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Using publicly available data to identify priority communities for a SARS-CoV-2 testing intervention in a southern U.S. state
BACKGROUND: The U.S. Southeast has a high burden of SARS-CoV-2 infections and COVID-19 disease. We used public data sources and community engagement to prioritize county selections for a precision population health intervention to promote a SARS-CoV-2 testing intervention in rural Alabama during Oct...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9915825/ https://www.ncbi.nlm.nih.gov/pubmed/36778309 http://dx.doi.org/10.1101/2023.01.31.23285248 |
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author | Matthews, Lynn T Long, Dustin M Pratt, Madeline C Yuan, Ya Heath, Sonya L Levitan, Emily B Grooms, Sydney Creger, Thomas Rana, Aadia Mugavero, Michael J Judd, Suzanne E |
author_facet | Matthews, Lynn T Long, Dustin M Pratt, Madeline C Yuan, Ya Heath, Sonya L Levitan, Emily B Grooms, Sydney Creger, Thomas Rana, Aadia Mugavero, Michael J Judd, Suzanne E |
author_sort | Matthews, Lynn T |
collection | PubMed |
description | BACKGROUND: The U.S. Southeast has a high burden of SARS-CoV-2 infections and COVID-19 disease. We used public data sources and community engagement to prioritize county selections for a precision population health intervention to promote a SARS-CoV-2 testing intervention in rural Alabama during October 2020 and March 2021. METHODS: We modeled factors associated with county-level SARS-CoV-2 percent positivity using covariates thought to associate with SARS-CoV-2 acquisition risk, disease severity, and risk mitigation practices. Descriptive epidemiologic data were presented to scientific and community advisory boards to prioritize counties for a testing intervention. RESULTS: In October 2020, SARS-CoV-2 percent positivity was not associated with any modeled factors. In March 2021, premature death rate (aRR 1.16, 95% CI 1.07, 1.25), percent Black residents (aRR 1.00, 95% CI 1.00, 1.01), preventable hospitalizations (aRR 1.03, 95% CI 1.00, 1.06), and proportion of smokers (aRR 0.231, 95% CI 0.10, 0.55) were associated with average SARS-CoV-2 percent positivity. We then ranked counties based on percent positivity, case fatality, case rates, and number of testing sites using individual variables and factor scores. Top ranking counties identified through factor analysis and univariate associations were provided to community partners who considered ongoing efforts and strength of community partnerships to promote testing to inform intervention. CONCLUSIONS: The dynamic nature of SARS-CoV-2 proved challenging for a modelling approach to inform a precision population health intervention at the county level. Epidemiological data allowed for engagement of community stakeholders implementing testing. As data sources and analytic capacities expand, engaging communities in data interpretation is vital to address diseases locally. |
format | Online Article Text |
id | pubmed-9915825 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Cold Spring Harbor Laboratory |
record_format | MEDLINE/PubMed |
spelling | pubmed-99158252023-02-11 Using publicly available data to identify priority communities for a SARS-CoV-2 testing intervention in a southern U.S. state Matthews, Lynn T Long, Dustin M Pratt, Madeline C Yuan, Ya Heath, Sonya L Levitan, Emily B Grooms, Sydney Creger, Thomas Rana, Aadia Mugavero, Michael J Judd, Suzanne E medRxiv Article BACKGROUND: The U.S. Southeast has a high burden of SARS-CoV-2 infections and COVID-19 disease. We used public data sources and community engagement to prioritize county selections for a precision population health intervention to promote a SARS-CoV-2 testing intervention in rural Alabama during October 2020 and March 2021. METHODS: We modeled factors associated with county-level SARS-CoV-2 percent positivity using covariates thought to associate with SARS-CoV-2 acquisition risk, disease severity, and risk mitigation practices. Descriptive epidemiologic data were presented to scientific and community advisory boards to prioritize counties for a testing intervention. RESULTS: In October 2020, SARS-CoV-2 percent positivity was not associated with any modeled factors. In March 2021, premature death rate (aRR 1.16, 95% CI 1.07, 1.25), percent Black residents (aRR 1.00, 95% CI 1.00, 1.01), preventable hospitalizations (aRR 1.03, 95% CI 1.00, 1.06), and proportion of smokers (aRR 0.231, 95% CI 0.10, 0.55) were associated with average SARS-CoV-2 percent positivity. We then ranked counties based on percent positivity, case fatality, case rates, and number of testing sites using individual variables and factor scores. Top ranking counties identified through factor analysis and univariate associations were provided to community partners who considered ongoing efforts and strength of community partnerships to promote testing to inform intervention. CONCLUSIONS: The dynamic nature of SARS-CoV-2 proved challenging for a modelling approach to inform a precision population health intervention at the county level. Epidemiological data allowed for engagement of community stakeholders implementing testing. As data sources and analytic capacities expand, engaging communities in data interpretation is vital to address diseases locally. Cold Spring Harbor Laboratory 2023-02-01 /pmc/articles/PMC9915825/ /pubmed/36778309 http://dx.doi.org/10.1101/2023.01.31.23285248 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use. |
spellingShingle | Article Matthews, Lynn T Long, Dustin M Pratt, Madeline C Yuan, Ya Heath, Sonya L Levitan, Emily B Grooms, Sydney Creger, Thomas Rana, Aadia Mugavero, Michael J Judd, Suzanne E Using publicly available data to identify priority communities for a SARS-CoV-2 testing intervention in a southern U.S. state |
title | Using publicly available data to identify priority communities for a SARS-CoV-2 testing intervention in a southern U.S. state |
title_full | Using publicly available data to identify priority communities for a SARS-CoV-2 testing intervention in a southern U.S. state |
title_fullStr | Using publicly available data to identify priority communities for a SARS-CoV-2 testing intervention in a southern U.S. state |
title_full_unstemmed | Using publicly available data to identify priority communities for a SARS-CoV-2 testing intervention in a southern U.S. state |
title_short | Using publicly available data to identify priority communities for a SARS-CoV-2 testing intervention in a southern U.S. state |
title_sort | using publicly available data to identify priority communities for a sars-cov-2 testing intervention in a southern u.s. state |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9915825/ https://www.ncbi.nlm.nih.gov/pubmed/36778309 http://dx.doi.org/10.1101/2023.01.31.23285248 |
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