Predicting life expectancy for community-dwelling older adults from Medicare claims data

Am J Epidemiol. 2013 Sep 15;178(6):974-83. doi: 10.1093/aje/kwt054. Epub 2013 Jul 12.


Estimates of life expectancy are useful in assessing whether different prevention strategies are appropriate in different populations. We developed sex-specific Cox proportional-hazard models that use Medicare claims data to predict life expectancy and risk of death at up to 10 years for older adults. We identified a cohort of Medicare beneficiaries 66-90 years of age from the 5% Medicare claims data in 2000 (n = 1,137,311) and tracked each subject's vital status until December 31, 2009. Subjects were split randomly into training and validation samples. Models were developed from the training sample and validated by comparison of predicted to actual survival in the validation sample. The C statistics for the models including predictors of age and Elixhauser comorbidities were 0.76-0.79 for men and women for prediction of death at the 1-, 5-, 7-, and 10-year follow-up periods. More than 80% of subjects with <25% risk of death at 5, 7, and 10 years survived longer than the chosen cutoff years. More than 80% of subjects with ≥75% risk of death at 5, 7, and 10 years died by those cutoff years. The models overestimated the risk of death at 1 year for the high-risk groups. Sex-specific models that use age and Elixhauser comorbidities can accurately predict patient life expectancy and risk of death at 5-10 years.

Keywords: Medicare; aged; comorbidity; life expectancy; mortality; prognosis.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Aged, 80 and over
  • Comorbidity
  • Female
  • Forecasting / methods
  • Humans
  • Insurance Claim Review
  • Kaplan-Meier Estimate
  • Life Expectancy*
  • Logistic Models
  • Male
  • Mass Screening / standards
  • Mass Screening / statistics & numerical data*
  • Medicare / statistics & numerical data*
  • Preventive Health Services / standards
  • Preventive Health Services / statistics & numerical data*
  • Proportional Hazards Models
  • Sex Distribution
  • United States / epidemiology