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An ecological study of socioeconomic predictors in detection of COVID-19 cases across neighborhoods in New York City

BACKGROUND: New York City was the first major urban center of the COVID-19 pandemic in the USA. Cases are clustered in the city, with certain neighborhoods experiencing more cases than others. We investigate whether potential socioeconomic factors can explain between-neighborhood variation in the CO...

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Autores principales: Whittle, Richard S., Diaz-Artiles, Ana
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7471585/
https://www.ncbi.nlm.nih.gov/pubmed/32883276
http://dx.doi.org/10.1186/s12916-020-01731-6
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author Whittle, Richard S.
Diaz-Artiles, Ana
author_facet Whittle, Richard S.
Diaz-Artiles, Ana
author_sort Whittle, Richard S.
collection PubMed
description BACKGROUND: New York City was the first major urban center of the COVID-19 pandemic in the USA. Cases are clustered in the city, with certain neighborhoods experiencing more cases than others. We investigate whether potential socioeconomic factors can explain between-neighborhood variation in the COVID-19 test positivity rate. METHODS: Data were collected from 177 Zip Code Tabulation Areas (ZCTA) in New York City (99.9% of the population). We fit multiple Bayesian Besag-York-Mollié (BYM) mixed models using positive COVID-19 tests as the outcome, a set of 11 representative demographic, economic, and health-care associated ZCTA-level parameters as potential predictors, and the total number of COVID-19 tests as the exposure. The BYM model includes both spatial and nonspatial random effects to account for clustering and overdispersion. RESULTS: Multiple regression approaches indicated a consistent, statistically significant association between detected COVID-19 cases and dependent children (under 18 years old), population density, median household income, and race. In the final model, we found that an increase of only 5% in young population is associated with a 2.3% increase in COVID-19 positivity rate (95% confidence interval (CI) 0.4 to 4.2%, p=0.021). An increase of 10,000 people per km(2) is associated with a 2.4% (95% CI 0.6 to 4.2%, p=0.011) increase in positivity rate. A decrease of $10,000 median household income is associated with a 1.6% (95% CI 0.7 to 2.4%, p<0.001) increase in COVID-19 positivity rate. With respect to race, a decrease of 10% in White population is associated with a 1.8% (95% CI 0.8 to 2.8%, p<0.001) increase in positivity rate, while an increase of 10% in Black population is associated with a 1.1% (95% CI 0.3 to 1.8%, p<0.001) increase in positivity rate. The percentage of Hispanic (p=0.718), Asian (p=0.966), or Other (p=0.588) populations were not statistically significant factors. CONCLUSIONS: Our findings indicate associations between neighborhoods with a large dependent youth population, densely populated, low-income, and predominantly black neighborhoods and COVID-19 test positivity rate. The study highlights the importance of public health management during and after the current COVID-19 pandemic. Further work is warranted to fully understand the mechanisms by which these factors may have affected the positivity rate, either in terms of the true number of cases or access to testing.
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spelling pubmed-74715852020-09-04 An ecological study of socioeconomic predictors in detection of COVID-19 cases across neighborhoods in New York City Whittle, Richard S. Diaz-Artiles, Ana BMC Med Research Article BACKGROUND: New York City was the first major urban center of the COVID-19 pandemic in the USA. Cases are clustered in the city, with certain neighborhoods experiencing more cases than others. We investigate whether potential socioeconomic factors can explain between-neighborhood variation in the COVID-19 test positivity rate. METHODS: Data were collected from 177 Zip Code Tabulation Areas (ZCTA) in New York City (99.9% of the population). We fit multiple Bayesian Besag-York-Mollié (BYM) mixed models using positive COVID-19 tests as the outcome, a set of 11 representative demographic, economic, and health-care associated ZCTA-level parameters as potential predictors, and the total number of COVID-19 tests as the exposure. The BYM model includes both spatial and nonspatial random effects to account for clustering and overdispersion. RESULTS: Multiple regression approaches indicated a consistent, statistically significant association between detected COVID-19 cases and dependent children (under 18 years old), population density, median household income, and race. In the final model, we found that an increase of only 5% in young population is associated with a 2.3% increase in COVID-19 positivity rate (95% confidence interval (CI) 0.4 to 4.2%, p=0.021). An increase of 10,000 people per km(2) is associated with a 2.4% (95% CI 0.6 to 4.2%, p=0.011) increase in positivity rate. A decrease of $10,000 median household income is associated with a 1.6% (95% CI 0.7 to 2.4%, p<0.001) increase in COVID-19 positivity rate. With respect to race, a decrease of 10% in White population is associated with a 1.8% (95% CI 0.8 to 2.8%, p<0.001) increase in positivity rate, while an increase of 10% in Black population is associated with a 1.1% (95% CI 0.3 to 1.8%, p<0.001) increase in positivity rate. The percentage of Hispanic (p=0.718), Asian (p=0.966), or Other (p=0.588) populations were not statistically significant factors. CONCLUSIONS: Our findings indicate associations between neighborhoods with a large dependent youth population, densely populated, low-income, and predominantly black neighborhoods and COVID-19 test positivity rate. The study highlights the importance of public health management during and after the current COVID-19 pandemic. Further work is warranted to fully understand the mechanisms by which these factors may have affected the positivity rate, either in terms of the true number of cases or access to testing. BioMed Central 2020-09-04 /pmc/articles/PMC7471585/ /pubmed/32883276 http://dx.doi.org/10.1186/s12916-020-01731-6 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Whittle, Richard S.
Diaz-Artiles, Ana
An ecological study of socioeconomic predictors in detection of COVID-19 cases across neighborhoods in New York City
title An ecological study of socioeconomic predictors in detection of COVID-19 cases across neighborhoods in New York City
title_full An ecological study of socioeconomic predictors in detection of COVID-19 cases across neighborhoods in New York City
title_fullStr An ecological study of socioeconomic predictors in detection of COVID-19 cases across neighborhoods in New York City
title_full_unstemmed An ecological study of socioeconomic predictors in detection of COVID-19 cases across neighborhoods in New York City
title_short An ecological study of socioeconomic predictors in detection of COVID-19 cases across neighborhoods in New York City
title_sort ecological study of socioeconomic predictors in detection of covid-19 cases across neighborhoods in new york city
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7471585/
https://www.ncbi.nlm.nih.gov/pubmed/32883276
http://dx.doi.org/10.1186/s12916-020-01731-6
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