Developing a National-Scale Exposure Index for Combined Environmental Hazards and Social Stressors and Applications to the Environmental Influences on Child Health Outcomes (ECHO) Cohort.

Tools for assessing multiple exposures across several domains (e.g., physical, chemical, and social) are of growing importance in social and environmental epidemiology because of their value in uncovering disparities and their impact on health outcomes. Here we describe work done within the Environmental influences on Child Health Outcomes (ECHO)-wide Cohort Study to build a combined exposure index. Our index considered both environmental hazards and social stressors simultaneously with national coverage for a 10-year period. Our goal was to build this index and demonstrate its utility for assessing differences in exposure for pregnancies enrolled in the ECHO-wide Cohort Study. Our unitless combined exposure index, which collapses census-tract level data into a single relative measure of exposure ranging from 0-1 (where higher values indicate higher exposure to hazards), includes indicators for major air pollutants and air toxics, features of the built environment, traffic exposures, and social determinants of health (e.g., lower educational attainment) drawn from existing data sources. We observed temporal and geographic variations in index values, with exposures being highest among participants living in the West and Northeast regions. Pregnant people who identified as Black or Hispanic (of any race) were at higher risk of living in a "high" exposure census tract (defined as an index value above 0.5) relative to those who identified as White or non-Hispanic. Index values were also higher for pregnant people with lower educational attainment. Several recommendations follow from our work, including that environmental and social stressor datasets with higher spatial and temporal resolutions are needed to ensure index-based tools fully capture the total environmental context.

Detected prenatal perfluorooctanoic acid (PFOA) exposure is associated with decreased fetal head biometric parameters in participants experiencing higher perceived stress during pregnancy in the MADRES cohort.

Background: Perfluoroalkyl substances (PFAS) are ubiquitous synthetic chemicals with long half-lives and are known to cross the placenta during pregnancy. We examined the influence of maternal PFAS levels on in utero fetal growth trajectories and assessed whether maternal stress modified these associations. Methods: Blood serum concentrations of five PFAS (PFOS, PFHxS, PFNA, PFOA, PFDA) were measured in 335 prenatal specimens (mean gestational age (GA): 21±9 weeks) in the MADRES cohort. Fetal growth outcomes (head circumference (HC), abdominal circumference (AC), biparietal diameter (BPD), femur length (FL), and estimated fetal weight (EFW)) were abstracted from ultrasound medical records and measured at the 3rd trimester study visit (N = 833 scans, GA range 10-42 weeks, mean 2.4 scans/participant). Adjusted linear mixed models with a GA quadratic growth curve were used for each PFAS exposure and growth outcome. PFOS and PFHxS were modeled continuously (100% sample detection), while PFOA, PFNA, and PFDA were modeled categorically (57-70% sample detection). Scores on the Perceived Stress Scale (PSS) measured in pregnancy were dichotomized at the median (<13 vs. ≥ 13) in stratified models. Results: Participants were on average 29±6 years old and predominately Hispanic (76%). Median serum concentrations of PFOS, PFHxS, PFNA, PFOA and PFDA were 1.34, 1.10, 0.07, 0.12, and 0.04 ng/mL, respectively. Participants with detected PFOA concentrations had fetuses with -2.5 mm (95% CI -4.2, -0.8) smaller HC and-0.7 mm (95% CI -1.3, -0.2) smaller BPD on average for a fixed GA than those without detected PFOA concentrations. In models stratified by PSS level, the effects of PFOA on fetal growth parameters were stronger and only significant in participants with higher stress levels (HC: ß= -3.5, 95% CI -5.8, -1.4; BPD: ß = -0.8, 95% CI -1.6, -1.1). Conclusions: Prenatal PFOA exposure adversely impacted fetal head biometric parameters in participants experiencing higher stress during pregnancy.

Prenatal Perfluorooctanoic Acid (PFOA) Exposure Is Associated With Lower Infant Birthweight Within the MADRES Pregnancy Cohort.

