Participatory COVID-19 Surveillance Tool in Rural Appalachia

INTRODUCTION: Few US studies have examined the usefulness of participatory surveillance during the coronavirus disease 2019 (COVID-19) pandemic for enhancing local health response efforts, particularly in rural settings. We report on the development and implementation of an internet-based COVID-19 participatory surveillance tool in rural Appalachia. METHODS: A regional collaboration among public health partners culminated in the design and implementation of the COVID-19 Self-Checker, a local online symptom tracker. The tool collected data on participant demographic characteristics and health history. County residents were then invited to take part in an automated daily electronic follow-up to monitor symptom progression, assess barriers to care and testing, and collect data on COVID-19 test results and symptom resolution. RESULTS: Nearly 6500 county residents visited and 1755 residents completed the COVID-19 Self-Checker from April 30 through June 9, 2020. Of the 579 residents who reported severe or mild COVID-19 symptoms, COVID-19 symptoms were primarily reported among women (n = 408, 70.5%), adults with preexisting health conditions (n = 246, 70.5%), adults aged 18-44 (n = 301, 52.0%), and users who reported not having a health care provider (n = 131, 22.6%). Initial findings showed underrepresentation of some racial/ethnic and non-English-speaking groups. PRACTICAL IMPLICATIONS: This low-cost internet-based platform provided a flexible means to collect participatory surveillance data on local changes in COVID-19 symptoms and adapt to guidance. Data from this tool can be used to monitor the efficacy of public health response measures at the local level in rural Appalachia.

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Journal
UNC Libraries
Published
2025-05-20
DOI
https://doi.org/10.17615/16er-ad36
Primary Topic
Data-Driven Disease Surveillance
Type
article
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article

Participatory COVID-19 Surveillance Tool in Rural Appalachia

Fletcher Tove, Jennifer Mullendore, Ellis Vaughan, Jennifer D. Runkle et al.
UNC Libraries
Data-Driven Disease Surveillance
article

Participatory COVID-19 Surveillance Tool in Rural Appalachia

Fletcher Tove, Jennifer Mullendore, Ellis Vaughan, Jennifer D. Runkle, Steve Valeika, Bryan Hodge, Terri March, Garrett Graham, Margaret M. Sugg, Martha Salyers
article en

Abstract

INTRODUCTION: Few US studies have examined the usefulness of participatory surveillance during the coronavirus disease 2019 (COVID-19) pandemic for enhancing local health response efforts, particularly in rural settings. We report on the development and implementation of an internet-based COVID-19 participatory surveillance tool in rural Appalachia. METHODS: A regional collaboration among public health partners culminated in the design and implementation of the COVID-19 Self-Checker, a local online symptom tracker. The tool collected data on participant demographic characteristics and health history. County residents were then invited to take part in an automated daily electronic follow-up to monitor symptom progression, assess barriers to care and testing, and collect data on COVID-19 test results and symptom resolution. RESULTS: Nearly 6500 county residents visited and 1755 residents completed the COVID-19 Self-Checker from April 30 through June 9, 2020. Of the 579 residents who reported severe or mild COVID-19 symptoms, COVID-19 symptoms were primarily reported among women (n = 408, 70.5%), adults with preexisting health conditions (n = 246, 70.5%), adults aged 18-44 (n = 301, 52.0%), and users who reported not having a health care provider (n = 131, 22.6%). Initial findings showed underrepresentation of some racial/ethnic and non-English-speaking groups. PRACTICAL IMPLICATIONS: This low-cost internet-based platform provided a flexible means to collect participatory surveillance data on local changes in COVID-19 symptoms and adapt to guidance. Data from this tool can be used to monitor the efficacy of public health response measures at the local level in rural Appalachia.

UNC Libraries
University of North Carolina at Chapel Hill (US)
Openalex Percentile: Top 64%
Data-Driven Disease Surveillance
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Participatory COVID-19 Surveillance Tool in Rural Appalachia — Fletcher Tove, Jennifer Mullendore, et al. · UNC Libraries (2025) | TGRS Research Map | TGRS