Eric Coker

Modeling spatial effects of PM2.5 on term low birth weight in Los Angeles County

Air pollution epidemiological studies suggest that elevated exposure to fine particulate matter (PM2.5) is associated with higher prevalence of term low birth weight (TLBW). Previous studies have generally assumed the exposure-response of PM2.5 on TLBW to be the same throughout a large geographical area. Health effects related to PM2.5 exposures, however, may not be uniformly distributed spatially, creating a need for studies that explicitly investigate the spatial distribution of the exposure-response relationship between individual-level exposure to PM2.5 and TLBW. Here, we examine the overall and spatially varying exposure-response relationship between PM2.5 and TLBW throughout urban Los Angeles (LA) County, California. We estimated PM2.5 from a combination of land use regression (LUR), aerosol optical depth from remote sensing, and atmospheric modeling techniques. Exposures were assigned to LA County individual pregnancies identified from electronic birth certificates between the years 1995-2006 (N=1,359,284) provided by the California Department of Public Health. We used a single pollutant multivariate logistic regression model, with multilevel spatially structured and unstructured random effects set in a Bayesian framework to estimate global and spatially varying pollutant effects on TLBW at the census tract level. Overall, increased PM2.5 level was associated with higher prevalence of TLBW county-wide. The spatial random effects model, however, demonstrated that the exposure-response for PM2.5 and TLBW was not uniform across urban LA County. Rather, the magnitude and certainty of the exposure-response estimates for PM2.5 on log odds of TLBW were greatest in the urban core of Central and Southern LA County census tracts. These results suggest that the effects may be spatially patterned, and that simply estimating global pollutant effects obscures disparities suggested by spatial patterns of effects. Studies that incorporate spatial multilevel modeling with random coefficients allow us to identify areas where air pollutant effects on adverse birth outcomes may be most severe and policies to further reduce air pollution might be most effective.

Multi-pollutant exposure profiles associated with term low birth weight in Los Angeles County

Research indicates that multiple outdoor air pollutants and adverse neighborhood conditions are spatially correlated. Yet health risks associated with concurrent exposure to air pollution mixtures and clustered neighborhood factors remain underexplored. Statistical models to assess the health effects from pollutant mixtures remain limited, due to problems of collinearity between pollutants and area-level covariates, and increases in covariate dimensionality. Here we identify pollutant exposure profiles and neighborhood contextual profiles within Los Angeles (LA) County. We then relate these profiles with term low birth weight (TLBW). We used land use regression to estimate NO2, NO, and PM2.5 concentrations averaged over census block groups to generate pollutant exposure profile clusters and census block group-level contextual profile clusters, using a Bayesian profile regression method. Pollutant profile cluster risk estimation was implemented using a multilevel hierarchical model, adjusting for individual-level covariates, contextual profile cluster random effects, and modeling of spatially structured and unstructured residual error. Our analysis found 13 clusters of pollutant exposure profiles. Correlations between study pollutants varied widely across the 13 pollutant clusters. Pollutant clusters with elevated NO2, NO, and PM2.5 concentrations exhibited increased log odds of TLBW, and those with low PM2.5, NO2, and NO concentrations showed lower log odds of TLBW. The spatial patterning of pollutant cluster effects on TLBW, combined with between-pollutant correlations within pollutant clusters, imply that traffic-related primary pollutants influence pollutant cluster TLBW risks. Furthermore, contextual clusters with the greatest log odds of TLBW had more adverse neighborhood socioeconomic, demographic, and housing conditions. Our data indicate that, while the spatial patterning of high-risk multiple pollutant clusters largely overlaps with adverse contextual neighborhood cluster, both contribute to TLBW while controlling for the other.