Introduction: Perfluoroalkyl and polyfluoroalkyl substances (PFAS) are persistent synthetic chemicals found in household products that can cross the placenta during pregnancy. We investigated whether PFAS exposure during pregnancy was associated with infant birth outcomes in a predominantly urban Hispanic population. Methods: Serum concentrations of perfluorooctanoic acid (PFOA), perfluorooctanesulfonic acid (PFOS), perfluorohexanesulfonic acid (PFHxS), perfluorononanoic acid (PFNA), and perfluorodecanoic acid (PFDA) were measured in 342 prenatal biospecimens (mean gestational age: 21 ± 9 weeks) from participants in the ongoing Maternal And Developmental Risks from Environmental and Social Stressors (MADRES) cohort. PFAS compounds were modeled continuously or categorically, depending on the percentage of samples detected. The birth outcomes assessed were birthweight, gestational age at birth, and birthweight for gestational age (BW-for-GA) z-scores that accounted for parity or infant sex. Single pollutant and multipollutant linear regression models were performed to evaluate associations between PFAS exposures and birth outcomes, adjusting for sociodemographic, perinatal, and study design covariates. Results: Maternal participants (n = 342) were on average 29 ± 6 years old at study entry and were predominantly Hispanic (76%). Infants were born at a mean of 39 ± 2 weeks of gestation and weighed on average 3,278 ± 522 g. PFOS and PFHxS were detected in 100% of the samples while PFNA, PFOA, and PFDA were detected in 70%, 65%, and 57% of the samples, respectively. PFAS levels were generally lower in this cohort than in comparable cohorts. Women with detected levels of PFOA during pregnancy had infants weighing on average 119.7 g less (95% CI -216.7, -22.7) than women with undetected levels of PFOA in adjusted single pollutant models. PFOA results were also statistically significant in BW-for-GA z-score models that were specific for sex or parity. In models that were mutually adjusted for five detected PFAS compounds, PFOA results remained comparable; however, the association was only significant in BW-for-GA z-scores that were specific for parity (ß = -0.3; 95% CI -0.6, -0.01). We found no significant adjusted associations with the remaining PFAS concentrations and the birth outcomes assessed. Conclusion: Prenatal exposure to PFOA was associated with lower birthweight in infants, suggesting that exposure to these chemicals during critical periods of development might have important implications for children's health.

Prenatal ambient air pollution and maternal depression at 12 months postpartum in the MADRES pregnancy cohort.

BACKGROUND: Depression is the leading cause of mental health-related morbidity and affects twice as many women as men. Hispanic/Latina women in the US have unique risk factors for depression and they have lower utilization of mental health care services. Identifying modifiable risk factors for maternal depression, such as ambient air pollution, is an urgent public health priority. We aimed to determine whether prenatal exposure to ambient air pollutants was associated with maternal depression at 12 months after childbirth. METHODS: One hundred eighty predominantly low-income Hispanic/Latina women participating in the ongoing MADRES cohort study in Los Angeles, CA were followed from early pregnancy through 12 months postpartum through a series of phone questionnaires and in-person study visits. Daily prenatal ambient pollutant estimates of nitrogen dioxide (NO2), ozone (O3), and particulate matter (PM10 and PM2.5) were assigned to participant residences using inverse-distance squared spatial interpolation from ambient monitoring data. Exposures were averaged for each trimester and across pregnancy. The primary outcome measure was maternal depression at 12 months postpartum, as reported on the 20-item Center for Epidemiologic Studies-Depression (CES-D) scale. We classified each participant as depressed (n = 29) or not depressed (n = 151) based on the suggested cutoff of 16 or above (possible scores range from 0 to 60) and fitted logistic regression models, adjusting for potential confounders. RESULTS: We found over a two-fold increased odds of depression at 12 months postpartum associated with second trimester NO2 exposure (OR = 2.63, 95% CI: 1.41-4.89) and pregnancy average NO2 (OR = 2.04, 95% CI: 1.13-3.69). Higher second trimester PM2.5 exposure also was associated with increased depression at 12 months postpartum (OR = 1.56, 95% CI: 1.01-2.42). The effect for second trimester PM10 was similar and was borderline significant (OR = 1.58, 95% CI: 0.97-2.56). CONCLUSIONS: In a low-income cohort consisting of primarily Hispanic/Latina women in urban Los Angeles, we found that prenatal ambient air pollution, especially mid-pregnancy NO2 and PM2.5, increased the risk of depression at 12 months after childbirth. These results underscore the need to better understand the contribution of modifiable environmental risk factors during potentially critical exposure periods.

Estimating hourly PM2.5 concentrations at the neighborhood scale using a low-cost air sensor network: A Los Angeles case study.

