Estimating increment-decrement life tables with multiple covariates from panel data: the case of active life expectancy.
A fundamental limitation of current multistate life table methodology-evident in recent estimates of active life expectancy for the elderly-is the inability to estimate tables from data on small longitudinal panels in the presence of multiple covariates (such as sex, race, and socioeconomic status). This paper presents an approach to such an estimation based on an isomorphism between the structure of the stochastic model underlying a conventional specification of the increment-decrement life table and that of Markov panel regression models for simple state spaces. We argue that Markov panel regression procedures can be used to provide smoothed or graduated group-specific estimates of transition probabilities that are more stable across short age intervals than those computed directly from sample data. We then join these estimates with increment-decrement life table methods to compute group-specific total, active, and dependent life expectancy estimates. To illustrate the methods, we describe an empirical application to the estimation of such life expectancies specific to sex, race, and education (years of school completed) for a longitudinal panel of elderly persons. We find that education extends both total life expectancy and active life expectancy. Education thus may serve as a powerful social protective mechanism delaying the onset of health problems at older ages.
Land
K C
KC
Department of Sociology, Duke University, Durham, NC 27708-0088.
Guralnik
J M
JM
Blazer
D G
DG
D000203:Activities of Daily Living / Q000145:classification; D000368:Aged; D000369:Aged, 80 and over; D000704:Analysis of Variance; D002423:Cause of Death; D005260:Female; D006801:Humans; D008017:Life Expectancy; D015993:Life Tables; D008137:Longitudinal Studies; D008297:Male; D008390:Markov Chains; D015233:Models, Statistical; D009026:Mortality; D009657:North Carolina / Q000453:epidemiology; D012959:Socioeconomic Factors; D013269:Stochastic Processes; D016019:Survival Analysis