Association between Pesticide Profiles Used on Agricultural Fields near Maternal Residences during Pregnancy and IQ at Age 7 Years

We previously showed that potential prenatal exposure to agricultural pesticides was associated with adverse neurodevelopmental outcomes in children, yet the effects of joint exposure to multiple pesticides is poorly understood. In this paper, we investigate associations between the joint distribution of agricultural use patterns of multiple pesticides (denoted as “pesticide profiles”) applied near maternal residences during pregnancy and Full-Scale Intelligence Quotient (FSIQ) at 7 years of age. Among a cohort of children residing in California’s Salinas Valley, we used Pesticide Use Report (PUR) data to characterize potential exposure from use within 1 km of maternal residences during pregnancy for 15 potentially neurotoxic pesticides from five different chemical classes. We used Bayesian profile regression (BPR) to examine associations between clustered pesticide profiles and deficits in childhood FSIQ. BPR identified eight distinct clusters of prenatal pesticide profiles. Two of the pesticide profile clusters exhibited some of the highest cumulative pesticide use levels and were associated with deficits in adjusted FSIQ of −6.9 (95% credible interval: −11.3, −2.2) and −6.4 (95% credible interval: −13.1, 0.49), respectively, when compared with the pesticide profile cluster that showed the lowest level of pesticides use. Although maternal residence during pregnancy near high agricultural use of multiple neurotoxic pesticides was associated with FSIQ deficit, the magnitude of the associations showed potential for sub-additive effects. Epidemiologic analysis of pesticides and their potential health effects can benefit from a multi-pollutant approach to analysis.

A Narrative Review on the Human Health Effects of Ambient Air Pollution in Sub-Saharan Africa: An Urgent Need for Health Effects Studies

An important aspect of the new sustainable development goals (SDGs) is a greater emphasis on reducing the health impacts from ambient air pollution in developing countries. Meanwhile, the burden of human disease attributable to ambient air pollution in sub-Saharan Africa is growing, yet estimates of its impact on the region are possibly underestimated due to a lack of air quality monitoring, a paucity of air pollution epidemiological studies, and important population vulnerabilities in the region. The lack of ambient air pollution epidemiologic data in sub-Saharan Africa is also an important global health disparity. Thousands of air pollution health effects studies have been conducted in Europe and North America, rather than in urban areas that have some of the highest measured air pollution levels in world, including urban areas in sub-Saharan Africa. In this paper, we provide a systematic and narrative review of the literature on ambient air pollution epidemiological studies that have been conducted in the region to date. Our review of the literature focuses on epidemiologic studies that measure air pollutants and relate air pollution measurements with various health outcomes. We highlight the gaps in ambient air pollution epidemiological studies conducted in different sub-regions of sub-Saharan Africa and provide methodological recommendations for future environmental epidemiology studies addressing ambient air pollution in the region.

Multi-pollutant Modeling Through Examination of Susceptible Subpopulations Using Profile Regression

Purpose of ReviewThe inter-correlated nature of exposure-based risk factors in environmental health studies makes it a challenge to determine their combined effect on health outcomes. As such, there has been much research of late regarding the development and utilization of methods in the field of multi-pollutant modeling. However, much of this work has focused on issues related to variable selection in a regression context, with the goal of identifying which exposures are the “bad actors” most responsible for affecting the health outcome of interest. However, the question addressed by these approaches does not necessarily represent the only or most important questions of interest in a multi-pollutant modeling context, where researchers may be interested in health effects from co-exposure patterns and in identifying subpopulations associated with patterns defined by different levels of constituent exposures.Recent FindingsOne approach to analyzing multi-pollutant data is to use a method known as Bayesian profile regression, which aids in identifying susceptible subpopulations associated with exposure mixtures defined by different levels of each exposure. Identification of exposure-level patterns that correspond to a location may provide a starting point for policy-based exposure reduction. Also, in a spatial context, identification of locations with the most health-relevant exposure-mixture profiles might provide further policy relevant information.SummaryIn this brief report, we review and describe an approach that can be used to identify exposures in subpopulations or locations known as Bayesian profile regression. An example is provided in which we examine associations between air pollutants, an indicator of healthy food retailer availability, and indicators of poverty in Los Angeles County. A general tread suggesting that vulnerable individuals are more highly exposed and have limited access to healthy food retailers is observed, though the associations are complex and non-linear.

