ArticleBMJ open2021
Evaluating the contributions of strategies to prevent SARS-CoV-2 transmission in the healthcare setting: a modelling study.
Article in BMJ open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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.
Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.
- COVID-19 Prevention and Control Measures in Workplace Settings: A Rapid Review and Meta-Analysis.International journal of environmental research and public health · 2021Pooled it
- Minimising the duration of N95 respirator use during hospital SARS-CoV-2 outbreaks: A mixed-effects analysis of post-screening infection reduction.Infection prevention in practice · 2025Article
- Assessing the Reasons and Adverse Effects of Self-Medication in the Context of the COVID-19 Pandemic in Sierra Leone. A Case Study of Moriba Town Section.Risk management and healthcare policy · 2024Article
- Knowledge, attitudes and practices of Iranian people about food safety and hygiene during covid-19 pandemic.BMC public health · 2022Article
- Risk stratification by anamnesis increases SARS-CoV-2 test efficiency in cancer patients.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2022Article
- Interventions to control nosocomial transmission of SARS-CoV-2: a modelling study.BMC medicine · 2021Article
Corrections and comments
- Update of
Authors and funding
4 authors at 3 institutions in 3 countries.
Funding
Abstract
introductionSince its onset, the COVID-19 pandemic has caused significant morbidity and mortality worldwide, with particularly severe outcomes in healthcare institutions and congregate settings. To mitigate spread, healthcare systems have been cohorting patients to limit contacts between uninfected patients and potentially infected patients or healthcare workers (HCWs). A major challenge in managing the pandemic is the presence of currently asymptomatic/presymptomatic individuals capable of transmitting the virus, who could introduce COVID-19 into uninfected cohorts. The optimal combination of personal protective equipment (PPE), testing and other approaches to prevent these events is unclear, especially in light of ongoing limited resources.
methodsUsing stochastic simulations with a susceptible-exposed-infected-recovered dynamic model, we quantified and compared the impacts of PPE use, patient and HCWs surveillance testing and subcohorting strategies.
resultsIn the base case without testing or PPE, the healthcare system was rapidly overwhelmed, and became a net contributor to the force of infection. We found that effective use of PPE by both HCWs and patients could prevent this scenario, while random testing of apparently asymptomatic/presymptomatic individuals on a weekly basis was less effective. We also found that even imperfect use of PPE could provide substantial protection by decreasing the force of infection. Importantly, we found that creating smaller patient/HCW-interaction subcohorts can provide additional resilience to outbreak development with limited resources.
conclusionThese findings reinforce the importance of ensuring adequate PPE supplies even in the absence of testing and provide support for strict subcohorting regimens to reduce outbreak potential in healthcare institutions.
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Registered trials
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.