Developing and Validating a Measure to Estimate Poverty in Medicare Administrative Data.

Area of Science:

• Health Services Research
• Biostatistics
• Public Health

Background:

• Identifying poverty status among Medicare beneficiaries is crucial for health disparities research.
• Existing methods often rely on indirect measures or external data, limiting their utility in large-scale administrative data analyses.

Purpose of the Study:

• To develop and validate a novel measure for estimating individual-level poverty directly within Medicare administrative data.
• To create a tool that enhances researchers' capacity to study the impact of poverty on Medicare beneficiaries.

Main Methods:

• Utilized Medicare Current Beneficiary Survey data linked to Medicare fee-for-service claims and census data (2008-2013).
• Developed a logistic regression model predicting poverty status based on dual eligibility, Part D low-income subsidy, and demographic/administrative factors.
• Validated the model's performance using sensitivity, specificity, positive predictive value, and c-statistic, comparing it against existing proxies.

Main Results:

• The derived poverty score demonstrated strong performance, with a c-statistic of 0.84 in derivation and validation datasets.
• The score achieved 58% sensitivity, 94% specificity, and 84% positive predictive value (at a threshold of >0.5).
• Outperformed traditional proxies like Medicaid enrollment and zip code-based poverty measures.

Conclusions:

• A validated poverty score can be reliably calculated from Medicare administrative data.
• This measure offers a valuable, continuous or binary, tool for researchers investigating poverty within Medicare populations.
• Enhances the identification and study of poverty-related health outcomes and disparities in Medicare claims data.