County-level longitudinal clustering of COVID-19 mortality to incidence ratio in the United States

Abstract As of November 12, 2020, the mortality to incidence ratio (MIR) of COVID-19 was 5.8% in the US. A longitudinal model-based clustering system on the disease trajectories over time was used to identify “vulnerable” clusters of counties that would benefit from allocating additional resources b...

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Autores principales:	Nasim Vahabi, Masoud Salehi, Julio D. Duarte, Abolfazl Mollalo, George Michailidis
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/714dc68c8436436e912b5be6eea3f40b
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spelling	oai:doaj.org-article:714dc68c8436436e912b5be6eea3f40b2021-12-02T10:44:15ZCounty-level longitudinal clustering of COVID-19 mortality to incidence ratio in the United States10.1038/s41598-021-82384-02045-2322https://doaj.org/article/714dc68c8436436e912b5be6eea3f40b2021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-82384-0https://doaj.org/toc/2045-2322Abstract As of November 12, 2020, the mortality to incidence ratio (MIR) of COVID-19 was 5.8% in the US. A longitudinal model-based clustering system on the disease trajectories over time was used to identify “vulnerable” clusters of counties that would benefit from allocating additional resources by federal, state and county policymakers. County-level COVID-19 cases and deaths, together with a set of potential risk factors were collected for 3050 U.S. counties during the 1st wave of COVID-19 (Mar25–Jun3, 2020), followed by similar data for 1344 counties (in the “sunbelt” region of the country) during the 2nd wave (Jun4–Sep2, 2020), and finally for 1055 counties located broadly in the great plains region of the country during the 3rd wave (Sep3–Nov12, 2020). We used growth mixture models to identify clusters of counties exhibiting similar COVID-19 MIR growth trajectories and risk-factors over time. The analysis identifies “more vulnerable” clusters during the 1st, 2nd and 3rd waves of COVID-19. Further, tuberculosis (OR 1.3–2.1–3.2), drug use disorder (OR 1.1), hepatitis (OR 13.1), HIV/AIDS (OR 2.3), cardiomyopathy and myocarditis (OR 1.3), diabetes (OR 1.2), mesothelioma (OR 9.3) were significantly associated with increased odds of being in a more vulnerable cluster. Heart complications and cancer were the main risk factors increasing the COVID-19 MIR (range 0.08–0.52% MIR↑). We identified “more vulnerable” county-clusters exhibiting the highest COVID-19 MIR trajectories, indicating that enhancing the capacity and access to healthcare resources would be key to successfully manage COVID-19 in these clusters. These findings provide insights for public health policymakers on the groups of people and locations they need to pay particular attention while managing the COVID-19 epidemic.Nasim VahabiMasoud SalehiJulio D. DuarteAbolfazl MollaloGeorge MichailidisNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-22 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Nasim Vahabi Masoud Salehi Julio D. Duarte Abolfazl Mollalo George Michailidis County-level longitudinal clustering of COVID-19 mortality to incidence ratio in the United States
description	Abstract As of November 12, 2020, the mortality to incidence ratio (MIR) of COVID-19 was 5.8% in the US. A longitudinal model-based clustering system on the disease trajectories over time was used to identify “vulnerable” clusters of counties that would benefit from allocating additional resources by federal, state and county policymakers. County-level COVID-19 cases and deaths, together with a set of potential risk factors were collected for 3050 U.S. counties during the 1st wave of COVID-19 (Mar25–Jun3, 2020), followed by similar data for 1344 counties (in the “sunbelt” region of the country) during the 2nd wave (Jun4–Sep2, 2020), and finally for 1055 counties located broadly in the great plains region of the country during the 3rd wave (Sep3–Nov12, 2020). We used growth mixture models to identify clusters of counties exhibiting similar COVID-19 MIR growth trajectories and risk-factors over time. The analysis identifies “more vulnerable” clusters during the 1st, 2nd and 3rd waves of COVID-19. Further, tuberculosis (OR 1.3–2.1–3.2), drug use disorder (OR 1.1), hepatitis (OR 13.1), HIV/AIDS (OR 2.3), cardiomyopathy and myocarditis (OR 1.3), diabetes (OR 1.2), mesothelioma (OR 9.3) were significantly associated with increased odds of being in a more vulnerable cluster. Heart complications and cancer were the main risk factors increasing the COVID-19 MIR (range 0.08–0.52% MIR↑). We identified “more vulnerable” county-clusters exhibiting the highest COVID-19 MIR trajectories, indicating that enhancing the capacity and access to healthcare resources would be key to successfully manage COVID-19 in these clusters. These findings provide insights for public health policymakers on the groups of people and locations they need to pay particular attention while managing the COVID-19 epidemic.
format	article
author	Nasim Vahabi Masoud Salehi Julio D. Duarte Abolfazl Mollalo George Michailidis
author_facet	Nasim Vahabi Masoud Salehi Julio D. Duarte Abolfazl Mollalo George Michailidis
author_sort	Nasim Vahabi
title	County-level longitudinal clustering of COVID-19 mortality to incidence ratio in the United States
title_short	County-level longitudinal clustering of COVID-19 mortality to incidence ratio in the United States
title_full	County-level longitudinal clustering of COVID-19 mortality to incidence ratio in the United States
title_fullStr	County-level longitudinal clustering of COVID-19 mortality to incidence ratio in the United States
title_full_unstemmed	County-level longitudinal clustering of COVID-19 mortality to incidence ratio in the United States
title_sort	county-level longitudinal clustering of covid-19 mortality to incidence ratio in the united states
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/714dc68c8436436e912b5be6eea3f40b
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County-level longitudinal clustering of COVID-19 mortality to incidence ratio in the United States

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