Mathematica helps Medicare assess whether a new pay-for-performance model reduces heart attacks and strokes for high-risk Medicare beneficiaries.
Keith Kranker
Principal Researcher
Expertise
- Program Evaluation
- Impact Analysis Design and Implementation
- Econometrics and Predictive Analytics
- Primary Care
- Maternal and Child Health
Focus Areas
- Health
- Medicaid and CHIP
- Medicare
About Keith
Keith Kranker focuses on quantitative research analyses, having designed or implemented more than a dozen impact evaluations, including individual and cluster randomized designs, quasi-experimental studies, and observational studies. His work has focused on the effects of the Centers for Medicare & Medicaid Services’ programs on health care utilization, spending, and health outcomes for Medicare and Medicaid beneficiaries, and it has incorporated analyses of survey and administrative data. Kranker currently leads several tasks related to measuring impacts on beneficiaries and providers for the evaluation of the Million Hearts Cardiovascular Disease Risk Reduction Model. He has studied maternal and infant health outcomes in response to a variety of interventions, including the Special Supplemental Nutrition Program for Women, Infants, and Children, mobile health technology, and home visiting programs.
Before joining Mathematica, Kranker held positions at the Millennium Challenge Corporation, the University of Maryland, and the World Bank Group. His research has been included in numerous Centers for Medicare & Medicaid Services reports and published in multiple venues, such as the Journal of Health Economics, the Journal of Human Resources, Health Services Research, and JAMA Internal Medicine. He lives in Denver, Colorado.
Key Publications
Estimating the Incidence of Unintended Births and Pregnancies at the Sub-State Level to Inform Program Design
Oct 15, 2020
Improving Effect Estimates by Limiting the Variability in Inverse Propensity Score Weights
Apr 14, 2020
Key Projects
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FNS WIC-Medicaid Study IIWe use linked administrative data from WIC, Medicaid, and vital records to analyze associations between (1) WIC participation during pregnancy and birth outcomes, health care use/costs, and maternal behaviors and risk factors; and (2) child WIC participation and health care use and costs.