A Bayesian framework for incorporating exposure uncertainty into health analyses with application to air pollution and stillbirth.

Studies of the relationships between environmental exposures and adverse health outcomes often rely on a two-stage statistical modeling approach, where exposure is modeled/predicted in the first stage and used as input to a separately fit health outcome analysis in the second stage. Uncertainty in these predictions is frequently ignored, or accounted for in an overly simplistic manner when estimating the associations of interest. Working in the Bayesian setting, we propose a flexible kernel density estimation (KDE) approach for fully utilizing posterior output from the first stage modeling/prediction to make accurate inference on the association between exposure and health in the second stage, derive the full conditional distributions needed for efficient model fitting, detail its connections with existing approaches, and compare its performance through simulation. Our KDE approach is shown to generally have improved performance across several settings and model comparison metrics. Using competing approaches, we investigate the association between lagged daily ambient fine particulate matter levels and stillbirth counts in New Jersey (2011-2015), observing an increase in risk with elevated exposure 3 days prior to delivery. The newly developed methods are available in the R package KDExp.

Exposure to atmospheric metals using moss bioindicators and neonatal health outcomes in Portland, Oregon.

Studying the impacts of prenatal atmospheric heavy-metal exposure is challenging, because biological exposure monitoring does not distinguish between specific sources, and high-resolution air monitoring data is lacking for heavy metals. Bioindicators - animal or plant species that can capture environmental quality - are a low-cost tool for evaluating exposure to atmospheric heavy-metal pollution that have received little attention in the public-health literature. We obtained birth records for Portland, Oregon live births (2008-2014) and modeled metal concentrations derived from 346 samples of moss bioindicators collected in 2013. Exposure estimates were assigned using mother's residential address at birth for six metals with known toxic and estrogenic effects (arsenic, cadmium, chromium, cobalt, nickel, lead). Associations were evaluated for continuous (cts) and quartile-based (Q) metal estimates and three birth outcomes (preterm birth (PTB; <37 weeks)), very PTB (vPTB; <32 weeks), small for gestational age (SGA; 10th percentile of weight by age and sex)) using logistic regression models with adjustment for demographic characteristics, and stratified by maternal race. Chromium and cobalt were associated with increased odds of vPTB (chromium - odds ratio (OR)cts = 1.09, 95% CI: 1.00, 1.17; cobalt - ORQ4vsQ1 = 1.33, 95% CI: 1.03, 1.71). Cobalt, chromium and cadmium were significantly associated with odds of SGA, although the direction of association differed by metal (cobalt - ORcts = 1.04, 95% CI: 1.01, 1.07; chromium - ORQ3vsQ1 = 0.91, 95% CI: 0.83, 0.99; cadmium - ORcts = 0.96, 95% CI: 0.93, 1.00). In stratified analyses, odds of SGA were significantly different among non-white mothers compared to white mothers with exposure to chromium, cobalt, lead and nickel. This novel application of a moss-based exposure metric found that exposure to some atmospheric metals is associated with adverse birth outcomes. These findings are consistent with previous literature and suggest that moss bioindicators are a useful complement to traditional exposure-assessment methods.

Bringing Big Data to Bear in Environmental Public Health: Challenges and Recommendations.

Understanding the role that the environment plays in influencing public health often involves collecting and studying large, complex data sets. There have been a number of private and public efforts to gather sufficient information and confront significant unknowns in the field of environmental public health, yet there is a persistent and largely unmet need for findable, accessible, interoperable, and reusable (FAIR) data. Even when data are readily available, the ability to create, analyze, and draw conclusions from these data using emerging computational tools, such as augmented and artificial inteligence (AI) and machine learning, requires technical skills not currently implemented on a programmatic level across research hubs and academic institutions. We argue that collaborative efforts in data curation and storage, scientific computing, and training are of paramount importance to empower researchers within environmental sciences and the broader public health community to apply AI approaches and fully realize their potential. Leaders in the field were asked to prioritize challenges in incorporating big data in environmental public health research: inconsistent implementation of FAIR principles in data collection and sharing, a lack of skilled data scientists and appropriate cyber-infrastructures, and limited understanding of possibilities and communication of benefits were among those identified. These issues are discussed, and actionable recommendations are provided.