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Sources and Quality of Data for Mortality and Health Studies

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Abstract

Mortality and health data can be obtained both from the general sources for demographic data, such as censuses, vital statistics registrations, and general sample surveys, and from specialized sources, such as national sample surveys on health, administrative records on health and mortality (e.g., hospital discharge records; records of hospital inpatient stays), epidemiological studies, and clinical trials. These sources provide quantitative macrodata (aggregate data) and microdata (individual data) on mortality and health. I consider each of them in turn and then briefly discuss qualitative sources of information on health.

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Notes

  1. 1.

    A public dataset is available from the Population Research Center, University of Chicago/NORC and is downloadable from their website (src.uchicago.edu/prc/chfls.php).

  2. 2.

    For a detailed history of the Framingham Heart Study, see Daniel Levy and Susan Brink, A Change of Heart: How the People of Framingham, Massachusetts Helped Unravel the Mysteries of Cardiovascular Disease, NewYork: Alfred A. Knopf, 2005.

  3. 3.

    Sometimes population groups are used as units in the clinical trial for testing treatment protocols. In community studies random samples of population groups may be studied as paired units (one being a control). Schools or even cities may be the population units sampled. The sample in one city may be subjected to the health program being tested while the sample in another city is not. A sample of schools in a city may be divided into schools subject to the treatment program and those used as controls. Alternatively, all schools in a city may be included in a study, but only a sample of students in each school is subject to the program and another sample in each school is carried as controls. In another variation, a single sample of the population may be followed longitudinally and examined before and after a health program is put into effect.

References and Suggested Readings

  • Belloc, N. B., & Breslow, L. (1972). Relationship of physical health status and health practices. Preventive Medicine, 1(3), 409–421.

    Article  Google Scholar 

  • Berkman, L. F., & Breslow, L. (1983). Health and ways of living. New York: Oxford University Press.

    Google Scholar 

  • Costa, D. L. (2000). Understanding the twentieth-century decline in chronic conditions among older men. Demography, 37(1), 53–72.

    Article  Google Scholar 

  • Crimmins, E. M., & Seeman, T. E. (2004). Integrating biology into the study of health disparities In L. J. Waite (Ed.), Aging, health, and public policy: Demographic and economic perspectives: A supplement to Vol. 30. Population and development review (pp. 89–107). New York: Population Council.

    Google Scholar 

  • Elman, C., & Myers, G. (1997). Age and sex differentials in morbidity at the start of an epidemiologic transition. Social Science and Medicine, 45, 943–956.

    Article  Google Scholar 

  • Elman, C., & Myers, G. (1999). Geographic morbidity differentials in late nineteenth-century America. Demography, 36(4), 429–443.

    Article  Google Scholar 

  • Geronimus, A. T., Bound, J., Waidmann, T. A., Colen, C. G., & Steffick, D. (2001). Inequality in life expectancy, functional status, and active life expectancy across selected black and white populations in the United States. Demography, 38(2), 227–251.

    Article  Google Scholar 

  • Hayflick, L. (1994). How and why we age. New York: Ballantine Books.

    Google Scholar 

  • Hayward, M. D., & Heron, M. (1999). Racial inequality in active life among adult Americans. Demography, 36(1), 77–91.

    Article  Google Scholar 

  • ICF Macro (Macro International). www.measuredhs.com.

  • Kaplan, G. A., Seeman, T. E., Cohen, R. D., Knudsen, L. P., & Guralnik, J. (1987). Mortality among elderly in the Alameda County study: Behavioral and demographic risk factors. American Journal of Public Health, 77(3), 307–312.

    Article  Google Scholar 

  • Kroeger, A. (1983). Health interview surveys in developing countries: A review of the methods and results. International Journal of Epidemiology, 12, 465–481.

    Article  Google Scholar 

  • Lenfant, C. (1999, October 27). Dietary fiber, weight gain, and cardiovascular disease risk factors in young adults. Journal of the American Medical Association, 282(16), 1539–1546.

    Google Scholar 

  • Lloyd, C. B., & Marquette, C. M. (1992). Directory of surveys in developing countries: Data on families and households, 1975–1992. A population and development review (Supplement, Vol. 27). New York: Population Council.

    Google Scholar 

  • Manton, K., & Soldo, B. (1992). Disability and mortality among the oldest-old: Implications for current and future health and long-term care service needs. In R. M. Suzman, D. P. Willis, & K. G. Manton (Eds.), The oldest-old (pp. 199–250). New York: Oxford University Press.

