Evidence map›Paper›PMID 33398293›Full record

ArticlemedRxiv : the preprint server for health sciences2020

Patterns of SARS-CoV-2 testing preferences in a national cohort in the United States.

Matthew L Romo, Rebecca Zimba, Sarah Kulkarni, Amanda Berry, William You, Chloe Mirzayi, Drew Westmoreland, Angela M Parcesepe, Levi Waldron, Madhura Rane and 6 more

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

16 authors.

Matthew L RomoInstitute for Implementation Science in Population Health (ISPH), City University of New York (CUNY); New York, NY, 10027 USA.ORCID 0000-0002-3038-0644
Rebecca ZimbaInstitute for Implementation Science in Population Health (ISPH), City University of New York (CUNY); New York, NY, 10027 USA.ORCID 0000-0001-5278-3930
Sarah KulkarniInstitute for Implementation Science in Population Health (ISPH), City University of New York (CUNY); New York, NY, 10027 USA.
Amanda BerryInstitute for Implementation Science in Population Health (ISPH), City University of New York (CUNY); New York, NY, 10027 USA.ORCID 0000-0001-9084-7771
William YouInstitute for Implementation Science in Population Health (ISPH), City University of New York (CUNY); New York, NY, 10027 USA.
Chloe MirzayiInstitute for Implementation Science in Population Health (ISPH), City University of New York (CUNY); New York, NY, 10027 USA.ORCID 0000-0002-9817-2868
Drew WestmorelandInstitute for Implementation Science in Population Health (ISPH), City University of New York (CUNY); New York, NY, 10027 USA.ORCID 0000-0003-4085-7941
Angela M ParcesepeDepartment of Maternal and Child Health, Gillings School of Public Health, University of North Carolina, Chapel Hill, NC, 27599 USA.
Levi WaldronInstitute for Implementation Science in Population Health (ISPH), City University of New York (CUNY); New York, NY, 10027 USA.ORCID 0000-0003-2725-0694
Madhura RaneInstitute for Implementation Science in Population Health (ISPH), City University of New York (CUNY); New York, NY, 10027 USA.
Shivani KochharInstitute for Implementation Science in Population Health (ISPH), City University of New York (CUNY); New York, NY, 10027 USA.
McKaylee RobertsonInstitute for Implementation Science in Population Health (ISPH), City University of New York (CUNY); New York, NY, 10027 USA.ORCID 0000-0002-8426-6572
Andrew R MarokoInstitute for Implementation Science in Population Health (ISPH), City University of New York (CUNY); New York, NY, 10027 USA.ORCID 0000-0002-9398-2386
Christian GrovInstitute for Implementation Science in Population Health (ISPH), City University of New York (CUNY); New York, NY, 10027 USA.
Denis NashInstitute for Implementation Science in Population Health (ISPH), City University of New York (CUNY); New York, NY, 10027 USA.
CHASING COVID Cohort Study Team

Funding

Following a U.S. National Cohort of Vulnerable Men to Improve HIV Prevention and CareUH3AI133675 · NIAID · GRADUATE SCHOOL OF PUBLIC HEALTH AND HEALTH POLICY · PI GROV, CHRISTIAN · 2019 to 2021
$6.9M
NIAID NIH HHS UH3 AI133675
6 · The paper itself

Abstract

In order to understand preferences about SARS-CoV-2 testing, we conducted a discrete choice experiment among 4793 participants in the Communities, Households, and SARS-CoV-2 Epidemiology (CHASING COVID) Cohort Study from July 30-September 8, 2020. We used latent class analysis to identify distinct patterns of preferences related to testing and conducted a simulation to predict testing uptake if additional testing scenarios were offered. Five distinct patterns of SARS-CoV-2 testing emerged. "Comprehensive testers" (18.9%) ranked specimen type as most important and favored less invasive specimen types, with saliva most preferred, and also ranked venue and result turnaround time as highly important, with preferences for home testing and fast result turnaround time. "Fast track testers" (26.0%) ranked result turnaround time as most important and favored immediate and same day turnaround time. "Dual testers" (18.5%) ranked test type as most important and preferred both antibody and viral tests. "Non-invasive dual testers" (33.0%) ranked specimen type and test type as similarly most important, preferring cheek swab specimen type and both antibody and viral tests. "Home testers" (3.6%) ranked venue as most important and favored home-based testing. By offering less invasive (saliva specimen type), dual testing (both viral and antibody tests), and at home testing scenarios in addition to standard testing scenarios, simulation models predicted that testing uptake would increase from 81.7% to 98.1%. We identified substantial differences in preferences for SARS-CoV-2 testing and found that offering additional testing options, which consider this heterogeneity, would likely increase testing uptake. SIGNIFICANCE: During the COVID-19 pandemic, diagnostic testing has allowed for early detection of cases and implementation of measures to reduce community transmission of SARS-CoV-2 infection. Understanding individuals' preferences about testing and the service models that deliver tests are relevant in efforts to increase and sustain uptake of SARS-CoV-2 testing, which, despite vaccine availability, will be required for the foreseeable future. We identified substantial differences in preferences for SARS-CoV-2 testing in a discrete choice experiment among a large national cohort of adults in the US. Offering additional testing options that account for or anticipate this heterogeneity in preferences (e.g., both viral and antibody tests, at home testing), would likely increase testing uptake. CLASSIFICATION: Biological Sciences (major); Psychological and Cognitive Sciences (minor).

Identifiers

PMID33398293
PMCPMC7781336

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.