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Journal Article Epublish 3 citations

PubMed: 37928455 DOI: 10.3389/fmed.2023.1166074

Evaluating the impact of testing strategies for the detection of nosocomial COVID-19 in English hospitals through data-driven modeling.

Front Med (Lausanne) 10:1166074

Authors: Stephanie Evans, James Stimson, Diane Pople, Mark H Wilcox, Russell Hope, Julie V Robotham

Affiliations

  • HCAI, Fungal, AMR, AMU and Sepsis Division, UK Health Security Agency, London, United Kingdom.
  • Statistics, Modelling and Economics, UK Health Security Agency, London, United Kingdom.
  • NIHR Health Protection Research Unit in Modelling and Health Economics at Imperial College London in Partnership With UKHSA and the London School of Hygiene and Tropical Medicine, London, United Kingdom.
  • Healthcare-Associated Infections Research Group, Leeds Institute of Medical Research, University of Leeds, Leeds, United Kingdom.
  • Microbiology, Leeds Teaching Hospitals, Leeds, United Kingdom.
  • NIHR Health Protection Research Unit in Healthcare-Associated Infections and Antimicrobial Resistance at University of Oxford in Partnership with UKHSA, Oxford, United Kingdom.

Abstract

Introduction

During the first wave of the COVID-19 pandemic 293,204 inpatients in England tested positive for SARS-CoV-2. It is estimated that 1% of these cases were hospital-associated using European centre for disease prevention and control (ECDC) and Public Health England (PHE) definitions. Guidelines for preventing the spread of SARS-CoV-2 in hospitals have developed over time but the effectiveness and efficiency of testing strategies for preventing nosocomial transmission has not been explored.

Methods

Using an individual-based model, parameterised using multiple datasets, we simulated the transmission of SARS-CoV-2 to patients and healthcare workers between March and August 2020 and evaluated the efficacy of different testing strategies. These strategies were: 0) Testing only symptomatic patients on admission; 1) Testing all patients on admission; 2) Testing all patients on admission and again between days 5 and 7, and 3) Testing all patients on admission, and again at days 3, and 5-7. In addition to admissions testing, patients that develop a symptomatic infection while in hospital were tested under all strategies. We evaluated the impact of testing strategy, test characteristics and hospital-related factors on the number of nosocomial patient infections.

Results

Modelling suggests that 84.6% (95% CI: 84.3, 84.7) of community-acquired and 40.8% (40.3, 41.3) of hospital-associated SARS-CoV-2 infections are detectable before a patient is discharged from hospital. Testing all patients on admission and retesting after 3 or 5 days increases the proportion of nosocomial cases detected by 9.2%. Adding discharge testing increases detection by a further 1.5% (relative increase). Increasing occupancy rates, number of beds per bay, or the proportion of admissions wrongly suspected of having COVID-19 on admission and therefore incorrectly cohorted with COVID-19 patients, increases the rate of nosocomial transmission. Over 30,000 patients in England could have been discharged while incubating a non-detected SARS-CoV-2 infection during the first wave of the COVID-19 pandemic, of which 3.3% could have been identified by discharge screening. There was no significant difference in the rates of nosocomial transmission between testing strategies or when the turnaround time of the test was increased.

Discussion

This study provides insight into the efficacy of testing strategies in a period unbiased by vaccines and variants. The findings are relevant as testing programs for SARS-CoV-2 are scaled back, and possibly if a new vaccine escaping variant emerges.

MeSH

Infections, Research Design, Vaccines, Pandemics, Covid-19, Sars-Cov-2

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