Multivariate Risk Adjustment of Primary Care Patient Panels in a Public Health Setting: A Comparison of Statistical Models. Issue 4 (October 2016)

• Record Type: Journal Article
• Authors: Hirozawa, Anne M.; Montez-Rath, Maria E.; Johnson, Elizabeth C.; Solnit, Stephen A.; Drennan, Michael J.; Katz, Mitchell H.; Marx, Rani
• Is Part Of: Journal of ambulatory care management. Volume 39:Issue 4(2016)
• Publication Date: 2016-10
• Subjects: multivariate analysis -- primary health care -- public sector -- regression analysis -- risk adjustment -- safety net providers -- statistical models; Ambulatory medical care -- Administration -- Periodicals; Ambulatory medical care -- United States -- Administration -- Periodicals; Community health services -- Administration -- Periodicals; Community health services -- United States -- Administration -- Periodicals; Health planning -- Periodicals; Health planning -- United States -- Periodicals; 362.12
• DOI: 10.1097/JAC.0000000000000065
• Languages: English
• ISSNs: 0148-9917

Abstract

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.