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A Bayesian Difference-in-Difference Framework for the Impact of Primary Care Redesign on Diabetes Outcomes

Author

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  • James Normington
  • Eric Lock
  • Caroline Carlin
  • Kevin Peterson
  • Bradley Carlin

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 certified as PCMHs by 2011 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 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. An ad-hoc 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. Supplementary materials for this article are available online.

Suggested Citation

  • James Normington & Eric Lock & Caroline Carlin & Kevin Peterson & Bradley Carlin, 2019. "A Bayesian Difference-in-Difference Framework for the Impact of Primary Care Redesign on Diabetes Outcomes," Statistics and Public Policy, Taylor & Francis Journals, vol. 6(1), pages 55-66, January.
    • Handle: RePEc:taf:usppxx:v:6:y:2019:i:1:p:55-66
    DOI: 10.1080/2330443X.2019.1626310
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    Cited by:

    1. Krzysztof Drachal & Daniel González Cortés, 2022. "Estimation of Lockdowns’ Impact on Well-Being in Selected Countries: An Application of Novel Bayesian Methods and Google Search Queries Data," IJERPH, MDPI, vol. 20(1), pages 1-24, December.

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