Patterns of SARS-CoV-2 Testing Preferences in a National Cohort in the United States: Latent Class Analysis of a Discrete Choice Experiment
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Abstract
Inadequate 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.
Objective
This study aimed to identify patterns of preferences for SARS-CoV-2 screening and diagnostic testing prior to widespread vaccine availability and uptake.
Methods
We 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.
Results
Of 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.
Conclusions
We 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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SciScore for 10.1101/2020.12.22.20248747: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement IRB: All study procedures were approved by the CUNY Graduate School of Public Health and Health Policy Institutional Review Board. Randomization Preferences for the 3 total options for the first simulation and 6 total options for the second simulation were generated using randomized first choice models whereby utilities were summed across all levels for each scenario and then exponentiated and rescaled to sum to 100.29 Finally, we computed descriptive statistics (frequencies and proportions) for demographic characteristics, previous SARS-CoV-2 testing, previous COVID-19 diagnosis, and concern about infection stratified by the 5 preference patterns and compared … SciScore for 10.1101/2020.12.22.20248747: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement IRB: All study procedures were approved by the CUNY Graduate School of Public Health and Health Policy Institutional Review Board. Randomization Preferences for the 3 total options for the first simulation and 6 total options for the second simulation were generated using randomized first choice models whereby utilities were summed across all levels for each scenario and then exponentiated and rescaled to sum to 100.29 Finally, we computed descriptive statistics (frequencies and proportions) for demographic characteristics, previous SARS-CoV-2 testing, previous COVID-19 diagnosis, and concern about infection stratified by the 5 preference patterns and compared distributions of these variables using Pearson’s Chi-Square tests. Blinding not detected. Power Analysis not detected. Sex as a biological variable The median age was 39 years, 51.5% were female, 62.8% were non-Hispanic white, 16.5% were Hispanic, 9.8% were non-Hispanic Black, and 7.1% were Asian.9 At enrollment, 29.3% of participants resided in the Northeast, 28.6% in the South, 24.1% in the West, and 17.9% in the Midwest. Table 2: Resources
Software and Algorithms Sentences Resources For this analysis, age was categorized into 3 groups (18-39 years, 40-59 years, and ≥60 years); gender was recategorized into 3 groups (male, female, transgender/non-binary/other); race/ethnicity was ascertained by two questions related to Hispanic heritage and race, and was categorized into 5 groups (Hispanic, non-Hispanic Black, Asian/Pacific Islander, non-Hispanic white, and other); education was ascertained by asking about highest grade or year of school completed (less than high school diploma, grade 12 or GED [high school graduate], college 1-3 years [some college or technical school], college 4 years or more [college graduate]); region was ascertained based on US state or territory of residence and categorized into 4 census regions (Northeast, Midwest, South, West) and Puerto Rico; urbanicity was categorized based on city locale assignments from the National Center for Education Statistics using ZIP Code Tabulation Areas with urban defined as living in an urbanized area and inside a principal city;23 and having any comorbidities (yes/no) was based on a question about ever being told by a healthcare professional that the participant had any chronic conditions in a list (i.e., coronary artery disease, diabetes, hypertension, cancer, asthma, chronic obstructive pulmonary disease, kidney disease, HIV/AIDS, immunosuppression, and depression). Islandersuggested: (Islander, RRID:SCR_007758)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our results should be interpreted in the context of their limitations. First, an important limitation of our analysis is related to latent class analysis in general, as best practices for using it to study heterogeneity in preferences in health-related research are still evolving.10 For example, regarding number of classes, for which there is no consensus, we selected 5, after comparing sample size, fit statistics, and overall interpretability of 2-10 classes. It is therefore possible that there were some patterns of preferences that went unidentified. Second, participants’ preferences about SARS-CoV-2 testing may change over time. Research on other topics has demonstrated that choices stated in a DCE are generally consistent, with good test-retest reliability;16,17 however, knowledge about SARS-CoV-2 and COVID-19 is rapidly evolving and is widely disseminated in mainstream media,18 which could plausibly impact preferences. For example, reports of re-infection and the potential waning of antibodies, such as the first such report in the US in October 2020,19 approximately one month after the completion of our DCE, might influence preferences about antibody testing, as might the availability of highly efficacious vaccines in December 2020.20 Third, stated preferences regarding SARS-CoV-2 testing in our DCE may not necessarily align with actual behavior (i.e., revealed preferences); however, a systematic review and meta-analysis found that, in general, stated preferences in DCEs...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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