A cardiovascular disease risk prediction algorithm for use with the Medicare current beneficiary survey

UMMS Affiliation

Meyers Primary Care Institute; Department of Population and Quantitative Health Sciences

Publication Date


Document Type



Cardiology | Cardiovascular Diseases | Epidemiology | Health Policy | Health Services Research | Translational Medical Research


OBJECTIVE: To develop a cardiovascular disease (CVD) risk score that can be used to quantify CVD risk in the Medicare Current Beneficiary Survey (MCBS).

DATA SOURCES: We used 1999-2013 MCBS data.

STUDY DESIGN: We used a backward stepwise approach and cox proportional hazards regressions to build and validate a new CVD risk score, similar to the Framingham Risk Score (FRS), using only information available in MCBS. To assess its performance, we calculated C statistics and examined calibration plots.

DATA COLLECTION/EXTRACTION METHODS: We studied 21 968 community-dwelling Medicare beneficiaries aged 65 years or older without pre-existing CVD. We obtained risk factors from both survey and claims data. We used claims data to derive "CVD event within 3 years" following the FRS definition of CVD.

PRINCIPAL FINDINGS: About five percent of MCBS participants developed a CVD event over a mean follow-up period of 348 days. Our final MCBS-based model added morbidity burden, reported general health status, and functional limitation to the traditional FRS predictors of CVD. This model had relatively fair discrimination (C statistic = 0.69; 95% confidence interval [CI], 0.67-0.71) and performed well on validation (C = 0.68; CI, 0.66-0.70). More importantly, the plot of observed CVD outcomes versus predicted ones showed that this model had a good calibration.

CONCLUSIONS: Our new CVD risk score can be calculated using MCBS data, thereby extending the survey's ability to quantify CVD risk in the Medicare population and better inform both health policy and health services research.


UMCCTS funding, cardiovascular diseases, health policy, health risk assessment, proportional hazards models, survey methods

DOI of Published Version



Fouayzi H, Ash AS, Rosen AK. A cardiovascular disease risk prediction algorithm for use with the Medicare current beneficiary survey. Health Serv Res. 2020 Apr 14. doi: 10.1111/1475-6773.13290. Epub ahead of print. PMID: 32285938. Link to article on publisher's site

Journal/Book/Conference Title

Health services research

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Link to Article in PubMed

PubMed ID