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18722 Association between neighborhood overcrowdedness, multigenerational households, and COVID-19 in New York City

Published online by Cambridge University Press:  30 March 2021

Arnab K. Ghosh
Affiliation:
Department of Medicine, Weill Cornell Medical College, Cornell University, 525 E 68th St., New York, NY, USA10065
Sara Venkatraman
Affiliation:
Department of Statistics and Data Science, Cornell University, 129 Garden Ave., Ithaca, NY, USA14853
Orysya Soroka
Affiliation:
Department of Medicine, Weill Cornell Medical College, Cornell University, 525 E 68th St., New York, NY, USA10065
Evgeniya Reshetnyak
Affiliation:
Department of Medicine, Weill Cornell Medical College, Cornell University, 525 E 68th St., New York, NY, USA10065
Mangala Rajan
Affiliation:
Department of Medicine, Weill Cornell Medical College, Cornell University, 525 E 68th St., New York, NY, USA10065
Anjile An
Affiliation:
Department of Population Health Sciences, Weill Cornell Medical College, Cornell University, 402 E 67th St., New York, NYUSA10065
John K. Chae
Affiliation:
Department of Medicine, Weill Cornell Medical College, Cornell University, 525 E 68th St., New York, NY, USA10065
Christopher Gonzalez
Affiliation:
Department of Medicine, Weill Cornell Medical College, Cornell University, 525 E 68th St., New York, NY, USA10065
Jonathan Prince
Affiliation:
Silberman School of Social Work at Hunter College, City University of New York, 2180 Third Ave, New York, NYUSA10035
Charles DiMaggio
Affiliation:
Department of Surgery, New York University School of Medicine, 462 First Ace, NBV 15, New York, NYUSA10016
Said Ibrahim
Affiliation:
Department of Population Health Sciences, Weill Cornell Medical College, Cornell University, 402 E 67th St., New York, NYUSA10065
Monika M. Safford
Affiliation:
Department of Medicine, Weill Cornell Medical College, Cornell University, 525 E 68th St., New York, NY, USA10065
Nathaniel Hupert
Affiliation:
Department of Medicine, Weill Cornell Medical College, Cornell University, 525 E 68th St., New York, NY, USA10065 Department of Population Health Sciences, Weill Cornell Medical College, Cornell University, 402 E 67th St., New York, NYUSA10065
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Abstract

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ABSTRACT IMPACT: Patients living in overcrowded zip codes were at increased risk of contracting severe COVID-19 after controlling for confounding disease and socioeconomic factors OBJECTIVES/GOALS: This study sought to examine whether residences in over-crowded zip codes with higher reported over-crowding represented an independent risk factor for severe COVID-19 infection, defined by presentation to an emergency department. METHODS/STUDY POPULATION: In this zip code tabulated area (ZCTA)-level analysis, we used NYC Department of Health disease surveillance data in March 2020 merged with data from the CDC and ACS to model suspected COVID-19 case rates by zip code over-crowdedness (households with greater than 1 occupant per room, in quartiles). We defined suspected COVID-19 cases as emergency department reported cases of pneumonia and influenza-like illness. Our final model employed a multivariate Poisson regression models with controls for known COVID-19 clinical (prevalence of obesity, coronary artery disease, and smoking) and related socioeconomic risk factors (percentage below federal poverty line, median income by zip-code, percentage White, and proportion of multigenerational households) after accounting for multicollinearity. RESULTS/ANTICIPATED RESULTS: Our analysis examined 39,923 suspected COVID-19 cases across 173 ZCTAs in NYC between March 1 and March 30 2020. We found that, after adjusted analysis, for every quartile increase in defined over-crowdedness, case rates increased by 32.8% (95% CI: 22.7%% to 34.0%, P < 0.001). DISCUSSION/SIGNIFICANCE OF FINDINGS: Over-crowdedness by zip code may be an independent risk factor for severe COVID-19. Social distancing measures such as school closures that increase house-bound populations may inadvertently worsen the risk of COVID-19 contraction in this setting.

Type
Health Equity & Community Engagement
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Association for Clinical and Translational Science 2021
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