SARS-CoV-2 infection in central North Carolina: Protocol for a population-based longitudinal cohort study and preliminary participant results.
Public health surveillance systems likely underestimate the true prevalence and incidence of SARS-CoV-2 infection due to limited access to testing and the high proportion of subclinical infections in community-based settings. This ongoing prospective, observational study aimed to generate accurate e...
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2021
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oai:doaj.org-article:ee6a3236f679499aa26b218c56f17aab2021-12-02T20:07:46ZSARS-CoV-2 infection in central North Carolina: Protocol for a population-based longitudinal cohort study and preliminary participant results.1932-620310.1371/journal.pone.0259070https://doaj.org/article/ee6a3236f679499aa26b218c56f17aab2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0259070https://doaj.org/toc/1932-6203Public health surveillance systems likely underestimate the true prevalence and incidence of SARS-CoV-2 infection due to limited access to testing and the high proportion of subclinical infections in community-based settings. This ongoing prospective, observational study aimed to generate accurate estimates of the prevalence and incidence of, and risk factors for, SARS-CoV-2 infection among residents of a central North Carolina county. From this cohort, we collected survey data and nasal swabs every two weeks and venous blood specimens every month. Nasal swabs were tested for the presence of SARS-CoV-2 virus (evidence of active infection), and serum specimens for SARS-CoV-2-specific antibodies (evidence of prior infection). As of June 23, 2021, we have enrolled a total of 153 participants from a county with an estimated 76,285 total residents. The anticipated study duration is at least 24 months, pending the evolution of the pandemic. Study data are being shared on a monthly basis with North Carolina state health authorities and future analyses aim to compare study data to state-wide metrics over time. Overall, the use of a probability-based sampling design and a well-characterized cohort will enable collection of critical data that can be used in planning and policy decisions for North Carolina and may be informative for other states with similar demographic characteristics.Elyse M MillerElle A LawRawan AjeenJaclyn KarasikCarmen MendozaHaley AbernathyHaley GarrettElise KingJohn WallaceMichael ZelekJessie K EdwardsKhou XiongCherese BeattyAaron T FleischauerEmily J CicconeBonnie E Shook-SaAllison E AielloRoss M BoycePublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 10, p e0259070 (2021) |
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Medicine R Science Q Elyse M Miller Elle A Law Rawan Ajeen Jaclyn Karasik Carmen Mendoza Haley Abernathy Haley Garrett Elise King John Wallace Michael Zelek Jessie K Edwards Khou Xiong Cherese Beatty Aaron T Fleischauer Emily J Ciccone Bonnie E Shook-Sa Allison E Aiello Ross M Boyce SARS-CoV-2 infection in central North Carolina: Protocol for a population-based longitudinal cohort study and preliminary participant results. |
description |
Public health surveillance systems likely underestimate the true prevalence and incidence of SARS-CoV-2 infection due to limited access to testing and the high proportion of subclinical infections in community-based settings. This ongoing prospective, observational study aimed to generate accurate estimates of the prevalence and incidence of, and risk factors for, SARS-CoV-2 infection among residents of a central North Carolina county. From this cohort, we collected survey data and nasal swabs every two weeks and venous blood specimens every month. Nasal swabs were tested for the presence of SARS-CoV-2 virus (evidence of active infection), and serum specimens for SARS-CoV-2-specific antibodies (evidence of prior infection). As of June 23, 2021, we have enrolled a total of 153 participants from a county with an estimated 76,285 total residents. The anticipated study duration is at least 24 months, pending the evolution of the pandemic. Study data are being shared on a monthly basis with North Carolina state health authorities and future analyses aim to compare study data to state-wide metrics over time. Overall, the use of a probability-based sampling design and a well-characterized cohort will enable collection of critical data that can be used in planning and policy decisions for North Carolina and may be informative for other states with similar demographic characteristics. |
format |
article |
author |
Elyse M Miller Elle A Law Rawan Ajeen Jaclyn Karasik Carmen Mendoza Haley Abernathy Haley Garrett Elise King John Wallace Michael Zelek Jessie K Edwards Khou Xiong Cherese Beatty Aaron T Fleischauer Emily J Ciccone Bonnie E Shook-Sa Allison E Aiello Ross M Boyce |
author_facet |
Elyse M Miller Elle A Law Rawan Ajeen Jaclyn Karasik Carmen Mendoza Haley Abernathy Haley Garrett Elise King John Wallace Michael Zelek Jessie K Edwards Khou Xiong Cherese Beatty Aaron T Fleischauer Emily J Ciccone Bonnie E Shook-Sa Allison E Aiello Ross M Boyce |
author_sort |
Elyse M Miller |
title |
SARS-CoV-2 infection in central North Carolina: Protocol for a population-based longitudinal cohort study and preliminary participant results. |
title_short |
SARS-CoV-2 infection in central North Carolina: Protocol for a population-based longitudinal cohort study and preliminary participant results. |
title_full |
SARS-CoV-2 infection in central North Carolina: Protocol for a population-based longitudinal cohort study and preliminary participant results. |
title_fullStr |
SARS-CoV-2 infection in central North Carolina: Protocol for a population-based longitudinal cohort study and preliminary participant results. |
title_full_unstemmed |
SARS-CoV-2 infection in central North Carolina: Protocol for a population-based longitudinal cohort study and preliminary participant results. |
title_sort |
sars-cov-2 infection in central north carolina: protocol for a population-based longitudinal cohort study and preliminary participant results. |
publisher |
Public Library of Science (PLoS) |
publishDate |
2021 |
url |
https://doaj.org/article/ee6a3236f679499aa26b218c56f17aab |
work_keys_str_mv |
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