OBJECTIVE

To develop and validate a measure that estimates individual level poverty in Medicare administrative data that can be used in studies of Medicare claims.

DATA SOURCES

A 2008 to 2013 Medicare Current Beneficiary Survey linked to 2008 to 2013 Medicare fee-for-service beneficiary summary file and census data.

STUDY DESIGN AND METHODS

We used the Medicare Current Beneficiary Survey to define individual level poverty status and linked to Medicare administrative data (N=38,053). We partitioned data into a measure derivation dataset and a validation dataset. In the derivation data, we used a logistic model to regress poverty status on measures of dual eligible status, part D low-income subsidy, and demographic and administrative data, and modeled with and without linked census and nursing home data. Each beneficiary receives a predicted poverty score from the model. Performance was evaluated in derivation and validation data and compared with other measures used in the literature. We present a measure for income-only poverty as well as one for income and asset poverty.

PRINCIPAL FINDINGS

A score (predicted probability of income poverty) >0.5 yielded 58% sensitivity, 94% specificity, and 84% positive predictive value in the derivation data; our score yielded very similar results in the validation data. The model's c-statistic was 0.84. Our poverty score performed better than Medicaid enrollment, high zip code poverty, and zip code median income. The income and asset version performed similarly well.

CONCLUSIONS

A poverty score can be calculated using Medicare administrative data for use as a continuous or binary measure. This measure can improve researchers' ability to identify poverty in Medicare administrative data.