Identifying county-level effect modifiers of the association between heat waves and preterm birth using a Bayesian spatial meta regression approach.

High temperature is associated with adverse health outcomes, particularly for vulnerable subpopulations including pregnant individuals and their unborn babies. Several recent studies have investigated the association between temperature and preterm birth at different geographic scales and across different spatial locations. However, there has been less focus on characterizing spatial heterogeneity in risks and identifying modifiable factors that contribute to the observed variation.

Preterm and early-term delivery after heat waves in eight US states: A case-crossover study using the High-resolution Urban Meteorology for Impacts Dataset (HUMID).

Background: Heat wave frequency and intensity is increasing and this trend is more pronounced in urban areas. Heat waves may be acutely associated with early birth.

Objectives: To examine the acute relationship between heat waves and preterm (<37 weeks) and early-term (37-38 weeks) birth in eight states: California, Florida, Georgia, Kansas, Nevada, New Jersey, North Carolina, and Oregon.

The High-resolution Urban Meteorology for Impacts Dataset (HUMID) daily for the Conterminous United States.

Many current gridded surface meteorological datasets are inadequate for quantifying near-surface spatiotemporal variability because they do not fully represent the impacts of land surface heterogeneity. Of note, explicit representation of the spatial structure and magnitude of local urban warming are usually lacking. Here we enhance the representation of spatial meteorological variability over urban areas in the conterminous United States (CONUS) by employing the High-Resolution Land Data Assimilation System (HRLDAS), which accounts for the fine-scale impacts of spatiotemporally varying land surfaces on weather.