Effectiveness of rapid SARS-CoV-2 genome sequencing in supporting infection control for hospital-onset COVID-19 infection: Multicentre, prospective study

  1. Oliver Stirrup
  2. James Blackstone
  3. Fiona Mapp
  4. Alyson MacNeil
  5. Monica Panca
  6. Alison Holmes
  7. Nicholas Machin
  8. Gee Yen Shin
  9. Tabitha Mahungu
  10. Kordo Saeed
  11. Tranprit Saluja
  12. Yusri Taha
  13. Nikunj Mahida
  14. Cassie Pope
  15. Anu Chawla
  16. Maria-Teresa Cutino-Moguel
  17. Asif Tamuri
  18. Rachel Williams
  19. Alistair Darby
  20. David L Robertson
  21. Flavia Flaviani
  22. Eleni Nastouli
  23. Samuel Robson
  24. Darren Smith
  25. Matthew Loose
  26. Kenneth Laing
  27. Irene Monahan
  28. Beatrix Kele
  29. Sam Haldenby
  30. Ryan George
  31. Matthew Bashton
  32. Adam A Witney
  33. Matthew Byott
  34. Francesc Coll
  35. Michael Chapman
  36. Sharon J Peacock
  37. COG-UK HOCI Investigators
  38. The COVID-19 Genomics UK (COG-UK) consortium
  39. Joseph Hughes
  40. Gaia Nebbia
  41. David G Partridge
  42. Matthew Parker
  43. James Richard Price
  44. Christine Peters
  45. Sunando Roy
  46. Luke B Snell
  47. Thushan I de Silva
  48. Emma Thomson
  49. Paul Flowers
  50. Andrew Copas
  51. Judith Breuer  Is a corresponding author
  1. Institute for Global Health, University College London, United Kingdom
  2. The Comprehensive Clinical Trials Unit, University College London, United Kingdom
  3. Imperial College Healthcare NHS Trust, United Kingdom
  4. Manchester University NHS Foundation Trust, United Kingdom
  5. University College London Hospitals NHS Foundation Trust, United Kingdom
  6. Royal Free London NHS Foundation Trust, United Kingdom
  7. University Hospital Southampton NHS Foundation Trust, United Kingdom
  8. Sandwell & West Birmingham Hospitals NHS Trust, United Kingdom
  9. Department of Virology and Infectious Diseases, Newcastle-upon-Tyne Hospitals NHS Foundation Trust, United Kingdom
  10. Nottingham University Hospitals NHS Trust, United Kingdom
  11. St George’s University Hospitals NHS Foundation Trust, United Kingdom
  12. Liverpool University Hospitals NHS Foundation Trust, United Kingdom
  13. Barts Health NHS Trust, United Kingdom
  14. Research Computing, University College London, United Kingdom
  15. Department of Genetics and Genomic Medicine, UCL Great Ormond Street Institute of Child Health, University College London, United Kingdom
  16. Centre for Genomic Research, University of Liverpool, United Kingdom
  17. MRC-University of Glasgow Centre For Virus Research, University of Glasgow, United Kingdom
  18. Guy's and St Thomas' Hospital NHS Foundation Trust, United Kingdom
  19. Centre for Enzyme Innovation and School of Pharmacy and Biomedical Science, University of Portsmouth, United Kingdom
  20. Department of Applied Sciences, Northumbria University, United Kingdom
  21. School of Life Sciences, University of Nottingham, United Kingdom
  22. Institute for Infection and Immunity, St George’s University of London, United Kingdom
  23. The Hub for Biotechnology in the Built Environment, Department of Applied Sciences, Northumbria University, United Kingdom
  24. Department of Infection Biology, Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, United Kingdom
  25. Health Data Research UK Cambridge Hub, United Kingdom
  26. Department of Medicine, University of Cambridge, United Kingdom
  27. Sheffield Teaching Hospitals NHS Foundation Trust, United Kingdom
  28. Sheffield Bioinformatics Core, University of Sheffield, United Kingdom
  29. NHS Greater Glasgow and Clyde, United Kingdom
  30. Department of Infection, Immunity and Inflammation, UCL Great Ormond Street Institute of Child Health, University College London, United Kingdom
  31. Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, United Kingdom
  32. School of Psychological Sciences and Health, University of Strathclyde, United Kingdom

Abstract

Background:

Viral sequencing of SARS-CoV-2 has been used for outbreak investigation, but there is limited evidence supporting routine use for infection prevention and control (IPC) within hospital settings.

Methods:

We conducted a prospective non-randomised trial of sequencing at 14 acute UK hospital trusts. Sites each had a 4-week baseline data collection period, followed by intervention periods comprising 8 weeks of ‘rapid’ (<48 hr) and 4 weeks of ‘longer-turnaround’ (5–10 days) sequencing using a sequence reporting tool (SRT). Data were collected on all hospital-onset COVID-19 infections (HOCIs; detected ≥48 hr from admission). The impact of the sequencing intervention on IPC knowledge and actions, and on the incidence of probable/definite hospital-acquired infections (HAIs), was evaluated.

Results:

A total of 2170 HOCI cases were recorded from October 2020 to April 2021, corresponding to a period of extreme strain on the health service, with sequence reports returned for 650/1320 (49.2%) during intervention phases. We did not detect a statistically significant change in weekly incidence of HAIs in longer-turnaround (incidence rate ratio 1.60, 95% CI 0.85–3.01; p=0.14) or rapid (0.85, 0.48–1.50; p=0.54) intervention phases compared to baseline phase. However, IPC practice was changed in 7.8 and 7.4% of all HOCI cases in rapid and longer-turnaround phases, respectively, and 17.2 and 11.6% of cases where the report was returned. In a ‘per-protocol’ sensitivity analysis, there was an impact on IPC actions in 20.7% of HOCI cases when the SRT report was returned within 5 days. Capacity to respond effectively to insights from sequencing was breached in most sites by the volume of cases and limited resources.

Conclusions:

While we did not demonstrate a direct impact of sequencing on the incidence of nosocomial transmission, our results suggest that sequencing can inform IPC response to HOCIs, particularly when returned within 5 days.

Funding:

COG-UK is supported by funding from the Medical Research Council (MRC) part of UK Research & Innovation (UKRI), the National Institute of Health Research (NIHR) (grant code: MC_PC_19027), and Genome Research Limited, operating as the Wellcome Sanger Institute.

Clinical trial number:

NCT04405934.

Editor's evaluation

This article contains valuable information on the potential value of real-time genome sequencing to inform infection control practices. The study, unique in its size, addresses the implementation of this approach during the height of the COVID-19 pandemic. Naturally, the extreme situation limited the options for choices in infection control practices.

https://doi.org/10.7554/eLife.78427.sa0

Introduction

Viral sequencing has played an important role in developing our understanding of the emergence and evolution of the SARS-CoV-2 pandemic (Oude Munnink et al., 2021). Sequencing technologies can now be used for local outbreak investigation in near real time, and this was implemented by some research centres for evaluation of nosocomial transmission from the early stages of the pandemic (Meredith et al., 2020). It has been demonstrated that sequencing can provide additional information on outbreak characteristics and transmission routes in comparison to traditional epidemiological investigation alone (Meredith et al., 2020; Lucey et al., 2021; Snell et al., 2022b). However, limited data are available on the feasibility of routine use of sequencing for infection prevention and control (IPC), or on its direct impact on IPC actions and nosocomial transmission rates.

Throughout the pandemic, nosocomial transmission of SARS-CoV-2 has been a major concern (Abbas et al., 2021), with hospital-acquired infections (HAIs) accounting for more than 5% of lab-confirmed cases from March to August 2020 in the UK (Bhattacharya et al., 2021) and representing 11% of COVID-19 cases within hospitals in this period (Read et al., 2021). HAIs also frequently occur within a very vulnerable population with high levels of mortality (Bhattacharya et al., 2021; Oliver, 2021b; Ponsford et al., 2021). There is therefore an unmet need to develop interventions that can reduce the occurrence of nosocomial transmission. The aims of this study were to determine the effectiveness of SARS-CoV-2 sequencing in informing acute IPC actions and reducing the incidence of HAIs when used prospectively in routine practice, and to record the impact of sequencing reports on the actions of IPC teams.

When this study was planned in the summer of 2020, there was imperfect knowledge regarding the dominant mode of transmission of SARS-CoV-2 (Greenhalgh et al., 2021), and it was not possible to predict the future course of the pandemic. In conducting this study, substantial difficulties were encountered in implementing the intervention and in responding effectively to any insights generated. As such, this report serves as a record of the challenge of conducting research within a pandemic as well as being a conventional study summary report.

Methods

We conducted a prospective multiphase non-randomised trial to evaluate the implementation and impact of SARS-CoV-2 sequencing for IPC within 14 acute NHS hospital groups in the UK. All sites were linked to a COG-UK sequencing hub, 13 were university hospitals and 1 a district general hospital. We implemented a bespoke sequence reporting tool (SRT) intervention, developed and previously evaluated for this study (Stirrup et al., 2021), and assessed the importance of turnaround time for sequencing and reporting. The study included integral health economic and qualitative process evaluation (Flowers et al., 2021).

