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Race, Ethnicity, and Air pollution in COVID-19 Hospitalization OUTcomes (REACH OUT)

Principal Investigator: 
,

Columbia University

In this study the investigators will conduct a retrospective evaluation of the interactions between long-term exposure to air pollution and neighborhood vulnerability to adverse COVID-19 outcomes. They will investigate both single and multipollutant air pollution exposures in relation to COVID-19 hospitalization, inpatient length of stay, ICU admission, ventilator use, and death among a racially diverse population in New York City.

Funded under
Status: 
Ongoing
Abstract

Greater prevalence of COVID-19 infection and mortality among Black and Latinx individuals has further illuminated health disparities that have a long historical context. Black and Latinx communities are often more highly exposed to air pollution due to policies of environmental injustice and racism. Issues of racism also contribute to the lower socioeconomic status and higher prevalence of chronic disease observed in these communities.

Determining whether chronic exposure to air pollution contributes to observed disparities in COVID-19 outcomes requires a holistic approach that integrates multiple determinants of COVID-19 vulnerability and investigates how they interact with chronic air pollution exposure. Our multidisciplinary team of investigators from 4 different institutions across New York City (NYC) is well poised to investigate the intersection between chronic air pollution exposure, race, ethnicity, and neighborhood-level vulnerability as risk factors for severe COVID-19. We will leverage existing data sources to efficiently examine the combined effect of air pollution exposure and neighborhood vulnerability on severe COVID-19 outcomes among a racially-diverse population.

Our study population is derived from harmonized electronic health records (EHR) within the INSIGHT clinical research network (INSIGHT-CRN). INSIGHT-CRN collects and harmonizes clinical records across 5 large NYC health systems and includes over 37,000 COVID-positive patients to date. Chronic air pollution exposure will be assigned using the NYC Community Air Survey (NYCCAS), the largest on-going study of intra-urban variation in air pollution in the world. Pollution sampling data are combined within a land-use regression model to construct annual averages of fine particulate matter, black carbon, nitrogen dioxide, nitrogen oxide, ozone and sulfur dioxide at a 300m resolution. We will construct a neighborhood vulnerability index using a profiling and clustering approach known as the toxicological prioritization index that can synthesize multiple sources of geospatial data into an integrated profile. The index will be created using neighborhood-level demographics, residential density, prevalence of chronic diseases, other measures of neighborhood health and neighborhood economic indicators.

Our COVID-19 outcomes will include: risk of hospitalization, length of stay, intensive care unit admission and length of stay, mechanical ventilation, and mortality. Our approach will include: Aim 1) evaluating interactions between chronic air pollution exposure and neighborhood vulnerability on COVID-19 outcomes using emergency department, hospitalization and ambulatory care data, in both single and multi-pollutant contexts Sub-aim 1) validation of the quality of harmonized EHR data by reviewing a sample of medical records at individual institutions and Aim 2) an excess-deaths analysis combining air pollution monitoring data with all-cause mortality data from the NYC Department of Health. NYC is the ideal setting for this investigation of pollution, race and ethnicity in COVID-19 due to: 1) the large number of COVID-19 cases, 2) the variability in exposures in combination with the racial, ethnic, and socioeconomic status diversity of neighborhoods and 3) the ability to examine multiple phases of the pandemic due to changes in COVID-19 incidence over time in NYC.

This study will generate knowledge on the potential vulnerability to COVID-19 conferred by chronic air pollution exposure. It will help identify neighborhoods with the greatest risk of severe outcomes in potential later phases. Most notably, this study establishes a multi-institutional collaboration aimed at efficiently using existing data on COVID-19 to address timely questions related to health-equity and environmental justice as the pandemic progresses.