The Biomedical and Health Data Sciences Collaborative (BHDSC), a cross-disciplinary group formed by the Tufts Clinical and Translational Science Institute (CTSI) and Institute for Clinical Research and Health Policy Studies (ICRHPS) at Tufts Medical Center, invites you to attend a virtual seminar on Wednesday, July 26 from 2:00PM-3:00PM.
Rachel Nethery, PhD will give a talk titled “Estimating and forecasting the causal effects of extreme weather events on health.”
To minimize the health threats presented by extreme weather events, we must generate high-precision insights and tools to inform strategic preparedness efforts. Currently, our limited understanding of the epidemiology of these events inhibits progress in reducing health risks. We propose an integrated causal and predictive statistical modeling approach that, when applied to today’s wealth of historic weather and health data, enables standardized, high-resolution quantification of the health impacts of historic extreme weather episodes and characterizes how features of the events and the impacted communities explain variation in health risks. This method enables high-resolution prediction of future extreme weather-related health impacts, which can inform strategic preparedness and aid in identifying high-risk communities in advance of future events. We apply our method to a rich data platform containing detailed historic tropical cyclone exposure information for the US and Medicare claims data to investigate health effects of past tropical cyclones and identify features predictive of tropical cyclone-related health risks.
Dr. Rachel Nethery is an Assistant Professor in the Department of Biostatistics at the Harvard T. H. Chan School of Public Health. Her research is focused on the development of statistical methods that enable rigorous and impactful analyses of environmental health data and thereby inform new, evidence-based environmental policy and clinical guidelines to protect public health. Methodologically, her work spans the domains of causal inference, machine learning, Bayesian methods, latent variable models, spatial statistics, and time series analysis.
Wednesday, July 26, 2023
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