Evidence map›Paper›PMID 33886605›Full record

ArticlePloS one2021

Preventing COVID-19 spread in closed facilities by regular testing of employees-An efficient intervention in long-term care facilities and prisons?

Henri Christian Junior Tsoungui Obama, Nessma Adil Mahmoud Yousif, Looli Alawam Nemer, Pierre Marie Ngougoue Ngougoue, Gideon Akumah Ngwa, Miranda Teboh-Ewungkem, Kristan Alexander Schneider

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 3 pooled it
3.3field-weighted citation impact, top 7% 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

18 citing papers in PubMed, 3 syntheses or guidelines pooled it, 41 citations in OpenAlex.

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  10. Rates of SARS-CoV-2 transmission between and into California state prisons.medRxiv : the preprint server for health sciences · 2023
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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

7 authors at 3 institutions in 3 countries.

Henri Christian Junior Tsoungui ObamaDepartment of Applied Computer- and Biosciences, University of Applied Sciences Mittweida, Mittweida, Germany.
Nessma Adil Mahmoud YousifDepartment of Applied Computer- and Biosciences, University of Applied Sciences Mittweida, Mittweida, Germany.
Looli Alawam NemerAfrican Institute for Mathematical Sciences Cameroon, Limbe, Cameroon.
Pierre Marie Ngougoue NgougoueDepartment of Applied Computer- and Biosciences, University of Applied Sciences Mittweida, Mittweida, Germany.
Gideon Akumah NgwaDepartment of Applied Computer- and Biosciences, University of Applied Sciences Mittweida, Mittweida, Germany.
Miranda Teboh-EwungkemDepartment of Mathematics, Lehigh University, Bethlehem, Pennsylvania, United States of America.ORCID 0000-0002-0765-4969
Kristan Alexander SchneiderDepartment of Applied Computer- and Biosciences, University of Applied Sciences Mittweida, Mittweida, Germany.ORCID 0000-0003-4138-1180
Hochschule Mittweida · DEAfrican Institute for Mathematical Sciences · CMLehigh University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDifferent levels of control measures were introduced to contain the global COVID-19 pandemic, many of which have been controversial, particularly the comprehensive use of diagnostic tests. Regular testing of high-risk individuals (pre-existing conditions, older than 60 years of age) has been suggested by public health authorities. The WHO suggested the use of routine screening of residents, employees, and visitors of long-term care facilities (LTCF) to protect the resident risk group. Similar suggestions have been made by the WHO for other closed facilities including incarceration facilities (e.g., prisons or jails), wherein parts of the U.S., accelerated release of approved inmates is taken as a measure to mitigate COVID-19. METHODS AND

findingsHere, the simulation model underlying the pandemic preparedness tool CovidSim 1.1 (http://covidsim.eu/) is extended to investigate the effect of regularly testing of employees to protect immobile resident risk groups in closed facilities. The reduction in the number of infections and deaths within the risk group is investigated. Our simulations are adjusted to reflect the situation of LTCFs in Germany, and incarceration facilities in the U.S. COVID-19 spreads in closed facilities due to contact with infected employees even under strict confinement of visitors in a pandemic scenario without targeted protective measures. Testing is only effective in conjunction with targeted contact reduction between the closed facility and the outside world-and will be most inefficient under strategies aiming for herd immunity. The frequency of testing, the quality of tests, and the waiting time for obtaining test results have noticeable effects. The exact reduction in the number of cases depends on disease prevalence in the population and the levels of contact reductions. Testing every 5 days with a good quality test and a processing time of 24 hours can lead up to a 40% reduction in the number of infections. However, the effects of testing vary substantially among types of closed facilities and can even be counterproductive in U.S. IFs.

conclusionsThe introduction of COVID-19 in closed facilities is unavoidable without a thorough screening of persons that can introduce the disease into the facility. Regular testing of employees in closed facilities can contribute to reducing the number of infections there, but is only meaningful as an accompanying measure, whose economic benefit needs to be assessed carefully.

Indexed as

Nursing HomesPrisonsCOVID-19COVID-19 TestingHumansLong-Term CareMass ScreeningSARS-CoV-2

Identifiers

PMID33886605
PMCPMC8062045
OpenAlexW3154613385

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.