Evidence map›Paper›PMID 38714946›Full record

ArticleBMC infectious diseases2024

Impact of interventions to reduce nosocomial transmission of SARS-CoV-2 in English NHS Trusts: a computational modelling study.

Stephanie Evans, James Stimson, Diane Pople, Peter J White, Mark H Wilcox, Julie V Robotham

Abstract read
In one paragraph

Article in BMC infectious diseases, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

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  4. An analysis of spatial and temporal uncertainty propagation in agent-based models.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025
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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

6 authors.

Stephanie EvansFungal, HCAI, AMU & Sepsis Division, UK Health Security Agency, London, AMR, UK. stephanie.evans@ukhsa.gov.uk.
James StimsonFungal, HCAI, AMU & Sepsis Division, UK Health Security Agency, London, AMR, UK.
Diane PopleFungal, HCAI, AMU & Sepsis Division, UK Health Security Agency, London, AMR, UK.
Peter J WhiteStatistics, Modelling and Economics, UK Health Security Agency, London, UK.
Mark H WilcoxHealthcare-Associated Infections Research Group, Leeds Institute of Medical Research, University of Leeds, Leeds, LS1 9JT, UK.
Julie V RobothamFungal, HCAI, AMU & Sepsis Division, UK Health Security Agency, London, AMR, UK.

Funding

Medical Research Council MR/R015600/1Medical Research Council (MRC) Centre for Global Infectious Disease Analysis MR/X020258/1National Institute for Health and Care Research (NIHR) Health Protection Research Unit (HPRU) in Modelling and Health Economics NIHR200908National Institute for Health and Care Research (NIHR) Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance NIHR200915
6 · The paper itself

Abstract

backgroundPrior to September 2021, 55,000-90,000 hospital inpatients in England were identified as having a potentially nosocomial SARS-CoV-2 infection. This includes cases that were likely missed due to pauci- or asymptomatic infection. Further, high numbers of healthcare workers (HCWs) are thought to have been infected, and there is evidence that some of these cases may also have been nosocomially linked, with both HCW to HCW and patient to HCW transmission being reported. From the start of the SARS-CoV-2 pandemic interventions in hospitals such as testing patients on admission and universal mask wearing were introduced to stop spread within and between patient and HCW populations, the effectiveness of which are largely unknown. MATERIALS/

methodsUsing an individual-based model of within-hospital transmission, we estimated the contribution of individual interventions (together and in combination) to the effectiveness of the overall package of interventions implemented in English hospitals during the COVID-19 pandemic. A panel of experts in infection prevention and control informed intervention choice and helped ensure the model reflected implementation in practice. Model parameters and associated uncertainty were derived using national and local data, literature review and formal elicitation of expert opinion. We simulated scenarios to explore how many nosocomial infections might have been seen in patients and HCWs if interventions had not been implemented. We simulated the time period from March-2020 to July-2022 encompassing different strains and multiple doses of vaccination.

resultsModelling results suggest that in a scenario without inpatient testing, infection prevention and control measures, and reductions in occupancy and visitors, the number of patients developing a nosocomial SARS-CoV-2 infection could have been twice as high over the course of the pandemic, and over 600,000 HCWs could have been infected in the first wave alone. Isolation of symptomatic HCWs and universal masking by HCWs were the most effective interventions for preventing infections in both patient and HCW populations. Model findings suggest that collectively the interventions introduced over the SARS-CoV-2 pandemic in England averted 400,000 (240,000 - 500,000) infections in inpatients and 410,000 (370,000 - 450,000) HCW infections.

conclusionsInterventions to reduce the spread of nosocomial infections have varying impact, but the package of interventions implemented in England significantly reduced nosocomial transmission to both patients and HCWs over the SARS-CoV-2 pandemic.

Indexed as

COVID-19Cross InfectionHealth PersonnelSARS-CoV-2Computer SimulationEnglandHumansInfection ControlMasksState MedicineAgent-based modelHealthcare-associated infectionIndividual-based modelInfection prevention and controlIPCMasking

Identifiers

PMID38714946
PMCPMC11075183

What Socratic 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 Socratic graph.