Irina Degtiar combines statistical and health policy expertise to advance patient and public health. She is an expert in generalizability (e.g., estimating the impacts of scaling up interventions). She develops and applies Bayesian and frequentist semiparametric estimators to address causal questions in Medicare and Medicaid policy evaluation.
Degtiar joined Mathematica in 2021 after an internship in 2019. During the internship, she developed a novel approach for estimating the impacts of scaling up the Comprehensive Primary Care Plus evaluation and worked on projects related to the Comprehensive Care for Joint Replacement Model.
Before coming to Mathematica, Degtiar’s research focused on addressing the causal impact of Medicaid insurance plans on patient costs using semiparametric double-robust and g-computation estimators. She also conducted health economics and outcomes research, assessing the cost-effectiveness of personalized medicine diagnostics across mental health, rheumatology, and oncology. Degtiar holds a Ph.D. in biostatistics from Harvard University.
The appendices to the final report provide detailed information about the data, methods, analyses, and findings from the independent evaluation of the five years of CPC+ for the 2,905 practices in regions that began CPC+ in 2017.
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