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.

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.

Ozone-Related Respiratory Morbidity in a Low-Pollution Region.

OBJECTIVE: We evaluated the effects of ozone on respiratory-related hospital admissions in three counties in Washington State from 1990 to 2006. We further examined vulnerability to ozone by key demographic factors. METHOD: Using linked hospital admission and ambient monitoring data, we estimated the age-, sex-, and health insurance-stratified associations between ozone (0 to 3 days' lag) and respiratory-related hospital admissions in King, Spokane, and Clark County, Washington. RESULTS: The adjusted relative risk (RR) for a 10 ppb increase in ozone at 3 days' lag was 1.04 (95% confidence interval [CI]: 1.02, 1.07) for Clark County, 1.03 (95% CI: 1.01, 1.05) for Spokane County, and 1.02 (95% CI: 1.01, 1.03) for King County. There was consistent evidence of effect modification by age. CONCLUSION: Ozone at levels below federal standards contributes to respiratory morbidity among high-risk groups in Washington.