Subway Ridership, Crowding, or Population Density : Determinants of COVID-19 Infection Rates in New York City
Copyright © 2021 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved..
INTRODUCTION: This study aims to determine whether subway ridership and built environmental factors, such as population density and points of interests, are linked to the per capita COVID-19 infection rate in New York City ZIP codes, after controlling for racial and socioeconomic characteristics.
METHODS: Spatial lag models were employed to model the cumulative COVID-19 per capita infection rate in New York City ZIP codes (N=177) as of April 1 and May 25, 2020, accounting for the spatial relationships among observations. Both direct and total effects (through spatial relationships) were reported.
RESULTS: This study distinguished between density and crowding. Crowding (and not density) was associated with the higher infection rate on April 1. Average household size was another significant crowding-related variable in both models. There was no evidence that subway ridership was related to the COVID-19 infection rate. Racial and socioeconomic compositions were among the most significant predictors of spatial variation in COVID-19 per capita infection rates in New York City, even more so than variables such as point-of-interest rates, density, and nursing home bed rates.
CONCLUSIONS: Point-of-interest destinations not only could facilitate the spread of virus to other parts of the city (through indirect effects) but also were significantly associated with the higher infection rate in their immediate neighborhoods during the early stages of the pandemic. Policymakers should pay particularly close attention to neighborhoods with a high proportion of crowded households and these destinations during the early stages of pandemics.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:60 |
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| Enthalten in: |
American journal of preventive medicine - 60(2021), 5 vom: 27. Mai, Seite 614-620 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Hamidi, Shima [VerfasserIn] |
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| Anmerkungen: |
Date Completed 03.06.2021 Date Revised 29.03.2022 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.amepre.2020.11.016 |
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| 245 | 1 | 0 | |a Subway Ridership, Crowding, or Population Density |b Determinants of COVID-19 Infection Rates in New York City |
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| 500 | |a Date Revised 29.03.2022 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2021 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved. | ||
| 520 | |a INTRODUCTION: This study aims to determine whether subway ridership and built environmental factors, such as population density and points of interests, are linked to the per capita COVID-19 infection rate in New York City ZIP codes, after controlling for racial and socioeconomic characteristics | ||
| 520 | |a METHODS: Spatial lag models were employed to model the cumulative COVID-19 per capita infection rate in New York City ZIP codes (N=177) as of April 1 and May 25, 2020, accounting for the spatial relationships among observations. Both direct and total effects (through spatial relationships) were reported | ||
| 520 | |a RESULTS: This study distinguished between density and crowding. Crowding (and not density) was associated with the higher infection rate on April 1. Average household size was another significant crowding-related variable in both models. There was no evidence that subway ridership was related to the COVID-19 infection rate. Racial and socioeconomic compositions were among the most significant predictors of spatial variation in COVID-19 per capita infection rates in New York City, even more so than variables such as point-of-interest rates, density, and nursing home bed rates | ||
| 520 | |a CONCLUSIONS: Point-of-interest destinations not only could facilitate the spread of virus to other parts of the city (through indirect effects) but also were significantly associated with the higher infection rate in their immediate neighborhoods during the early stages of the pandemic. Policymakers should pay particularly close attention to neighborhoods with a high proportion of crowded households and these destinations during the early stages of pandemics | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 700 | 1 | |a Hamidi, Iman |e verfasserin |4 aut | |
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