Predicting PM2.5 concentrations at a fine spatial and temporal resolution (i.e., neighborhood, hourly) is challenging. Recent growth in low cost sensor networks is providing increased spatial coverage of air quality data that can be used to supplement data provided by monitors of regulatory agencies. We developed an hourly, 500 × 500 m gridded PM2.5 model that integrates PurpleAir low-cost air sensor network data for Los Angeles County. We developed a quality control scheme for PurpleAir data. We included spatially and temporally varying predictors in a random forest model with random oversampling of high concentrations to predict PM2.5. The model achieved high prediction accuracy (10-fold cross-validation (CV) R2 = 0.93, root mean squared error (RMSE) = 3.23 µg/m3; spatial CV R2 = 0.88, spatial RMSE = 4.33 µg/m3; temporal CV R2 = 0.90, temporal RMSE = 3.85 µg/m3). Our model was able to predict spatial and diurnal patterns in PM2.5 on typical weekdays and weekends, as well as non-typical days, such as holidays and wildfire days. The model allows for far more precise estimates of PM2.5 than existing methods based on few sensors. Taking advantage of low-cost PM2.5 sensors, our hourly random forest model predictions can be combined with time-activity diaries in future studies, enabling geographically and temporally fine exposure estimation for specific population groups in studies of acute air pollution health effects and studies of environmental justice issues.

Demographic predictors of urinary arsenic in a low-income predominantly Hispanic pregnancy cohort in Los Angeles.

BACKGROUND: Arsenic (As) is a contaminant of top public health concern, due to its range of detrimental health effects. Arsenic exposure has not been well-characterized among the US Hispanic populations and has been particularly understudied in this population during pregnancy. METHODS: As part of the MADRES ongoing pregnancy cohort of predominantly lower-income, Hispanic women in Los Angeles, CA, we examined levels of maternal first trimester urinary As, including total As and As metabolites (inorganic (iAs), monomethylated (MMA) and dimethylated As (DMA)), in relation to participant demographics, lifestyle characteristics, and rice/seafood consumption, to identify factors that may influence As exposure and its metabolites during pregnancy (N = 241). RESULTS: Total As concentrations ranged from low to high (0.8-506.2 µg/L, mean: 9.0 µg/L, SD: 32.9) in our study population. Foreign-born Hispanic women had 8.6% higher %DMA (95% CI: 3.3%, 13.9%) and -7.7% lower %iAs (95% CI: -12.6%, -2.9%) than non-Hispanic women. A similar trend was observed for US-born Hispanic women. In addition, maternal age was associated with 0.4% higher %iAs (95% CI: 0.1%, 0.6%) and 0.4% lower %DMA (95% CI: -0.7%, -0.1%) per year, which may indicate poor As methylation capacity. CONCLUSION: Individual factors may predict As exposure and metabolism in pregnancy, and in turn, greater risk of adverse health effects.

Air Pollution Monitoring for Health Research and Patient Care. An Official American Thoracic Society Workshop Report.

Air quality data from satellites and low-cost sensor systems, together with output from air quality models, have the potential to augment high-quality, regulatory-grade data in countries with in situ monitoring networks and provide much-needed air quality information in countries without them. Each of these technologies has strengths and limitations that need to be considered when integrating them to develop a robust and diverse global air quality monitoring network. To address these issues, the American Thoracic Society, the U.S. Environmental Protection Agency, the National Aeronautics and Space Administration, and the National Institute of Environmental Health Sciences convened a workshop in May 2017 to bring together global experts from across multiple disciplines and agencies to discuss current and near-term capabilities to monitor global air pollution. The participants focused on four topics: 1) current and near-term capabilities in air pollution monitoring, 2) data assimilation from multiple technology platforms, 3) critical issues for air pollution monitoring in regions without a regulatory-quality stationary monitoring network, and 4) risk communication and health messaging. Recommendations for research and improved use were identified during the workshop, including a recognition that the integration of data across monitoring technology groups is critical to maximizing the effectiveness (e.g., data accuracy, as well as spatial and temporal coverage) of these monitoring technologies. Taken together, these recommendations will advance the development of a global air quality monitoring network that takes advantage of emerging technologies to ensure the availability of free, accessible, and reliable air pollution data and forecasts to health professionals, as well as to all global citizens.