This Project Grant award, funded by the National Institute of Environmental Health Sciences (NIEHS) under the Environmental Health (CFDA 93.113) program, aims to develop novel statistical and deep learning methods to understand and mitigate health disparities related to climate threats such as extreme heat and air pollution. Specifically, the $150,416 award to Harvard T.H. Chan School of Public Health will:
- Develop new topological deep learning (TDL) techniques to process multi-resolution spatial data,
- Leverage TDL approaches to improve spatiotemporal causal inference, and
- Establish a framework to jointly utilize TDL-based spatial and individual-level health data representations.
The research will utilize large-scale datasets including Medicare records from 2000-2020, with the goal of informing policies and interventions to address climate-related health disparities affecting marginalized populations. The award period is from July 2024 to April 2028.
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