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

Abstract

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.

Associations of Meteorology with Adverse Pregnancy Outcomes: A Systematic Review of Preeclampsia, Preterm Birth and Birth Weight

“…This work was recently extended [ 90 ]; it was estimated that under projected climate change, mean birth weight will decrease by 0.44%–1.05% per °C increase in temperature. A similar analysis was conducted in the USA and reported an inverse association between annual average temperature of and mean birth weight at the county resolution [ 92 ].…”

“…The number of days with temperature < 25° F during the first trimester was also associated with a decrement in mean birth weight, suggesting a possible inverse U-shaped relationship. A subsequent analysis that explored even more extreme events (days <20 °F and >90 °F) confirmed such relationships [ 92 ].…”

“…The effects of temperature on birth weight were assessed in thirteen studies, with different questions explored by several approaches: some studies focused on the association between birth weight and the climate (or “temperature regime” [ 90 ]) prevailing in different locations, that was typically reflected by mean annual temperature [ 90 , 91 , 92 ]. These analyses were based on geographical contrasts in annual mean birth weights between locations of different climates.…”

“…These analyses were based on geographical contrasts in annual mean birth weights between locations of different climates. Other studies focused on temporal contrasts in exposure in fixed settings, with a focus either on the temporal contrasts in temperature typically experienced between different trimesters of pregnancy [ 80 ], or on the occurrence of extreme climatic events [ 92 , 93 ].…”

Disparity of Imputed Data from Small Area Estimate Approaches – A Case Study on Diabetes Prevalence at the County Level in the U.S.

“…In addition, none of the three SAE methods considered spatial clustering. However, we know when data are fairly complete (e.g., births and deaths), model-based estimates would be substantially biased when spatial clustering or spatial association effects were not removed (Lin and Zhang 2012). Future studies should examine the effects of spatial clusters or clustering effects on SAEs, and remedies to reduce potential biases.…”