Abstract
A great number and variety of terms are used to refer to health conditions. The principal terms are disease, illness, sickness, disorder, pathology, health condition, morbid condition, impairment, injury, disability, and handicap. Dependency, frailty, and the standard categories of self-perceived health (excellent, …, poor) also describe health conditions, but in more global terms. Health demographers and epidemiologists have given some of these terms formal definitions and we shall note the definitions that are available. Other terms are used more loosely and lack formal definition. The term morbidity has been used to refer both to health as a field of study and to specific health conditions. The terms health condition, pathology, morbid condition, and disorder are the other general terms among those enumerated but all can refer to a specific condition. They cannot be satisfactorily distinguished from one another.
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- 1.
Homeostasis is the ability of a cell, organ, or physiological system to maintain its internal equilibrium in a healthy state by adjusting its physiological processes in accordance with the changes in related physical structures. One might say, it is the balance achieved by the body through communication of the body’s parts to each other.
- 2.
Note that under steady-state conditions the prevalence ratio is directly proportional to the incidence rate and the average duration of the illness. A more precise formula for calculating the relation between the prevalence ratio, the incidence rate, and the average duration of illness than shown is:
$$\mbox{ Prevalence ratio} = \frac{\mbox{ incidence rate}\, {_\ast}\mbox{ average duration of illness}} {[1 + \mbox{ incidence rate}\, {_\ast}\mbox{ average duration of illness}]}$$If the incidence rate is low or if the disease has a high fatality rate, this formula will approximate formula (5.6a) (See Kleinbaum et al. 1982).
The relation between the prevalence ratio and the incidence rate, or prevalence cases and incidence cases, may be structured in other ways. By analogy, we may apply the conventional population estimating equation with appropriate modifications, as follows:
$${\mathrm{PR}}_{0} + \mathrm{I} -\mathrm{D} + \mathrm{Im} -\mathrm{Em} -\mathrm{R} ={ \mathrm{PR}}_{1}$$where the elements refer to prevalence of cases for a given disease at the start of a period (PRo), new cases during the period (I), deaths of persons who have the disease (D) during the period, immigrants with the condition (Im), emigrants with the condition (Em), recovering cases reduced by relapses (R), and prevalence of cases at the end of the period (PR1).
- 3.
Hyperplasia is the abnormal increase in the number of cells in a tissue or organ, causing its enlargement.
- 4.
Stenosis is the blockage of a blood vessel, typically an artery, with possible negative consequences in the form of angina (pain in chest), claudication (pain in calf or foot), heart attack, or stroke. Angioplasty is the procedure of catheterizing (coronary, peripheral, abdominal) arteries in order to reduce the degree of stenosis and improve the flow of blood though the arteries.
- 5.
See Chap. 7 for further explanation of logistic and Poisson regression.
- 6.
The latest version is: World Health Organization (1992, 1993, 1994).
- 7.
The SSA definition requires the impairment to be of a degree of severity that renders the individual unable to engage in any kind of substantial gainful work that exists anywhere in the national economy. If the determination of disability cannot be made on the basis of medical evidence only, consideration is given to the person’s age, education, and work experience.
- 8.
Ferrucci et al. (2005) believe that the intrinsic cause of frailty should be sought in common pathways for multiple impairments, such as hormonal changes, inflammation, disequilibrium between the production and scavenging of free radicals, and failures of the dynamic equilibrium between the complementary parts of the autonomic nervous system (i.e., the sympathetic and parasympathetic systems).
- 9.
Whitson et al. (2007), Bergman et al. (2007), and Rockwood et al. (2007) suggest other ways of composing a frailty index or defining freshly in their contributions to a special section of the Journal of Gerontology: Medical Sciences, Vol. 62A, No. 7, July, No. 7.
- 10.
Harmonization in this context refers to the adjustment of the data, definitions, and measures so as to achieve comparability and consistency between countries.
References and Suggested Readings
American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders, DSM-IV. Washington, DC: American Psychiatric Association.
