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Many communities are located near multiple sources of pollution, including current and former industrial sites, major roadways, and agricultural operations. Populations in such locations are predominantly low-income, with a large percentage of minorities and non-English speakers. These communities face challenges that can affect the health of their residents, including limited access to health care, a shortage of grocery stores, poor housing quality, and a lack of parks and open spaces. Environmental exposures may interact with social stressors, thereby worsening health outcomes. Age, genetic characteristics, and preexisting health conditions increase the risk of adverse health effects from exposure to pollutants. There are existing approaches for characterizing cumulative exposures, cumulative risks, and cumulative health impacts. Although such approaches have merit, they also have significant constraints. New developments in exposure monitoring, mapping, toxicology, and epidemiology, especially when informed by community participation, have the potential to advance the science on cumulative impacts and to improve decision making.

Background: The health-risk assessment paradigm is shifting from single stressor evaluation towards cumulative assessments of multiple stressors. Recent efforts to develop broad-scale public health hazard datasets provide an opportunity to develop and evaluate multiple exposure hazards in combination.Methods: We performed a multivariate study of the spatial relationship between 12 indicators of environmental hazard, 5 indicators of socioeconomic hardship, and 3 health outcomes. Indicators were obtained from CalEnviroScreen (version 3.0), a publicly available environmental justice screening tool developed by the State of California Environmental Protection Agency. The indicators were compared to the total rate of hospitalization for 14 ICD-9 disease categories (a measure of disease burden) at the zip code tabulation area population level. We performed principal component analysis to visualize and reduce the CalEnviroScreen data and spatial autoregression to evaluate associations with disease burden.Results: CalEnviroScreen was strongly associated with the first principal component (PC) from a principal component analysis (PCA) of all 20 variables (Spearman ρ = 0.95). In a PCA of the 12 environmental variables, two PC axes explained 43% of variance, with the first axis indicating industrial activity and air pollution, and the second associated with ground-level ozone, drinking water contamination and PM2.5. Mass of pesticides used in agriculture was poorly or negatively correlated with all other environmental indicators, and with the CalEnviroScreen calculation method, suggesting a limited ability of the method to capture agricultural exposures. In a PCA of the 5 socioeconomic variables, the first PC explained 66% of variance, representing overall socioeconomic hardship. In simultaneous autoregressive models, the first environmental and socioeconomic PCs were both significantly associated with the disease burden measure, but more model variation was explained by the socioeconomic PCs.Conclusions: This study supports the use of CalEnviroScreen for its intended purpose of screening California regions for areas with high environmental exposure and population vulnerability. Study results further suggest a hypothesis that, compared to environmental pollutant exposure, socioeconomic status has greater impact on overall burden of disease. [ABSTRACT FROM AUTHOR]