Air Pollution and COVID-19 Mortality in New York City

The health impact of coronavirus disease (COVID-19) appears disproportionate across populations. Biological mechanisms of prognosisofCOVID-19(1) suggest that long-termexposure toambient air pollution may contribute to health disparities. Mortality impacts includingthosefrompneumoniaandcardiovasculareventsduetolongterm exposure to air pollution are well established (2–5). Early studies coveringmultipleneighborhoods (e.g.,U.S. counties) suggest that longtermexposure toparticulatematter<2.5mminaerodynamic diameter (PM2.5) and ozone (O3) may be linked with higher risk of COVID-19 mortality (6–8). However, many questions remain regarding linkages between COVID-19 and air pollution. Particularly, pollution levels vary by community, and evidence based on high spatially resolved data such as for neighborhoods is lacking (9). Potential determinants of transmission and prognosis of COVID-19 (e.g., urbanicity, socioeconomic conditions, race/ethnicity, health behaviors, access to health care) are disproportionately distributed across neighborhoods. These factors may have impacted findings of early studies because of possible residual confounding. We conducted cross-sectional analysis to investigate association between long-term exposure to PM2.5 and O3 and COVID-19 confirmedmortality in 177neighborhoods (i.e.,five-digitmodifiedZIP code tabulation areas [MZCTAs]) in New York City, New York. COVID-19 data from February 29, 2020, to January 5, 2021, were obtained from the New York City (NYC) Health Department’s COVID-19 DataWebsite. The date of the first confirmed COVID-19 case anddeath is February 29 andMarch 11, 2020, respectively. Annual average PM2.5 levels fromDecember 2008 to December 2018 and summer (June toAugust) averageO3 levels from2009 to 2018 in 300m rasterwere obtained fromNYCOpenData.MZCTA-level associations between PM2.5 and O3 and COVID-19 confirmed mortality rate were estimated using Poisson regression.We considered nonlinear terms (e.g., natural cubic spline) for associations between pollutants and COVID-19 mortality. A randomMZCTA-level intercept and coordinates were included to consider overdispersion and adjust for spatial autocorrelation.We applied generalized propensity score (GPS) weighting to adjust for transmission (i.e., COVID-19 confirmed case rate), age, sex, race/ethnicity, population density, socioeconomic position, smoking, obesity, preexisting diseases (stroke, lung cancer, asthma, and poorly controlled diabetes), and healthcare availability (model 1). A list of variables is shown in Table 1 and its footnote. GPS was estimated by linear regression of the logarithm of PM2.5 or O3 on covariates. To test mediation through COVID-19 transmission and/or preexisting diseases, we omitted these variables (models 2–4). We further adjusted for covariates whose GPS-weighted absolute correlations with air pollutants exceeded 0.10. MZCTA-level associations between variables other than air pollution andCOVID-19 mortality were also evaluated. Data were obtained fromNYC Neighborhood Health Atlas, Community Health Profiles, American Community Survey 2014–2018 five-year estimate, hospital beds of Definitive Healthcare of Esri COVID-19 GIS Hub, and the New York State Health Department. We used area-weighting to align different spatial tabulations. From February 29, 2020, to January 5, 2021, the average crude COVID-19mortality rate was 226.8 deaths per 100,000 persons. Crude COVID-19 mortality rates by neighborhood ranged from 0 (financial district in Manhattan) to 744.9 cases per 100,000 persons (East New York neighborhood of Brooklyn). MZCTA-level long-term PM2.5 and O3rangedfrom7.21to13. 45mg/m and22.22to38. 67ppb,respectively. Correlation between PM2.5 and O3 was20.90. For O3, covariates, including PM2.5, were generally well balanced. A fewvariablesneeded tobeadditionally adjustedusingdiseasemodels. Figure 1 presents MZCTA-level associations between long-term O3 exposure andCOVID-19mortality risk frommodels 1–4. Fourmodels showed consistent associations. We should note that preexisting diseases andCOVID-19 case ratewerewell balancedbyGPS-weighting even formodels 2–4.Thesewell-balanced covariates suggest inability to isolate O3–COVID-19 infection and O3–preexisting disease condition mediation pathways. In model 2, a 1-ppb increase in O3 concentration was associated with a 10.43% (95% confidence interval, 5.97–15.08%) increase in COVID-19 mortality. For PM2.5, covariates were not well balanced, so covariate adjustment was needed. A 1-mg/m increase of PM2.5 was associated with a25.00% (95% confidence interval, 213.62% to 4.47%) increase in COVID-19 mortality. Table 1 presents MZCTA-level associations between neighborhood characteristics and COVID-19 mortality. We excluded preexisting disease variables because of their high correlation with obesity (.0.70) except for lung cancer. Results suggest that neighborhoodswith a higher percentage of Black individuals, Hispanic individuals, individuals aged>65 years, individuals living below the poverty level, and individuals without health insurance are linked with higher COVID-19 mortality risk. Our results indicate that populations living inneighborhoodswith higher O3 levels may have a poorer prognosis of COVID-19. This association was present at pollution levels below Environmental Protection Agency regulatory standards. We speculate some reasons why we did not find associations for PM2.5. First, PM2.5 was highly correlated with O3, suggesting possible residual confoundingbyO3.O3waspoorly balancedbyGPS-weighting This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License 4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/). For commercial usage and reprints, please contact Diane Gern (dgern@thoracic.org).