: 7662395]
• Gillum RF. The association between serum albumin and HDL and total cholesterol. Journal of the National Medical Association. 1993;85(4):290–292. [PMC free article: PMC2571905] [PubMed: 8478970]
• Gillum RF, Ingram D. Relation between serum-albumin concentration and stroke incidence and death: the NHANES-I epidemiological follow-up study. American Journal of Epidemiology. 1994;140(10):876–888. [PubMed: 7977275]
• Gradman T, Laws A, Thompson L, Reaven G. Verbal learning and/or memory improves with glycemic control in older subjects with non-insulin-dependent diabetes mellitus. Journal of American Geriatrics Society. 1993;41:1305–1312. [PubMed: 8227912]
• Greendale G, Unger JB, Rowe JW, Seeman T. The relation between cortisol excretion and fractures in healthy older peope: Results from the MacArthur Studies of Successful Aging. Journal of American Geriatrics Society. 1999;47(7):799–803. [PubMed: 10404922]
• Grossman M. The Demand for Health: A Theoretical and Empirical Investigation. New York: National Bureau of Economic Research; 1972.
• Henry J. Coronary heart disease and arousal of the adrenal cortical axis. In: Dembroski T, Schmidt T, Blumchen G, editors. Biobehavioral Bases of Coronary Heart Disease. Basel: Karger; 1983. pp. 365–381.
• Herzog R, Wallace R. Measures of cognitive-functioning in the AHEAD study. Journal of Gerontology: Psychological Sciences. 1997;52B:293–293. [PubMed: 9215356]
• Hofman A, Ott A, Breteler M, Bots M, Slooter A, Harskamp F, Dujin C, Broeckhoven C, Brobbee D. Atherosclerosis, apolipoprotein E, and prevalence of dementia and Alzheimer's disease in the Rotterdam Study. Lancet. 1997;349:151–154. [PubMed: 9111537]
• Houston B. Control over stress, locus of control, and response to stress. Journal of Personality and Social Psychology. 1972;21:249–255. [PubMed: 5010407]
• Howe G. Practical uses of biomarkers in population studies. In: Mendelsohn M, Mohr L, Peeters J, editors. Biomarkers: Medical and Workplace Applications. Washington, DC: Joseph Henry Press; 1998. pp. 41–49.
• Hummer R. Black-white differences in health and mortality: A review and conceptual model. The Sociological Quarterly. 1996;37:105–125.
• Hyman B, Gomez-Isla T, Briggs M, Chung H, Nichols S, Kohour F, Wallace R. Apolipoprotein E and cognitive change in an elderly population. Annals of Neurology. 1996;40:55–66. [PubMed: 8687193]
• Hypertension Detection, and Follow-up Program Cooperative Group (HDFP). Variability of blood pressure and results of screening in HDFP program. Journal of Chronic Diseases. 1978;31:651–667. [PubMed: 730822]
• Iribarren C, Sidney S, Bild DE, Liu K, Markovitz JH, Roseman JM, Matthews KP. Association of hostility with coronary artery calcification in young adults: The Cardia Study. Journal of American Medical Association. 2000;283(19):2546–2551. [PubMed: 10815118]
• Jacques P, Riggs K. B vitamins as risk factors for age-related diseases. In: Rosenberg IH, editor. Nutritional Assessment of Elderly Populations: Measure and Function. New York: Raven Press; 1995.
