The health expectancies of older Australians

Abstract

Health expectancies of the states 'Disability-free' and 'Disabled' are estimated for Australian females and males aged 60 and over, both by cohort from 1980 and current for survey years 1981, 1988, 1993 and 1998. Modifications of recently developed logistic regression techniques are used rather than the standard 1971 method due to Sullivan. Results from the three later surveys are broadly similar and differ in important respects from those of the 1981 survey. Based on the last three surveys our estimates support the view that, depending on age, two-thirds or more of the increase in female life expectancy over the decade 1988-1998 is spent in the Disabled state. The situation is worse for elderly men, for whom all of the increased years of expected life are estimated to be spent in the Disabled state. The findings do not support rectangularization of the survival curve or Disability-free survival curve.Keywords: Disability, health expectancy, logistic regression, multistate life tables, life expectancy, regression analysis, Australia Health expectancies are the future durations that a person expects to spend in different states of health before the inevitable outcome of death and thus are components of life expectancies. It is well known that life expectancies have been increasing, and a proposition that has attracted some support is that the extra years are ones of ill-health. Bebbington (1988:321) provides a succinct summary of alternative views, while a wider discussion can be found in Crimmins, Hayward and Saito (1994) and Verbrugge (1984).Australian health expectancies in 1981, 1988 and 1993 were estimated by Mathers (1996) following an earlier treatment using data for the first two of the three survey years (Mathers 1991). He found that, unlike other countries for which data were then available, Australian expectations of years with disability had increased both per se and relative to life expectancies. The development of new methods and the availability of updated and compatible data enables these findings to be re-examined. This is the main aim of this paper. Our findings are that the situation is complex but, depending on age and sex, two-thirds or more of the increased years of life expectancy are spent in a disabled state.

Estimating Processes of Counts From Cross‐sectional Aggregate Data, With an Application to Multistate Life Tables and Health Expectancies

“…Proofs of the results presented in this section are in Davis, Heathcote & O'Neill (2001) and more simply and in greater generality in the working paper , with an application in Heathcote et al (2003). The notation of those papers is generally followed, simplified when possible.…”

“…Mathers (1996) gave a comparative account of health expectancies in 1981, 1988 and 1993 using the lifetable-based method of Sullivan (1971). Heathcote et al (2003) considered the system with the three states 'disability-free', 'disabled' and 'dead' for persons aged 60 and over by the methods of this paper. With access to more extensive data, this section and the next give an account of survival curves and expectancies for 1988, 1993 and 1998 for ages 0-105.…”

“…Consequently the covariance matrices V (y) are estimated using different sample sizes as well as different probabilities. The (0) differ from the population sizes used by and Heathcote et al (2003). For a discussion of the sampling schemes and related matters see ABS (1998) and ABS (2001).…”

Multistate worklife expectancies

“…In Australia, estimates of health expectancies based on survey data supported the view that, depending on age, two-thirds or more of the increase in female life expectancy over the decade 1988-1998 was spent in the disabled state (41). The situation was worse for older men (ie, aged 60 years and over), for whom all of the increased years of expected life were estimated to be spent in a disabled state.…”

“…The method of Heathcote & Higgins (9), which is a specialization of the Davis at al multistate approach to the case in which there are only two states (alive and dead), models the risk of death specific to both age and year and develops a large-sample weighted least squares technique for fitting mortality surfaces. These are parameterized measures of mortality indexed by age and year, whose random elements are generated by stochastic processes defined on the diagonals of the Lexis plane (41).…”