Multivariate Risk Adjustment of Primary Care Patient Panels in a Public Health Setting: A Comparison of Statistical Models. Issue 4 (October 2016) - Hirozawa, Anne M.; Montez-Rath, Maria E.; Johnson, Elizabeth C.; Solnit, Stephen A.; Drennan, Michael J.; Katz, Mitchell H.; Marx, Rani
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 ( R 2 = 0.20). This model, designed specifically for safety net patients, may prove useful for panel adjustment in other public health settings. Abstract : Supplemental Digital Content is Available in the Text.