Estimating health expectancy in presence of missing data: an application using HID survey

Cristina Giudici, Maria Felice Arezzo () and Nicolas Brouard

Statistical Methods & Applications, 2013, vol. 22, issue 4, 517-534

Abstract: In this article we estimate health transition probabilities using longitudinal data collected in France for the survey on handicaps, disabilities and dependencies from 1998 to 2001. Life expectancies with and without disabilities are estimated using a Markov-based multi-state life table approach with two non-absorbing states: able to perform all activities of daily living (ADLs) and unable or in need of help to perform one or more ADLs, and the absorbing state of death. The loss of follow-up between the two waves induces biases in the probabilities estimates: mortality estimates were biased upwards; also the incidence of recovery and the onset of disability seemed to be biased. Since individuals were not missing completely at random, we correct this bias by estimating health status for drop-outs using a non parametric model. After imputation, we found that at the age of 70 disability-free life expectancy decreases by 0.5 years, whereas the total life expectancy increases by 1 year. The slope of the stable prevalence increases, but it remains lower than the slope of the cross sectional prevalence. The gender differences on life expectancy did not change significantly after imputation. Globally, there is no evidence of a general reduction in ADL disability, as defined in our study. The added value of the study is the reduction of the bias induced by sample attrition. Copyright Springer-Verlag Berlin Heidelberg 2013

Keywords: Healthy life expectancy; Classification and regression trees; Sample attrition (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1007/s10260-013-0233-8

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