ABSTRACT
Background: The COVID-19 pandemic has been marked by its disparate impacts on residents in lower income neighborhoods. To better understand reasons for disparities in disease severity, we asked: To what extent does the neighborhood built environment independently predict COVID-19 hospitalizations for people with SARS-CoV-2? Methods: Retrospective analysis of all cases of SARS-CoV-2 diagnosed within the University of Colorado Health and Denver Health healthcare systems from 3/1/2020-12/15/2020. Electronic health records were queried for positive SARS-CoV-2 PCR results for individuals aged ≥18 years during the study period. Demographic and health variables were extracted. Home addresses were matched with social vulnerability indices and built environment variables including population density and crowding, environmental hazards and amenities, and mobility options. Logistic regression was used to identify factors of the neighborhood built environment associated with hospitalization after a positive SARS-CoV-2 result. Results: Among the two systems, 39,304 individuals had positive SARS-CoV-2 tests;14,604 (37.16%) had full demographic data;4,101 (28%) were hospitalized. Odds of hospitalization were higher for individuals living in apartments and in census blocks with lower residential density and higher percentage of multi-family housing units. See Table. Higher particulate matter (PM2.5) levels were associated with higher odds of being hospitalized but living within mile of a highway was not. Living within 1/2 mile of a park and more park acreage in the neighborhood were associated with lower odds of hospitalization. Odds of being hospitalized were higher for individuals in neighborhoods with a lower Walk Score ®, lower Bike Score ®, and higher Transit Score ®. Effects were more pronounced for Latinx individuals. Conclusion: Among those with SARS-CoV-2 infection in Denver, living in areas with high levels of PM2.5, less park access, and lower Walk® and Bike Scores® were found to be independent risk factors for hospitalization when controlling for income and medical comorbidities.
ABSTRACT
The temporary decrease of fine particulate matter (PM2.5) concentrations in many parts of the world due to the COVID-19 lockdown spurred discussions on urban air pollution and health. However there has been little focus on sub-Saharan Africa, as few African cities have air quality monitors and if they do, these data are often not publicly available. Spatial differentials of changes in PM2.5 concentrations as a result of COVID also remain largely unstudied. To address this gap, we use a serendipitous mobile air quality monitoring deployment of eight Sensirion SPS 30 sensors on motorbikes in the city of Nairobi starting on 16 March 2020, before a COVID-19 curfew was imposed on 25 March and continuing until 5 May 2020. We developed a random-forest model to estimate PM2.5 surfaces for the entire city of Nairobi before and during the COVID-19 curfew. The highest PM2.5 concentrations during both periods were observed in the poor neighborhoods of Kariobangi, Mathare, Umoja, and Dandora, located to the east of the city center. Changes in PM2.5 were heterogeneous over space. PM2.5 concentrations increased during the curfew in rapidly urbanizing, the lower-middle-class neighborhoods of Kahawa, Kasarani, and Ruaraka, likely because residents switched from LPG to biomass fuels due to loss of income. Our results indicate that COVID-19 and policies to address it may have exacerbated existing air pollution inequalities in the city of Nairobi. The quantitative results are preliminary, due to sampling limitations and measurement uncertainties, as the available data came exclusively from low-cost sensors. This research serves to highlight that spatial data that is essential for understanding structural inequalities reflected in uneven air pollution burdens and differential impacts of events like the COVID pandemic. With the help of carefully deployed low-cost sensors with improved spatial sampling and at least one reference-quality monitor for calibration, we can collect data that is critical for developing targeted interventions that address environmental injustice in the African context.