Multivariate Risk Adjustment of Primary Care Patient Panels in a Public Health Setting: A Comparison of Statistical Models

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Abstract

We compared prospective risk adjustment models for adjusting patient panels at the San Francisco Department of Public Health. We used 4 statistical models (linear regression, two-part model, zero-inflated Poisson, and zero-inflated negative binomial) and 4 subsets of predictor variables (age/gender categories, chronic diagnoses, homelessness, and a loss to follow-up indicator) to predict primary care visit frequency. Predicted visit frequency was then used to calculate patient weights and adjusted panel sizes. The two-part model using all predictor variables performed best (R2 = 0.20). This model, designed specifically for safety net patients, may prove useful for panel adjustment in other public health settings.

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