The study design comprised a planned 4-week baseline data collection period, followed by intervention periods defined by the time from diagnostic sampling to return of sequence data to IPC teams, comprising 8 weeks of ‘rapid’ (<48 hr) turnaround sequencing and 4 weeks of ‘longer’ (5–10 days) turnaround sequencing for each site. Target turnaround time was 48 hr from diagnostic sampling to return of the SRT report during the ‘rapid’ sequencing phase, and 5–10 days for the ‘longer-turnaround’ phase. Eight sites implemented ‘rapid’ followed by ‘longer-turnaround’ phases with five doing the opposite. One site did not implement longer-turnaround sequencing because they considered it a reduction in their standard practice, comprising outbreak sequencing with weekly meetings to discuss phylogenetic analyses; they nonetheless completed the baseline phase of the study without use of the SRT or automated feedback to IPC teams on all hospital-onset COVID-19 infection (HOCI) cases. The order of the intervention phases was pragmatically determined in some sites by the need to first run the ‘longer-turnaround’ phase to develop sample transport and sequencing procedures before attempting the ‘rapid’ sequencing phase, and the ordering was decided in the remaining sites to ensure a mixture of intervention phases over calendar time – there was no randomisation process in deciding the order of study phases.

Data were recorded in all phases for all patients meeting the definition of a HOCI, that is, first confirmed test for SARS-CoV-2 >48 hr after admission and without suspicion of COVID-19 at the time of admission. During the intervention phases, and for at least 3 weeks prior to any intervention period to enable linkage to recent cases, participating sites aimed to sequence all SARS-CoV-2 cases including both HOCI and non-HOCI cases.

The SRT aimed to integrate sequence and patient data to produce concise and immediately interpretable feedback about cases to IPC teams via a one-page report. Sites were also able to apply other methods (e.g. phylogenetics) to the sequence data generated, where this was part of their usual practice. Guidance regarding IPC actions was not specified as part of this study. Sites were expected to follow current national guidelines, which evolved throughout the course of the pandemic. Sequencing data from healthcare workers (HCWs) could be utilised in the SRT system, and this was implemented by 8/14 sites. Whether this was done depended on availability of HCW samples for each lab as staff testing was generally managed separately to patient testing. HCW testing protocols followed national guidelines.

Data collection on patient characteristics and on implementation and impact of the intervention was conducted using a central study-specific database. Ethical approval for the study was granted by NHS HRA (REC 20/EE/0118), and the study was prospectively registered (ClinicalTrials.gov Identifier: NCT04405934).

The primary outcomes of the study as defined in the protocol (Blackstone et al., 2022) were (1) incidence of IPC-defined SARS-CoV-2 HAIs per week per 100 currently admitted non-COVID-19 inpatients, and (2) for each HOCI, identification of linkage to individuals within an outbreak of SARS-CoV-2 nosocomial transmission using sequencing data as interpreted through the SRT that was not identified by pre-sequencing IPC evaluation during intervention phases. The second outcome used all observed HOCI cases as the denominator, and so represented the proportion of cases in which sequencing provided information regarding potential transmission routes where none had been previously uncovered.

Secondary outcomes were (1) incidence of IPC-defined SARS-CoV-2 hospital outbreaks per week per 1000 non-COVID-19 inpatients; (2) for each HOCI, any change to IPC actions following receipt of SRT report during intervention phases; and (3) any recommended change to IPC actions (regardless of whether changes were implemented). There was considered to be an impact on IPC actions if this was recorded for any of a number of predefined outcomes (e.g. enhanced cleaning, visitor and staffing restrictions, provision of personal protective equipment), or if it was stated that the report had effected any change to IPC practice on that ward or elsewhere within the hospital. The proportion of HOCI cases for which IPC reported the SRT report to be ‘useful’ was added as a further outcome.

To support standardisation across sites, ‘IPC-defined SARS-CoV-2 HAIs’ were considered to be all HOCIs with ≥8 days from admission to symptom onset (if known) or sample date (i.e. UK Health Security Agency definition of a probable/definite HAI; Public Health England, 2020).

An IPC-defined SARS-CoV-2 hospital outbreak was defined as at least two HOCI cases on the same ward, with at least one having ≥8 days from admission to symptom onset or sample date. Outbreak events were considered to be concluded once there was a period of 28 days prior to observation of another HOCI (Public Health England, 2020).

Further details of outcome definitions are given in Appendix 1.

Statistical analysis

We used three approaches: intention-to-treat analysis to assess the overall impact of sequencing on IPC activity and the incidence of HAIs, per protocol site-based analysis on a subset of high-performance sites, and pooled analysis to describe how turnaround time was related to impact on IPC irrespective of study phase. Inclusion of sites in the per protocol analysis was based on the proportion of sequence reports returned and speed of return in the rapid phase. Thresholds to define this group were determined following review of the data but before analysis of outcomes.

Incidence outcomes were analysed using mixed effects negative binomial regression models, which in this context correspond to Poisson regression with an additional overdispersion parameter. Data for the first week of each intervention period, or in the first week of return to intervention following a break, were considered transition periods and not considered as direct evidence regarding the intervention effect. Analysis was conducted with calendar time divided into ‘study weeks’ running Monday–Sunday. Models were adjusted for calendar time, the proportion of current inpatients that were SARS-CoV-2 positive, as well as local community SARS-CoV-2 incidence for each study site, using 5 knot restricted cubic splines (Kahan et al., 2016). The number of inpatients not positive for SARS-CoV-2 was considered an exposure variable (defining ‘person-time’ at risk of nosocomial infection). Differences between study phases were evaluated using adjusted incidence rate ratios.

The primary outcome of identification of SARS-CoV-2 nosocomial transmission using sequencing data and secondary outcomes relating to changes to IPC actions and the ‘usefulness’ of SRT reports were analysed using mixed effects logistic regression models, without covariable adjustment or removal of cases from the first week of each intervention phase. Marginal proportions from fitted models were reported for rapid- and longer-turnaround intervention phases, and differences in outcomes between these phases were evaluated. If the SRT report was not returned, this was interpreted as a ‘failure’, that is, no change to IPC action; however, we also present percentages for these outcomes restricted to HOCIs where the SRT report was returned.

For both incidence and ‘per HOCI’ outcomes, we accounted for the structure of the data with hierarchical exchangeable normally distributed random effects for each study site, and for each study phase within each study site. Analyses were conducted using Stata V16, with figures generated using the ggplot2 package for R V4.0.

Results

A total of 2170 HOCIs were recorded for the study between 15 October 2020 and 26 April 2021. These cases had median age of 76.7 (interquartile range [IQR] 64.4–85.6) years, and 80% had at least one clinically significant comorbidity (Table 1).

Table 1
Demographic and baseline characteristics of the participants by study phase.
Characteristic at screeningStudy phaseTotal
BaselineLonger-turnaroundRapid
N HOCI cases8503739472170
N HOCI cases per site, median (range); N sites36 (1–207); 1419 (0–86); 1330.5 (4-297); 14103.5 (40-451); 14
HAI classification, n (%)
 Indeterminate (3–7 days)362 (42.6)166 (44.5)371 (39.2)899 (41.4)
 Probable (8–14 days)236 (27.8)121 (32.4)270 (28.5)627 (28.9)
 Definite (>14 days)252 (29.6)86 (23.1)306 (32.3)644 (29.7)
Age (years), median (IQR, range)77.5 (65.4–85.6, 0.4–100.5)77.6 (64.6–86.7, 0.7–100.7)76.4 (62.6–85.5, 0.6–103.5)76.7 (64.4–85.6, 0.4–103.5)
Age ≥70 years, n/N (%)589/850 (69.3)240/373 (64.3)598/947 (63.1)1427/2170 (65.8)
Sex at birth: female, n/N (%)457/850 (53.8)177/372 (47.6)460/947 (48.6)1094/2169 (50.4)
Ethnicity, n (%)
 White668 (78.6)275 (73.7)732 (77.3)1675 (77.2)
 Mixed ethnicity9 (1.1)6 (1.6)8 (0.8)23 (1.1)
 Asian46 (5.4)26 (7.0)34 (3.6)106 (4.9)
 Black Caribbean or African36 (4.2)18 (4.8)46 (4.9)100 (4.6)
 Other6 (0.7)1 (0.3)4 (0.4)11 (0.5)
 Unknown85 (10.0)47 (12.6)123 (13.0)255 (11.8)
Symptomatic at time of sampling, n/N (%)167/739 (22.6)58/322 (18.0)106/659 (16.1)331/1720 (19.2)
Significant comorbidity present, n/N (%)650/776 (83.8)260/323 (80.5)574/757 (75.8)1484/1856 (80.0)
Pregnant, n/N (%)6/451 (1.3)1/177 (0.6)4/445 (0.9)11/1073 (1.0)
Hospital admission route, n (%)
 Emergency department605 (71.2)258 (69.2)549 (58.0)1412 (65.1)
 Hospital transfer59 (6.9)21 (5.6)51 (5.4)131 (6.0)
 Care home3 (0.4)0 (0)0 (0)3 (0.1)
 GP referral38 (4.5)15 (4.0)76 (8.0)129 (5.9)
 Outpatient clinic ref.27 (3.2)20 (5.4)30 (3.2)77 (3.5)
 Other42 (4.9)9 (2.4)48 (5.1)99 (4.6)
 Unknown76 (8.9)50 (13.4)193 (20.4)319 (14.7)
  1. GP, general practitioner; HAI, hospital-acquired infection; HOCI, hospital-onset COVID-19 infection.

