Uncertainty reduction and environmental justice in air pollution
epidemiology: the importance of minority representation
Abstract
Ambient air pollution is an increasing threat to society, with rising
numbers of adverse outcomes and exposure inequalities across the globe.
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
fine particulate matter (PM2.5) in North Carolina for 2001-2016,
employing a case-crossover design with inputs from an individual-level
mortality dataset and high-resolution gridded datasets of PM2.5 and
weather covariates. We find a significant association between mortality
and PM2.5, and the information tradeoffs indicate that in this case
increasing information from mortality may reduce model uncertainty at a
faster rate than increasing information from air pollution. 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
(NHW) 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.