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

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  • Cristina Giudici
  • Maria Arezzo
  • Nicolas Brouard

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

Suggested Citation

  • Cristina Giudici & Maria Arezzo & Nicolas Brouard, 2013. "Estimating health expectancy in presence of missing data: an application using HID survey," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 22(4), pages 517-534, November.
    • Handle: RePEc:spr:stmapp:v:22:y:2013:i:4:p:517-534
    DOI: 10.1007/s10260-013-0233-8
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    References listed on IDEAS

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    1. Scheuren, Fritz, 2005. "Multiple Imputation: How It Began and Continues," The American Statistician, American Statistical Association, vol. 59, pages 315-319, November.
    2. Vanessa Yong & Yasuhiko Saito, 2012. "Are There Education Differentials in Disability and Mortality Transitions and Active Life Expectancy Among Japanese Older Adults? Findings From a 10-Year Prospective Cohort Study," The Journals of Gerontology: Series B, The Gerontological Society of America, vol. 67(3), pages 343-353.
    3. Flávia C. D. Andrade, 2010. "Measuring the Impact of Diabetes on Life Expectancy and Disability-Free Life Expectancy Among Older Adults in Mexico," The Journals of Gerontology: Series B, The Gerontological Society of America, vol. 65(3), pages 381-389.
    4. repec:dau:papers:123456789/449 is not listed on IDEAS
    5. Agnes Lievre & Nicolas Brouard & Christopher Heathcote, 2003. "The Estimation Of Health Expectancies From Cross-Longitudinal Surveys," Mathematical Population Studies, Taylor & Francis Journals, vol. 10(4), pages 211-248.
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    1. Cristina Giudici & Silvia Polettini & Alessandra Rose & Nicolas Brouard, 2019. "Which Aspects of Elderly Living Conditions are Important to Predict Mortality? The Complex Role of Family Ties at Home and in Institutions," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 142(3), pages 1255-1283, April.

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