Evidence map›Paper›PMID 38368339›Full record

Observational studyBMC public health2024

Implementing a pilot study of COVID-19 self-testing in high-risk populations and remote locations: results and lessons learnt.

Elena Marbán-Castro, Vladimer Getia, Maia Alkhazashvili, Maia Japaridze, Ia Jikia, Berra Erkosar, Paula Del Rey-Puech, Guillermo Z Martínez-Pérez, Paata Imnadze, Amiran Gamkrelidze and 3 more

Open access · goldAbstract readObservational Study
In one paragraph

Observational study in BMC public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.9field-weighted citation impact, top 15% of its field
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

4 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors at 2 institutions in 2 countries.

Elena Marbán-CastroFIND, Geneva, Switzerland.
Vladimer GetiaNational Center for Disease Control and Public Health (NCDC), Tbilisi, Georgia.
Maia AlkhazashviliNational Center for Disease Control and Public Health (NCDC), Tbilisi, Georgia.
Maia JaparidzeFIND, Geneva, Switzerland.
Ia JikiaFIND, Geneva, Switzerland.
Berra ErkosarFIND, Geneva, Switzerland.
Paula Del Rey-PuechFIND, Geneva, Switzerland.
Guillermo Z Martínez-PérezFIND, Geneva, Switzerland.
Paata ImnadzeNational Center for Disease Control and Public Health (NCDC), Tbilisi, Georgia.
Amiran GamkrelidzeNational Center for Disease Control and Public Health (NCDC), Tbilisi, Georgia.
Olga DenisiukFIND, Geneva, Switzerland.
Elena Ivanova ReipoldFIND, Geneva, Switzerland.
Sonjelle ShiltonFIND, Geneva, Switzerland. sonjelle.shilton@finddx.org.
FIND · CHNational Center for Disease Control and Public Health · GE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRapid antigen-detection tests for SARS-CoV-2 self-testing represent a useful tool for pandemic control and expanding access to community-level case screening. COVID-19 self-tests have been extensively used in high-income countries since 2021; however, their introduction and programmatic implementation in low- and middle-income countries was delayed. We aimed to identify and continuously improve a weekly COVID-19 self-testing model among staff at healthcare facilities and schools.

methodsThis mixed-methods, observational prospective study was conducted in 5 healthcare centres and 24 schools in Georgia, between June and December 2022. The study comprised the integration of COVID-19 self-testing into the national mandatory testing programme for high-risk groups, with primary distribution of self-tests among staff performed weekly, plus secondary distribution to their household members. These use cases were selected because NCDC was seeking to strengthen their already strong weekly testing programme, by investigating self-testing to ease the burden of testing in the healthcare system. Online surveys and semi-structured interviews were used for data collection.

resultsIn total, 2156 participants were enrolled (1963 female, 72%). At baseline and mid- and end-points, 88%, 97% and 99%, respectively, of participants agreed/strongly agreed they would self-test. Similarly, the majority were willing to report their self-testing results (88%, 98% and 96% at baseline and mid- and end-points, respectively). Weekly reporting of test results to the national COVID-19 database was high during all the implementation. There were 622 COVID-19 positive results reported, and linked to care, from 601 individuals (282 participants and 319 household members). Findings from qualitative interviews showed great satisfaction with self-testing for its convenience, ease of use, trust in the results, no need to travel for diagnostics, and increased perception of safety.

conclusionsOur findings contribute to the evidence-base regarding self-testing strategies conducted via workplaces and secondary distribution to households. Willingness to perform a COVID-19 self-test increased after implementation. This pilot enhanced pandemic preparedness through expansion of the national self-testing reporting system, development of communications materials, changes in the national legal framework and coordination mechanisms, and improved perceptions around self-care in the community. The lessons learnt can inform operational aspects of the introduction and scale-up of self-care strategies.

Indexed as

COVID-19FemaleHumansPilot ProjectsProspective StudiesSARS-CoV-2Self-TestingCOVID-19DiagnosticHealthcareImplementationSARS-CoV-2SchoolScreeningSelf-testingTesting

Identifiers

PMID38368339
PMCPMC10874527
OpenAlexW4391914595

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.