Self-tests for Covid-19

What is the evidence? A living systematic review and meta-analysis (2020–2023)

Bibliographic Data

ID19592876
AuthorsApoorva Anand (0000-0003-3782-9472, McGill University Health Centre, corresponding author), Fiorella Vialard (0000-0001-8575-436X, McGill University Health Centre, corresponding author), Aliasgar Esmail (0000-0002-1835-9252, University of Cape Town, corresponding author), F A Khan (0000-0003-0473-8734, McGill University Health Centre, corresponding author), Patrick O’byrne (0000-0002-0587-1409, University of Ottawa, corresponding author), Jean-Pierre Routy, Jean‐Pierre Routy (0000-0001-9897-7589, McGill University Health Centre, corresponding author), Keertan Dheda (0000-0001-7709-5341, University of Cape Town, corresponding author), Nitika Pant Pai (0000-0002-4672-0500, McGill University Health Centre, corresponding author)
EditorsCollins Otieno Asweto (0000-0003-3514-9836)
Year2024
Volume4
Issue2
Pagese0002336
Publication date2024-02-07
Open AccessYes
TypeARTICLE
VenuePLOS Global Public Health (JOURNAL)
Journal identifiersISSN: 2767-3375 • E-ISSN: 2767-3375
PublisherPublic Library of Science (PLoS) (PUBLISHER)
DOI10.1371/journal.pgph.0002336
PMID38324519
OpenAlexW4391594869
LanguageEN
Citations received2
References cited45

COVID-19 self-testing strategy (COVIDST) can rapidly identify symptomatic and asymptomatic SARS-CoV-2-infected individuals and their contacts, potentially reducing transmission. In this living systematic review, we evaluated the evidence for real-world COVIDST performance. Two independent reviewers searched six databases (PubMed, Embase, Web of Science, World Health Organization database, Cochrane COVID-19 registry, Europe PMC) for the period April 1 st , 2020, to January 18 th , 2023. Data on studies evaluating COVIDST against laboratory-based conventional testing and reported on diagnostic accuracy, feasibility, acceptability, impact, and qualitative outcomes were abstracted. Bivariate random effects meta-analyses of COVIDST accuracy were performed (n = 14). Subgroup analyses (by sampling site, symptomatic/asymptomatic infection, supervised/unsupervised strategy, with/without digital supports) were conducted. Data from 70 included studies, conducted across 25 countries with a median sample size of 817 (range: 28–784,707) were pooled. Specificity and DOR was high overall, irrespective of subgroups (98.37–99.71%). Highest sensitivities were reported for: a) symptomatic individuals (73.91%, 95%CI: 68.41–78.75%; n = 9), b) mid-turbinate nasal samples (77.79%, 95%CI: 56.03–90.59%; n = 14), c) supervised strategy (86.67%, 95%CI: 59.64–96.62%; n = 13), and d) use of digital interventions (70.15%, 95%CI: 50.18–84.63%; n = 14). Lower sensitivity was attributed to absence of symptoms, errors in test conduct and absence of supervision or a digital support. We found no difference in COVIDST sensitivity between delta and omicron pre-dominant period. Digital supports increased confidence in COVIDST reporting and interpretation (n = 16). Overall acceptability was 91.0–98.7% (n = 2) with lower acceptability reported for daily self-testing (39.5–51.1%). Overall feasibility was 69.0–100.0% (n = 5) with lower feasibility (35.9–64.6%) for serial self-testing. COVIDST decreased closures in school, workplace, and social events (n = 4). COVIDST is an effective rapid screening strategy for home-, workplace- or school-based screening, for symptomatic persons, and for preventing transmission during outbreaks. These data will guide COVIDST policy. Our review demonstrates that COVIDST has paved the way for self-testing in pandemics worldwide

2019-20 coronavirus outbreak · Biology · MEDLINE · Meta-analysis · Systematic review · COVID-19 Clinical Research Studies · COVID-19 diagnosis using AI · Medicine · Psychology · SARS-CoV-2 detection and testing · Internal Medicine · Virology

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