Uncertainty Reduction and Environmental Justice in Air Pollution Epidemiology: The Importance of Minority Representation

Ambient air pollution is an increasing threat to society, with rising numbers of adverse outcomes and exposure inequalities worldwide. Reducing uncertainty in health outcomes models and exposure disparity studies is therefore essential to develop policies effective in protecting the most affected places and populations. This study uses the concept of information entropy to study tradeoffs in mortality uncertainty reduction from increasing input data of air pollution versus health outcomes. We study a case scenario for short-term mortality from particulate matter (PM2.5) in North Carolina for 2001-2016, employing a case-crossover design with inputs from an individual-level mortality data set and high-resolution gridded data sets of PM2.5 and weather covariates. We find a significant association between mortality and PM2.5, and the information tradeoffs indicate that a 10% increase in mortality information reduces model uncertainty three times more than increased resolution of the air pollution model from 12 to 1 km. We also find that Non-Hispanic Black (NHB) residents tend to live in relatively more polluted census tracts, and that the mean PM2.5 for NHB cases in the mortality model is significantly higher than that of Non-Hispanic White cases. The distinct distribution of PM2.5 for NHB cases results in a relatively higher information value, and therefore faster uncertainty reduction, for new NHB cases introduced into the mortality model. This newfound influence of exposure disparities in the rate of uncertainty reduction highlights the importance of minority representation in environmental research as a quantitative advantage to produce more confident estimates of the true effects of environmental pollution. We study how estimates of the relationship between air pollution and mortality may be improved with more information on air pollution concentrations or death records, and compare the impacts of improved air pollution data alone versus improved death data alone. We also study the effect of social inequalities by comparing what happens when there is missing data in the majority demographic (in this case, Non-Hispanic White, NHW) versus missing data in a minoritized demographic group (in this case, Non-Hispanic Black, NHB). We find that, because NHW and NHB populations are exposed to different levels of air pollution, the data from the NHB minority is, statistically speaking, more informative, as it provides new information that cannot be obtained by only looking at the NHW majority. This finding highlights the importance of ensuring that studies of air pollution and health effects are representative of both majority and minoritized populations. Having data that represent everyone allows us to develop better assessments of environmental health impacts, and also to do research that treats environmental health as a fundamental right for all humans regardless of their race, income, or other differences. We used information entropy to study efficient pathways for uncertainty reduction in an air pollution-mortality model for PM2.5We compared the uncertainty reduction effect of adding new data for Non-Hispanic Black (NHB) versus Non-Hispanic White casesIntroducing new NHB cases results in faster uncertainty reduction because of the differential PM2.5 exposure in the NHB population