A risk-based model to assess environmental justice and coronary heart disease burden from traffic-related air pollutants

James P Fabisiak,Erica M Jackson,Luann L Brink,Albert A Presto

doi:10.1186/s12940-020-00584-z

James P Fabisiak, Erica M Jackson + Show 2 more

Open Access

https://doi.org/10.1186/s12940-020-00584-z

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Abstract

BackgroundCommunities need to efficiently estimate the burden from specific pollutants and identify those most at risk to make timely informed policy decisions. We developed a risk-based model to estimate the burden of black carbon (BC) and nitrogen dioxide (NO2) on coronary heart disease (CHD) across environmental justice (EJ) and non-EJ populations in Allegheny County, PA.MethodsExposure estimates in census tracts were modeled via land use regression and analyzed in relation to US Census data. Tracts were ranked into quartiles of exposure (Q1-Q4). A risk-based model for estimating the CHD burden attributed to BC and NO2 was developed using county health statistics, census tract level exposure estimates, and quantitative effect estimates available in the literature.ResultsFor both pollutants, the relative occurrence of EJ tracts (> 20% poverty and/or > 30% non-white minority) in Q2 – Q4 compared to Q1 progressively increased and reached a maximum in Q4. EJ tracts were 4 to 25 times more likely to be in the highest quartile of exposure compared to the lowest quartile for BC and NO2, respectively. Pollutant-specific risk values (mean [95% CI]) for CHD mortality were higher in EJ tracts (5.49 × 10− 5 [5.05 × 10− 5 – 5.92 × 10− 5]; 5.72 × 10− 5 [5.44 × 10− 5 – 6.01 × 10− 5] for BC and NO2, respectively) compared to non-EJ tracts (3.94 × 10− 5 [3.66 × 10− 5 – 4.23 × 10− 5]; 3.49 × 10− 5 [3.27 × 10− 5 – 3.70 × 10− 5] for BC and NO2, respectively). While EJ tracts represented 28% of the county population, they accounted for about 40% of the CHD mortality attributed to each pollutant. EJ tracts are disproportionately skewed toward areas of high exposure and EJ residents bear a greater risk for air pollution-related disease compared to other county residents.ConclusionsWe have combined a risk-based model with spatially resolved long-term exposure estimates to predict CHD burden from air pollution at the census tract level. It provides quantitative estimates of effects that can be used to assess possible health disparities, track temporal changes, and inform timely local community policy decisions. Such an approach can be further expanded to include other pollutants and adverse health endpoints.

Highlights

Communities need to efficiently estimate the burden from specific pollutants and identify those most at risk to make timely informed policy decisions
By coupling spatial exposure and health effect estimates with demographic US census data we calculate the hypothetical excess coronary heart disease (CHD) burden arising from Nitrogen Dioxide (NO2) and black carbon (BC) exposure and show that environmental justice (EJ) sensitive areas are were more numerous in areas of higher exposure and bear a disproportionate amount of risk of CHD from air pollution compared to non-EJ areas
Descriptive statistics describing the distribution of BC and NO2 census tract exposures including mean, median, and range for total and individual quartiles are provided in Tables S1, S2 (Supplementary Data)

Summary

Introduction

Communities need to efficiently estimate the burden from specific pollutants and identify those most at risk to make timely informed policy decisions. The adverse health effects from elevations in ambient air pollution are well established. For which National Ambient Air Quality Standards are set by the US EPA, include ozone, sulfur dioxide, nitrogen dioxide (NO2), lead, carbon monoxide and particulate matter (PM). The relationship between exposure to air pollution and adverse health outcomes, some severe, has been demonstrated in multiple epidemiological and other studies. Elevations in exposure to several of the criteria air pollutants have been associated with premature overall mortality [1, 2], exacerbation of respiratory disease like asthma [3,4,5], adverse birth outcomes [6, 7], increased rate of hospitalizations [8, 9] and death from cardiovascular disease [10,11,12]. Practical limitations in routinely conducting these largescale health effect studies, include the requirements of a large population and long study duration in order to achieve statistical power, sufficient access to patient health records over the study period, and exposure estimates are often limited by poor spatial resolution

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