A Bayesian Difference-In-Differences Framework for Measuring the Impact of Primary Care Redesign on Diabetes Outcomes

• Activity Number: 334 - Health Policy Statistics Student Paper Awards
• Type: Topic Contributed
• Date/Time: Tuesday, July 30, 2019 : 10:30 AM to 12:20 PM
• Sponsor: Health Policy Statistics Section
• Author(s): James Normington* and Eric Lock and Caroline Carlin and Kevin Peterson and Bradley Carlin
• Companies: Univ of Minnesota and University of Minnesota and University of Minnesota and University of Minnesota and Counterpoint Statistical Consulting, LLC
• Keywords: Bayesian hierarchical modeling; diabetes management; difference-in-differences; errors in covariates; patient-centered medical home; primary care redesign

Abstract

Although national measures of the quality of diabetes care delivery demonstrate improvement, progress has been slow. In 2008, the Minnesota legislature endorsed the patient-centered medical home (PCMH) as the preferred model for primary care redesign. In this work, we investigate the effect of PCMH-related clinic redesign and resources on diabetes outcomes from 2008 to 2012 among Minnesota clinics by using a Bayesian framework for a continuous difference-in-differences model. Data from the Physician Practice Connections-Research Survey were used to assess a clinic’s maturity in primary care transformation, and diabetes outcomes were obtained from the MN Community Measurement (MNCM) program. These data have several characteristics that must be carefully considered from a modeling perspective, including the inability to match patients over time, the potential for dynamic confounding, and the hierarchical structure of clinics. A simple correlational analysis suggests a significant correlation between PCMH-related clinic redesign and resources on diabetes outcomes; however, this effect is not detected after properly accounting for different sources of variability and confounding.