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High-income ZIP codes in New York City demonstrate higher case rates during off-peak Covid-19 waves

Bibliographic Data

ID22075368
AuthorsSteven T L Tung (Stony Brook School), Mosammat M Perveen (University of Pikeville), Kirsten N Wohlars, Kirsten Wohlars (Donald & Barbara Zucker School of Medicine at Hofstra/Northwell), Robert A Promisloff (Drexel University), Mary F Lee-Wong, Mary Lee‐Wong (0000-0001-8404-1851, Maimonides Medical Center), Anthony M Szema (0000-0001-5992-5479, Donald & Barbara Zucker School of Medicine at Hofstra/Northwell, corresponding author)
Year2024
Volume12
Pages1384156-1384156
Publication date2024-06-20
Open AccessYes
TypeARTICLE
VenueFrontiers in Public Health (JOURNAL)
Journal identifiersISSN: 2296-2565 • E-ISSN: 2296-2565
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2024.1384156
PMID38966700
OpenAlexW4399868066
LanguageEN
References cited15

Introduction: Our study explores how New York City (NYC) communities of various socioeconomic strata were uniquely impacted by the COVID-19 pandemic. Methods: New York City ZIP codes were stratified into three bins by median income: high-income, middle-income, and low-income. Case, hospitalization, and death rates obtained from NYCHealth were compared for the period between March 2020 and April 2022. Results: COVID-19 transmission rates among high-income populations during off-peak waves were higher than transmission rates among low-income populations. Hospitalization rates among low-income populations were higher during off-peak waves despite a lower transmission rate. Death rates during both off-peak and peak waves were higher for low-income ZIP codes. Discussion: This study presents evidence that while high-income areas had higher transmission rates during off-peak periods, low-income areas suffered greater adverse outcomes in terms of hospitalization and death rates. The importance of this study is that it focuses on the social inequalities that were amplified by the pandemic

Demographic economics · Disease · Economic inequality · Economics · Geography · Household income · Inequality · Low income · Mortality rate · Pandemic · Population · Socioeconomic status · Socioeconomics · Sociology · Telecommunications · Zip code · COVID-19 epidemiological studies · Demography · Food Security and Health in Diverse Populations · Mathematics · Medicine · Vaccine Coverage and Hesitancy

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