# SCH: Synergizing Topological Deep Learning and Spatio-Temporal Causal Inference to Unpack Health

> **NIH NIH R01** · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · 2024 · $300,393

## Abstract

PROJECT SUMMARY (See instructions):
This research aims to provide novel statistical and deep learning (DL) tools to address open
methodological challenges in understanding and mitigating health disparities in the United States in the
context of the climate crisis. It focuses especially on the health effects of extreme heat and air pollution.
Marginalized populations often bear the brunt of climate threats. It is thus vital to identify the communities
and individuals that are most vulnerable. To enable such analysis, this multi-disciplinary research brings
together computer scientists and biostatisticians to promote principled and synergistic advancements in
Topological Deep Learning (TDL) and spatial Causal Inference (Cl) to address open technical challenges
in the fields of TDL, Cl, and Spatial Statistics. The project is centered around three thrusts. Thrust 1:
Develop new TDL methods to process multi-resolution irregular areal data; Thrust 2: Leverage TDL in
spatiotemporal causal inference; Thrust 3: Establish a methodological framework to jointly leverage
TDL-based spatial and individual-level representations. Innovations include new TDL methods to address
key gaps in DL for areal spatial data, the first theoretically grounded framework leveraging TDL for
learning from aggregated outcomes under spatial heterogeneity in areal data, and new TDL methods for
causal inference using spatiotemporal data. This project will result in the first multi-modal scalable
framework for learning joint representations of geospatial data and individual-level health records.
Large-scale case studies utilizing Medicare data from 2000 to 2020 and a vast amount of spatial and
longitudinal data will allow the translation of research results into actionable information to inform policies
and interventions to reduce health disparities. The successful completion of this research will address
scientific policy-relevant questions such as: Which social and structural determinants of health lead to
higher vulnerability to hospitalization and death caused by extreme heat, and, therefore, more urgently
need mitigation policies to ensure climate justice? How does exposure to air pollution exacerbate
geographic health disparities due to exposure to heat over the short- and long-term? Does individual-level
medical history contribute to recurrent hospitalizations among individuals of low socioeconomic status?

## Key facts

- **NIH application ID:** 11061942
- **Project number:** 1R01ES037156-01
- **Recipient organization:** HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH
- **Principal Investigator:** Francesca Dominici
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $300,393
- **Award type:** 1
- **Project period:** 2024-07-06 → 2028-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11061942

## Citation

> US National Institutes of Health, RePORTER application 11061942, SCH: Synergizing Topological Deep Learning and Spatio-Temporal Causal Inference to Unpack Health (1R01ES037156-01). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/11061942. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*