Evidence map›Paper›PMID 36018589›Full record

ArticleJAMA network open2022

Use of a Digital Assistant to Report COVID-19 Rapid Antigen Self-test Results to Health Departments in 6 US Communities.

Carly Herbert, Qiming Shi, Vik Kheterpal, Chris Nowak, Thejas Suvarna, Basyl Durnan, Summer Schrader, Stephanie Behar, Syed Naeem, Seanan Tarrant and 17 more

Abstract read
In one paragraph

Article in JAMA network open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
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  7. Leveraging School Infection Data to Address Community COVID-19 Data Gaps.Journal of the Pediatric Infectious Diseases Society · 2023
    Article
  8. Article
  9. Observational
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

27 authors.

Carly HerbertProgram in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester.
Qiming ShiProgram in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester.
Vik KheterpalCareEvolution, Ann Arbor, Michigan.
Chris NowakCareEvolution, Ann Arbor, Michigan.
Thejas SuvarnaCareEvolution, Ann Arbor, Michigan.
Basyl DurnanCareEvolution, Ann Arbor, Michigan.
Summer SchraderCareEvolution, Ann Arbor, Michigan.
Stephanie BeharProgram in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester.
Syed NaeemProgram in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester.
Seanan TarrantProgram in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester.
Ben KalibalaProgram in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester.
Aditi SinghProgram in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester.
Ben GerberDepartment of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester.
Bruce BartonDepartment of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester.
Honghuang LinProgram in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester.
Michael Cohen-WolkowiezDepartment of Pediatrics, Duke University School of Medicine, Durham, North Carolina.
Giselle Corbie-SmithCenter for Health Equity Research, Department of Social Medicine, Department of Medicine, University of North Carolina School of Medicine, Chapel Hill.
Warren KibbeDepartment of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina.
Juan MarquezWashtenaw County Health Department, Washtenaw, Michigan.
Jonggyu BaekDepartment of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester.
Nathaniel HaferCenter for Clinical and Translational Science, University of Massachusetts Chan Medical School, Worcester.
Laura GibsonDivision of Infectious Disease, Department of Medicine, University of Massachusetts Chan Medical School, Worcester.
Laurel O'ConnorDepartment of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester.
John BroachDepartment of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester.
William HeetderksNational Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, via contract with Kelly Services, Bethesda, Maryland.
David McManusDivision of Cardiology, Department of Medicine, University of Massachusetts Chan Medical School, Worcester.
Apurv SoniProgram in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester.

Funding

The Center for Advancing Point of Care in Heart, Lung, Blood and Sleep DiseasesU54HL143541 · NHLBI · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI Nathaniel Scott Hafer · 2018 to 2026
$141.0M
University of Massachusetts Center for Clinical Science and Translational SupplementUL1TR001453 · NCATS · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI LUZURIAGA, KATHERINE F · 2015 to 2024
$39.0M
University of Massachusetts Center for Clinical and Translational ScienceTL1TR001454 · NCATS · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI LEWIS, BRIAN C · 2015 to 2024
$3.6M
Assessing Alzheimer disease risk and heterogeneity using multimodal machine learning approachesU01AG068221 · NIA · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI DESTEFANO, ANITA L, LIN, HONGHUANG · 2021 to 2024
$2.5M
NCATS NIH HHS TL1 TR001454NCATS NIH HHS UL1 TR001453NHLBI NIH HHS U54 HL143541NIA NIH HHS U01 AG068221
6 · The paper itself

Abstract

Importance: Widespread distribution of rapid antigen tests is integral to the US strategy to address COVID-19; however, it is estimated that few rapid antigen test results are reported to local departments of health. Objective: To characterize how often individuals in 6 communities throughout the United States used a digital assistant to log rapid antigen test results and report them to their local departments of health. Design, Setting, and Participants: This prospective cohort study is based on anonymously collected data from the beneficiaries of the Say Yes! Covid Test program, which distributed more than 3 000 000 rapid antigen tests at no cost to residents of 6 communities (Louisville, Kentucky; Indianapolis, Indiana; Fulton County, Georgia; O'ahu, Hawaii; Ann Arbor and Ypsilanti, Michigan; and Chattanooga, Tennessee) between April and October 2021. A descriptive evaluation of beneficiary use of a digital assistant for logging and reporting their rapid antigen test results was performed. Interventions: Widespread community distribution of rapid antigen tests. Main Outcomes and Measures: Number and proportion of tests logged and reported to the local department of health through the digital assistant. Results: A total of 313 000 test kits were distributed, including 178 785 test kits that were ordered using the digital assistant. Among all distributed kits, 14 398 households (4.6%) used the digital assistant, but beneficiaries reported three-quarters of their rapid antigen test results to their state public health departments (30 965 tests reported of 41 465 total test results [75.0%]). The reporting behavior varied by community and was significantly higher among communities that were incentivized for reporting test results vs those that were not incentivized or partially incentivized (90.5% [95% CI, 89.9%-91.2%] vs 70.5%; [95% CI, 70.0%-71.0%]). In all communities, positive tests were less frequently reported than negative tests (60.4% [95% CI, 58.1%-62.8%] vs 75.5% [95% CI, 75.1%-76.0%]). Conclusions and Relevance: These results suggest that application-based reporting with incentives may be associated with increased reporting of rapid tests for COVID-19. However, increasing the adoption of the digital assistant may be a critical first step.

Indexed as

COVID-19Data CollectionGeorgiaHumansProspective StudiesSelf-TestingUnited States

Identifiers

PMID36018589
PMCPMC9419013

What OpenQuestion holds

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Registered trials

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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.