All 14 sites completed baseline and rapid sequencing intervention phases (Appendix 1—figure 1). Thirteen sites completed the longer-turnaround sequencing intervention phase. 49.2% (650/1320) SRT reports for HOCIs were returned in the intervention phases, with only 9.3% (123/1320) returned within the target time frames (Table 2). This figure was greater in the longer-turnaround phase at 21.2% (79/373) than in the rapid phase (4.6%; 44/947). The median turnaround time from diagnostic sampling for reports returned was 5 days in the rapid phase and 13 days in the longer-turnaround phase, substantially longer than the targets of 48 hr and 5–10 days, respectively. A detailed breakdown of reporting turnaround times is reported separately (Colton et al., 2022).

Table 2
Per hospital-onset COVID-19 infection (HOCI) implementation and outcome summary by study intervention phase, overall and within the 7/14 sites included in the ’per protocol’ sensitivity analysis.
All study sitesSensitivity analysis
Study phaseTotalStudy phase
Longer-turnaroundRapidLonger-turnaroundRapid
N HOCI cases3739471320143533
Implementation
Sequence returned within expected timeline, n (%)*229 (61.4)377 (39.8)606 (45.9)81 (56.6)204 (38.3)
Sequence returned within study period, n (%)*277 (74.3)596 (62.9)873 (66.1)98 (68.5)347 (65.1)
SRT report returned within target timeline (10 days for longer-turnaround, 2 days for rapid), n (%)79 (21.2)44 (4.6)123 (9.3)35 (24.5)44 (8.3)
SRT report returned within study period, n (%)215 (57.6)435 (45.9)650 (49.2)92 (64.3)317 (59.5)
Time from sample to report return (days), median (IQR, range) [n]13 (9–15, 0–36) [215]5 (3-11, 2-84) [430]9 (4-14, 0-84) [645]13 (9–17, 6–29) [92]4 (3-6, 2-64) [312]
Sequencing results
SRT-suggestive patient acquired infection post-admission, n/N (%)196/212 (92.5)384/423 (90.8)580/635 (91.3)85/92 (92.4)287/311 (92.3)
SRT-suggestive patient is part of ward outbreak, n/N (%)151/212 (71.2)260/423 (61.5)411/635 (64.7)65/92 (70.7)202/311 (65.0)
Linkage identified not suspected at initial IPC investigation:
 All HOCIs in phase n/N (%, 95% CI)24/348 (6.8, 1.7–11.8)46/915 (6.7, 2.0–11.3)70/1263 (5.5)11/139 (7.9, 3.4–12.4)39/512 (7.6, 5.3–9.9)
 When SRT returned n/N (%)24/190 (12.6)46/403 (11.4)70/593 (11.8)11/88 (12.5)39/296 (13.2)
SRT excluded IPC-identified hospital outbreak, n/N (%)14/213 (6.6)27/428 (6.3)41/641 (6.4)9/92 (9.8)25/310 (8.1)
Impact on IPC
SRT changed IPC practice:
 All HOCIs in phase n/N (%, 95% CI)25/373 (7.4, 1.1–13.6)74/941 (7.8, 2.4–13.2)99/1314 (7.5)1/143 (0.7, 0.0–2.1)52/527 (9.9, 7.3–12.4)
 When SRT returned n/N (%)25/215 (11.6)74/429 (17.2)99/644 (15.4)1/92 (1.1)52/311 (16.7)
SRT changed IPC practice for ward, n/N (%)13/215 (6.0)31/429 (7.2)44/644 (6.8)0/92 (0.0)28/311 (9.0)
SRT used in IPC decisions beyond ward, n/N (%)12/215 (5.6)45/428 (10.5)57/643 (8.9)1/92 (1.1)27/310 (8.7)
IPC team reported SRT to be useful, n/N (%)
 Yes107/215 (49.8)303/428 (70.8)410/643 (63.8)25/92 (27.2)245/310 (79.0)
 No67/215 (31.2)71/428 (16.6)138/643 (21.5)50/92 (54.3)57/310 (18.4)
 Unsure41/215 (19.1)54/428 (12.6)95/643 (14.8)17/92 (18.5)8/310 (2.6)
HCW absence on ward
Proportion of HCWs on sick leave due to COVID-19, median (IQR, range) [n]0.09 (0.00–0.15, 0.00–0.30) [49]0.13 (0.07–0.29, 0.00–1.00) [162]0.13 (0.04–0.27, 0.00–1.00) [321] 0.09 (0.00–0.15, 0.00–0.30) [49]0.13 (0.08–0.29, 0.00–1.00) [143]
  1. HCW, healthcare worker; HOCI, hospital-onset COVID-19 infection; IPC, infection prevention and control; IQR, interquartile range; SRT, sequence reporting tool.

  2. *

    As recorded by site, not based on recorded date or availability on central CLIMB server.

  3. Estimated marginal value from mixed effects model, not raw %, evaluated on intention-to-treat basis with lack of SRT report classified as ‘no’.

  4. Includes data for baseline phase: 0.13 (0.00–0.30, 0.00–0.88) [110].

Ordering the sites by proportion of cases with sequencing results returned and median turnaround time during the rapid phase (Figure 1) identified no obvious clustering of highest vs. lowest performing sites. We therefore also carried out a ’per protocol’ sensitivity analysis on the seven highest performing sites; these sites returned ≥40% of SRTs within a median time from diagnostic sample of ≤8 days within their rapid phase. The criteria for this analysis were decided after data collection but prior to data analysis, as per the statistical analysis plan (SAP). However, we acknowledge that the ‘higher performing sites’ did not meet the target turnaround time for reporting in the rapid phase; criteria were therefore set to split the sites into upper and lower 50% based on the level of implementation.

Plots of the median turnaround time against the percentage of hospital-onset COVID-19 infection (HOCI) cases with sequence reporting tool (SRT) reports returned for the rapid (left panel) and longer-turnaround (right panel) sequencing phases across the 14 study sites.

The size of each circle plotted indicates the number of HOCI cases observed within each phase for each site, with letter labels corresponding to study site. The criteria for inclusion in our sensitivity analysis are displayed as the green rectangle in the rapid phase plot, and sites on the longer-turnaround phase plot are colour-coded by their inclusion. In the rapid phase, SRT reports were returned for 0/4 HOCI cases recorded for site H. Site N did not have a longer-turnaround phase, Site A observed 0 HOCI cases, and sites D and E returned SRT reports for 0/1 and 0/2 HOCI cases, respectively, in this phase.

We did not detect a statistically significant change in weekly incidence of HAIs in the longer-turnaround (incidence rate ratio 1.60, 95% CI 0.85–3.01; p=0.14) or rapid (0.85, 0.48–1.50; 0.54) intervention phases in comparison to baseline phase across the 14 sites (Table 3), and incidence rate ratios were comparable in our ‘per protocol’ analysis. Similarly, there was only weak evidence for an effect of phase on incidence of outbreaks in both intention-to-treat and ‘per protocol’ analyses, with wide confidence intervals inclusive of no difference in incidence (Table 3).

Table 3
Incidence outcomes by study intervention phase, overall and within the 7/14 sites included in the ’per protocol’ sensitivity analysis.
Study phaseIRR (95% CI, p-value)
BaselineLonger-turnaroundRapidLonger-turnaround vs. baselineRapid vs. baseline
All sites
n HOCI cases850373947
n IPC-defined HAIs488207576
Weekly incidence of IPC-defined HAIs per 100 inpatients, mean (median, IQR, range)* [primary outcome]1.0 (0.5, 0.0–1.4, 0.0–5.6)0.7 (0.3, 0.0–0.7, 0.0–7.6) 0.6 (0.3, 0.0–0.8, 0.0–5.3) 1.60 (0.85–3.01; 0.14)0.85 (0.48–1.50; 0.54)
n IPC-defined outbreak events12933114
Weekly incidence of IPC-defined outbreak events per 1000 inpatients, mean (median, IQR, range)*2.7 (1.1, 0.0–4.1, 0.0–23.0)0.8 (0.0, 0.0–1.0, 0.0–8.9) 0.7 (0.0, 0.0–0.0, 0.0–8.9) 1.09 (0.38–3.16; 0.86)0.58 (0.24–1.39; 0.20)
n IPC + sequencing-defined outbreak events40133
Weekly incidence of IPC + sequencing-defined outbreak events per 1000 inpatients, mean (median, IQR, range)*1.1 (0.0, 0.0–1.5, 0.0–13.4) 0.9 (0.0, 0.0–1.4, 0.0–7.6)
Sensitivity analysis
n HOCI cases290143533
n IPC-defined HAIs17991337
Weekly incidence of IPC-defined HAIs per 100 inpatients, mean (median, IQR, range)* [primary outcome]0.3 (0.0, 0.0–0.3, 0.0–3.0)0.3 (0.0, 0.0–0.0, 0.0–3.4) 0.4 (0.0, 0.0–0.3, 0.0–5.3) 2.21 (0.82–5.92; 0.10)1.75 (0.75–4.08; 0.16)
n IPC-defined outbreak events581455
Weekly incidence of IPC-defined outbreak events per 1000 inpatients, mean (median, IQR, range)*1.1 (0.0, 0.0–1.3, 0.0–12.9)0.3 (0.0, 0.0–0.0, 0.0–5.7) 0.4 (0.0, 0.0–0.0, 0.0–8.9) 0.83 (0.14–4.93; 0.80)0.46 (0.11–1.86; 0.21)
n IPC + sequencing-defined outbreak events1467
Weekly incidence of IPC + sequencing-defined outbreak events per 1000 inpatients, mean (median, IQR, range)*0.3 (0.0, 0.0–0.0, 0.0–5.7) 0.5 (0.0, 0.0–0.0, 0.0–7.6)
  1. IPC-defined HAIs are considered to be ‘probable’ or ‘definite’ HAIs.

