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

Hirozawa, Anne M. MPH; Montez-Rath, Maria E. PhD; Johnson, Elizabeth C. MD; Solnit, Stephen A. BA; Drennan, Michael J. MD; Katz, Mitchell H. MD; Marx, Rani PhD, MPH

Journal of Ambulatory Care Management: October/December 2016 - Volume 39 - Issue 4 - p 333–342
doi: 10.1097/JAC.0000000000000065
Original Articles

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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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).

Correspondence: Anne M. Hirozawa, MPH, San Francisco Department of Public Health, 25 Van Ness Ave, Suite 500, San Francisco, CA 94102 (

Statistical programming and analysis for this article was funded in part by the Kaiser Permanente Northern California Fund for Community Benefit of the East Bay Community Foundation (grant 20100626) and the California Health Care Foundation (grant 06-1644). The authors thank Sharareh Modaressi for sharing her knowledge about risk adjustment of patient panels and Paul Barnett for his explanations and other valuable feedback regarding statistical models. The authors also thank Fred Strauss for his work on International Classification of Diseases, Ninth Revision, code diagnosis categories.

There are no conflicts of interest for any of the authors.

The views expressed herein do not necessarily reflect the official policies of the City and County of San Francisco; nor does mention of the San Francisco Department of Public Health imply its endorsement.

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