Eric Stallard

The impact of heterogeneity in individual frailty on the dynamics of mortality

Life table methods are developed for populations whose members differ in their endowment for longevity. Unlike standard methods, which ignore such heterogeneity, these methods use different calculations to construct cohort, period, and individual life tables. The results imply that standard methods overestimate current life expectancy and potential gains in life expectancy from health and safety interventions, while underestimating rates of individual aging, past progress in reducing mortality, and mortality differentials between pairs of populations. Calculations based on Swedish mortality data suggest that these errors may be important, especially in old age.

Limits to human life expectancy: evidence prospects and implications.

Identifying limits to human life expectancy and life span is difficult because survival is determined by the individuals physiology exogenous influences and their interaction over time. To explore theoretical limits the authors examine the life expectancy of selected populations with good health behavior and apply a multivariate risk-factor model to longitudinal data. The risk-factor model and the population data produce consistent estimates of a lower bound of the theoretical limit to human life expectancy. The results suggest that such limits may be higher than estimates obtained by extrapolating human mortality trends which necessarily are dependent on historical conditions. The investigation emphasizes the need to use information on individual physiological processes and health changes prior to death in addition to mortality or endpoint data in making estimates. The low-risk populations studied are from the United States and Japan. (SUMMARY IN FRE AND SPA) (EXCERPT)

Changes in the age dependence of mortality and disability: Cohort and other determinants

Though the general trend in the United States has been toward increasing life expectancy both at birth and at age 65, the temporal rate of change in life expectancy since 1900 has been variable and often restricted to specific population groups. There have been periods during which the age- and gender-specific risks of particular causes of death have either increased or decreased. These periods partly reflect the persistent effects of population health factors on specific birth cohorts. It is important to understand the ebbs and flows of cause-specific mortality rates because general life expectancy trends are the product of interactions of multiple dynamic period and cohort factors. Consequently, we first review factors potentially affecting cohort health back to 1880 and explore how that history might affect the current and future cohort mortality risks of major chronic diseases. We then examine how those factors affect the age-specific linkage of disability and mortality in three sets of birth cohorts assessed using the 1982, 1984, and 1989 National Long Term Care Surveys and Medicare mortality data collected from 1982 to 1991. We find large changes in both mortality and disability in those cohorts. providing insights into what changes might have occurred and into what future changes might be expected.

Methods For Comparing The Mortality Experience of Heterogeneous Populations

Methods are presented which produce Maximum Likelihood Estimates (MLE) of the degree of heterogeneity in individual mortality risks under a variety of assumptions about the age trajectory of those mortality risks. With these estimates of the degree of population heterogeneity it is possible to adjust comparisons of mortality risks across populations for the effects of population heterogeneity, differential mortality selection, and different age trajectories of the force of mortality. These methods are demonstrated by applying a variety of standard assumptions about the age trajectory of the force of mortality to the analysis of a broad range of cohort mortality data for the U.S. and Swedish populations. The estimates of the degree of heterogeneity, produced under all of the selected force of mortality models, consistently indicated a considerable degree of heterogeneity in mortality risks.

A variance components approach to categorical data models with heterogeneous cell populations: analysis of spatial gradients in lung cancer mortality rates in North Carolina counties.

A mixed categorical-continuous variable model is proposed for the analysis of mortality rates. This model differs from other available models, such as weighted least squares and loglinear models, in that the within-cell populations are assumed to be heterogeneous in their levels of mortality risk. Heterogeneity implies that, in addition to the sampling variance considered in other available models, there will be a second component of variance due solely to within-cell heterogeneity. Maximum likelihood procedures are presented for the estimation of the model parameters. These procedures are based on the assumption that the distribution function for each cell death count is the negative binomial probability function. This assumption is shown to be equivalent to assuming a mixture of Poisson processes with the differential risk levels among individuals within each cell being governed by a two-parameter gamma distribution. The model is applied to data on lung cancer mortality for 1970-1975 for the 100 counties of North Carolina. The analysis shows that, though a gradient in lung cancer mortality rates exist in space, the gradient is restricted to specific demographic categories identified by race, age and sex.

