# Population-Based Assessment of the Health Effects of Climate Exposure Using Hyperlocal Predictive Models

> **NIH NIH R01** · STATE UNIVERSITY NEW YORK STONY BROOK · 2024 · $332,562

## Abstract

`
Project Summary
 We aim to develop a daily temperature model on 30m road segments for all roads in the
contiguous United States from 2000 to 2020. We will use satellite data, land use features,
elevation, etc. as predictors in a stacked machine learning approach which will be trained on tens
of thousands of weather stations to estimate hyperlocal predictions. We will in turn use this model
to identify urban heat islands, hot and cold spots, and identify populations most vulnerable to sub-
optimal temperature exposure. This model will be made publicly available to all stakeholders
including community members, researchers, and policy makers to help examine and address
structural environmental injustice in exposure to temperature and its health effects.
 Next, we will use this exposure data in epidemiological studies to see whether short- and
long-term temperature exposures are associated with health outcomes using causal modeling
and mixtures approaches. We will conduct a case-crossover analysis looking at short-term
exposures and geocoded deaths in thirteen US states (to residence in most states and census
block groups in the rest). For long-term exposure, we will use a variation of a causal difference-
in-difference model which can account for both measured and unmeasured confounding between
spatial units over time to assess how changes in area-level temperature affect changes in rates
of morbidity and mortality. This will be followed up by a grouped weighted quantile sum approach
which can group correlated exposures and identify their joint effects as a mixture as well as the
contribution of each individual component. We will look at several simultaneous mixtures: climate
exposures, air pollution exposures, and socioeconomic exposures.
 Finally, we will conduct extensive sensitivity and subgroup analyses. We will use negative
controls assure that associations are not due to confounding. We will correct for measurement
error using regression calibration, stratified by state and season. We will examine how the
associations are modified by race/ethnicity, poverty, measures of social vulnerability and
deprivation, and land use characteristics. Our morbidity data includes all state in-patient data and
emergency department visits from several states across years. This allows us to examine the
effects of temperature on non-fatal outcomes across the life span. Our large population-based
datasets ensure that we will have enough power to detect associations in our subgroup and
stratified analyses. This project will greatly enhance our understanding of the full health effects of
exposure to temperature in numerous population groups using causal methodology and mixtures
approaches.

## Key facts

- **NIH application ID:** 10944448
- **Project number:** 1R01ES036566-01
- **Recipient organization:** STATE UNIVERSITY NEW YORK STONY BROOK
- **Principal Investigator:** Mahdieh Danesh Yazdi
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $332,562
- **Award type:** 1
- **Project period:** 2024-07-13 → 2029-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10944448

## Citation

> US National Institutes of Health, RePORTER application 10944448, Population-Based Assessment of the Health Effects of Climate Exposure Using Hyperlocal Predictive Models (1R01ES036566-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10944448. Licensed CC0.

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