Ferlay, J., Bray, F., Pisani, P., & Parkin, D. M. (2001). GLOBOCAN 2000: Cancer incidence, mortality and prevalence worldwide, Version 1.0 IARC (International Association of Cancer Registries) CancerBase No. 5. Lyons, France: IARC Press, Limited version available online at: http://www-dep.iarc.fr/globocan/globocan.htm.
Mausner, J. S., & Kramer, S. (1985). Epidemiology: An introductory text. Philadelphia: W. B. Saunders.
World Health Organization (WHO). (1980). International classification of impairments, disabilities, and handicaps (ICIDH). Geneva, Switzerland: World Health Organization.
World Health Organization. (1984). International classification of impairments, disabilities, and handicaps: A manual of classification relating to the consequences of disease. Geneva, Switzerland: World Health Organization.
World Health Organization. (1992, 1993, 1994). International statistical classification of diseases and related problems. Vol. I: ICD-10, Tabular List (1992); Vol. II: Instructional manual (1993); Vol. III: Alphabetical Index (1994). Geneva, Switzerland: World Health Organization.
World Health Organization. (2001). International classification of functioning, disability, and health (ICF). Designated ICIDH-2 and available via the World Wide Web.
Idler, E. L., & Benyamini, Y. (1997). Self-rated health and mortality: A review of 27 community studies. Journal of Health and Social Behavior, 38, 21–37.
Idler, E., & Kasl, S. (1991). Health perceptions and survival: Do global evaluations of health status really predict mortality? Journal of Gerontology: Social Sciences, 46B, S55–S65.
Rahman, M. O., & Barsky, A. J. (2003). Self-reported health among older Bangladeshis: How good a health indicator is it? Gerontologist, 43(6), 856–863.
Wolinsky, F. & Johnson, R. (1992). Perceived health status and mortality among older men and women. Journal of Gerontology: Social Sciences, 47(6), S304–S312.
Amella, E. J. (2003). Presentation of illness in older adults. American Journal of Nursing, 104(10), 40–51.
Ansello, E. F., & Eustis, N. N. (Eds.). (1992). Aging and disabilities: Seeking common ground. Amityville, NY: Baywood Publishing Company.
Bassuk, S. S. (2000). Cognitive impairment and mortality in the community-dwelling elderly. American Journal of Epidemiology, 151, 676–688.
Bergman, H., Ferrucci, L., Guralnik, J., et al. (2007). Frailty, an emerging research and clinical paradigm: Issues and controversies. Journal of Gerontology: Medical Sciences, 62A(7), 731–737.
Bortz, W. M. (2002). A conceptual framework of frailty: A review. Journals of Gerontology: Medical Sciences, 57A, M283–M288.
Branch, L. G., Katz, S., Kniepmann, K., & Papsidero, J.A. (1984) A prospective study of functional status among community elders. American Journal of Public Health, 74(3), 266–268.
Constantino, J. P., Gail, M. H., Pee, D., et al. (1999). Validation studies for models projecting the risk of invasive and total breast cancer incidence. Journal of the National Cancer Institute, 91(18), 1541–1548.
Dye, C., Scheele, S., Dolin, P., Pathania, V., & Raviglione, M. C. (1999). Global burden of tuberculosis: Estimated incidence, prevalence, and mortality by country. Journal of the American Medical Association, 282, 677–686.
Ferrucci, L., Windham, B. G., & Fried, L. P. (2005). Frailty in older persons. Genus, LXI(1), 39–53.
Freedman, V. A., Martin, L. G., & Schoeni, R. F. (2002). Recent trends in disability and functioning among older adults in the United States: a systematic review. Journal of the American Medical Association, 288(24), 3137–3146.
Fried, L. P., Tangen, C. M., Walston, J., et al. (2001). Frailty in older adults: Evidence for a phenotype. Journal of Gerontology: Medical Sciences, 56A(3), M146–M156.
Fries, J. F. (2003). Measuring and monitoring success in compressing morbidity. Annals of Internal Medicine, 139(5, Pt. 2), 455–459.
