As noted at the outset of this chapter, biodemography pertains to two different fields, namely biological demography and biomedical demography. These two fields are as distinct as biology is from biomedicine. This chapter has emphasized the concepts and findings of biological demography, in part because the concepts and findings are less familiar to most demographers. The last few pages of this chapter will now turn to the other branch of biodemography.
The number of demographers working in the area of biomedical demography is at least as large as the number working in biological demography. Grant funding is substantially greater and publications are at least as numerous. The field of biomedical demography is innovative and important, with the potential for making contributions that help improve public health. The field can essentially be characterized as the interface between demography and epidemiology. Demography and epidemiology intersect and overlap. Demographers more frequently focus on how diseases and disabilities influence the structure and dynamics of a population, whereas epidemiologists are typically concerned with how population patterns of a specific disease of interest shed light on the etiology, prevention, and cure of the disease. Many demographers have acquired a substantial knowledge of the biology of various diseases and disabilities and have developed models of morbidity and mortality. Some relate disease and disability patterns and trends in a population to consequences for health care systems. Demographers and epidemiologists often collaborate on designing better surveys, questionnaires, and health measurements.
The field of biomedical demography emerged over the past two decades and is now flourishing. This development was fostered by funding from the Behavioral and Social Science branch of the U.S. National Institute on Aging. The head of this branch, Richard Suzman, deserves credit for recognizing and supporting the role of demographers in biomedical research. Other sources of inspiration and funding have been the Italian National Institute on Aging, headed by Claudio Franceschi, and the epidemiology and demography program at the University of Southern Denmark, currently under the leadership of Kaare Christensen and Bernard Jeune.
A key event in the history of biomedical demography was a National Research Council workshop in 2000 called Cells and Surveys: Should Biological Measures be Included in Social Science Research? The workshop was organized and chaired by Caleb Finch, James Vaupel, and Kevin Kinsella (Finch et al. 2000). The workshop sought to address questions such as the following: What can social science in general and demography in particular reasonably expect to learn from biomedical information? Which genetic, pedigree, historical, and environmental data ought to be collected in order to be most useful to a wide range of scientists? The volume published from this workshop (Finch et al. 2000) includes chapters on the use of bioindicators in demographic and social research, the potential of using genetic information in demography, research on aging human subjects, the relevance of animal models for human populations, value-added survey research, and consent and privacy issues.
Currently, several major research projects are underway that are headed or co-headed by biomedical demographers. In the United States the three most notable are the Health and Retirement Survey (HRS), the National Long Term Care Survey (NLTCS), and the MacArthur Study of Successful Aging. Soldo played a major role in designing the HRS, Manton has long directed the NLTCS, and Crimmins, Haywood, and Singer have worked with the MacArthur data. The very large Chinese Longitudinal Survey of Healthy Longevity was devised by Zeng and Vaupel. Vaupel (and colleagues) participated in the design, funding, and analysis of large longitudinal studies of aging among older Danish twins, very old Sardinians, and elderly Russians living in Moscow and St. Petersburg. Weinstein and Goldman are the leaders of the Taiwan Study of the Elderly (Weinstein and Willis 2000).
A main contribution of biomedical demographers has been the development of models. Manton has played a leadership role in the elaboration of dynamic models for analyzing complicated longitudinal data. The numerous publications of Manton and colleagues are summarized (Manton and Yashin 2000). Also notable are the modeling contributions of Ewbank (2000) and the work of biological demographers (Carey et al. 1998; Muller et al. 1997).
Demographers over the past half century have increasingly become involved with the design of surveys and the analysis of survey data, especially pertaining to fertility or morbidity and mortality. Recently, various kinds of physical measurements (height and weight), physiological measurements (of blood pressure and cholesterol levels), nutritional status (assessed by the analysis of blood or urine and other methods), physical performance (hand-grip strength or ability to pick a coin up from the floor), and genetic makeup (as determined by analysis of DNA) have been added to surveys, including those conducted by Christensen, Goldman, Weinstein, Zeng, and others. Such biological measurements can be used as covariates in demographic analyses in much the same way that social and economic information is used. These kinds of analyses are an important activity of biomedical demographers (Finch et al. 2000).
In particular, there has been a rapid growth of interest in using genetic information in medical-demographic research (Ewbank 2000). Of particular interest is the information from DNA about specific genes, as in research by Ewbank (2001), and Yashin (Yashin et al. 2000). Information from DNA about genetic polymorphisms (i.e., mutations) can be used to determine the genetic structure of a population and to make inferences about the influence of migration and inbreeding on the population. A central goal of such ''molecular demography'' is to identify genetic polymorphisms that affect mortality, morbidity, functioning, fecundity, and other sources of demographic change. Much of this research to date, as illustrated by analyses conducted by Ewbank, Vaupel, and Yashin (cited above), has focused on finding genetic variants that influence longevity. This relationship can be studied by analyzing changes with age in the proportion of survivors who have some specific allele (i.e., version of a gene). If in a given cohort the allele becomes more frequent with age, that allele may be associated with lower mortality.
It should not be forgotten, however, that much can be learned about genetics even if DNA is unavailable. The genetic and common environment components of these variations in life spans, fertility, and other demographic characteristics can be analyzed in humans using demographic data on twins, siblings, cousins, and other relatives of various degree. These data are available in genealogies and in twin, household, parish, and other population registries. Required is information about the proportion of genes shared by two individuals and about shared nongenetic influences. Analysis of variance methods, correlated frailty approaches, and nested event-history models have been applied by demographers. Kohler and Rodgers (2003) have studied how much of the variation in number of children can be attributed to genetic variation in family size preferences among potential parents, and Anatoli Yashin has analyzed genetic variation as it is related to susceptibility to various diseases and to mortality in general (Yashin and Iachine 1997; Yashin et al. 2001).
In sum, both the biomedical-demography branch of biodemography and the biological-demography branch are vibrant areas of demographic research that are rapidly growing and that have great potential to enrich and enlarge the domain of demography. Not only can demographers learn much from biologists and epidemiologists, they are also capable of contributing much to research on life in general, as opposed to humans in particular, and to research on population health.
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