Abstract
Goal: To estimate the probabilities of transition among self-rated health states for older adults, and examine how they vary by age and sex. Methods: We used self-rated health (excellent, very good, good, fair, poor, dead) collected in two longitudinal studies of older adults (mean age 75) to estimate the probability of transition in 2 years. We used the estimates to project future health for selected cohorts. Findings: These older adults were most likely to be in the same health state 2 years later, but a substantial proportion changed in both directions. Transition probabilities varied by initial health state, age and sex. Men were more likely than women to transition to excellent or dead. Women were more likely than men to transition to good or fair health. Although women aged 70 will have more years of life and more years of healthy life than men, they also have more years of unhealthy life, and the proportion of remaining life that is healthy is slightly higher for men. When observed and predicted years of healthy life (YHL) were compared in various subgroups, the YHL of persons with less favorable baseline characteristics was lower than predicted, and vice-versa. Differences, however, were small (about 5%). Conclusions: These transition probability estimates can be used to predict the future health of individuals or groups as a function of current age, sex, and self-rated health.
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Rogers RG, Rogers A, Belanger A. Active life among the elderly in the United States: multistate life-table estimates and population projections. Milbank Q 1989; 67: 370–411.
Krause NM, Jay GM. What do global self-rated health items measure? Med Care 1994; 32: 930–942.
Gold M, Franks P, Erickson P. Assessing the health of the nation: the predictive validity of a preference-based measure and self-rated health. Med Care 1996; 34: 163–177.
Idler EL, Benyamini Y. Self-rated health and mortality: a review of twenty-seven community studies. J Health Social Behav 1997; 38: 21–37.
Patrick DL, Grembowski D, Durham M, et al. Cost and outcomes of Medicare re-imbursement for preventive services in an HMO. Health Care Financing Rev 1999; 20: 25–43.
Durham M, Beresford S, Diehr P, Grembowski D, Hecht J, Patrick D. Participation of higher users in a randomized trial of Medicare re-imbursement for preventive services. Gerontologist 1991; 31: 603–606.
Fried L, Nemar O, Enright P, et al. The cardiovascular health study: design and rationale. Ann Epidemiol 1991; 1: 263–276.
Ives G, Fitzpatrick A, Bild D, et al. Surveillance and ascertainment of cardiovascular events: the cardiovascular health study. Ann Epidemiol 1995; 5: 278–285.
Tell GS, Fried LP, Hermanson B, Manolio TA, Newman AB, Borhani NO. Recruitment of adults 65 years and older as participants in the cardiovascular health study. Ann Epidemiol 1993; 3: 358–366.
Mundahl JM. Imputation of missing longitudinal data: a comparison of methods. Master's Thesis, University of Washington, 1998.
Diehr P, Williamson J, Patrick DL, Bild DE, Burke GL. Patterns of self-rated health in older adults before and after sentinel health events. J Am Geriatr Soc 2001; 49: 36–44.
Diehr P, Patrick DL, Bild DE, Burke GL, Williamson JD. Predicting future years of healthy life for older adults. J Clin Epidemiology 1998; 51: 343–353.
Diehr P, Patrick DL. Probabilities of transition among health states for older adults. Technical Report # 168, Department of Biostatistics, University of Washington, Seattle, WA, 2000.
Kovar MG, Fitti JE, Chyba MM. The longitudinal study of aging: 1984–1990. National Center for Health Statistics. Vital and Health Statistics 1992; 1(28): 1–248.
Diehr P, Patrick DL, Spertus J, Kiefe C, McDonell M, Fihn S. Transforming self-rated health and the SF-36 scales to include death and improve interpretability. Med Care 2001 39: 670–680.
Chiang CL. Making annual indexes of health. Health Services Res 1976; 11: 442–451.
Rogers A, Rogers RG, Branch LG. A multistate analysis of active life expectancy. Public Health Rep 1989; 104: 222–226.
Hisanick JJ. Changes over time in the ADL status of elderly US veterans. Age Aging 1994; 23: 505–511.
Biritwum RB, Odoom SI. Application of Markov process modeling to health status switching behavior of infants. Int J Epidemiol 1995; 24: 177–182.
Beckett LA, Brock DB, Lemke JH, et al. Analysis of change in self-reported physical function among older persons in four population studies. Am J Epidemiol 1996; 143: 766–778.
Mendes de Leon CF, Beckett LA, Fillenbaum GG, et al. Black-white differences in risk of becoming disabled and recovering from disability in old age: a longitudinal analysis of two EPESE populations. Am J Epidemiol 1997; 145: 488–497.
Glasziou PP, Cole BF, Gelber RD, Hilden J, Simes RJ. Quality adjusted survival analysis with repeated quality of life measures. Stat Med 1998; 17(11): 1215–1229.
Crimmins EM, Hayward MD, Saito Y. Differentials in active life expectancy in the older population of the United States. J Gerontol B Psychol Sci Soc Sci 1996; 51: S111–S120.
Manton KD, Stallard E. Cross-sectional estimates of active life expectancy for the US elderly and oldest-old populations. J Gerontol 1991; 46: S170–S182.
Crimmins EM, Hayward MD, Saito Y. Changing mortality and morbidity rates and the health status and life expectancy of older populations. Demography 1994; 31: 159–175.
McHorney CA. Measuring and monitoring general health status in elderly persons: practical and methodological issues in using the SF-36 Health Survey. Gerontologist 1996; 36: 571–583.
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Diehr, P., Patrick, D.L. Probabilities of transition among health states for older adults. Qual Life Res 10, 431–442 (2001). https://doi.org/10.1023/A:1012566130639
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DOI: https://doi.org/10.1023/A:1012566130639