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Identifying county-level effect modifiers of the association between heat waves and preterm birth using a Bayesian
Shuqi Lin1, Howard H Chang2, Lyndsey A Darrow3
1Department of Biostatistics, Yale School of Public Health, Yale University, New Haven, CT.
Medrxiv : the Preprint Server for Health Sciences
|July 17, 2025
Summary
Extreme heat increases preterm birth risks, especially for vulnerable groups. Factors like housing quality and social vulnerability modify these heat-related risks, highlighting areas for public health intervention.
Area of Science:
- Environmental Health
- Epidemiology
- Biostatistics
Background:
- High temperatures pose significant health risks, particularly to pregnant individuals and fetuses.
- Existing research on heat and preterm birth lacks focus on spatial variations and modifying factors.
Purpose of the Study:
- To estimate county-level associations between heat waves and preterm birth across eight US states.
- To explore county-level factors that modify heat wave-preterm birth associations using spatial meta-regression.
Main Methods:
- A two-stage modeling approach was employed.
- Hierarchical Bayesian spatial meta-regression, incorporating conditional autoregressive models, was used to account for spatial dependence.
- An R package, SpMeta, was developed for synthesizing area-level risk estimates.
Main Results:
- Significant spatial heterogeneity in heat wave-associated preterm birth risks was observed.
- Factors such as housing quality, energy affordability, and social vulnerability (minority status, language barriers) were identified as significant modifiers of these risks.
Conclusions:
- Heat wave-associated preterm birth risks vary spatially and are influenced by socioeconomic and environmental factors.
- Identifying and addressing these modifiable factors is crucial for mitigating adverse birth outcomes in vulnerable populations.
Keywords:
conditional autoregressive modelextreme temperaturemeta-regressionpreterm birthspatial statisticsMore Related Videos
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