• Jarvik G, Wijsman E, Kukull W, Schellenberg G, Yu C, Larson E. Interactions of apolipoprotein E genotype, total cholesterol level, age, and sex in prediction of Alzheimer's disease: A case-control study. Neurology. 1995;45:1092–1098. [PubMed: 7783869]
• Jesse R, et al. Dehydroepiandrosterone inhibits human platelet aggregation in vitro and in vivo. Annals of the New York Academy of Sciences. 1994;774:281–290. [PubMed: 8597466]
• Kannel W, Wolf P, Castelli W, D'Agostino R. Fibrinogen and risk of cardiovascular disease: The Framingham Study. Journal of American Medical Association. 1987;258:1183–1186. [PubMed: 3626001]
• Kaplan G, Keil J. Socioeconomic factors and cardiovascular disease. Circulation. 1993;88:1973–1998. [PubMed: 8403348]
• Kehoe R, Wu S, Leske C, Chylack L Jr. Comparing self-reported and physician-reported medical history. American Journal of Epidemiology. 1994;139(8):813–818. [PubMed: 8178794]
• Kendall M, Rajman I, Maxwell S. Cardioprotective therapeutics: Drugs used in hypertension, hyperlipidaemia, thromboembolism, arrhythmias, the postmenopausal state and as anti-oxidants. Postgraduate Medical Journal. 1994;70:329–343. [PMC free article: PMC2397611] [PubMed: 8016003]
• Kimonides V, Khatibi N, Sofroniew M, Herbert J. Dehydroepiandrosterone (DHEA) and DHEA-sulfate (DHEAS) protect hippocampal neurons against excitatory amino acid-induced neurotoxicity. Proceedings of the National Academy of Sciences of the United States of America. 1998;95:1852–1857. [PMC free article: PMC19202] [PubMed: 9465106]
• Klahr S. The kidney in hypertension—villain and victim. New England Journal of Medicine. 1989;320:731–733. [PubMed: 2922017]
• Koenig H, Cohen H, George L, Hays J, Larson D, Blazer D. Attendance at religious services, interleukin-6, and other biological parameters of immune function in older adults. International Journal of Psychiatry in Medicine. 1997;27:233–250. [PubMed: 9565726]
• Kohlmeier L, Kark J, Gomez-Gracia E, Martin B, Steck S, Kardinaal A, Ringstad J, Thamm M, Masaev V, Riemersma R, Martin-Moreno J, Huttunen J, Kok F. Lycopene and myocardial infarction risk in the Euramic Study. American Journal of Epidemiology. 1997;146:618–626. [PubMed: 9345115]
• Krantz DS, Manuck SB. Acute psychophysiological reactivity and risk of cardiovascular disease: A review and methodologic critique. Psychological Bulletin. 1984;96:435–464. [PubMed: 6393178]
• Krsulovic AM. Investigations of catecholamine metabolism using high performance liquid chromatography: Analytical methodology and clinical applicators. Journal of Chromatography. 1983;9:1–34. [PubMed: 7085817]
• Kruk Z, Pycock C. Neurotransmitters and Drugs. Baltimore: University Park Press; 1983.
• Kuh D, Ben-Shlomo B. Life Course Approach to Chronic Disease Epidemiology. Oxford: Oxford University Press; 1997.
• Kuller L, Eichner J, Orchard T, Grandits G, McCallum L, Tracy R. The relation between serum albumin levels and risk of coronary heart disease in the Multiple Risk Factor Intervention Trial. American Journal of Epidemiology. 1991;134:1266–1277. [PubMed: 1755441]
• Kuller L, Tracy R, Shaten J, Meilahn E. Relation of C-reactive protein and coronary heart disease in the MRFIT nested case-control study. American Journal of Epidemiology. 1996;144:537–547. [PubMed: 8797513]
• LeMay L, Vander A, Kluger M. The effects of psychological stress on plasma interleukin-6 activity in rats. Physiology and Behavior. 1990;47:957–961. [PubMed: 2388952]
• Lindeman R, Tobin J, Shock N. Longitudinal studies on the rate of decline in renal function with age. Journal of American Geriatrics Society. 1985;33:278–286. [PubMed: 3989190]
• Link BG, Phelan J. Social conditions as fundamental causes of disease. Journal of Health and Social Behavior. 1995;36(extra issue):80–94. [PubMed: 7560851]
• Little RR, England J, Wiedmeyer H, Goldstein D. Effects of whole blood storage on results of glycosolated hemoglobin as measured by ion exchange chromatography, affinity chromatography and colorimetry. Clinical Chemistry. 1983;29:1080–1082. [PubMed: 6851106]
• Lohman TG, Roche AF, Martorell R. Anthropometric Standardization Reference Manual. Champaign, IL: Human Kinetics Books; 1988.
• Lu R, Crimmins E, Seeman T. Measurement of Hypertension in Sample Surveys of the U.S. Elderly Population. Paper presented at the annual meeting of the Gerontological Society of America; Philadelphia. PA. 1998.
