Journal Article Epublish 5 citations
PubMed: 38191324 DOI: 10.1186/s12879-023-08948-9
The effectiveness and efficiency of asymptomatic SARS-CoV-2 testing strategies for patient and healthcare workers within acute NHS hospitals during an omicron-like period.
BMC Infect Dis 24(1):64
Authors: Stephanie Evans, Nichola R Naylor, Tom Fowler, Susan Hopkins, Julie Robotham
Affiliations
- Clinical and Public Health Group, UK Health Security Agency, London, UK.
- Data, Analytics and Surveillance Group, UK Health Security Agency, London, UK.
- Clinical and Public Health Group, UK Health Security Agency, London, UK.
- Data, Analytics and Surveillance Group, UK Health Security Agency, London, UK.
- Clinical and Public Health Group, UK Health Security Agency, London, UK.
- William Harvey Research Institute, Queen Mary University of London, London, UK.
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford in partnership with the UK Health Security Agency, Oxford, UK.
- Data, Analytics and Surveillance Group, UK Health Security Agency, London, UK.
Abstract
BACKGROUND
Asymptomatic SARS-CoV-2 testing of hospitalised patients began in April-2020, with twice weekly healthcare worker (HCW) testing introduced in November-2020. Guidance recommending asymptomatic testing was withdrawn in August-2022. Assessing the impact of this decision from data alone is challenging due to concurrent changes in infection prevention and control practices, community transmission rates, and a reduction in ascertainment rate from reduced testing. Computational modelling is an effective tool for estimating the impact of this change.
METHODS
Using a computational model of SARS-CoV-2 transmission in an English hospital we estimate the effectiveness of several asymptomatic testing strategies, namely; (1) Symptomatic testing of patients and HCWs, (2) testing of all patients on admission with/without repeat testing on days 3 and 5-7, and (3) symptomatic testing plus twice weekly asymptomatic HCW testing with 70% compliance. We estimate the number of patient and HCW infections, HCW absences, number of tests, and tests per case averted or absence avoided, with differing community prevalence rates over a 12-week period.
RESULTS
Testing asymptomatic patients on admission reduces the rate of nosocomial SARS-CoV-2 infection by 8.1-21.5%. Additional testing at days 3 and 5-7 post admission does not significantly reduce infection rates. Twice weekly asymptomatic HCW testing can reduce the proportion of HCWs infected by 1.0-4.4% and monthly absences by 0.4-0.8%. Testing asymptomatic patients repeatedly requires up to 5.5Â million patient tests over the period, and twice weekly asymptomatic HCW testing increases the total tests to almost 30Â million. The most efficient patient testing strategy (in terms of tests required to prevent a single patient infection) was testing asymptomatic patients on admission across all prevalence levels. The least efficient was repeated testing of patients with twice weekly asymptomatic HCW testing in a low prevalence scenario, and in all other prevalence levels symptomatic patient testing with regular HCW testing was least efficient.
CONCLUSIONS
Testing patients on admission can reduce the rate of nosocomial SARS-CoV-2 infection but there is little benefit of additional post-admission testing. Asymptomatic HCW testing has little incremental benefit for reducing patient cases at low prevalence but has a potential role at higher prevalence or with low community transmission. A full health-economic evaluation is required to determine the cost-effectiveness of these strategies.
MeSH
Research Design, Sars-Cov-2, Absenteeism, Prevalence, Health Personnel, Severe Acute Respiratory Syndrome-Related Coronavirus, Covid-19 Testing, Communicable Diseases, Infections
Cited by
- Comparing the effectiveness of universal admission testing and risk-based testing at emergency admission for preventing nosocomial COVID-19: a multicenter retrospective cohort study in Japan. Infect Control Hosp Epidemiol, 2024
- Early detection of disease outbreaks and non-outbreaks using incidence data: A framework using feature-based time series classification and machine learning. PLoS Comput Biol, 2025
- Acceptance of SARS-CoV-2 Surveillance Testing Among Patients Receiving Dialysis: A Cluster Randomized Trial. JAMA Netw Open, 2024