  2. HAI, hospital-acquired infection; HOCI, hospital-onset COVID-19 infection; IPC, infection prevention and control; IQR, interquartile range; IRR, incidence rate ratio.

  3. *

    Descriptive data over all week-long periods at all study sites.

  4. Adjusted for proportion of current inpatients at site that are COVID-19 cases, community incidence rate, and calendar time (as displayed in Appendix 1—figure 5 and Appendix 1—figure 6 for all sites).

  5. Not including data from the first week of each intervention period or in the week following any break in the intervention period.

We compared HOCI-level impacts of the sequence report between phases. Nosocomial linkage to other individual cases, where initial IPC investigation had not correctly identified any such linkage, was identified in 6.7 and 6.8% of all HOCI cases in the rapid and longer-turnaround phases, respectively (OR for ‘rapid vs. longer-turnaround’ 0.98, 95% CI 0.46–2.08; p=0.95) (Table 2) and in 11.4 and 12.6% respectively of cases where the report was returned. For 25 cases in the rapid and 5 cases in the longer-turnaround phase, phylogenetic trees were used for sequences with <90% genome coverage, with 3 from the rapid phase showing previously unidentified linkage.

IPC practices were changed in 7.8 and 7.4% of all HOCI cases in the rapid and longer-turnaround phases, respectively (OR for ‘rapid vs. longer-turnaround’ 1.07, 0.34–3.38; p=0.90), and 17.2 and 11.6%, respectively, of cases where the report was returned. No one specific change to IPC action dominated those recorded among the options included within study reporting forms (Appendix 1—table 2, Appendix 1—table 3). When restricted to higher performing sites (i.e. ‘per protocol’), IPC practice was changed in a greater proportion of all HOCI cases in the rapid (9.9%) in comparison to the longer-turnaround (0.7%) sequencing phase (OR for ‘rapid vs. longer-turnaround’ 15.55, 1.30–1.85; p=0.01) and 16.7 and 1.1%, respectively, of cases where SRT reports were returned. The impact of phase on detecting nosocomial linkage was similar.

IPC teams more commonly reported finding the sequence reports useful in the rapid sequencing, 303/428 (70.8%) compared to the longer-turnaround phase, 107/215 (49.8%) (although this association was reversed on analysis within the multi-level mode specified, OR 0.82 rapid vs. longer-turnaround, 0.12–5.46; p=0.82), and the difference was more pronounced in the ‘per protocol’ analysis (79.0 vs. 27.2%, respectively; OR 3.44, 0.28–42.61; p=0.41). We explored this association further using the actual time to return of the reports, going beyond the analyses pre-specified in the SAP (Figure 2). In the ‘per protocol’ analysis, an impact on IPC actions was observed in 20.7% (45/217) of HOCI cases in which the SRT report was returned within 5 days, but in very few cases beyond this, with this trend less apparent when data from all sites were considered. Figure 2 also displays a strong decline in reported usefulness of the SRT with increasing turnaround time, both across all sites and in the ‘per protocol’ analysis. Sequence reports were considered useful in 79.1% (182/230) of cases if returned within 5 days for all sites (169/216, 78%, in ‘per protocol’ analysis). However, we note that many of the HOCI cases with SRT returned within 5 days were from a single study site, and some sites did not seem to have clearly differentiated ‘useful’ SRT reports when completing data collection (Appendix 1—figure 2 and Appendix 1—figure 3).

Plots of the proportion of returned sequence reporting tool (SRT) reports that had an impact on infection prevention and control (IPC) actions (a, b) and that were reported to be useful by IPC teams (c, d).

Data are shown for all sites in (a) and (c), and for the seven sites included in the ’per protocol’ sensitivity analysis in (b) and (d). Results are only shown up to turnaround times of ≤28 days, and grouped proportions are shown for ≥9 days because of data sparsity at higher turnaround times. Error bars show binomial 95% CIs. ‘Yes’ and ‘No’ outcomes for individual hospital-onset COVID-19 infection (HOCI) cases are displayed, colour-coded by rapid (red) and longer-turnaround (blue) intervention phases and with random jitter to avoid overplotting. ‘Unsure’ responses were coded as ‘No’ for (c) and (d).

SRT reports suggested that 91.3% of HOCI patients had acquired their infection post-admission (580/635, Table 2). In 91.9%, (589/641, Appendix 1—table 2) of cases, the reports were interpreted as supportive of IPC actions already taken. SRT reports also suggested post-admission infection in the majority of indeterminate HAIs (diagnosed 3–7 days from admission) (176/223, 78.9%).

Our analysis models reveal important findings beyond the effect of the intervention. The analysis model for the incidence of HAIs identified independent positive associations with the proportion of current SARS-CoV-2-positive inpatients, the local community incidence of new SARS-CoV-2 cases (which peaked from December 2020 to January 2021, Appendix 1—figure 4 and Appendix 1—figure 5), and calendar time (modelled as ‘study week’). Adding the proportion of local community cases that were Alpha (lineage B.1.1.7) variant did not lead to a statistically significant improvement in model fit (p=0.78). The observed weekly HOCI incidence rates varied substantially from 0 to 7.6 per 100 SARS-CoV-2 negative inpatients, with peaks aligning with those for local community incidence (Appendix 1—figure 4).

From modelling outbreaks, positive associations were similarly found for both hospital prevalence and community incidence of SARS-CoV-2 (Appendix 1—figure 6). The median number of HOCIs per IPC-defined outbreak event was four, with the largest observed outbreak including 43 HOCIs (Appendix 1—table 1).

Extensive qualitative analyses (Mapp et al., under review; Flowers et al., 2022) found high levels of acceptability for the SRT sequencing reports, which supported decision-making about IPC activity (e.g. stand down some IPC actions or continue as planned). In several sites, the major barriers to embedding and normalising the SRT within existing systems and processes were overcome. The SRT did provide new and valued insights into transmission events, outbreaks, and wider hospital functioning but mainly acted to offer confirmation and reassurance to IPC teams. Critically, given the context of the study within the pandemic timeline, the capacity to generate and respond to these insights effectively on a case-by-case basis was breached in most sites by the volume of HOCIs, and the limits of finite human and physical resource (e.g. hospital layout).

Cost of SARS-CoV-2 genome sequencing

Analysis of SARS-CoV-2 genome sequencing in the 10 laboratories which performed the tests for the sites included in the study showed that mean per-sample costs were on average higher for rapid (£78.11) vs. longer-turnaround (£66.94) sequencing. (Appendix 1—table 4). Consumables were the highest cost driver of the sequencing process accounting for 66% in rapid and 67% in longer-turnaround sequencing.

Several factors affected the costs of genome sequencing. There was a general tendency of increasing returns to scale, with average per-sample costs of genome sequencing tending to decrease as the batch size increases; cost per sample in reagents also depends highly on how many samples are processed per batch. Another factor was the sequencing platform and protocols used: some processes had been automated which reduced the hands-on input.

Discussion

This study constitutes the largest prospective multicentre evaluation study of viral whole-genome sequencing (WGS) for acute IPC investigation of nosocomial transmission conducted to date. The study was run as part of routine practice within the NHS, and the challenges faced in implementing the intervention reflected the context and barriers in winter 2020–2021 in the UK. We did not demonstrate a direct impact of sequencing on the primary outcome of the incidence of HAIs, either on full analysis or when restricted to the higher performing sites, and the overall proportion of cases with nosocomial transmission linkage identified using sequencing that had been missed by IPC investigation was <10% in the intervention phases. However, on post hoc exploratory investigation among those sites with the most effective implementation of the sequencing intervention we showed that feedback within 5 days of diagnosis allowed for maximal impact on IPC actions. IPC teams, particularly in the ‘per protocol’ analysis, were almost all positive in their perception of the utility of viral sequencing for outbreak investigation.