    Google Scholar 

  • Population Association of America. (2003). PAA affairs. Winter 2003:7–8.

    Google Scholar 

  • Preston, S. H., & Haines, M. R. (1991). Fatal years: Child mortality in late nineteenth century America. Princeton, NJ: Princeton University Press.

    Google Scholar 

  • Wiley, J. A., & Camacho, T. C. (1980). Life style and future health: Evidence from the Alameda Country Study. Preventive Medicine, 9(1), 1–21.

    Article  Google Scholar 

  • Garibotti, G., Smith, K. R., Kerber, R. A., Boucher, K. M. (2006). Longevity and correlated frailty in multigenerational families. Journal of Gerontology: Biological Sciences, 61A(12): 1253–1261.

    Article  Google Scholar 

  • Gavrilov, L. A., Garvrilova, N. S., Olshansky, S. J., & Carnes, B. A. (2002). Genealogical data and the biodemography of human longevity. Social Biology, 49, 160–173.

    Google Scholar 

  • Hoyert, D. L., & Martin, J. A. (2002, February). Vital statistics as a data source. Seminars in Perinatology, 26(1), 12–16.

    Google Scholar 

  • North American Association of Central Cancer Registries. (2001). CiNA + [Cancer in North America]. Online at www.naaccr.org/cinap/index.htm > 

  • Schatz, I. J. (1995). Internal medicine residency program violations of the minimum autopsy rate. Journal of the American Medical Association, 273, 1092.

    Article  Google Scholar 

  • Shanks, J. H., McCluggage, G., Anderson, N. H., & Toner, P. G. (1990). Value of the necropsy in perioperative deaths. Journal of Clinical Pathology, 43, 193–195.

    Article  Google Scholar 

  • U.S. National Center for Health Statistics. (2001). The autopsy, medicine, and mortality statistics. By D. Hoyert. Vital and Health Statistics, 3(32) (DHHS Publication No. (PHS) 2001-1416).

    Google Scholar 

  • U.S. National Center for Health Statistics. (2006). Summary health statistics for the U.S. population: National Health Interview Survey, 2004. By P. F. Adams & P. M. Barnes. Vital and Health Statistics, 10(229).

    Google Scholar 

  • U.S. National Office of Vital Statistics. (1950). International recommendations on definitions of live birth and fetal death. Washington, DC: U.S. Public Health Service.

    Google Scholar 

  • New England Research Institute. (1994). Network. Summer/Fall.

    Google Scholar 

  • New England Research Institute. (1996). Network. Spring/summer.

    Google Scholar 

  • U.S. Bureau of the Census. (2001). International database. census.gov/ipc/www/idbnew.html.

  • U.S. Department of Health and Human Services. (2000). International health data reference guide, 1999 (DHHS Publication No. (PHS) 2000-1007).

    Google Scholar 

  • U.S. National Center for Health Statistics. (2002). National center for health statistics: programs and activities (DHHS Publication No. (PHS) 2002-1200).

    Google Scholar 

  • United Nations. (1994). Demographic yearbook 1993. New York: United Nations.

    Google Scholar 

  • World Health Organization. (2005). World health report, 2004. Geneva, Switzerland: World Health Organization.

    Google Scholar 

  • Andrews, F. M., & Herzog, A. R. (1986). The quality of survey data as related to age of respondent Journal of the American Statistical Association, 81, 403–410.

    Google Scholar 

  • Carp, F. M. (1989). Maximizing data quality in community studies of older people. pp. 93–122 In M. P. Lawton & A. R. Herzog (Eds.), Special research methods for gerontology. Amityville, NJ: Baywood.

    Google Scholar 

  • Czaja, R. F., Snowden, C. B., & Casady, R. J. (1986). Reporting bias and sampling errors in a survey of a rare population using multiplicity counting rules. Journal of the American Statistical Association, 81(394), 411–419.

    Article  Google Scholar 

  • Droitcour, J., Larson, E. M., & Scheuren, F. J. (2001). The three card method: Estimating sensitive survey items–with permanent anonymity of response. Proceedings of the American Statistical Association, August 5–9, 2001, Alexandria, VA.

    Google Scholar 

  • Glantz, S. A. (2005). Primer of biostatistics. (6th ed.). New York: McGraw-Hill.

