Evidence map›Paper›PMID 36797293›Full record

ArticleScientific reports2023

Optimized workplace risk mitigation measures for SARS-CoV-2 in 2022.

Rowland Pettit, Bo Peng, Patrick Yu, Peter G Matos, Alexander L Greninger, Julie McCashin, Christopher Ian Amos

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.3field-weighted citation impact, top 41% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. 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

7 authors at 5 institutions in 2 countries.

Rowland Pettit *Institute for Clinical and Translational Research, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA.
Bo Peng *Institute for Clinical and Translational Research, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA.
Patrick YuCorporate Medical Advisors, Houston, TX, USA.
Peter G MatosCorporate Medical Advisors, Houston, TX, USA.
Alexander L GreningerDepartment of Laboratory Medicine and Pathology, University of Washington, Seattle, USA.
Julie McCashinInternational S.O.S., Houston, TX, USA.
Christopher Ian AmosInstitute for Clinical and Translational Research, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA. chrisa@bcm.edu.ORCID 0000-0002-8540-7023
Baylor College of Medicine · USHealthcentric Advisors · USSt. John's School · USUniversity of British Columbia · CAUniversity of Washington · US

Funding

Translating Molecular and Clinical Data to Population Lung Cancer Risk AssessmentU19CA203654 · NCI · UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR · PI Christopher I. Amos, Rayjean J. Hung · 2017 to 2026
$23.7M
Training in Precision Environmental Health SciencesT32ES027801 · NIEHS · BAYLOR COLLEGE OF MEDICINE · PI Cheryl L. Walker · 2018 to 2026
$2.8M
NCI NIH HHS U19 CA203654NIEHS NIH HHS T32 ES027801
6 · The paper itself

Abstract

596 million SARS-CoV-2 cases have been reported and over 12 billion vaccine doses have been administered. As vaccination rates increase, a gap in knowledge exists regarding appropriate thresholds for escalation and de-escalation of workplace COVID-19 preventative measures. We conducted 133,056 simulation experiments, evaluating the spread of SARS-CoV-2 virus in hypothesized working environments subject to COVID-19 infections from the community. We tested the rates of workplace-acquired infections based on applied isolation strategies, community infection rates, methods and scales of testing, non-pharmaceutical interventions, variant predominance, vaccination coverages, and vaccination efficacies. When 75% of a workforce is vaccinated with a 70% efficacious vaccine against infection, then no masking or routine testing + isolation strategies are needed to prevent workplace-acquired omicron variant infections when the community infection rate per 100,000 persons is ≤ 1. A CIR ≤ 30, and ≤ 120 would result in no workplace-acquired infections in this same scenario against the delta and alpha variants, respectively. Workforces with 100% worker vaccination can prevent workplace-acquired infections with higher community infection rates. Identifying and isolating workers with antigen-based SARS-CoV-2 testing methods results in the same or fewer workplace-acquired infections than testing with slower turnaround time polymerase chain reaction methods. Risk migration measures such as mask-wearing, testing, and isolation can be relaxed, or escalated, in commensurate with levels of community infections, workforce immunization, and risk tolerance. The interactive heatmap we provide can be used for immediate, parameter-based case count predictions to inform institutional policy making. The simulation approach we have described can be further used for future evaluation of strategies to mitigate COVID-19 spread.

Indexed as

COVID-19SARS-CoV-2COVID-19 TestingHumansWorkplace

Identifiers

PMID36797293
PMCPMC9933026
OpenAlexW4321107949

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.