The study was undertaken during a period of extreme strain on the NHS, with hospitals described as being ‘in the eye of a COVID-19 storm’ (Oliver, 2021a). Sites reported that they lacked the additional resources, in terms of staff and bed space, needed to respond effectively to insights generated by sequencing. Furthermore, if the study were repeated now then IPC teams would have more evidence-backed tools at their disposal, such as increasing respirator usage. As such, we do not believe that the null result for the impact on incidence of nosocomial transmission should be taken as strong evidence for a general lack of effectiveness of viral WGS for IPC.

Outbreak investigations are inherently complex and must take account of uncertainty regarding transmission links, even in the presence of high-quality genomic data (Lindsey et al., 2022). Interventions centred on IPC practices often need to be evaluated at the hospital level in order to allow for impacts on transmission across an institution as a whole (O’Hara et al., 2019), meaning that large multicentre studies are required to generate high-quality evidence. Standardisation of data collection with complex structures across multiple hospital sites is a considerable challenge. A review of IPC practice guidelines conducted prior to the SARS-CoV-2 pandemic found that most recommendations were based on evidence from descriptive studies, expert opinion, and other low-quality evidence (Mitchell et al., 2020).

The use of viral WGS for public health surveillance has become firmly established in the UK for SARS-CoV-2 (The COVID-19 Genomics UK consortium, 2020). This enabled early detection of the increased transmissibility and health impact of the Alpha variant (Volz et al., 2021) and subsequent monitoring of the Delta (Mishra et al., 2021) and Omicron variants (Andrews et al., 2022). However, whilst viral WGS for acute outbreak investigation has been shown for both SARS-CoV-2 (Meredith et al., 2020; Snell et al., 2022b; Lumley et al., 2021; Li et al., 2021) and other viruses (Brown et al., 2019; Houldcroft et al., 2018; Roy et al., 2019) to better identify sources of hospital-acquired infections and transmission chains, its impact on the management and outcome of nosocomial infection has not previously been quantified. Our study provides a substantial body of evidence regarding the introduction of viral WGS into routine IPC practice, its potential usage for outbreak management, and the challenges that need to be overcome to achieve implementation across the UK.

There are several limitations that may have impacted the results of this study. The study was conducted between October 2020 and April 2021. In this period, the local community incidence for the study sites ranged from <50 to >1200 weekly cases per 100,000 people. There were corresponding large variations in the healthcare burden of COVID-19, with several sites recording weeks when more than half of all inpatients were SARS-CoV-2 positive. High community infection rates and associated increases in the incidence of HOCI cases contributed to difficulties for site research teams in generating good quality viral sequences and reports for all HOCI cases within target time frames.

Our qualitative analyses also found that the capacity of sites to react to information generated by the sequencing intervention was breached by the volume of HOCI and admitted COVID-19 cases (“we’ve been basically deluged”, IPC staff) in combination with the finite personnel resources and limited physical space for isolation that was available (“The trouble is when you have so many wards going down and such a high prevalence of COVID, your actions are kind of the same regardless”, IPC staff). It may therefore be more achievable to develop effective systems for rapid viral WGS and feedback for endemic respiratory viruses at lower and more consistent levels, and more timely reporting of results might be associated with greater impact on IPC actions. As well as acute changes to IPC actions, there is the potential for routine pathogen sequencing to allow prospective IPC practice and policies to be refined. This could enable a longer-term reduction in the incidence of nosocomial infection at any given site, and such effects would be less dependent on turnaround time of sequencing in any given case. However, the capacity of sites to make such informed adjustments to IPC practice was limited during peaks in incidence of SARS-CoV-2 over the time scale of this study.

Planning this study and developing the data collection forms during the early stages of a novel viral pandemic was challenging, as in the summer of 2020 there were still ongoing debates around the primary mode of viral transmission and optimal IPC practice, and global supply chains for personal protective equipment were strained. In the planning of an equivalent study now, there would be a greater focus on adjustments to ventilation (Allen and Ibrahim, 2021), air filtration (Conway Morris et al., 2022), and respirator (Ferris et al., 2021) usage. It would also be possible to be more prescriptive and standardised regarding the recommended changes to IPC practice in response to sequencing findings, with the potential that our improved knowledge and available tools might facilitate a measurable impact on the incidence of nosocomial transmission.

The peak in SARS-CoV-2 levels from December 2020 to January 2021 corresponded to the rise of the highly transmissible Alpha variant in the UK (Volz et al., 2021). We did not find that the local prevalence of the Alpha variant was associated with the incidence rate of HAIs, beyond any effect mediated by higher community incidence. This matches the conclusions of a previously reported sub-study analysis using data from our sites (Boshier et al., 2021).

The study intervention made use of a bespoke SRT (Stirrup et al., 2021). The SRT combined both patient metadata and sequencing data, providing a single-page, easily interpretable report for IPC teams. It also facilitated standardisation of data collection across sites. Interestingly, while HOCIs diagnosed 3–7 days after admission are generally excluded from assessments of nosocomial SARS-CoV-2 infections (Oliver, 2021b), because of difficulty in distinguishing them from community-acquired infections, the SRT reported the majority (78.9%) of these indeterminate HAIs as being hospital-acquired. This confirms findings from a retrospective study using genomic linkage (Lumley et al., 2021), and may reflect a shorter incubation time for the Alpha variant compared to earlier variants (Snell et al., 2022a; although this remains uncertain; Blanquart et al., 2022), indicating that definitions used for monitoring and reporting may need to be kept under active review. Variants with shorter incubation times would lead to a greater importance for the rapidity of feedback in informing adjustments to IPC actions.

A number of limitations of the SRT were recognised, and work is ongoing to rectify these for future studies. The SRT’s probability calculations did not include patient and HCW movements. The SRT gave feedback on cases that could plausibly form part of the same outbreak but did not identify direct transmission pairs or networks, as has been done in other studies (Lindsey et al., 2022; Illingworth et al., 2022). HCW sequencing data could not be incorporated at all sites due to logistical and data management and access constraints. Implementation of an improved tool with these features might help to better identify routes of transmission within a hospital that could be interrupted, for example, through changes to the management of ward transfers for patients, isolation policies or identification of areas within the hospital linked to high risk of transmission. Finally, samples with less than 90% genome coverage were not included within the reporting system, despite the fact that they may still be useful for phylogenetic analyses.

The study sites varied in their ability to process sequence and metadata and generate and distribute reports in a timely manner (Figure 1), and the targeted turnaround times for reporting were not achieved at any of the sites for the majority of HOCIs in either the ‘rapid’ or ‘longer turnaround’ phases. Sites that had established teams with existing genomics expertise and on-site sequencing facilities were generally more successful at implementing the SRT into clinical practice (Mapp et al., under review). There is a need to focus on how sequencing and reporting processes can be integrated within local infrastructure and tailoring of local processes to ensure clear chains of communication from diagnostic labs through to the IPC team. Precisely understanding the barriers to achieving rapid turnaround times is key to future IPC use of viral WGS and is currently being analysed in a follow-up secondary analysis. Standardising and automating more of the SRT production pipeline will also help reduce the implementation burden at sites.

The study covered a period in which a national vaccination programme was initiated for HCWs and the elderly population in the UK, commencing with those ≥80 years from 8 December 2020. We had planned to include data on the proportion of HCWs who had received at least one vaccine dose as a variable in the analysis of incidence outcomes. This was subsequently not included because data was only available from 10 sites, for which rollout of HCW vaccination was broadly consistent. As such, any effect of HCW or patient vaccination on the incidence outcomes would form part of the estimated association with calendar time.

With the sequencing technology now available and high levels of interest in viral genomics for public health, there is the potential to incorporate viral WGS into routine IPC practice. Many publications have already highlighted the utility of viral sequence data for changing IPC policy and auditing the management of outbreaks (Meredith et al., 2020; Snell et al., 2022b; Lumley et al., 2021; Li et al., 2021; Brown et al., 2019; Houldcroft et al., 2018; Roy et al., 2019). We did not demonstrate an effect of our sequencing intervention on our primary outcome of the incidence of HAIS, and there were challenges in the implementation of the intervention. However, our study provides the first prospective evidence that with faster turnaround times, viral sequences can inform ongoing IPC actions in managing nosocomial infections; on post hoc exploratory analysis results returned within ≤5 days from sampling to result changed the actions of IPC teams in around 20% of cases. The SRT, by rapidly combining sequence and patient metadata, was also better able than standard IPC definitions alone to distinguish hospital and community-acquired infections within a clinically relevant time scale. The difference in the cost of rapid compared with longer-turnaround hospital sample sequencing is low relative to the overall cost level at present (Appendix 1—table 4). Assuming SARS-CoV-2 sequencing for public health purposes continues, the added cost of rapid sequencing for IPC purposes could potentially be offset by the benefits accrued.

