Evidence map›Paper›PMID 39030676›Full record

ArticleJMIR public health and surveillance2024

Factors Associated With Surveillance Testing in Individuals With COVID-19 Symptoms During the Last Leg of the Pandemic: Multivariable Regression Analysis.

Timothy Dotson, Brad Price, Brian Witrick, Sherri Davis, Emily Kemper, Stacey Whanger, Sally Hodder, Brian Hendricks

Abstract read
In one paragraph

Article in JMIR public health and surveillance, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Timothy DotsonWest Virginia Clinical and Translational Sciences Institute, Morgantown, WV, United States.ORCID 0000-0002-7594-0545
Brad PriceWest Virginia Clinical and Translational Sciences Institute, Morgantown, WV, United States.ORCID 0000-0002-0619-3347
Brian WitrickWest Virginia Clinical and Translational Sciences Institute, Morgantown, WV, United States.ORCID 0000-0002-3800-8695
Sherri DavisWest Virginia Clinical and Translational Sciences Institute, Morgantown, WV, United States.ORCID 0000-0002-8131-2270
Emily KemperWest Virginia Clinical and Translational Sciences Institute, Morgantown, WV, United States.ORCID 0009-0008-6377-1968
Stacey WhangerAmerican Diabetes Association, Arlington, VA, United States.ORCID 0000-0001-7707-3513
Sally HodderWest Virginia Clinical and Translational Sciences Institute, Morgantown, WV, United States.ORCID 0000-0002-0728-5550
Brian HendricksWest Virginia Clinical and Translational Sciences Institute, Morgantown, WV, United States.ORCID 0000-0001-6682-1694

Funding

West Virginia IDEA-CTRU54GM104942 · NIGMS · WEST VIRGINIA UNIVERSITY · PI Stephenie Kay Kennedy-Rea · 2012 to 2026
$81.0M
Identifying COVID-19 vaccine deserts using Machine Learning and Geospatial Analyses to target Community -engaged testing for vulnerable rural populations to prevent localized outbreaksU01MD017419 · NIMHD · WEST VIRGINIA UNIVERSITY · PI HENDRICKS, BRIAN, PRICE, BRADLEY · 2022 to 2023
$2.2M
NIGMS NIH HHS U54 GM104942NIMHD NIH HHS U01 MD017419
6 · The paper itself

Abstract

Background: Rural underserved areas facing health disparities have unequal access to health resources. By the third and fourth waves of SARS-CoV-2 infections in the United States, COVID-19 testing had reduced, with more reliance on home testing, and those seeking testing were mostly symptomatic. Objective: This study identifies factors associated with COVID-19 testing among individuals who were symptomatic versus asymptomatic seen at a Rapid Acceleration of Diagnostics for Underserved Populations phase 2 (RADx-UP2) testing site in West Virginia. Methods: Demographic, clinical, and behavioral factors were collected via survey from tested individuals. Logistic regression was used to identify factors associated with the presence of individuals who were symptomatic seen at testing sites. Global tests for spatial autocorrelation were conducted to examine clustering in the proportion of symptomatic to total individuals tested by zip code. Bivariate maps were created to display geographic distributions between higher proportions of tested individuals who were symptomatic and social determinants of health. Results: Among predictors, the presence of a physical (adjusted odds ratio [aOR] 1.85, 95% CI 1.3-2.65) or mental (aOR 1.53, 95% CI 0.96-2.48) comorbid condition, challenges related to a place to stay/live (aOR 307.13, 95% CI 1.46-10,6372), no community socioeconomic distress (aOR 0.99, 95% CI 0.98-1.00), no challenges in getting needed medicine (aOR 0.01, 95% CI 0.00-0.82) or transportation (aOR 0.23, 95% CI 0.05-0.64), an interaction between community socioeconomic distress and not getting needed medicine (aOR 1.06, 95% CI 1.00-1.13), and having no community socioeconomic distress while not facing challenges related to a place to stay/live (aOR 0.93, 95% CI 0.87-0.99) were statistically associated with an individual being symptomatic at the first test visit. Conclusions: This study addresses critical limitations to the current COVID-19 testing literature, which almost exclusively uses population-level disease screening data to inform public health responses.

Indexed as

COVID-19COVID-19 TestingAdolescentAdultAgedFemaleHumansMaleMiddle AgedMultivariate AnalysisPandemicsRural PopulationWest VirginiaYoung Adultadolescentadolescentsasymptomaticbehavioralbivariate mapchildchildrenclinicalCOVID-19cross-sectional studydemographicdigital healthhealth disparitieslogistic regressionmachine learningmental healthmHealthmobile healthphysical healthpublic healthRADxRapid Acceleration of Diagnosticsregression analysisruralSARS-CoV-2surveillancesurveysymptomaticteenteenagerteenagersteenstestingUnited Statesyouth

Identifiers

PMID39030676
PMCPMC11270129

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.