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

Research output: Contribution to journalArticle

20 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Pages (from-to)974-983
Number of pages10
JournalAmerican Journal of Epidemiology
Volume178
Issue number6
StatePublished - Sep 15 2013
Externally publishedYes

Fingerprint

Independent Living
Medicare
Life Expectancy
Comorbidity
Proportional Hazards Models
Survival
Population

ASJC Scopus subject areas

  • Epidemiology

Cite this

Predicting life expectancy for community-dwelling older adults from Medicare claims data. / Tan, Alai; Kuo, Yong Fang; Goodwin, James.

In: American Journal of Epidemiology, Vol. 178, No. 6, 15.09.2013, p. 974-983.

Research output: Contribution to journalArticle

@article{cb9f91da0aba4e7c83f29752e8e07c66,
title = "Predicting life expectancy for community-dwelling older adults from Medicare claims data.",
abstract = "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.",
author = "Alai Tan and Kuo, {Yong Fang} and James Goodwin",
year = "2013",
month = "9",
day = "15",
language = "English (US)",
volume = "178",
pages = "974--983",
journal = "American Journal of Epidemiology",
issn = "0002-9262",
publisher = "Oxford University Press",
number = "6",

}

TY - JOUR

T1 - Predicting life expectancy for community-dwelling older adults from Medicare claims data.

AU - Tan, Alai

AU - Kuo, Yong Fang

AU - Goodwin, James

PY - 2013/9/15

Y1 - 2013/9/15

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84887384102&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84887384102&partnerID=8YFLogxK

M3 - Article

C2 - 23851579

AN - SCOPUS:84887384102

VL - 178

SP - 974

EP - 983

JO - American Journal of Epidemiology

JF - American Journal of Epidemiology

SN - 0002-9262

IS - 6

ER -