Drinking water contaminants in California and hypertensive disorders in pregnancy.

Environmental pollutants have been associated with hypertensive disorders in pregnancy including gestational hypertension, preeclampsia, and eclampsia, though few have focused on drinking water contamination. Water pollution can be an important source of exposures that may contribute to adverse pregnancy outcomes. METHODS: We linked water quality data on 13 contaminants and two violations from the California Communities Environmental Health Screening Tool to birth records from vital statistics and hospital discharge records (2007-2012) to examine the relationship between drinking water contamination and hypertensive disorders in pregnancy. We examined contaminants in single- and multipollutant models. Additionally, we examined if the relationship between water contamination and hypertensive disorders in pregnancy differed by neighborhood poverty, individual socioeconomic status, and race/ethnicity. RESULTS: Arsenic, nitrate, trihalomethane, hexavalent chromium, and uranium were detected in a majority of water systems. Increased risk of hypertensive disorders in pregnancy was modestly associated with exposure to cadmium, lead, trihalomethane, and hexavalent chromium in drinking water after adjusting for covariates in single pollutant models with odds ratios ranging from 1.01 to 1.08. In multipollutant models, cadmium was consistent, lead and trihalomethane were stronger, and additional contaminants were associated with hypertensive disorders in pregnancy including trichloroethylene, 1,2-Dibromo-3-chloropropane, nitrate, and tetrachloroethylene. Other contaminants either showed null results or modest inverse associations. The relationship between water contaminants and hypertensive disorders in pregnancy did not differ by neighborhood poverty. CONCLUSIONS: We found increased risk of hypertensive disorders in pregnancy associated with exposure to several contaminants in drinking water in California. Results for cadmium, lead, trihalomethane, and hexavalent chromium were robust in multipollutant models.

BMI, Physical Inactivity, and Pap Test Use in Asian Women in the U.S.

INTRODUCTION: In the U.S., limited epidemiologic studies have investigated associations between BMI and physical inactivity and Pap test use among Asian women. The aim was to disentangle associations using data from the Behavioral Risk Factor Surveillance System between 2014 and 2016. METHODS: In the Behavioral Risk Factor Surveillance System, BMI was categorized into four levels (<18.5, 18.5 to <25, 25 to <30, and ≥30) and inactivity was defined as having no physical activity in addition to the individual's regular job during the past month. Analyses were conducted in June 2018. Weighted percentages of covariates were used to descriptively summarize the data. Multivariable logistic regression corrected for sampling weight was used to estimate associations between BMI and inactivity and Pap test use. Subgroup analysis was conducted by income and education. RESULTS: The analysis included 9,424 women and 59.6% of them had their last Pap test within 3 years. OR in the mutually adjusted model suggested underweight (BMI <18.5 compared with normal weight) was inversely associated with Pap test use within the last 3 years (OR=0.56, 95% CI=0.36, 0.88). Inactivity (compared with activity) was not associated with Pap test use within the last 3 years (OR=0.80, 95% CI=0.60, 1.06). Different association patterns of BMI and inactivity were observed by education. CONCLUSIONS: This study suggests that being underweight, rather than overweight or obesity, is associated with a lower rate of Pap test use in U.S. Asian women. Health interventions to facilitate Pap test use in Asian women should explore other potential targets, not aiming to just prevent obesity or change physical inactivity.

Environmental pollution and social factors as contributors to preterm birth in Fresno County.

BACKGROUND: Environmental pollution exposure during pregnancy has been identified as a risk factor for preterm birth. Most studies have evaluated exposures individually and in limited study populations. METHODS: We examined the associations between several environmental exposures, both individually and cumulatively, and risk of preterm birth in Fresno County, California. We also evaluated early (< 34 weeks) and spontaneous preterm birth. We used the Communities Environmental Health Screening Tool and linked hospital discharge records by census tract from 2009 to 2012. The environmental factors included air pollution, drinking water contaminants, pesticides, hazardous waste, traffic exposure and others. Social factors, including area-level socioeconomic status (SES) and race/ethnicity were also evaluated as potential modifiers of the relationship between pollution and preterm birth. RESULTS: In our study of 53,843 births, risk of preterm birth was associated with higher exposure to cumulative pollution scores and drinking water contaminants. Risk of preterm birth was twice as likely for those exposed to high versus low levels of pollution. An exposure-response relationship was observed across the quintiles of the pollution burden score. The associations were stronger among early preterm births in areas of low SES. CONCLUSIONS: In Fresno County, we found multiple pollution exposures associated with increased risk for preterm birth, with higher associations among the most disadvantaged. This supports other evidence finding environmental exposures are important risk factors for preterm birth, and furthermore the burden is higher in areas of low SES. This data supports efforts to reduce the environmental burden on pregnant women.