• Luc E, Bard J, Arveiler D, Evans A, Cambou J, Bingham A, Amouyel P, Schaffer P, Ruidavets J, Cambien F. Impact of apolipoprotein E polymorphism on lipoproteins and risk of myocardial infarction: The ECTIM study. Arteriosclerosis and Thrombosis. 1994;14:1412–1419. [PubMed: 8068601]
• Lundberg U, Frankenhaeuser M. Psychophysiological reactions to noise as modified by personal control over stimulus intensity. Biological Psychology. 1978;6:51–59. [PubMed: 623858]
• Luoto R, Pekkanen J, Utela A, Tuomilehto J. Cardiovascular risks and socioeconomic status: Differences between men and women in Finland. Journal of Epidemiology and Community Health. 1994;48:348–354. [PMC free article: PMC1059982] [PubMed: 7964332]
• Lupien S, LeCours A, Lussier I, Schwartz G, Nair N, Meaney M. Basal cortisol levels and cognitive deficits in human aging. Journal of Neuroscience. 1994;14:2893–2903. [PMC free article: PMC6577490] [PubMed: 8182446]
• Lynch J, Kaplan G, Cohen R, Tuomilehto J, Salonen J. Do cardiovascular risk factors explain the relation between socioeconomic status and risk of all-cause mortality, cardiovascular mortality, and acute myocardial infarction? American Journal of Epidemiology. 1996;144:934–942. [PubMed: 8916504]
• Maestre G, Ottman R, Stern Y, Gurland B, Chun M, Tang M, Shelanski M, Tycko B, Mayeux R. Apolipoprotein-E and Alzheimer's disease: Ethnic variation in genotype risks. Annals of Neurology. 1995;37:254–259. [PubMed: 7847867]
• Manning C, Hall J, Gold P. Glucose effects on memory and other neuropsychological tests in elderly humans. Psychological Science. 1990;5:307–311.
• Markowe H, Marmot M, Shipley M, Bulpitt C, Meade T, Stirling Y, Vickers M, Semmence A. Fibrinogen: A possible link between social class and coronary heart disease. British Medical Journal. 1985;291:1312–1314. [PMC free article: PMC1417417] [PubMed: 3933646]
• Marmot M, Ryff C, Bumpass L, Shipley M, Marks N. Social inequalities in health: Next question and converging evidence. Social Science and Medicine. 1997;44:901–910. [PubMed: 9080570]
• Marx J. Oxygen free radicals linked to many diseases. Science. 1987;235:529–531. [PubMed: 3810154]
• Matthews K, Weiss S, Detre T, et al. Handbook of Stress, Reactivity, and Cardiovascular Disease. New York: John Wiley & Sons; 1986.
• McEwen B. Protective and damaging effects of stress mediators. New England Journal of Medicine. 1998;338:171–179. [PubMed: 9428819]
• McEwen B, Stellar E. Stress and the individual: Mechanisms leading to disease. Archives of Internal Medicine. 1993;153:2093–2101. [PubMed: 8379800]
• Meites J. Changes in neuroendocrine control of anterior pituitary function during aging. Neuroendocrinology. 1982;34:151–156. [PubMed: 6280091]
• Mendall M, Patel P, Ballam L, Strachan D, Northfield T. C-reactive protein and its relation to cardiovascular risk factors: A population based cross sectional study. British Medical Journal. 1996;312:1049–1050. [PMC free article: PMC2350910] [PubMed: 8616412]
• Morrow LE, McClellan JL, Conn CA, Kluger MJ. Glucocorticoids alter fever and IL-6 responses to psychological stress and to lipopolysaccharide. American Journal of Physiology. 1992;264:R1010–R1016. [PubMed: 8498588]
• Mosley WH, Chen LC. An analytical framework for the study of child survival in developing countries. Population Development Review. 1984;10(Suppl):25–45.
• Naditch M. Locus of control, relative discontent and hypertension. Social Psychiatry. 1974;9:111–117.
