Dataset Information

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.

SUBMITTER: Tan A

PROVIDER: S-EPMC3775541 | biostudies-literature | 2013 Sep

REPOSITORIES: biostudies-literature

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

Tan Alai A Kuo Yong-Fang YF Goodwin James S JS

American journal of epidemiology 20130712 6

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. Subject ...[more]

PMID: 23851579

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Project description:ObjectiveTo systematically review and critically appraise prognostic models for falls in community-dwelling older adults.Eligibility criteriaProspective cohort studies with any follow-up period. Studies had to develop or validate multifactorial prognostic models for falls in community-dwelling older adults (60+ years). Models had to be applicable for screening in a general population setting.Information sourceMEDLINE, EMBASE, CINAHL, The Cochrane Library, PsycINFO and Web of Science for studies published in English, Danish, Norwegian or Swedish until January 2020. Sources also included trial registries, clinical guidelines, reference lists of included papers, along with contacting clinical experts to locate published studies.Data extraction and risk of biasTwo authors performed all review stages independently. Data extraction followed the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist. Risk of bias assessments on participants, predictors, outcomes and analysis methods followed Prediction study Risk Of Bias Assessment Tool.ResultsAfter screening 11 789 studies, 30 were eligible for inclusion (n=86 369 participants). Median age of participants ranged from 67.5 to 83.0 years. Falls incidences varied from 5.9% to 59%. Included studies reported 69 developed and three validated prediction models. Most frequent falls predictors were prior falls, age, sex, measures of gait, balance and strength, along with vision and disability. The area under the curve was available for 40 (55.6%) models, ranging from 0.49 to 0.87. Validated models' The area under the curve ranged from 0.62 to 0.69. All models had a high risk of bias, mostly due to limitations in statistical methods, outcome assessments and restrictive eligibility criteria.ConclusionsAn abundance of prognostic models on falls risk have been developed, but with a wide range in discriminatory performance. All models exhibited a high risk of bias rendering them unreliable for prediction in clinical practice. Future prognostic prediction models should comply with recent recommendations such as Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis.Prospero registration numberCRD42019124021.

Dataset Information

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

Publications

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

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