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Application of a Propensity Score Approach for Risk Adjustment in Profiling Multiple Physician Groups on Asthma Care

Authors

  • I-Chan Huang,

  • Constantine Frangakis,

  • Francesca Dominici,

  • Gregory B. Diette,

  • Albert W. Wu

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    • Address Correspondence to Albert W. Wu, M.D., M.P.H, Department of Health Policy and Management, Bloomberg School of Public Health, The Johns Hopkins University, 624 North Broadway/Room 633, Baltimore, MD 21205-1901. I-Chan Huang, Ph.D., is with the Department of Health Policy and Management, Bloomberg School of Public Health, The Johns Hopkins University, Baltimore. Constantine Frangakis, Ph.D. and Francesca Dominici, Ph.D. are with the Department of Biostatistics, Bloomberg School of Public Health, The Johns Hopkins University, Baltimore. Gregory B. Diette, M.D., M.H.S. and Albert W. Wu, M.D., M.P.H. are with the Department of Epidemiology, Bloomberg School of Public Health, The Johns Hopkins University, Baltimore and also with Department of Medicine, School of Medicine, The Johns Hopkins University, Baltimore.


  • Conflicts of interests: There are no conflicts of interests for any authors. Presented in part at the following conferences: the 19th Annual Research Meeting of AcademyHealth, June 23–25, 2002, Washington DC, and the 2003 Joint Statistical Meeting (JSM), August 3–7, 2003, San Francisco.

Abstract

Objectives. To develop a propensity score-based risk adjustment method to estimate the performance of 20 physician groups and to compare performance rankings using our method to a standard hierarchical regression-based risk adjustment method.

Data Sources/Study Setting. Mailed survey of patients from 20 California physician groups between July 1998 and February 1999.

Study Design. A cross-sectional analysis of physician group performance using patient satisfaction with asthma care. We compared the performance of the 20 physician groups using a novel propensity score-based risk adjustment method. More specifically, by using a multinomial logistic regression model we estimated for each patient the propensity scores, or probabilities, of having been treated by each of the 20 physician groups. To adjust for different distributions of characteristics across groups, patients cared for by a given group were first stratified into five strata based on their propensity of being in that group. Then, strata-specific performance was combined across the five strata. We compared our propensity score method to hierarchical model-based risk adjustment without using propensity scores. The impact of different risk-adjustment methods on performance was measured in terms of percentage changes in absolute and quintile ranking (AR, QR), and weighted κ of agreement on QR.

Results. The propensity score-based risk adjustment method balanced the distributions of all covariates among the 20 physician groups, providing evidence for validity. The propensity score-based method and the hierarchical model-based method without propensity scores provided substantially different rankings (75 percent of groups differed in AR, 50 percent differed in QR, weighted κ=0.69).

Conclusions. We developed and tested a propensity score method for profiling multiple physician groups. We found that our method could balance the distributions of covariates across groups and yielded substantially different profiles compared with conventional methods. Propensity score-based risk adjustment should be considered in studies examining quality comparisons.

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