The synergistic health impacts of exposure to multiple stressors in Tulare County, California

Abstract Background
Tulare County is located in the Central Valley region of California. Its population is exposed to stressors that include high levels of air, water, and soil pollution, socioeconomic strain, and poor access to walkable areas and healthy foods. As a result, this population suffers from a high disease burden compared to other California counties.
Objective
We hypothesize that environmental and socioeconomic stressors interact in complex ways to raise the burden of disease in the Tulare population beyond additive impacts.
Method
We used CalEnviroScreen to select Tulare County as the subject of the study and characterized the geographical interaction of stressors. The CalEnviroScreen indicators provided the basis for population-weighted average calculations to determine the most critical environmental and socioeconomic stressors in Tulare County. We also analyzed and interpreted walkability and dietary access through open-source data. In addition, we compared disease-based mortality in Tulare County to California state averages.
Results
Our evaluation reveals that the population living within the census tracts of Tulare County is exposed to environmental stressors at significantly higher levels relative to many other Californian census tracts, specifically for PM2.5, ozone, and drinking water quality. Relatively high exposures to socioeconomic stressors can compound resulting health impacts. We use dose-response curves and stressor mapping to characterize how multiple stressors may augment a population's vulnerability and effective doses from exposure to multiple stressors.
Significance
Previous health-impact studies have linked individual environmental stressors to their respective measures of disease. However, many communities continue to be exposed daily to numerous stressors that individually are within regulatory limits but could significantly magnify risk due to the synergistic effects. Dose-response curves tailored to population vulnerability provide a basis for quantifying the synergistic risks of multiple stressors on specific measures of disease.