While we did not show an impact of sequencing on the numbers of HAIs or outbreaks, the evidence that these correlated with the high community SARS-CoV-2 rates suggests that factors beyond the control of IPC were influential. Our study nonetheless provides valuable evidence regarding the implementation and utility of this technology for IPC, and potentially it will have a greater positive impact on IPC practice outside of the burdens and resource constraints imposed by a pandemic. Importantly for future research, we provide a wealth of data on why the study worked better at some sites than others, and the challenges that would need to be overcome to make full use of viral genome sequencing for IPC practice more widely. It remains to be demonstrated that viral sequencing can have a direct impact on clinical outcomes such as the incidence of HAIs, and further prospective studies with refined implementation of similar interventions are required to address this.

Appendix 1

Appendix 1 contains further details of statistical analysis methods, and supplementary figures and tables describing additional study data.

Methods

Sample size estimation

There was uncertainty in the number of HOCIs that would be identified at each site during each of the intervention periods, with the rapid sequencing phase being 8 weeks’ duration. We assumed that there may be an average of 10 HOCIs/week per site during this intervention period, a total of 80 per site. Within a typical site this would allow us to estimate the proportion of HOCIs with genotypic linkage to another case(s) not detected by IPC processes with minimum precision of ±9.4%. Similarly, we would be able to estimate the proportion of HOCIs where an action is taken that would not have occurred without sequencing within ±9.4%, with a pooled estimate of key proportions across the 14 sites implementing rapid sequencing within ±6.5% assuming an intra-cluster correlation coefficient of 0.05.

Comparing the proportion of HOCIs with genotypic linkage to another case(s) not detected by IPC processes between rapid testing and delayed testing phases across all sites, the study aimed for at least 80% power to detect a percentage point difference of 11% (two-sided test with alpha = 0.05, considering proportions of 55.5% vs. 44.5% which would be associated with minimum power for a difference of this magnitude).

For the outcome of weekly incidence of IPC-defined HOCIs, using an approximate normal distribution for weekly counts there was 86.7% power to demonstrate a reduction from 12 IPC-defined HOCIs per week in the baseline phase to 10 per week during the rapid testing phase across all sites, under 5% significance level two-tailed testing. However, these calculations correspond to a variance of 12 for weekly counts based on the Poisson distribution, but the presence of over-dispersion of weekly counts would lead to a lower power to detect a difference. Using an overdispersion parameter of 0.82 based on retrospective analysis of data from Sheffield and Glasgow (dataset as described by Stirrup et al., 2021) resulted in 81% power to detect a reduction in mean weekly incidence from 12.5 to 10.

Planned secondary outcomes dropped from formal analysis

We did not carry out formal statistical analysis for the following planned secondary outcomes:

  • Weekly incidence of IPC + sequencing-defined SARS-CoV-2 hospital outbreaks, measured as incidence rate per week per 100 non-COVID-19 inpatients, during each phase of the study based on case report forms.

  • The number of HCW periods of sickness/self-isolation as assessed as a proportion of the number of staff usually on those wards impacted by HOCI cases, for all phases of the study.

The first of these was dropped prior to analysis because of incomplete sequencing coverage of HOCI cases in the intervention phases – it was not felt that this would add useful information given the level of sequencing achieved and the null results for other incidence outcomes. The second was dropped (again prior to any statistical analysis) because of low levels of data completion at most of the study sites. Data collection on HCW absence was discontinued whilst the study was ongoing in order to reduce the administrative burden for sites and due to difficulties in accessing this data for research staff (with staff data being recorded on separate systems to patient data).

We also omitted specific reporting of the secondary outcome of ‘Ideal changes to IPC actions following receipt of sequencing report’. This was because no recommended changes to IPC actions were recorded that were also recorded as ‘not implemented’. As such, this outcome was identical to ‘changes to IPC actions following receipt of sequencing report’.

Coding of primary and secondary outcomes

Primary outcome 1

Incidence of IPC-defined SARS-CoV-2 HAIs

In order to standardise this measure across sites, ‘IPC-defined SARS-CoV-2 HAIs’ were considered to be all HOCIs with an interval of ≥8 days from admission to symptom onset (if known) or sample date (i.e. those meeting the PHE definition of a probable or definite HAI; Public Health England, 2020). Incidence was expressed ‘per 100 non-COVID-19 inpatients per site per week’ and was evaluated for study baseline and intervention phases.

Primary outcome 2

Identification of SARS-CoV-2 nosocomial transmission using sequencing data

For each HOCI case during the intervention phases, the occurrence of this outcome was defined as positive where the following two answers had been recorded in the Hospital Transmission section of the relevant clinical reporting form (CRF04):

  • “Is sequencing report suggestive that patient is part of a hospital outbreak (i.e. involving ≥2 patients or HCWs in the hospital)?: Yes”

  • &

  • “If yes, was linkage to one or more of these patients suspected at initial IPC investigation?: No”

The occurrence of this outcome was considered to be negative if the following answer was recorded:

  • “Is sequencing report suggestive that patient is part of a hospital outbreak (i.e. involving ≥2 patients or HCWs in the hospital)?: No”

Or if the following combination was recorded:

  • “Is sequencing report suggestive that patient is part of a hospital outbreak (i.e. involving ≥2 patients or HCWs in the hospital)?: Yes”

  • &

  • “If yes, was linkage to one or more of these patients suspected at initial IPC investigation?: Yes”

The outcome will be considered missing if either the first question was not answered, or if the first question was answered ‘Yes’ and the second question was not answered or was answered ‘unknown’.

The outcome was also considered negative if the viral sequence and sequence report had not been returned during the period of study data collection.

This outcome was only evaluated for study sequencing intervention periods.

Secondary outcome 1

Incidence of IPC-defined SARS-CoV-2 hospital outbreaks

An IPC-defined SARS-CoV-2 hospital outbreak was defined as the occurrence of at least two HOCI cases on the same ward, with at least one having an interval of ≥8 days from admission to symptom onset (if known) or sample date, and with the outbreak event considered to be concluded if there was a gap of 28 days before the observation of another HOCI case (Public Health England, 2020). This was evaluated using the ward location recorded at patient registration into the study (CRF01) for HOCI cases, cross-checked against patient movement data to confirm location at diagnostic sampling. Outbreak events were considered to have occurred on the date of diagnosis of the first HOCI case. This outcome was evaluated for study baseline and intervention phases.

Secondary outcome 2

Changes to IPC actions following receipt of sequencing report

For each HOCI case, the occurrence of this outcome was defined as positive if ‘Yes’ is the answer to either of the following two questions in the ‘Sequencing report impact on IPC team’ section of CRF04:

  • “Overall, did the sequencing report change IPC practice for this ward?”

  • And/or

  • “Has the sequencing report information been used in IPC decisions beyond this patient’s ward?”

And/or, if any specific changes to IPC practice were recorded on CRF04.

The occurrence of the outcome was considered negative if at least one of these questions was answered ‘No’ and neither is answered ‘Yes’, and it was considered missing if neither were answered.

This outcome was only evaluated for study sequencing intervention periods.

Secondary outcome 3

Ideal changes to IPC actions following receipt of sequencing report

A binary outcome was defined for each HOCI patient. This was based on the value of secondary outcome 3, but was additionally defined as positive (whether secondary outcome 3 was negative or missing) if an ‘increase’ or ‘decrease’ that was not implemented was recorded for any of the actions in the ‘other recommended changes to IPC protocols’ section of CRF04.

This outcome was only evaluated for study sequencing intervention periods.

Secondary outcome 4

Incidence of IPC + sequencing-defined SARS-CoV-2 hospital outbreaks

An IPC + sequencing-defined SARS-CoV-2 hospital outbreak was defined as the occurrence of at least two HOCI cases on the same ward that form a genetic cluster with maximum viral sequence pairwise SNP distance of 2 between each individual included and their nearest neighbour within the cluster. This was evaluated using the ward location recorded at patient registration into the study (CRF01), with HOCI cases sorted into outbreak groups using the lists of close sequence matches on unit-ward as returned by the SRT and recorded in CRF03.

Outbreak events were considered to have occurred on the date of diagnosis of the first HOCI case. This outcome was evaluated for study sequencing intervention periods for all sites.

Secondary outcome 5

HCW sickness

The proportion of HCWs on sick leave due to COVID-19 was calculated using the ‘Current staffing levels on ward’ section of CRF02. Analysis was performed using the first available data within each IPC-defined SARS-CoV-2 hospital outbreak (as per secondary outcome 1), so as to provide a measure of the level of staff absence at the start of each outbreak. This outcome was evaluated for study baseline and intervention phases.

Changes with respect to the SAP

It was planned that the cumulative proportion of HCWs vaccinated at each site for each study week would also be included as a covariate for the analysis models of incidence outcomes. However, this was dropped because these supplementary data could not be obtained from four sites (and one site was only able to provide partial data). Where available, the data showed the rollout of HCW vaccination to be broadly consistent across sites. As such, any effect of HCW vaccination on the incidence outcomes would be incorporated into estimates of variation in relation to calendar time.

Local community incidence was not included within the SAP, but was added as an adjustment factor for incidence models because within-hospital prevalence of patients admitted for SARS-CoV-2 did not correlate perfectly with local incidence (e.g. due to triage of COVID-19 patients to different hospitals).

The proportion of HOCI cases in which the sequencing report feedback was considered to be ‘useful’ was added as a secondary outcome.

