title: Multivariate Risk Adjustment of Primary Care Patient Panels in a Public Health Setting: A Comparison of Statistical Models.
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 = 0.20). This model, designed specifically for safety net patients, may prove useful for panel adjustment in other public health settings.
authors: Community Oriented Primary Care (Ms Hirozawa and Drs Marx, Johnson, and Drennan) and Community Behavioral Health Services (Mr Solnit), San Francisco Department of Public Health, San Francisco, California; Division of Nephrology, Stanford University School of Medicine, Stanford, California (Dr Montez-Rath); and Los Angeles County Department of Health Services, Los Angeles, California (Dr Katz).
authors: Anne M
authors: AM
authors: Hirozawa
authors: Maria E
authors: ME
authors: Montez-Rath
authors: Elizabeth C
authors: EC
authors: Johnson
authors: Stephen A
authors: SA
authors: Solnit
authors: Michael J
authors: MJ
authors: Drennan
authors: Mitchell H
authors: MH
authors: Katz
authors: Rani
authors: R
authors: Marx
mesh_terms: D000328:Adult; D005260:Female; D006801:Humans; D008297:Male; D015233:Models, Statistical; D017060:Patient Satisfaction; D011320:Primary Health Care / Q000592:standards*; D011446:Prospective Studies; D011634:Public Health; D012044:Regression Analysis; D020379:Risk Adjustment; D064876:Safety-net Providers; D012495:San Francisco