Quantitative Health Sciences Calendar

Abstract: Massachusetts was one of the first states in the US to obtain a waiver to allow Medicaid to pay for nutrition and housing programs for medically complex members with food insecurity and housing insecurity. This presentation will focus on new research analyzing changes in hospitalizations, emergency department visits, and total costs of care among 30,000 Medicaid members who received services from 2020-2023 under the Flexible Services Program. 
Bio: Dr. Hager’s research focuses on structural determinants of health, food insecurity, and nutrition and health insurance policies. He is currently evaluating the Flexible Services Program, which addresses food and housing insecurity under Massachusetts’s Medicaid Section 1115 Waiver. Dr. Hager has training in nutritional epidemiology and his recent studies have included policy modeling, economic evaluations, and quasi-experimental studies.
Bio: Dr. Sabatino is a recent graduate of the Population Health Sciences program, where she contributed to data collection and analytic efforts for the independent evaluation of the Massachusetts Section 1115 Medicaid Demonstration. Her current research examines the effects of Medicaid policy reforms on health outcomes among low-income populations, with particular emphasis on individuals with behavioral health conditions.

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Abstract: Accurate and complete race/ethnicity data are central to measuring and reducing health inequities, but self-reported race/ethnicity data generally are incomplete for health plans. High-quality imputations are therefore critical. In this talk, we present a Bayesian machine learning imputation model that improves imputation accuracy compared to existing approaches by flexibly integrating information from all available data sources without being constrained by assumptions of linearity or additivity. Our method enables more precise measurement of inequities, ultimately enhancing the effectiveness of health equity programs. 
Bio: Dr. Mariel Finucane is the Director of Statistical Methods at Blue Cross Blue Shield of Massachusetts. She is a collaborative statistician and health services researcher, with expertise in heterogeneous treatment effects, generalizability, quality measurement, and Bayesian adaptive and factorial design. She has published widely, including first-author papers in the Journal of the American Statistical Association, Statistical Science, Statistical Methods in Medical Research, and the Journal of Evaluation; a first-author Lancet article on global trends in body mass index has been cited 6,000 times. Dr. Finucane holds a Ph.D. in biostatistics from the Harvard School of Public Health.

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Description: Cardiovascular disease (CVD) has been the leading cause of death in the U.S. for over a century, but its burden is not equitably distributed. Non-Hispanic Black adults experience significantly higher rates of CVD-related mortality, including 4–5 times higher hypertension-related deaths and greater stroke risk, compared to non-Hispanic White adults. These disparities are not solely due to individual behaviors, but stem from structural inequities—such as residential segregation and social vulnerability—that shape cardiovascular risk across the life course. This dissertation research proposal investigates how key upstream structural determinants, specifically, neighborhood residential segregation (a result of historically discriminatory housing policies) and social vulnerability, contribute to CVD outcomes and cardiovascular health risk factors among non-Hispanic Black adults in the Coronary Artery Risk Development in Young Adults (CARDIA) Study.

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Description: This study leverages longitudinal data from over 2,500 Bahamian adolescents to model how risk behaviors evolve in tandem over time. Using group-based multi-trajectory modeling, the project examines how distinct patterns of risk behavior are shaped by early social-contextual influences (e.g., parental monitoring, peer norms, neighborhood risk) and cognitive-motivational factors rooted in Protection Motivation Theory. Findings aim to inform targeted, culturally relevant HIV prevention strategies for high-risk youth populations.

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Description: Leveraging individual-level data from the nationally representative MDS 3.0 and publicly available data from Nursing Home Compare and Provider of Service datasets (facility level) and the American Community Survey (regional level) we seek to better understand the mental health of Latino nursing home residents with limited English proficiency. 

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Description: The proposal will present research aims on the use and comparative safety of gabapentinoids co-prescribed with opioids in nursing homes, using the Minimum Data Set 3.0 and Medicare claims data.

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