Examining Extreme Weather Effects on Birth Weight From the Individual Effect to Spatiotemporal Aggregation Effects

Extreme weather events are related to low birth weight. Monitoring this relationship in the context of climate change has a wide range of public health implications, as birth weight is a key indicator of many life course health outcomes, and climate change increases both frequency and intensity of extreme weather events. However, most birth weight data are not available with sufficient spatial and temporal resolution. The current study examined the relationship between birth weight and weather variables in a series of aggregations, from individual birth outcomes to month-county, season-county, and county-only mean birth weights. Data were based on a 20 % sample of White mothers aged 19 to 38 from the United States Natality Data Files, and the baseline model was for the 1974–1978 and 1984–1988 periods with 2,269,009 and 2,652,552 individual birth records, respectively. The evaluation was based on multiple regression for aggregation effects, and conditional autoregressive and spatial association models for spatial clustering effects. The results show that the number of extreme cold and hot days during the birth month is inversely associated with birth weight, and that temporal aggregation by month-county or season-county was likely to preserve the relationship between birth weight and extreme weather from the individual model. While both conditional autoregressive and spatial association models can remove some spatial autocorrelation, the spatial association approach may not work effectively without further modifying the existing method.