Gail, M. H., Brinton, L. A., Byar, D. P., et al. (1989). Projecting individualized probabilities of developing breast cancer for white females who are being examined annually. Journal of the National Cancer Institute, 81(24), 1879–1886.
Guralnik, J. M., Simonsick, E. M., et al. (1994). A short physical performance battery assessing lower extremity function: Association with self-reported disability and prediction of mortality and nursing home admission. Journal of Gerontology: Medical Sciences, 49(2), M85–M94.
Haddon, W. (1980). Advances in the epidemiology of injuries as a basis for public policy. Public Health Reports, 95(5), 411–421.
Humber, J. A., & Almeder, F. E. (Eds.). (1997). What is disease? Totowa, NJ: Humana Press.
Katz, S., Branch, L. G., Branson, M. H., Papsidero, J. A., et al. (1983). Active life expectancy. New England Journal of Medicine, 309, 1218–1221.
Knopman, D. S. (1989) A verbal memory test with high predictive accuracy. Archives of Neurology, 46, 141–145.
Lavery, L. L. (2005). The clock drawing test is an independent predictor of incident use of 24-hour care in a retirement community. Journal of Gerontology Series A: Biological and Medical Sciences, 7, 928–932.
Manton, K., Gu, X., & Lamb, V. (2006). Change in chronic disability from 1982 to 2004/05 as measured by long-term changes in function and health in the U.S. elderly population. Proceedings of the National Academy of Sciences, 103(48), 18374–18379.
Nagi, S. (1976). An epidemiology of disability among adults in the United States. Milbank Memorial Fund Quarterly/Health and Society, 54, 439–467.
Parker, M. G., & Thorsland, M. (2007). Health trends in the elderly population: Getting better and getting worse. Gerontologist, 47(2), 150–158.
Ravaud, J. -F., Letourney, A., & Ville, I. (2002). Identifying the population with disability: The approach of an INSEE survey on daily life. Population-E, 57(3), 29–554.
Robertson, L.S. (1998). Injury epidemiology. (2nd ed.). New York: Oxford University Press.
Robine, J. -M., Jagger, C., & Romieu, I. (2001). Disability-free life expectancies in the European Union countries: calculation and comparisons. Genus, LVII(2), 89–102.
Rockwood, K., Andrew, M., & Minitski, A. (2007). Frailty. A comparison of two approaches to measuring frailty in older people. Journal of Gerontology: Medical Sciences, 62A(7), 738–743.
Rockwood, K., & Minitski, A. (2007). Frailty in relation to the accompensation of deficts. Journal of Gerontology: Medical Sciences, 62A(7), 722–727.
Rosow, I., Breslau, N., & Guttman, A. (1966). A health scale for the aged. Journal of Gerontology, 21, 556–559.
Royall, D. R., Mulroy, A.R., Choido, L.K., & Folk, M.J. (1999). Clock drawing is sensitive to executive control: A comparison of six methods. Journal of Gerontology, 54, 328–333.
Ruzicka, L. T., Choi, C. Y., & Sadkowsky, K. (2004). Co-morbidity of suicides in the light of multiple cause of death reporting. Genus, LX(3–4), 143–160.
Shankie, W. R., Romney, A. K., Hara, J., Fortier, D., et al. (2005). Methods to improve the detection of mild cognitive improvement. Proceedings of the National Academy of Sciences, 102(13), 1919–1924.
Stallard, E. (2002, January 17–18). Underlying and multiple cause mortality at advanced ages: United States, 1980–1998. In CD of Proceedings of the Society of Actuaries, International Symposium: Living to 100 and Beyond: Mortality at Advanced Ages, Lake Buena Vista, FL.
UNICEF (2001). The state of the will’s children 2001. New York, United Nations.
U.S. National Center for Health Statistics. (1997). Negative mood and urban vs. rural residence: Using proximity to Metropolitan Statistical Areas as an alternative measure of residence. By B. S. Jones & R. W. Wilson. Advance Data No. 281, Vital and Health Statistics.
