ArticleJMIR public health and surveillance2021
Patterns of SARS-CoV-2 Testing Preferences in a National Cohort in the United States: Latent Class Analysis of a Discrete Choice Experiment.
Article in JMIR public health and surveillance, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
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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.
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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.
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Who cites it
12 citing papers in PubMed, 2 syntheses or guidelines pooled it, 15 citations in OpenAlex.
- The Evolving Landscape of Discrete Choice Experiments in Health Economics: A Systematic Review.PharmacoEconomics · 2025Pooled it
- The effect of sample site and collection procedure on identification of SARS-CoV-2 infection.The Cochrane database of systematic reviews · 2024Pooled it
- Syringe Services Participants' Preferences for Wound Care, HIV/Hepatitis C Virus Screening, and Opioid Use Disorder Treatment: A Discrete Choice Experiment.Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research · 2026Article
- SARS-CoV-2 testing behavior in symptomatic adults in a U.S. national cohort, 2020-2023: influence of exposure risk, susceptibility and healthcare access.BMC public health · 2025Article
- Suspected Mpox Symptoms and Testing in Men Who Have Sex With Men in the United States: Cross-Sectional Study.JMIR public health and surveillance · 2025Article
- Proposing the observational-implementation hybrid approach: designing observational research for rapid translation.Annals of epidemiology · 2023Article
- Implementing SARS-CoV-2 antigen testing scale-up in Rwanda: retrospective analysis of national programme data and qualitative findings.BMJ open · 2023Article
- Parental refusal and hesitancy of vaccinating children against COVID-19: Findings from a nationally representative sample of parents in the U.S.Preventive medicine · 2022Article
- SARS-CoV-2 pandemic in New York metropolitan area: the view from a major urgent care provider.Annals of epidemiology · 2022Article
- Trade-offs during the COVID-19 pandemic: A discrete choice experiment about policy preferences in Portugal.PloS one · 2022Article
- Article
- The emergence, surge and subsequent wave of the SARS-CoV-2 pandemic in New York metropolitan area: The view from a major region-wide urgent care provider.medRxiv : the preprint server for health sciences · 2021Article
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15 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundInadequate screening and diagnostic testing in the United States throughout the first several months of the COVID-19 pandemic led to undetected cases transmitting disease in the community and an underestimation of cases. Though testing supply has increased, maintaining testing uptake remains a public health priority in the efforts to control community transmission considering the availability of vaccinations and threats from variants.
objectiveThis study aimed to identify patterns of preferences for SARS-CoV-2 screening and diagnostic testing prior to widespread vaccine availability and uptake.
methodsWe conducted a discrete choice experiment (DCE) among participants in the national, prospective CHASING COVID (Communities, Households, and SARS-CoV-2 Epidemiology) Cohort Study from July 30 to September 8, 2020. The DCE elicited preferences for SARS-CoV-2 test type, specimen type, testing venue, and result turnaround time. We used latent class multinomial logit to identify distinct patterns of preferences related to testing as measured by attribute-level part-worth utilities and conducted a simulation based on the utility estimates to predict testing uptake if additional testing scenarios were offered.
resultsOf the 5098 invited cohort participants, 4793 (94.0%) completed the DCE. Five distinct patterns of SARS-CoV-2 testing emerged. Noninvasive home testers (n=920, 19.2% of participants) were most influenced by specimen type and favored less invasive specimen collection methods, with saliva being most preferred; this group was the least likely to opt out of testing. Fast-track testers (n=1235, 25.8%) were most influenced by result turnaround time and favored immediate and same-day turnaround time. Among dual testers (n=889, 18.5%), test type was the most important attribute, and preference was given to both antibody and viral tests. Noninvasive dual testers (n=1578, 32.9%) were most strongly influenced by specimen type and test type, preferring saliva and cheek swab specimens and both antibody and viral tests. Among hesitant home testers (n=171, 3.6%), the venue was the most important attribute; notably, this group was the most likely to opt out of testing. In addition to variability in preferences for testing features, heterogeneity was observed in the distribution of certain demographic characteristics (age, race/ethnicity, education, and employment), history of SARS-CoV-2 testing, COVID-19 diagnosis, and concern about the pandemic. Simulation models predicted that testing uptake would increase from 81.6% (with a status quo scenario of polymerase chain reaction by nasal swab in a provider's office and a turnaround time of several days) to 98.1% by offering additional scenarios using less invasive specimens, both viral and antibody tests from a single specimen, faster turnaround time, and at-home testing.
conclusionsWe identified substantial differences in preferences for SARS-CoV-2 testing and found that offering additional testing options would likely increase testing uptake in line with public health goals. Additional studies may be warranted to understand if preferences for testing have changed since the availability and widespread uptake of vaccines.
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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.