Racial/Ethnic Disparities in Nationwide P[M.sub.2.5] Concentrations: Perils of Assuming a Linear Relationship.

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From: Environmental Health Perspectives(Vol. 130, Issue 7)
Publisher: National Institute of Environmental Health Sciences
Document Type: Report
Length: 1,778 words
Lexile Measure: 1760L

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Introduction

Ambient air pollution, including particulate matter <2.5 micrometers in aerodynamic diameter [fine particulate matter (P[M.sub.2.5])], is a leading cause of morbidity and mortality in the United States. (1) The U.S. Environmental Protection Agency's (EPA) National Ambient Air Quality Standard for P[M.sub.2.5] (2022 annual standard = 12 [micro]g/[m.sup.3]) has resulted in consistently declining levels over the past decades but has not benefited racial/ethnic groups equally. (2,3)

The United States has a history of environmental injustice, and studies from the 1980s through the present document racial/ethnic disparities in air pollution exposure. (4-6) These studies have generally quantified disparities using population-weighted averages, (2-4) with a handful additionally including linear regression estimates, (7,8) which can adjust for known confounders. Population-weighted averages and linear regression models can mask the shape and magnitude of the relationship between racial/ethnic composition and ambient air pollution concentration. For example, as a result of zoning, racial segregation, and environmental racism, we might expect a 10% area-level increase in non-Hispanic Black individuals to be more strongly related to poor air quality in communities comprising 80-90% compared with communities comprising 30-40% Black residents. In this case, a linear model would yield a point estimate that respectively over- and underestimates the association at the lower and higher ends of the distribution.

Here, we explore departures from linearity in the relationship between racial/ethnic composition and P[M.sub.2.5] concentration across the United States. We also quantitatively compare the strength of the nonlinear association to the linear one and highlight implications for the quantification of resulting racial/ethnic disparities.

Methods

Our analysis covered urban census tracts [n = 58,030 tracts contained within core-based statistical areas (CBSAs)] in the contiguous United States in 2010. We obtained modeled publicly available annual surface-level concentrations of P[M.sub.2.5] for 2010 from a gridded (~1*1 km) data set,9 which we aggregated to census tracts. We compiled census tract-level percentage non-Hispanic Black and non-Hispanic White residents, percentage in poverty, and population density from the 2010 census.

We conducted descriptive analyses and evaluated the association between percentage racial/ethnic composition and P[M.sub.2.5] concentration using linear mixed models with cubic natural splines whose number of degrees of freedom were selected with the Akaike information criterion. We adjusted models for census tract-level poverty and population density and included state-specific fixed effects and CBSA-specific random intercepts. We also fit models with linear terms and calculated the bias in estimates had we used a linear P[M.sub.2.5] term by subtracting the linear point estimate from the nonlinear one. All analyses were conducted with R (version 4.1.2; R Development Core Team).

Results and Discussion

Across 58,030 U.S. urban census tracts in 2010, the median percentages of non-Hispanic Black and non-Hispanic White residents were 4.6% and 69.7%,...

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Gale Document Number: GALE|A712308612