Jiajia Chen

Jiajia Chen is a health policy researcher with expertise in Medicaid and Children’s Health Insurance Program (CHIP) analytics, maternal and infant health, and advanced analytic methods such as causal inference, machine-learning applications, and policy simulation modeling. He has extensive experience using large-scale survey and administrative data, such as insurance claims and electronic health records, to generate insights to support program decisions and conduct policy evaluations.

Chen has worked on projects at Mathematica ranging from COVID-19 cohort data analytics to the development of reusable analytic frameworks, such as algorithms for identifying pregnancy episodes and linking mothers’ and infants’ records in the Transformed Medicaid Statistical Information System (T-MSIS) Analytic Files (TAF). He also applied these approaches to studies of home- and community-based services, maternal vaccine feasibility, and Medicaid payment patterns. For the Centers for Medicare & Medicaid Services, Chen has written evaluation guidance documents on building comparison groups and on Section 1115 demonstrations to help evaluators design rigorous studies with Medicaid and CHIP data.

Before joining Mathematica, Chen completed the Steven M. Teutsch Prevention Effectiveness Fellowship at the Centers for Disease Control and Prevention (CDC), working on topics such as severe maternal morbidity, preterm birth and stillbirth, perinatal depression, and long-acting reversible contraception. His work was nominated for the Charles C. Shepard Science Award, the highest scientific award at CDC. His research has appeared in peer-reviewed publications, including Journal of Policy Analysis and Management, American Journal of Preventive Medicine, and JAMA Network Open. He also serves as a reviewer for journals such as Annals of Internal Medicine, Journal of Women’s Health, BMJ Open, and Southern Economic Journal. Chen holds a Ph.D. in economics from the University of Illinois at Chicago.