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Extreme urban temperature exposure and preterm birth: Spatial-temporal risk zone prediction using machine learning
Yuxiao Wang1, Lingchen Bu1, Zanmei Wei1
1Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China.
Abstract:
This study investigates temperature impacts on preterm birth (PTB) using residential address GPS coordinates for 311,972 pregnant women in Wuhan, China, coupled with daily environmental data. We developed a machine learning model to analyze the impact of environmental exposure on PTB. Results show PTB risks increase with temperatures below 14 °C or above 21 °C, excessive temperature variability, and acute exposure to extreme weather. Spatial analysis revealed heat-related risk zones concentrated in urban heat island areas, while cold-related risks were more widespread. This research provides novel insights into spatiotemporal patterns of temperature-related PTB risk, offering evidence-based recommendations for urban health planning and climate adaptation strategies. The integration of machine learning and spatial analysis represents a significant advancement in environmental health research methodology.
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