Community-Level Analysis of Drinking Water Data Highlights the Importance of Drinking Water Metrics for the State, Federal Environmental Health Justice Priorities in the United States

Perspective: Nutrition Health Disparities Framework: A Model to Advance Health Equity

Disparities in nutrition, such as poor diet quality and inadequate nutrient intake, arise from multiple factors and are related to adverse health outcomes such as obesity, diabetes, cardiovascular disease, and some cancers. The aim of the current perspective is to present a nutrition-centric socioecological framework that delineates determinants and factors that contribute to diet and nutrition-related disparities among disadvantaged populations. The Nutrition Health Disparities Framework (NHDF) describes the domains (biological, behavioral, physical/built environment, sociocultural environment, and healthcare system) that influence nutrition-related health disparities through the lens of each level of influence (that is, individual, interpersonal, community, and societal). On the basis of the scientific literature, the authors engaged in consensus decision making in selecting nutrition-related determinants of health within each domain and socioecological level when creating the NHDF. The framework identifies how neighborhood food availability and access (individual/built environment) intersect with cultural norms and practices (interpersonal/sociocultural environment) to influence dietary behaviors, exposures, and risk of diet-related diseases. In addition, the NHDF shows how factors such as genetic predisposition (individual/biology), family dietary practices (interpersonal/behavioral), and food marketing policies (societal) may impact the consumption of unhealthy foods and beverages and increase chronic disease risk. Family and peer norms (interpersonal/behavior) related to breastfeeding and early childhood nutrition interact with resource-poor environments such as lack of access to preventive healthcare settings (societal/healthcare system) and low usage of federal nutrition programs (societal/behavioral), which may increase risk of poor nutrition during childhood and food insecurity. The NHDF describes the synergistic interrelationships among factors at different levels of the socioecological model that influence nutrition-related outcomes and exacerbate health disparities. The framework is a useful resource for nutrition researchers, practitioners, food industry leaders, and policymakers interested in improving diet-related health outcomes and promoting health equity in diverse populations.

The Utility of Machine Learning Models for Predicting Chemical Contaminants in Drinking Water: Promise, Challenges, and Opportunities

PURPOSE OF REVIEW

This review aims to better understand the utility of machine learning algorithms for predicting spatial patterns of contaminants in the United States (U.S.) drinking water.

RECENT FINDINGS

We found 27 U.S. drinking water studies in the past ten years that used machine learning algorithms to predict water quality. Most studies (42%) developed random forest classification models for groundwater. Continuous models show low predictive power, suggesting that larger datasets and additional predictors are needed. Categorical/classification models for arsenic and nitrate that predict exceedances of pollution thresholds are most common in the literature because of good national scale data coverage and priority as environmental health concerns. Most groundwater data used to develop models were obtained from the United States Geological Survey (USGS) National Water Information System (NWIS). Predictors were similar across contaminants but challenges are posed by the lack of a standard methodology for imputation, pre-processing, and differing availability of data across regions. We reviewed 27 articles that focused on seven drinking water contaminants. Good performance metrics were reported for binary models that classified chemical concentrations above a threshold value by finding significant predictors. Classification models are especially useful for assisting in the design of sampling efforts by identifying high-risk areas. Only a few studies have developed continuous models and obtaining good predictive performance for such models is still challenging. Improving continuous models is important for potential future use in epidemiological studies to supplement data gaps in exposure assessments for drinking water contaminants. While significant progress has been made over the past decade, methodological advances are still needed for selecting appropriate model performance metrics and accounting for spatial autocorrelations in data. Finally, improved infrastructure for code and data sharing would spearhead more rapid advances in machine-learning models for drinking water quality.

Racial and social disparities in Ventura County, California related to agricultural pesticide applications and toxicity

Application of agricultural pesticides poses health concerns for farmworkers and for local communities due to pesticide drift from spraying or fumigation, pesticide volatilization into the air, contamination of household dust, as well as direct exposure for people who work in agriculture and their families. In this analysis of pesticide use records for Ventura County, California (USA) from 2016 to 2018, we identified the most prevalent toxicological effects of the pesticides applied. We also developed a cumulative toxicity index that incorporates specific toxicity endpoints for individual pesticides, the severity and strength of association for each endpoint, and the reliability of the data sources. Combining the toxicity index for each pesticide with the pounds applied within each square mile section in Ventura County, we calculated the total toxicity-weighted pesticide use and identified pesticides associated with higher potential risk to health. Analysis of U.S. Census data for Ventura County found a greater percentage of Hispanic/Latino, African American and Asian community members in township sections with a greater volume of pesticides applied and higher toxicity-weighted pesticide use. Similarly, areas with limited economic and social resources had elevated pesticide application overall and elevated toxicity-weighted pesticide use. The combination of toxicological and demographic analyses presented in this study provides information that can support the development of policies to protect public health from excessive exposure to pesticides and better environmental health protection for socially vulnerable populations.

Intersectional inequalities in industrial air toxics exposure in the United States

Environmental justice and health research demonstrate unequal exposure to environmental hazards at the neighborhood-level. We use an innovative method-eco-intersectional multilevel (EIM) modeling-to assess intersectional inequalities in industrial air toxics exposure across US census tracts in 2014. Results reveal stark inequalities in exposure across analytic strata, with a 45-fold difference in average exposure between most and least exposed. Low SES, multiply marginalized (high % Black, high % female-headed households) urban communities experienced highest risk. These inequalities were not described by additive effects alone, necessitating the use of interaction terms. We advance a critical intersectional approach to evaluating environmental injustices.