ArticleEClinicalMedicine2024
Estimating the return on investment of selected infection prevention and control interventions in healthcare settings for preparing against novel respiratory viruses: modelling the experience from SARS-CoV-2 among health workers.
Article in EClinicalMedicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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
2 citing papers in PubMed, 5 citations in OpenAlex.
- Overcoming barriers to NHS adoption of innovative IPC products: A qualitative study of SMEs in the Liverpool city region.PloS one · 2025Article
- The role of economic evaluation in modelling public health and social measures for pandemic policy: a systematic review.Cost effectiveness and resource allocation : C/E · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
Abstract
Background: Insufficient infection prevention and control (IPC) practices in healthcare settings increase the SARS-CoV-2 infection risk among health workers. This study aimed to examine the level of preparedness for future outbreaks. Methods: We modelled the experience from the COVID-19 pandemic and assessed the return on investment on a global scale of three IPC interventions to prevent SARS-CoV-2 infections among health workers: enhancing hand hygiene; increasing access to personal protective equipment (PPE); and combining PPE, with a scale-up of IPC training and education (PPE+). Our analysis covered seven geographic regions, representing a combination of World Health Organization (WHO) regions and the Organisation for Economic Co-operation and Development (OECD) countries. Across all regions, we focused on the first 180 days of the pandemic in 2020 between January 1st and June 30th. We used an extended version of a susceptible-infectious-recovered compartmental model to measure the level of IPC preparedness. Data were sourced from the WHO COVID-19 Detailed Surveillance Database. Findings: In all regions, the PPE + intervention would have averted the highest number of new SARS-CoV-2 infections compared to the other two interventions, ranging from 6562 (95% CI 4873-8779) to 38,170 (95% CI 33,853-41,901) new infections per 100,000 health workers in OECD countries and in the South-East Asia region, respectively. Countries in the South-East Asia region and non-OECD countries in the Western Pacific region were poised to achieve the highest level of savings by scaling up the PPE + intervention. Interpretation: Our results not only support efforts to make an economic case for continuing investments in IPC interventions to halt the COVID-19 pandemic and protect health workers, but could also contribute to efforts to improve preparedness for future outbreaks. Funding: This work was funded by WHO, with support by the German Federal Ministry of Health for the WHOResearch and Development Blueprint for COVID-19.
Indexed as
Identifiers
What OpenQuestion holds
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.