Extreme temperature, humidity, air pollution and spontaneous preterm birth

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PROJECT SUMMARY This project will extend methods for estimating daily temperature, humidity, and fine particulate matter air pollution at high resolution across large regions by utilizing NASA and new USGS satellite measurements to generate estimated exposures at the neighborhood level across the Northeast US. This detailed exposure record can be used in health studies to further consider extreme weather and air pollutant as a health risk. These new daily models will be developed for 2007-2023 at a <1 km resolution and thus include billions of point-day temperature, humidity, and particulate matter estimates. While it is well established that particulate matter increases the risk of preterm birth, less is known about how extreme weather events (including temperature and humidity) and air pollution contribute to the onset of spontaneous preterm birth. Pathophysiology builds throughout a lifetime & during pregnancy, yet the onset of preterm labor and/or rupture of membranes is acute. In this way, we investigate potential environmental triggers to ask, “Why today?”. The epidemiologic application of these novel exposure models will be demonstrated by testing the association of temperature, humidity, and particulate matter with a dataset of spontaneous preterm births and residential addresses from a comprehensive New York statewide administrative database. We will use address-level exposures in the 7 days prior to a spontaneous preterm delivery with matched days in the same fixed two- week period as a comparison (time stratified case-crossover design) for 17 years of spontaneous preterm births statewide – making this among the largest environmental epidemiology studies of spontaneous preterm birth with more than 170,000 cases. Given the large number of cases and the variation in population characteristics across New York State, the association between extreme weather, air pollution patterns and spontaneous preterm birth will be further tested for effect modification by sex, race, gestational age, urban/rural, and neighborhood deprivation. A follow-up analysis will use carefully phenotyped spontaneous preterm cases from large hospital systems in Philadelphia. Our epidemiologic models will improve on prior work on the acute impacts of extreme temperature and air pollution on preterm birth that have relied on coarse exposure assignments via ecological time series models, inappropriately long time strata for case-crossover comparison, or overly broad outcome definitions that included medically-indicated preterm births. Given the global coverage of satellite remote sensing, our approach – which can generate daily exposure estimates that are highly spatially resolved (<1 km) to capture ambient exposure at the home address – is broadly applicable to better understand the role of extreme weather and air pollution as a stressor in both chronic and acute health outcomes using big data registries.