It was stated in the SAP: “We will conduct sensitivity analyses excluding study sites and/or periods with suboptimal implementation of the trial intervention, both in terms of overall population sequencing coverage for HOCIs and the turnaround time for sequence reports being returned to IPC teams. The exact criteria for this will be decided amongst the study team before any analysis has been conducted.” This forms the basis for the ‘per protocol’ analyses presented. It was not possible to prespecify the exact criteria. After data collection for the study had been completed, it became clear that none of the sites had met target turnaround times for sequence reporting in the intervention phases, and so it was decided to set criteria to select the 50% ‘higher performing’ sites.

It was stated in the SAP: “If the target turnaround time for sequence generation and reporting is missed for a substantial proportion of HOCI cases in each of the intervention phases, then results [for Impact on IPC actions] will also be reported separately for the subset of cases for which the intervention was implemented within the target timeframe.” Because the proportion of HOCI cases with SRT report returned within the target time frame was low for the rapid intervention phases, we instead reported the association of this outcome with turnaround time more generally.

The SAP did not state that unadjusted estimates would be reported for the incidence rate ratios for HAIs and outtbreaks, but these have been added for completeness in response to the comments of a Reviewer (Appendix 1—table 5).

Weekly incidence rates for outbreak events are displayed as ‘/1000 inpatients’ rather than ‘/100 inpatients’ to improve display.

Small sample correction

The topic of small sample corrections for cluster randomised and other cluster-structured studies (e.g. stepped wedge trials) with outcomes that are not normally distributed is an area of ongoing active research. To our knowledge, there do not exist any studies regarding appropriate corrections for clustered data when analysing an outcome with negative binomial distribution. However, when calculating p-values and confidence intervals for the primary and secondary outcomes we will use a t-distribution with 12 or 13 degrees of freedom (n clusters – n relevant parameters) in order to ensure that there is not an inflated type 1 error rate. This correction has shown appropriate characteristics in simulation studies of analyses of binary outcomes using mixed effects models and generalised estimating equations (Li and Redden, 2015; Thompson et al., 2021).

Decision regarding continuation of study into final phase

A decision regarding the final phase of the study (Period 4) was planned for April 2021, with the options being: ending of the study at Period 3, a further phase of rapid sequencing at each site or a further phase of ‘baseline’ data collection without use of the SRT. A recommendation regarding this decision was made by the study investigators and agreed with the TSC-DMC. The decision was determined by the course of the epidemic and the progress of vaccination among key risk groups, and by the quantity of data collected by the end of Period 3. The decision was not based on any interim evaluation of the effect of the sequencing intervention under investigation on the incidence of nosocomial infection.

A decision was made to stop the study at the end of Period 3 because the total sample size was close to that projected for the study, and few new HOCI cases were being recorded at this point in time.

Further details for incidence model specification

The primary outcome of incidence of SARS-CoV-2 HAIs was analysed using a mixed effects negative binomial regression model. This acts as an extension of a Poisson regression model, with an additional parameter allowing for overdispersion (for a Poisson model the variance is always the mean for any given combination of covariables and conditional on random effect terms). The negative binomial regression model is a generalised linear model, which uses a log-link function for the expectation of the outcome variable. In our analysis, we used nested independent normally distributed random effects for each study site, and for each study phase within each study site, which were incorporated into the ‘linear predictor’ for the expectation of the outcome variable as for other forms of linear mixed effects model. The variance of the two random effects components of the model was estimated within the maximum likelihood estimation for the model.

The command used in Stata to run this model was of the form:

menbreg n_HAIs i.study_phase_analysis prop_cov_sp_* study_week_sp_* community_inc_sp_*, exposure(exposure_pw) || site_anon: || study_phase:, irr dispersion(constant)

Where the dataset under analysis included one row per study week at each site, with the variables defined as

  • n_HAIs: Number of HAIs observed at each sites in each study week

  • study_phase_analysis: Intervention phase for each study week, with separate categories for the first week of each intervention phase at each site (to allow for 1 week to pass before potential impact on incidence of HAIs)

  • prop_cov_sp_*: Spline basis variables for proportion of inpatients who were SARS-CoV-2 positive at each site in each study week

  • study_week_sp_*: Spline basis variables for adjustment for calendar time

  • community_inc_sp_*: Spline basis variables for local community incidence for each site in each study week

  • exposure_pw: person-weeks of SARS-CoV-2 negative inpatients at each site for each study week (i.e. sum of patient-time at-risk for nosocomial SARS-CoV-2 infection)

Qualitative analyses

An exploratory, qualitative process evaluation using iterative programme theory employed semi-structured interviews with 39 diverse healthcare professionals between December 2020 and June 2021. Participants were purposively sampled from 5/14 sites. Data collection and analysis (deductive and inductive thematic analysis) focused on the programme theory: intervention acceptability; contextual dependencies; issues of fidelity/adaption; insights into local implementation; and effects on outcomes.

Results

Appendix 1—figure 1
Flow diagram of study site enrolment and intervention implementation.

*Baseline phase extended for one site due to a complete lack of hospital-onset COVID-19 infection (HOCI) cases during the first few weeks of study period and omission of longer-turnaround sequencing phase. Rapid sequencing phase truncated at one site due to cessation of enrolment at all sites.

Appendix 1—figure 2
Plots of the proportion of returned sequence reporting tool (SRT) reports that had an impact on infection prevention and control (IPC) actions by study site.

Results are only shown up to turnaround times of ≤28 days, and grouped proportions are shown for ≥9 days because of data sparsity at higher turnaround times. Error bars show binomial 95% CIs. ‘Yes’ and ‘No’ outcomes for individual hospital-onset COVID-19 infection (HOCI) cases are displayed, colour-coded by rapid (red) and longer-turnaround (blue) intervention phases and with random jitter to avoid overplotting.

Appendix 1—figure 3
Plots of the proportion of returned sequence reporting tool (SRT) reports that were reported to be useful by infection prevention and control (IPC) teams by study site.

Results are only shown up to turnaround times of ≤28 days, and grouped proportions are shown for ≥9 days because of data sparsity at higher turnaround times. Error bars show binomial 95% CIs. ‘Yes’ and ‘No’ outcomes for individual hospital-onset COVID-19 infection (HOCI) cases are displayed, colour-coded by rapid (red) and longer-turnaround (blue) intervention phases and with random jitter to avoid overplotting. ‘Unsure’ responses were coded as ‘No’.

Appendix 1—figure 4
Weekly incidence of hospital-acquired infections (HAIs) at each site (●), with (a) proportion of all inpatients SARS-CoV-2+ve and (b) local community incidence of SARS-CoV-2+ve tests also plotted on the y-axis (purple line).

Horizontal bars show the duration of study phases (orange: baseline; blue: longer turnaround; green: rapid).

Appendix 1—figure 5
Adustment variables for analysis of weekly incidence of infection prevention and control (IPC)-defined hospital-acquired infections (HAIs) per 100 inpatients, as described in Table 3.

Incidence rate ratios are displayed relative to the median for (a) calendar time expressed as study week from 12 October 2020, (b) proportion of inpatients with positive SARS-CoV-2 test, and (c) local community incidence of SARS-CoV-2 (government surveillance data weighted by total set of postcodes for patients at each site). The spline curves shown are estimated simultaneously within the final analysis model and show how these factors have independent contributions to the prediction of the incidence rate for HAIs. The associations for each covariable indicated by model parameter point estimates are shown as solid lines, with 95% CIs shown as dashed lines. Adjustment for (c) was not pre-specified in the statistical analysis plan (SAP), but adding this variable to the model was associated with a statistically significant improvement in fit (p=0.01). The proportion of community-sampled cases in the region that were found to be the Alpha variant on sequencing was also considered, but adding this as a linear predictor did not lead to a statistically significant improvement in model fit (p=0.78).

Appendix 1—figure 6
Adustment variables for analysis of weekly incidence of infection prevention and control (IPC)-defined outbreak events per 1000 inpatients, as described in Table 3.

Incidence rate ratios are displayed relative to the median for (a) calendar time expressed as study week from 12 October 2020, (b) proportion of inpatients with positive SARS-CoV-2 test, and (c) local community incidence of SARS-CoV-2 (government surveillance data weighted by total set of postcodes for patients at each site). The spline curves shown are estimated simultaneously within the final analysis model and show how these factors have independent contributions to the prediction of the incidence rate for outbreaks. The associations for each covariable indicated by model parameter point estimates are shown as solid lines, with 95% CIs shown as dashed lines. Adjustment for (c) was not pre-specified in the statistical analysis plan (SAP), but adding this variable to the model was associated with a near-statistically significant improvement in fit (p=0.05) and was included for consistency with the analysis of individual hospital-acquired infections (HAIs). The proportion of community-sampled cases in the region that were found to be the Alpha variant on sequencing was also considered, but adding this as a linear predictor did not lead to a statistically significant improvement in model fit (p=0.80).

Appendix 1—figure 7
Weekly counts of enrolled hospital-onset COVID-19 infection (HOCI) cases by date of positive test return for each site, colour-coded by intervention phases.

Horizontal bars show the duration of study phases.