Spatial clustering of metal and metalloid mixtures in unregulated water sources on the Navajo Nation – Arizona, New Mexico, and Utah, USA

Contaminant mixtures are identified regularly in public and private drinking water supplies throughout the United States; however, the complex and often correlated nature of mixtures makes identification of relevant combinations challenging. This study employed a Bayesian clustering method to identify subgroups of water sources with similar metal and metalloid profiles. Additionally, a spatial scan statistic assessed spatial clustering of these subgroups and a human health metric was applied to investigate potential for human toxicity. These methods were applied to a dataset comprised of metal and metalloid measurements from unregulated water sources located on the Navajo Nation, in the southwest United States. Results indicated distinct subgroups of water sources with similar contaminant profiles and that some of these subgroups were spatially clustered. Several profiles had metal and metalloid concentrations that may have potential for human toxicity including arsenic, uranium, lead, manganese, and selenium. This approach may be useful for identifying mixtures in water sources, spatially evaluating the clusters, and help inform toxicological research investigating mixtures.

Determinants of Exposure to Pyrethroid Insecticides in the VHEMBE Cohort, South Africa

Exposure to pyrethroid insecticides has been linked to adverse health effects, and can originate from several sources, including indoor residual spraying (IRS) for malaria control, home pest control, food contamination, and occupational exposure. We aimed to explore the determinants of urinary pyrethroid metabolite concentrations in a rural population with high pesticide use. The Venda Health Examination of Mothers, Babies and their Environment (VHEMBE) is a birth cohort of 752 mother-child pairs in Limpopo, South Africa. We measured pyrethroid metabolites in maternal urine and collected information on several factors possibly related to pesticide exposure, including IRS, home pesticide use, and maternal factors (e.g., dietary habits and body composition). We performed statistical analysis using both conventional bivariate regressions and Bayesian variable selection methods. Urinary pyrethroid metabolites are consistently associated with pesticide factors around homes, including pesticide application in yards and food stocks, and IRS in the home during pregnancy, while more distant factors such as village spraying are not. High fat intake is associated with higher metabolite concentrations, and women from homes drawing water from wells or springs had marginally higher levels. Home pesticide use is the most consistent correlate of pyrethroid metabolite concentrations, but IRS, dietary habits, and household water source may also be important exposure determinants.

DNA methylation and socioeconomic status in a Mexican-American birth cohort

BackgroundMaternal social environmental stressors during pregnancy are associated with adverse birth and child developmental outcomes, and epigenetics has been proposed as a possible mechanism for such relationships.MethodsIn a Mexican-American birth cohort of 241 maternal-infant pairs, cord blood samples were measured for repeat element DNA methylation (LINE-1 and Alu). Linear mixed effects regression was used to model associations between indicators of the social environment (low household income and education, neighborhood-level characteristics) and repeat element methylation. Results from a dietary questionnaire were also used to assess the interaction between maternal diet quality and the social environment on markers of repeat element DNA methylation.ResultsAfter adjusting for confounders, living in the most impoverished neighborhoods was associated with higher cord blood LINE-1 methylation (β = 0.78, 95%CI 0.06, 1.50, p = 0.03). No other neighborhood-, household-, or individual-level socioeconomic indicators were significantly associated with repeat element methylation. We observed a statistical trend showing that positive association between neighborhood poverty and LINE-1 methylation was strongest in cord blood of infants whose mothers reported better diet quality during pregnancy (pinteraction = 0.12).ConclusionOur findings indicate a small yet unexpected positive association between neighborhood-level poverty during pregnancy and methylation of repetitive element DNA in infant cord blood and that this association is possibly modified by diet quality during pregnancy. However, our null findings for other adverse SES indicators do not provide strong evidence for an adverse association between early-life socioeconomic environment and repeat element DNA methylation in infants.