Using non-parametric Bayes shrinkage to assess relationships between multiple environmental and social stressors and neonatal size and body composition in the Healthy Start cohort

Abstract

Background Both environmental and social factors have been linked to birth weight and adiposity at birth, but few studies consider the effects of exposure mixtures. Our objective was to identify which components of a mixture of neighborhood-level environmental and social exposures were driving associations with birth weight and adiposity at birth in the Healthy Start cohort. Methods Exposures were assessed at the census tract level and included air pollution, built environment characteristics, and socioeconomic status. Prenatal exposures were assigned based on address at enrollment. Birth weight was measured at delivery and adiposity was measured using air displacement plethysmography within three days. We used non-parametric Bayes shrinkage (NPB) to identify exposures that were associated with our outcomes of interest. NPB models were compared to single-predictor linear regression. We also included generalized additive models (GAM) to assess nonlinear relationships. All regression models were adjusted for individual-level covariates, including maternal age, pre-pregnancy BMI, and smoking. Results Results from NPB models showed most exposures were negatively associated with birth weight, though credible intervals were wide and generally contained zero. However, the NPB model identified an interaction between ozone and temperature on birth weight, and the GAM suggested potential non-linear relationships. For associations between ozone or temperature with birth weight, we observed effect modification by maternal race/ethnicity, where effects were stronger for mothers who identified as a race or ethnicity other than non-Hispanic White. No associations with adiposity at birth were observed. Conclusions NPB identified prenatal exposures to ozone and temperature as predictors of birth weight, and mothers who identify as a race or ethnicity other than non-Hispanic White might be disproportionately impacted. However, NPB models may have limited applicability when non-linear effects are present. Future work should consider a two-stage approach where NPB is used to reduce dimensionality and alternative approaches examine non-linear effects.

Penalized distributed lag interaction model: Air pollution, birth weight, and neighborhood vulnerability

“…We investigate the potential for the association between maternal exposure to ambient air pollution and birth weight to be modified by area‐level factors that may influence vulnerability. Evidence shows that a myriad of neighborhood‐level factors (e.g., gentrification, poverty rate, green space, residential segregation, and healthcare accessibility) can modify the association between environmental exposures and health outcomes (Arcaya et al, 2016; Martenies, Hoskovec, et al, 2022; Schnake‐Mahl et al, 2020). Estimating the modification of many neighborhood factors is challenging because neighborhood‐level factors can be highly correlated with each other, making it difficult to differentiate the modification attributable to separate correlated factors.…”

Cumulative Exposures to Environmental and Socioeconomic Risk Factors in Milwaukee County, Wisconsin

“…Recently, several methodological approaches have been proposed to address the independent and joint contribution of environmental exposures and social factors to health outcomes (Martenies, Hoskovec, et al., 2022 ; Martenies, Zhang, et al., 2022 ; Martenies et al., 2023 ). Identification of relevant social or environmental factors associated with disease outcomes are an important pathway to identify effective intervention and mediation strategies to improve health.…”