• Newman AB, Enright P, McBurnie M, Bittner V, Tracy R, McNamara R. Predictors of 6 minute walk distance in a community-based cohort: The Cardiovascular Health Study. Journal of American Geriatrics Society. 1999;47:S79.
• Patel P, Carrington D, Strachan D, Leatham E, Goggin P, Northfield T, Mendall M. Fibrinogen: A link between chronic infection and coronary heart disease. Lancet. 1994;343(8913):1634–1635. [PubMed: 7911935]
• Patel P, Mendall M, Carrington D, Strachan D, Leatham E, Molineaux N. Association of helicobacter pylori and chlamydia pneumonia infections with coronary heart disease and cardiovascular risk factors. British Medical Journal. 1995;311:711–714. [PMC free article: PMC2550716] [PubMed: 7549683]
• Payami H, et al. Apolipoprotein E polymorphism and Alzheimer's disease. Lancet. 1992;342:697–699.
• Preston S, Taubman P. Socioeconomic differences in adult mortality and health status. In: Martin L, Preston S, editors. Demography of Aging. Washington, DC: National Academy Press; 1994. pp. 279–318. [PubMed: 25144016]
• Ravaglia G, Forti P, Maioli F, Boschi F, Cicognani A, Bernardi M, Pratelli L, Pizzoferrato A, Porcu S, Gasbarrini G. Determinants of functional status in healthy Italian nonagenarians and centenarians: A comprehensive functional assessment by the instruments of geriatric practice. Journal of American Geriatrics Society. 1997;45(10):1196–1202. [PubMed: 9329480]
• Reaven F. Banting lecture 1988: Role of insulin resistance in human disease. Diabetes. 1988;37:1595–1607. [PubMed: 3056758]
• Reaven G, Thompson L, Nahum D, Haskins E. Relationship between hyperglycemia and cognitive function in older NIDDM patients. Diabetes Care. 1990;13:16–21. [PubMed: 2298111]
• Reuben D, Ix J, Greendale G, Seeman T. The predictive value of combined hypoalbuminemia and hypocholesterolemia in high functioning community-dwelling older persons. Journal of American Geriatrics Society. 1999;47:402–406. [PubMed: 10203113]
• Reuben D, Chen A, Harris T, Seeman T. Peripheral blood markers of inflammation predict mortality and functional decline among high functioning community-dwelling older persons: MacArthur Studies of Successful Aging. 2000. (submitted)
• Reuben D, Talvi S, Seeman T. High urinary catecholamine excretion predicts mortality and functional decline in high functioning community-dwelling older persons: MacArthur Studies of Successful Aging. 2000. (submitted) [PubMed: 11034236]
• Ricklefs R, Finch C. Aging: A Natural History. New York: Scientific American Library; 1995.
• Ridker P, Cushman M, Stampfer M, Tracy R, Hennekens C. Inflammation, aspirin, and the risk of cardiovascular disease in apparently healthy men. New England Journal of Medicine. 1997;336:973–979. [PubMed: 9077376]
• Riggs KM, Spiro A, Tucker K, Rush D. Relations of vitamin B-12, vitamin B-6, folate and homocysteine to cognitive performance in the Normative Aging Study. American Journal of Clinical Nutrition. 1996;63:306–314. [PubMed: 8602585]
• Rodin J. Health, control, and aging. In: Baltes M, Baltes P, editors. The Psychology of Control and Aging. Hillsdale: Lawrence Erlbaum; 1986. pp. 139–161.
• Rogers R, Hummer R, Nam C. Living and Dying in the USA: Behavioral, Health, and Social Differentials of Adult Mortality. San Diego: Academic Press; 2000.
