Using Medicare Claims Data to Predict Life Expectancy Interview with: Alai Tan, MD, PhD

Assistant Professor, Dept. of Preventive Medicine & Community Health
Sr. Biostatistician, Sealy Center on Aging
Univerisity of Texas Medical Branch
301 University Blvd., Galveston, TX  77555-0177 What are the main findings of the study?

Dr. Tan: The study developed and validated sex-specific Cox proportional-hazards models with predictors of age and comorbidities to predict patient life expectancy using Medicare claims data. The predictive model was well-calibrated and showed good predictive discrimination for risk of mortality between 5 and 10 years. Were any of the findings unexpected?

Dr. Tan: The predicted risks of death within one year for the high-risk groups need to be re-calibrated to achieve more accurate prediction. What should clinicians and patients take away from your report?

Dr. Tan: Current age-based guidelines are insufficient to address the increasing heterogeneity in health and life expectancy among the elderly. Clinicians and patients need to take life expectancy in to consideration when making medical decisions. What recommendations do you have for future research as a result of this study?

Dr. Tan: One potential application of our predictive models is in assessing quality of preventive services using Medicare claims data.

Target populations that are appropriate or not appropriate for a service can be more accurately defined based on patient life expectancy than those based on age limit alone.

For example, tight glycemic control in diabetes mellitus to prevent microvascular complications is not recommended for patients with less than eight years of life expectancy, while colonoscopy screening for colon cancer is not recommended for patients with less than ten years of life expectancy.  Use of age, gender and comorbidity allows for a more accurate assessment of potential overuse and underuse of such preventive services than does age alone.


Predicting Life Expectancy for Community-dwelling Older Adults From Medicare Claims Data

Alai Tan, Yong-Fang Kuo, and James S. Goodwin

Am. J. Epidemiol. first published online July 12, 2013 doi:10.1093/aje/kwt054

Last Updated on February 3, 2014 by Marie Benz MD FAAD