    Google Scholar 

  • Gorelick, M. H. (2006). Bias arising from missing data in predictive models. Journal of Clinical Epidemiology, 59(10), 1115–1123.

    Article  Google Scholar 

  • Herzog, A. R., & Kulka, R. A. (1989). Telephone and mail surveys with older populations. A methodological overview. In M. P. Lawton & A. R. Herzog (Eds.), Special research methods for gerontology (pp. 63–89). Amityville, NJ: Baywood.

    Google Scholar 

  • Herzog, A. R., & Rodgers, W. L. (1988). Age and response rate to interview sample surveys. Journal of Gerontology: Social Sciences, 43, S200–S205.

    Google Scholar 

  • Herzog, T. N., Scheuren, F. J., & Winkler, W. E. (2007). Data quality and record linkage techniques. New York: Springer Publishing Company.

    Google Scholar 

  • Johansson, S. R. (1991). The health transition: The cultural inflation of morbidity during the decline of mortality. Health Transition Review, 1(1), 39–65.

    Google Scholar 

  • Kalter, H. (1992). The validation of interviews for estimating morbidity. Health Policy and Planning, 7, 30–39.

    Article  Google Scholar 

  • Magaziner, J., Simonsick, E. M., Kashner, T. M., & Hebel, J. R. (1988). Patient-proxy response comparability on measures of patient health and functional status. Journal of Clinical Epidemiology, 41, 1065–1074.

    Article  Google Scholar 

  • Marquis, K. H., Marquis, M. S., & Polich, J. M. (1986). Response bias and reliability in sensitive topic surveys. Journal of the American Statistical Association, 81(394), 381–389.

    Article  Google Scholar 

  • Obermeyer, C. M. (1996). A research agenda for reproductive health. Newsletter 54, Geneva, Switzerland: International Union for the Scientific Study of Population.

    Google Scholar 

  • Rabbitt, P., Watson, P., Donlan, C., Bent, N., & McInnes, L. (1994). Subject attrition in a longitudinal study of cognitive performance in community resident elderly people. In B. J. Vellas, J. L.  Abarende, & P. J. Gary (Eds.), Facts and research in gerontology: Epidemiology and aging (pp. 29–34). Paris: Serdi.

    Google Scholar 

  • Rabbitt, P., Lunn, M., & Wong, D. (2005). Neglect of dropout underestimates effects of death in longitudinal studies. Journal of Gerontology: Psychological Sciences, 60B(2), P106–P109.

    Article  Google Scholar 

  • Ravnskov, U. (1992). Cholesterol lowering trials on coronary heart disease: Frequency of citation and outcome. British Medical Journal, 131, 579–588.

    Google Scholar 

  • Riley, J. C. (1992). From a high mortality regime to a high morbidity regime: Is culture everything in sickness? Health Transition Review, 2(1), 71–77.

    Google Scholar 

  • Shimizu, I., & Bonham, G. (1978). Randomized response in a national survey. Journal of the American Statistical Association, 73, 35–39.

    Article  Google Scholar 

  • Siegel, J. S. (2002). Applied demography: Applications to business, government, law, and public policy. San Diego, CA: Academic Press.

    Google Scholar 

  • Sirken, M. G. (1970, March). Household surveys with multiplicity. Journal of the American Statistical Association, 65, 257–266.

    Google Scholar 

  • Sirken, M. G. (1975). Network surveys of rare and sensitive conditions. Advances in health survey research methods. Hyatsville, MD: National Center for Health Statistics.

    Google Scholar 

  • Thompson, S. G., & Pocock, S. J. (1991). Can meta-analysis be trusted? Lancet, 338, 1127–1130.

    Article  Google Scholar 

  • U.S. General Accounting Office. (1999, November). Survey methodology: An innovative technique for estimating sensitive survey items. GAO/GGD-00-30.

    Google Scholar 

  • Wayman, J. C. (2003). Multiple imputation for missing data: What is it and how can i use it?. Paper presented at the 2003 annual meeting of the American Educational Research Association, Chicago, IL. Baltimore, MD: Center for Social Organization of Schools, Johns Hopkins University.

    Google Scholar 

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Correspondence to Jacob S. Siegel .

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Siegel, J.S. (2012). Sources and Quality of Data for Mortality and Health Studies. In: The Demography and Epidemiology of Human Health and Aging. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-1315-4_2

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