• Rostand S, Brown G, Kirk K, Rutsky E, Dustan H. Renal insufficiency in treated essential hypertension. New England Journal of Medicine. 1989;320:684–688. [PubMed: 2922014]
• Saunders A, Strittmatter W, Schmechel D, George-Hyslop P, Pericak-Vance M. Association of apolipoprotein E allele e4 with late-onset familial and sporadic Alzheimer's disease. Neurology. 1993;43:1467–1472. [PubMed: 8350998]
• Schmidt R, Hayn M, Fazekas F, Kapeller P, Esterbauer H. Magnetic resonance imaging white matter hyperintensities in clinically normal elderly individuals: Correlations with plasma concentrations of naturally occurring antioxidants. Stroke. 1996;27:2043–2047. [PubMed: 8898813]
• Schrijvers C, Stronks K, van de Mheen D, Coebergh J, Mackenbach J. Validation of cancer prevalence data from a postal survey by comparison with cancer registry records. American Journal of Epidemiology. 1994;139(4):408–414. [PubMed: 8109575]
• Schulte P, Rothman N. Epidemiological validation of biomarkers of early biological effect and susceptibility. In: Mendelsohn M, Mohr L, Peeters J, editors. Biomarkers: Medical and Workplace Applications. Washington, DC: Joseph Henry Press; 1998. pp. 23–32.
• Seeman T, Berkman L, Blazer D, Rowe J. Social ties and support and neuroendocrine function: MacArthur Studies of Successful Aging. Annals of Behavioral Medicine. 1994;16:95–106.
• Seeman T, Charpentier P, Berkman L, Tinetti M, Guralnik J, Albert M, Blazer D, Rowe J. Predicting changes in physical performance in a high functioning elderly cohort: MacArthur studies of successful aging. Journal of Gerontology. 1994;49:M97–M108. [PubMed: 8169338]
• Seeman T, McEwen B. Social environment characteristics and neuroendocrine function: The impact of social ties and support on neuroendocrine regulation. Psychosomatic Medicine. 1996;58:459–471. [PubMed: 8902897]
• Seeman T, McEwen B, Singer B, Albert M, Rowe J. Increase in urinary cortisol excretion and declines in memory: MacArthur Studies of Successful Aging. Journal of Clinical Endocrinology and Metabolism. 1997;82:2458–2465. [PubMed: 9253318]
• Seeman T, Singer B, Horwitz R, McEwen B. The price of adaptation—allostatic load and its health consequences: MacArthur Studies of Successful Aging. Archives of Internal Medicine. 1997;157:2259–2268. [PubMed: 9343003]
• Singer B, Ryff C. Hierarchies of life histories and associated health risks. Annals of the New York Academy of Sciences. 1999;896:96–115. [PubMed: 10681891]
• Smith J, Kington R. Demographic and economic correlates of health in old age. Demography. 1997;34(1):159–170. [PubMed: 9074837]
• Smith J, Kington R. Race, socioeconomic status, and health in late life. In: Martin L, Soldo B, editors. Racial and Ethnic Differences in the Health of Older Americans. Washington, DC: National Academy Press; 1997. pp. 106–162. [PubMed: 23115810]
• Snowdon D, Gross M, Butler S. Antioxidants and reduced functional capacity in the elderly: Findings from the Nun Study. Journal of Gerontology: Medical Science. 1996;51A:M10–M16. [PubMed: 8548507]
• Sprott R. Biomarkers of aging. Journal of Gerontology: Biological Sciences. 1999;54:B464–B465. [PubMed: 10619309]
• Sternberg E, Chrousos G, Wilder R, Gold P. The stress response and the regulation of inflammatory disease. Annals of Internal Medicine. 1992;117:854–866. [PubMed: 1416562]
• Svec S, Lopez A. Antiglucocorticoid actions of dehydroepiandrosterone and low concentrations in Alzheimer's disease. Lancet. 1989;2:1335–1336. [PubMed: 2574281]
• Takaki A, Huang QH, Somogyvari VA, Arimura A. Immobilization stress may increase plasma interleukin-6 via central and peripheral catecholamines. Neuroimmunomodulation. 1994;1:335–342. [PubMed: 7671121]
• Taylor S, Repetti R, Seeman T. Health psychology: What is an unhealthy environment and how does it get under the skin? Annual Review of Psychology. 1997;48:s411–447. [PubMed: 9046565]
