Causes, Correlates and Consequences of Death Among Older by Jere R. Behrman

By Jere R. Behrman

All people ultimately die, yet existence expectations fluctuate through the years and between varied demographic teams. Teasing out a number of the factors and correlates of loss of life is a problem, and it truly is one we tackle during this e-book. a glance on the information on mortality is either attention-grabbing and suggestive of a few attainable relationships. In 1900 lifestyles expectations at delivery have been forty six. three and forty eight. three years for women and men respectively, a gender differential of a section below five percentage. lifestyles expectations for whites then have been approximately zero. three years longer than that of the total inhabitants, yet lifestyles expectations for blacks have been basically approximately 33 years for women and men. At age sixty five, the remainder existence expectations have been approximately 12 and eleven years for whites and blacks respectively. Fifty years later, lifestyles expectations at beginning had grown to sixty six and seventy one years for women and men respectively. the proportion differential among the sexes was once now nearly as much as 10 percentage. The existence expectations of whites have been approximately 12 months longer than that for the full inhabitants. the large switch was once for blacks, whose lifestyles expectancy had grown to over 60 years with black ladies residing approximately five percentage longer than their male opposite numbers. At age sixty five the rest anticipated lifestyles had elevated approximately years with a lot higher percent profits for blacks.

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Additional info for Causes, Correlates and Consequences of Death Among Older Adults: Some Methodological Approaches and Substantive Analyses

Example text

Possible solutions to these problems have followed two separate approaches. The first, used by Manton, et al. (1986), assumes a flexible parametric distribution for those fraility differences among individual that enter the hazard multiplicatively. The second, proposed by Heckman and Singer (1984), allows for the distribution of fraility differences to be estimated by a fmite support general probability estimator (Keifer and Wolfowitz, 1956). This estimator is consistent and approximated standard errors also can be generated (Heckman and Singer, 1984).

Heckman and Singer's Nonparametric Maximum Likelihood Estimator (NPMLE) estimator can be used to avoid the ad hoc specification of the mixing distribution 11(8) (Robbins, 1964; Laird, 1978; Lindsay, 1983; Heckman and Singer, 1982, 1984). Basically, this method reduces to the use of a fmite support histogram to model 11(8). , 1977) has often been used to solve the likelihood equations. Application to the frailty model is accomplished by treating the sequence of unobservables {8J as missing data.

Typically, a life-table analysis examines an age cohort of individuals, distinguished by a particular risk factor status. Observed age-specific death rates are compared with those expected from all causes using a chi-squared contingency table approach. , 1992). The null hypothesis tested is usually the independence of risk-factor status and rate of death. Although the life-table has been widely used as a survey prediction of an individual's risk of death, this technique suffers in design in that individuals are not required per se to survive in any consistent manner, as age-specific death rates are calculated independently of each other.

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