Cumulative Risk and Impact Modeling on Environmental Chemical and Social Stressors.

PURPOSE OF REVIEW: The goal of this review is to identify cumulative modeling methods used to evaluate combined effects of exposures to environmental chemicals and social stressors. The specific review question is: What are the existing quantitative methods used to examine the cumulative impacts of exposures to environmental chemical and social stressors on health? RECENT FINDINGS: There has been an increase in literature that evaluates combined effects of exposures to environmental chemicals and social stressors on health using regression models; very few studies applied other data mining and machine learning techniques to this problem. The majority of studies we identified used regression models to evaluate combined effects of multiple environmental and social stressors. With proper study design and appropriate modeling assumptions, additional data mining methods may be useful to examine combined effects of environmental and social stressors.

Associations between socio-demographic characteristics and chemical concentrations contributing to cumulative exposures in the United States.

Association rule mining (ARM) has been widely used to identify associations between various entities in many fields. Although some studies have utilized it to analyze the relationship between chemicals and human health effects, fewer have used this technique to identify and quantify associations between environmental and social stressors. Socio-demographic variables were generated based on U.S. Census tract-level income, race/ethnicity population percentage, education level, and age information from the 2010-2014, 5-Year Summary files in the American Community Survey (ACS) database, and chemical variables were generated by utilizing the 2011 National-Scale Air Toxics Assessment (NATA) census tract-level air pollutant exposure concentration data. Six mobile- and industrial-source pollutants were chosen for analysis, including acetaldehyde, benzene, cyanide, particulate matter components of diesel engine emissions (namely, diesel PM), toluene, and 1,3-butadiene. ARM was then applied to quantify and visualize the associations between the chemical and socio-demographic variables. Census tracts with a high percentage of racial/ethnic minorities and populations with low income tended to have higher estimated chemical exposure concentrations (fourth quartile), especially for diesel PM, 1,3-butadiene, and toluene. In contrast, census tracts with an average population age of 40-50 years, a low percentage of racial/ethnic minorities, and moderate-income levels were more likely to have lower estimated chemical exposure concentrations (first quartile). Unsupervised data mining methods can be used to evaluate potential associations between environmental inequalities and social disparities, while providing support in public health decision-making contexts.

Connecting the Dots: Linking Environmental Justice Indicators to Daily Dose Model Estimates.

Many different quantitative techniques have been developed to either assess Environmental Justice (EJ) issues or estimate exposure and dose for risk assessment. However, very few approaches have been applied to link EJ factors to exposure dose estimate and identify potential impacts of EJ factors on dose-related variables. The purpose of this study is to identify quantitative approaches that incorporate conventional risk assessment (RA) dose modeling and cumulative risk assessment (CRA) considerations of disproportionate environmental exposure. We apply the Average Daily Dose (ADD) model, which has been commonly used in RA, to better understand impacts of EJ indicators upon exposure dose estimates and dose-related variables, termed the Environmental-Justice-Average-Daily-Dose (EJ-ADD) approach. On the U.S. nationwide census tract-level, we defined and quantified two EJ indicators (poverty and race/ethnicity) using an EJ scoring method to examine their relation to census tract-level multi-chemical exposure dose estimates. Pollutant doses for each tract were calculated using the ADD model, and EJ scores were assigned to each tract based on poverty- or race-related population percentages. Single- and multiple-chemical ADD values were matched to the tract-level EJ scores to analyze disproportionate dose relationships and contributing EJ factors. We found that when both EJ indicators were examined simultaneously, ADD for all pollutants generally increased with larger EJ scores. To demonstrate the utility of using EJ-ADD on the local scale, we approximated ADD levels of lead via soil/dust ingestion for simulated communities with different EJ-related scenarios. The local-level simulation indicates a substantial difference in exposure-dose levels between wealthy and EJ communities. The application of the EJ-ADD approach can link EJ factors to exposure dose estimate and identify potential EJ impacts on dose-related variables.