• Tracy R, Bovill E, Yanez D, Psaty B, Fried L, Heiss G, Lee M, Polak J, Savage P., the Cardiovascular Health Study investigators. Fibrinogen and Factor VIII, but not Factor VII, are associated with measures of subclinical cardiovascular disease in the elderly: Results from the Cardiovascular Health Study. Arteriosclerosis, Thrombosis and Vascular Biology. 1995;15:1269–1279. [PubMed: 7670938]
• Tracy R, Lemaitre R, Psaty B, Ives D, Evans R, Cushman M, Meilahn E, Kuller L. Relationship of C-reactive protein to risk of cardiovascular disease in the elderly: Results from the Cardiovascular Health Study and the Rural Health Promotion Project. Arteriosclerosis, Thrombosis and Vascular Biology. 1997;17:1121–1127. [PubMed: 9194763]
• Troxler RG, Sprague EA, Albanese RA, Fuchs R, Thompson AJ. The association of elevated plasma cortisol and early atherosclerosis as demonstrated by coronary angiography. Atherosclerosis. 1977;26:151–162. [PubMed: 836352]
• Uchino B, Cacioppo J, Kiecolt-Glaser K. The relationship between social support and physiological processes: A review with emphasis on underlying mechanisms and implications for health. Psychological Bulletin. 1996;119:488–531. [PubMed: 8668748]
• Verbrugge L. The social roles of the sexes and their relative health and mortality. In: Lopez A, Ruzicka L, editors. Sex Differentials in Mortality: Trends, Determinants and Consequences. Canberra: Australian National University Press; 1983. pp. 221–245.
• Verbrugge L, Jette A. The disablement process. Social Science and Medicine. 1994;38(1):1–14. [PubMed: 8146699]
• Verhoef P, Stampfer M, Buring J, Gaziano J, Allen R, Stabler S, Reynolds R, Kok F, Hennekens C, Willett W. Homocysteine metabolism and risk of myocardial infarction: Relation with vitamins B₆, B₁₂, and folate. American Journal of Epidemiology. 1996;143:845–859. [PubMed: 8610698]
• Waldron I. What do we know about causes of sex differences in mortality? A review of the literature. Population Bulletin of the United States. 1986;8:59–76. [PubMed: 12314310]
• Weaver JD, Albert T, Harris M, Rowe JW, Seeman TE. Interleukin-6 as a predictor of cognitive function and cognitive decline. 2000. (submitted)
• Weinstein M, Willis R. Stretching Social Surveys to Include Bioindicators: Possibilities for the Health and Retirement Study, Experience from the Taiwan Study of the Elderly; Paper prepared for the Workshop on Collecting Biological Indicators and Genetic Information in Household Surveys; National Research Council, Washington, DC. February 10-11. 2000.
• Williams R Jr. Neuroendocrine response patterns and stress: Biobehavioral mechanisms of disease. In: Williams R Jr., editor. Perspectives on Behavioral Medicine: Neuroendocrine Control and Behavior. Vol. 2. Orlando, FL: Academic Press Inc.; 1985. pp. 71–101.
• Wilson T, Kaplan G, Kauhanen J, Cohen R, Wu M, Salonen R, Salonen J. Association between plasma fibrinogen concentration and five socioeconomic indices in the Kuopio Ischemic Heart Disease Risk Factor Study. American Journal of Epidemiology. 1993;137(3):292–300. [PubMed: 8452137]
• Winkleby M, Jatulis D, Frank E, Fortmann S. Socioeconomic status and health: How education, income and occupation contribute to risk factors for cardiovascular disease. American Journal of Public Health. 1992;82:816–820. [PMC free article: PMC1694190] [PubMed: 1585961]
• Wu T, Trevisan M, Genco R, Dorn J, Falkner K, Sempos C. Periodontal disease as a risk factor for CVD, CHD, and stroke: The first National Health and Nutrition Examination Survey (NHANES I) and its follow-up study. Circulation. 1999;99(8):1109.
• Zelinski E, Crimmins E, Reynolds S, Seeman T. Do medical conditions affect cognition in older adults? Health and Psychology. 1999;17(6):504–512. [PubMed: 9848800]
• Zhou D, Kusnecov A, Shurin M, DePaoli M, Rabin B. Exposure to physical and psychological stressors elevates plasma interleukin 6: Relationship to the activation of hypothalamic-pituitary-adrenal axis. Endocrinology. 1993;133:2523–2530. [PubMed: 8243274]