U.S. National Center for Health Statistics. (2003, April). Proceedings of the international collaborative effort on injury statistics, Vol. IV, Paris Meeting. Hyattsville, MD: U.S. National Center for Health Statistics. See pp. 2–1 to 2–8.
Verbrugge, L. & Jette, A. (1994). The disablement process. Social Science and Medicine, 38(1), 1–14.
Walston, J., Hadley, E. C., Ferrucci, L., et al. (2006). Research agenda for frailty in older adults: toward a better understanding of physiology and etiology: summary from the American Geriatrics Society/National Institute on Aging Research Conference on Frailty in Older Adults. Journal of the American Geriatrics Society, 54(6), 991–1001.
Whitson, H. E., Purser, J. L., & Cohen, H. J. (2007). Frailty thy name is …phrailty. Journal of Gerontology: Medical Sciences, 62A(7), 728–730.
Wolf, D. A., Mendes de Leon, C. F., & Glass, T. A. (2007). Trends in rates of onset of and recovery from disability at older ages: 1982–1994. Journal of Gerontology: Social Sciences, 62B(1), S3–S10.
Brady, H. E., & Elms, L. (1999). Age-period-cohort analysis with noisy, lumpy data. Paper presented at the annual meeting of the Political Methodology Group of the American Political Science Association, College Station, TX.
Carstensen, B. (2005). Demography and epidemiology: Age-period-cohort models in the computer age. www.pubhealth.ku.dk/~bxc
Cook, N. R., & Ware, J. H. (1983). Design and analysis methods for longitudinal research. Annual Review of Public Health, 4, 1–23.
Dorn, H. F., & Cutler, S. J. (1959). Morbidity from cancer in the United States (Public Health Monograph No. 56). Washington, DC: U.S, Government Printing Office.
Finch, C. E., & Crimmins, E. M. (2004). Inflammatory exposure and historical changes in human life-spans. Science, 305, 1736–1739.
Firebaugh, G. (1997). Analyzing repeated surveys. Sage University Paper Series No. 07-115. Thousand Oaks, CA
Frost, W. H. (1939). The age selection of mortality from tuberculosis in successive decades. American Journal of Hygiene, 30(3A), 91–96.
Glenn, N. D. (1976). Cohort Analysis’ futile quest: Statistical attempts to separate age-period-cohort effects? American Sociological Review, 41(October), 900–904.
Glenn, N. D. (2005). Cohort analysis. Quantitative applications in the social sciences (2nd ed.). Thousand Oaks, CA: Sage.
Halli, S. S., & Rao, K. V. (1992). Age, period, and cohort effects in demography. In Advanced Techniques of Population Analysis (Chap. 3). New York: Plenum Press.
Hobcraft, J., Menken, J., & Preston, S. (1982). Age, period, and cohort effects in demography: A review. Population Index, 48(Spring), 4–43.
Jones, H. (1956). Advances in biological and medical physics (Vol. 4, p. 281). New York: Academic.
Kermack, W. O., McKendrick, A. G., & McKinlay, P. L. (1934a). Death rates in Great Britain and Sweden: Some general regularities and their significance. Lancet, 1, 698–703.
Kermack, W. O., McKendrick, A. G., & McKinlay, P. L. (1934b). Death rates in Great Britain and Sweden: Expression of specific mortality rates as products of two factors, and some consequences thereof. Journal of Hygiene, 34, 433–457.
Kleinbaum, D. G., Kupper, L. L., & Morgenstern, H. (1982). Age, period, and cohort effects. In Epidemiologic research: Principles and quantitative methods (Sect. 7.3). Belmont, CA: Lifetime Learning Publishers.
Mason, W. M., & Fienberg, S. E. (Eds.). (1985). Cohort analysis in social research: Beyond the identification problem. New York: Springer. See esp. papers by W.M. Mason and S. E. Fienberg, Introduction, beyond the identification problem; S.E. Fienberg and W.M. Mason, Specification and implementation of age, period, and cohort models; and W. M. Mason and H.L. Smith, Age-period-cohort analysis and the study of deaths from pulmonary tuberculosis.