Education-Specific Estimates of Life Expectancy and Age-Specific Disability in the U.S. Elderly Population 1982 to 1991

The authors used mortality data for 1982 to 1991 linked to survey records from the 1982, 1984, and 1989 National Long Term Care Surveys to calculate gender differences over age in mortality and functional status for high (8 or more years of schooling) and low (less than 8 years of schooling) education subgroups. Males and females with high education maintained better functioning at later ages than those with low education. The authors also found that mortality was higher, after conditioning on disability, in both the male and female low-education than the male and female high-education groups. The size of the education effect on both disability and mortality was large, for example, about 7.6 years difference in female life expectancy at age 65; a roughly 2-year difference for males.

Dependent competing risks: a stochastic process model

Analyses of human mortality data classified according to cause of death frequently are based on competing risk theory. In particular, the times to death for different causes often are assumed to be independent. In this paper, a competing risk model with a weaker assumption of conditional independence of the times to death, given an assumed stochastic covariate process, is developed and applied to cause specific mortality data from the Framingham Heart Study. The results generated under this conditional independence model are compared with analogous results under the standard marginal independence model. Under the assumption that this conditional independence model is valid, the comparison suggests that the standard model overestimates by 4% the effect on life expectancy at age 30 due to the hypothetical elimination of cancer and by 7% the effect for cardiovascular/cerebrovascular disease. By age 80 the overestimates were 11% for cancer and 16% for heart disease. These results suggest the importance of avoiding the marginal independence assumption when appropriate data are available — especially when focusing on mortality at advanced ages.

Predicting 10‐Year Care Requirements for Older People with Suspected Alzheimer's Disease

OBJECTIVE: To describe the types and costs of care received for 10 years after the identification of an older person with suspected Alzheimers disease (AD) by using data from 3254 patients with suspected AD who participated in the National Long Term Care Survey (NLTCS).

Underlying and Multiple Case Mortality Advanced Ages: United States 1980-1998

Abstract This paper evaluates changes in cause-specific mortality for the general noninsured U.S. elderly population aged 65 years and older by sex and five-year age groups over the calendar years 1980, 1990, and 1998 for 14 leading causes of death coded according to the International Classification of Diseases (9th revision). The goals of the paper are substantive and methodological. Substantively, the goal is to assess the different contributions to the mortality decline made by diseases as underlying causes versus associated or contributing causes—as recorded in the multiple cause condition field of the death certificate. Methodologically, the goal is to introduce these data into actuarial practice and provide an initial set of tabulation methods that facilitate their use. The patterns of change over age and time of the 14 leading causes exhibited distinct characteristics in one or more of the tables presented, demonstrating unequivocally that the diseases are neither homogeneous nor independent. This suggests that standard models such as the multiple decrement life table model that assume independent competing risks may be invalid. However, the specification of realistic and accurate alternative models will be a major challenge because of the complexity of the morbid processes involved and the requirements for data linkages that are only beginning to be developed.

Should the presence of carcinogens in breast milk discourage breast feeding

Pollutant chemicals are commonly found in human milk at levels that would prevent its sale as a commercial food for infants. The chemicals found most commonly are dichlorodiphenyl-dichloroethene, polychlorinated biphenyls, dieldrin, chlordane, heptachlor, and polychlorinated dibenzodioxins. In general, the regulatory levels for these chemicals have been set to prevent cancer in adult humans from lifetime exposure. We compared lives saved in the postneonatal period by breast feeding to the estimated excess cancer deaths attributable to the contaminants in breast milk. The results of this analysis suggest that only extreme levels of contaminants in breast milk represent more of a hazard than failure to breast feed, but clinical considerations in individual cases might override this conclusion. Our analysis depends on assumptions about how the chemicals might cause cancer in humans and on whether breast feeding prevents some postneonatal mortality. Noncarcinogenic hazards from chemical exposure, other hazards from breast feeding such as transmission of viruses, and benefits of breast feeding other than reduction in mortality were not considered.