BMC Public Health (Jun 2020)
Self-assessed life expectancy among older adults in Côte d’Ivoire
Abstract
Abstract Background The purpose of this study was to estimate individuals’ expected longevity based on self-assessed survival probabilities and determine the predictors of such subjective life expectancy in a sample of elderly people (50 years and older) in Côte d’Ivoire. Methods Paper-based questionnaires were administered to a sample (n = 267) of older adults residing in the city of Dabou, Côte d’Ivoire in May 2017. Information on subjective expectations regarding health, comorbidities, and self-assessed survival probabilities was collected. We estimated self-assessed life expectancy and its determinants using a two-pronged approach by: (i) estimating individuals’ life expectancy using the self-assessed survival probabilities (SSPs), and (ii) applying a finite mixture of regression models to form homogenous groups of individuals (clusters/components) and investigate the determinants. A spline-based approach was used to estimate the overall distribution of life expectancy for each individual using two to four points of self-assessed survival probabilities. A finite mixture of regression models was used to identify homogeneous groups of individuals (i.e. clusters/components) of the overall subjective life expectancy distribution of the study participants. Results The mean subjective life expectancy in older people varied according to four components/clusters. The average subjective life expectancy among the elderly was 79.51, 78.89, 80.02, and 77.79 years in the first, second, third, and fourth component of the subjects’ overall subjective life expectancy, respectively. The effect of sociodemographic characteristics, comorbidities, and lifestyle on subjective life expectancy varied across components. For instance, a U-shape relationship between household per capita income and subjective life expectancy was found for individuals classified into the third component, and an inverse U-shape relationship was found for individuals classified into the fourth component. Conclusions We extended the estimation of subjective life expectancy by accounting for heterogeneity in the distribution of the estimated subjective life expectancy. This approach improved the usual methods for estimating individual subjective life expectancies and may provide insight into the elderly’s perception of aging, which could be used to forecast the demand for health services and long-term care needs.
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