Palmore, E. (1978). When can age, period, and cohort be separated? Social Forces, 57(September), 282–295.
Siegel, J.S. (1993). A generation of change: A profile of America’s older population. New York: Russell Sage Foundation.
Yang, Y., Land, K. C., & Fu, W. J. (2006, August 11). The intrinsic estimator for age-period-cohort analysis and how to use it. Paper presented at the annual meeting of the American Sociological Association, Montreal, Canada.
Baker III, G. T., & Sprott, R. L. (1988). Biomarkers of aging. Experimental Gerontology, 23, 223–239.
Goldman, N., Turra, C. M., Glei, D. A., Seplaki, C. M., et al. (2006). Predicting mortality from clinical and nonclinical biomarkers. Journal of Gerontology: Medical Sciences, 61A(10), 1070–1074.
Goodwin, J. S. (2003). Embracing complexity: A consideration of hypertension in the very old. Journal of Gerontology: Medical Sciences, 58A(7), 653–658. See also commentaries on this paper by W. S. Aronow, S. Denson, R. R. Hajjar, T. B. Harris, D. T. Lowenthal, J. -P. Michel et al., A. B. Newman, & D. T. Thomas.
Grundy, S. M., Brewer, H. B., Cleeman, J. I., Smith, S. C., & Lenfant, C. (2004). Definition of metabolic syndrome. Report of the National, Heart, Lung, and Blood Institute/American Heart Association Conference on scientific issues related to definition. Circulation, 109(2), 433–438.
Ingram, D. K. (2004). Biomarkers of aging: From marking time to moving forward. Workshop in Biomarkers of Aging. Alliance for Aging Research, October 20, 2004. Baltimore, MD: Gerontology Research Center, National Institute on Aging, National Institutes of Health.
Ingram, D. K., Nakamura, E., Smucny, D., et al. (2001). Strategies for identifying biomarkers of aging in long-lived species. Experimental Gerontology, 36, 1025–1035.
Karasik, D., Demissie, S., Cupples, L. A., & Kiel, D. P. (2005). Disentangling the genetic determinants of human aging: Biological age as an alternative to the use of survival measures. Journal of Gerontology: Biological Sciences, 60A(5), 574–587.
Karlamangla, A. S., Singer, B. H., McEwen, B. S., et al. (2002). Allostatic load as a predictor of functional decline: MacArthur studies of successful aging. Journal of Clinical Epidemiology, 55, 696–710.
McEwen, B. S., & Stellar, E. (1993). Stress and the individual mechanisms leading to disease. Archives of Internal Medicine, 153, 2093–2101.
Nakamura, E., & Miyao, K. (2007). A method for identifying biomarkers of aging and constructing an index of biological age in humans. Journal of Gerontology: Biological Sciences, 62A(10), 1096–1105.
Seeman, T. E., Singer, B., Rowe, J. W., et al. (1997). Price of adaptation – Allostatic load and its health consequences: McArthur studies of successful aging. Archives of Internal Medicine, 157, 2259–2268.
Seeman, T. E., McEwen, B. S., Rowe, J. W., & Singer, B. H. (2001). Allostatic load as a marker of cumulative biological risk: MacArthur studies of successful aging. Proceedings of the National Academy of Sciences, 98(8), 4770–4775.
Seplaki, C. L., Goldman, N., Weinstein, M., & Lin, Y. H. (2004). How are biomarkers related to physical and mental well-being? Journal of Gerontology: Medical Sciences, 59A, 201–207.
Sterling P., & Eyer, J. (1988). Allostasis: A new paradigm to explain arousal pathology. In S. Fisher & J. Reason (Eds.), Handbook of life stress, cognition, and health (pp. 631–651). New York: Wiley.
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Siegel, J.S. (2012). Measures of Health Status, Functioning, and Use of Health Services. In: The Demography and Epidemiology of Human Health and Aging. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-1315-4_5
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