Evidence map›Paper›PMID 34518835›Full record

ArticleResearch square2021

Optimal strategies to screen health care workers for COVID-19 in the US: a cost-effectiveness analysis.

Sigal Maya, Guntas Padda, Victoria Close, Trevor Wilson, Fareeda Ahmed, Elliot Marseille, James G Kahn

Abstract readPreprint
In one paragraph

Article in Research square, 2021. 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
–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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Sigal MayaUniversity of California San Francisco.
Guntas PaddaUniversity of California San Francisco.
Victoria CloseUniversity of Vermont College of Medicine.
Trevor WilsonSan Joaquin General Hospital.
Fareeda AhmedStanford University Graduate School of Business.
Elliot MarseilleHealth Strategies International.
James G KahnUniversity of California San Francisco.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTransmission of SARS-CoV-2 in health care facilities poses a challenge against pandemic control. Health care workers (HCWs) have frequent and high-risk interactions with COVID-19 patients. We undertook a cost-effectiveness analysis to determine optimal testing strategies for screening HCWs to inform strategic decision-making in health care settings.

methodsWe modeled the number of new infections, quality-adjusted life years lost, and net costs related to six testing strategies including no tests. We applied our model to four strata of HCWs, defined by the presence and timing of symptoms. We conducted sensitivity analyses to account for uncertainty in inputs.

resultsWhen screening recently symptomatic HCWs, conducting only a PCR test is preferable; it saves costs and improves health outcomes in the first week post-symptom onset, and costs $83,000 per quality-adjusted life year gained in the second week post-symptom onset. When screening HCWs in the late clinical disease stage, none of the testing approaches is cost-effective and thus no testing is preferable, yielding $11 and 0.003 new infections per 10 HCWs. For screening asymptomatic HCWs, antigen testing is preferable to PCR testing due to its lower cost.

conclusionsBoth PCR and antigen testing are beneficial strategies to identify infected HCWs and reduce transmission of SARS-CoV-2 in health care settings. IgG testing clinical value depends on test timing and immunity characteristics, however is not cost-effective in a low prevalence setting. As the context of the pandemic evolves, our study provides insight to health-care decision makers to keep the health care workforce safe and transmissions low.

Identifiers

PMID34518835
PMCPMC8437316

What OpenQuestion holds

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LicenceCC BY
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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.