Appendix 1—table 1
Per outbreak event outcomes by study intervention phase.
Study phaseTotal
BaselineLonger-turnaroundRapid
IPC-defined outbreak events
n outbreak events12933114276
n/N (%) of HOCI cases part of outbreak event682/850 (80.2)314/373 (84.2)763/947 (80.6)1759/2170 (81.1)
Number of HOCIs per outbreak event, median (IQR, range)5.0 (3–8, 2–43)5.0 (3–9, 2–24)4.0 (2–7, 2–31)4.0 (2–8, 2–43)
Proportion of HCWs on sick leave due to COVID-19, median (IQR, range) [n]0.13 (0.00–0.35, 0.00–0.50) [13]0.05 (0.00–0.18, 0.00–0.30) [7]0.20 (0.08–0.33, 0.00–0.89) [14]0.13 (0.00–0.31, 0.00–0.89) [34]
IPC + sequencing-defined outbreak events
n outbreak events41135176
n/N (%) of HOCI cases part of outbreak event292/373 (78.3)705/947 (74.4)997/1320 (75.5)
Number of HOCIs per outbreak event, median (IQR, range)5.0 (2-8, 2-23)3.0 (2-6, 2-29)3.0 (2-7, 2-29)
For first HOCI in outbreak:
 SRT changed IPC practice, n/N (%, 95% CI)4/41 (10.4, 0–21.0)19/133 (14.9, 6.6–23.2)23/174 (13.2)
 SRT changed IPC practice for ward, n/N (%)2/35 (5.7)6/82 (7.3)8/117 (6.8)
 SRT used in IPC decisions beyond ward, n/N (%)2/35 (5.7)10/82 (12.2)12/117 (10.3)
IPC team reported SRT to be useful, n/N (%)
  Yes20/35 (57.1)51/82 (62.2)71/117 (60.7)
  No9/35 (25.7)15/82 (18.3)24/117 (20.5)
  Unsure6/35 (17.1)16/82 (19.5)22/117 (18.8)
 SRT would ideally have changed IPC practice, n/N (%*, 95% CI)4/41 (9.8)19/133 (14.3)23/174 (13.2)
  1. Odds ratio of SRT changed IPC practice for ‘rapid vs. longer-turnaround’ phases 1.54 (95% CI 0.37–6.44; p=0.52).

  2. HCW, healthcare worker; HOCI, hospital-onset COVID-19 infection; IPC, infection prevention and control; IQR, interquartile range; SRT, sequence reporting tool.

  3. *

    Estimated marginal value from mixed effects model, not raw %, evaluated on intention-to-treat basis with lack of SRT report classified as ‘No’.

Appendix 1—table 2
Descriptive summary of impact of sequencing on IPC actions implemented during study intervention phases, as recorded on pre-specified study reporting forms.
Study phase
Longer-turnaround sequencingRapid turnaround sequencing
N HOCI cases373947
Review of IPC actions already takenSupportRefuteMissingSupportRefuteMissing
SRT results support or refute IPC actions already taken*200/213 (93.9)7/213 (3.3)2389/428 (90.9)9/428 (2.1)7
Changes to IPC practice following SRTTo enhancedTo routineNo changeTo enhancedTo routineNo change
Change to cleaning protocols on ward2/185 (1.1)0/185 (0.0)183/185 (98.9)7/341 (2.1)0/341 (0.0)334/341 (97.9)
To greaterTo fewerNo changeTo greaterTo fewerNo change
Change to visitor restrictions1/186 (0.5)0/186 (0.0)185/186 (99.5)1/340 (0.3)0/340 (0.0)339/340 (99.7)
To ‘cohort nursing’To ‘other restrictions’No changeTo ‘cohort nursing’To ‘other restrictions’No change
Change to staffing restrictions on ward0/186 (0.0)1/186 (0.5)185/186 (99.5)0/336 (0.0)1/336 (0.3)335/336 (99.7)
IncreaseDecreaseNo changeIncreaseDecreaseNo change
Hand hygiene audit frequency1/185 (0.5)0/185 (0.0)184/185 (99.5)10/335 (3.0)0/335 (0.0)325/335 (97.0)
IPC staff visits to ward1/185 (0.5)0/185 (0.0)184/185 (99.5)14/335 (4.2)0/335 (0.0)321/335 (95.8)
Assessment of alcogel stocks0/185 (0.0)0/185 (0.0)185/185 (100.0)2/335 (0.6)0/335 (0.0)333/335 (99.4)
Assessment of soap stocks0/185 (0.0)0/185 (0.0)185/185 (100.0)2/334 (0.6)0/334 (0.0)332/334 (99.4)
Assessment of aseptic non-touch technique compliance0/185 (0.0)0/185 (0.0)185/185 (100.0)8/335 (2.4)0/335 (0.0)327/335 (97.6)
Assessment of PPE supply1/185 (0.5)0/185 (0.0)184/185 (99.5)9/336 (2.7)0/336 (0.0)327/336 (97.3)
Availability of doffing and donning buddy0/185 (0.0)0/185 (0.0)185/185 (100.0)1/333 (0.3)0/333 (0.0)332/333 (99.7)
IPC signage assessment1/185 (0.5)0/185 (0.0)184/185 (99.5)12/336 (3.6)0/336 (0.0)324/336 (96.4)
IPC signage implementation1/185 (0.5)0/185 (0.0)184/185 (99.5)11/336 (3.3)0/336 (0.0)325/336 (96.7)
Training on IPC procedures0/185 (0.0)0/185 (0.0)185/185 (100.0)8/336 (2.4)0/336 (0.0)328/336 (97.6)
  1. Data shown as n or n/N (%). Overall impact on IPC actions per HOCI case is given in Table 2.

  2. HOCI, hospital-onset COVID-19 infection; IPC, infection prevention and control; PPE, personal protective equipment; SRT, sequence reporting tool.

  3. *

    Sites could select ‘Yes’ or ‘No’ for both ‘Support’ and ‘Refute’ as these were entered as separate data items.

Appendix 1—table 3
Descriptive summary of impact of sequencing on IPC actions implemented during study intervention phases, only including the first HOCI in each IPC+sequencing-defined outbreak event, as recorded on pre-specified study reporting forms.
Study phase
Longer-turnaround sequencingRapid turnaround sequencing
N HOCI cases41135
Review of IPC actions already takenSupportRefuteMissingSupportRefuteMissing
SRT results support or refute IPC actions already taken*30/35 (85.7)3/35 (8.6)071/82 (86.6)5/82 (6.1)2
Changes to IPC practice following SRTTo enhancedTo routineNo changeTo enhancedTo routineNo change
Change to cleaning protocols on ward1/34 (2.9)0/34 (0.0)33/34 (97.1)1/70 (1.4)0/70 (0.0)69/70 (98.6)
To greaterTo fewerNo changeTo greaterTo fewerNo change
Change to visitor restrictions0/34 (0.0)0/34 (0.0)34/34 (100.0)1/70 (1.4)0/70 (0.0)69/70 (98.6)
To ‘cohort nursing’To ‘other restrictions’No changeTo ‘cohort nursing’To ‘other restrictions’No change
Change to staffing restrictions on ward0/34 (0.0)1/34 (2.9)33/34 (97.1)0/70 (0.0)1/70 (1.4)69/70 (98.6)
IncreaseDecreaseNo changeIncreaseDecreaseNo change
Hand hygiene audit frequency0/34 (0.0)0/34 (0.0)34/34 (100.0)3/70 (4.3)0/70 (0.0)67/70 (95.7)
IPC staff visits to ward0/34 (0.0)0/34 (0.0)34/34 (100.0)5/70 (7.1)0/70 (0.0)65/70 (92.9)
Assessment of alcogel stocks0/34 (0.0)0/34 (0.0)34/34 (100.0)2/70 (2.9)0/70 (0.0)68/70 (97.1)
Assessment of soap stocks0/34 (0.0)0/34 (0.0)34/34 (100.0)2/70 (2.9)0/70 (0.0)68/70 (97.1)
Assessment of aseptic non-touch technique compliance0/34 (0.0)0/34 (0.0)34/34 (100.0)3/70 (4.3)0/70 (0.0)67/70 (95.7)
Assessment of PPE supply0/34 (0.0)0/34 (0.0)34/34 (100.0)3/70 (4.3)0/70 (0.0)67/70 (95.7)
Availability of doffing and donning buddy0/34 (0.0)0/34 (0.0)34/34 (100.0)1/70 (1.4)0/70 (0.0)69/70 (98.6)
IPC signage assessment0/34 (0.0)0/34 (0.0)34/34 (100.0)4/70 (5.7)0/70 (0.0)66/70 (94.3)
IPC signage implementation0/34 (0.0)0/34 (0.0)34/34 (100.0)4/70 (5.7)0/70 (0.0)66/70 (94.3)
Training on IPC procedures0/34 (0.0)0/34 (0.0)34/34 (100.0)4/70 (5.7)0/70 (0.0)66/70 (94.3)
  1. Data shown as n or n/N (%). Overall impact on IPC actions per HOCI case is given in Appendix 1—table 1.

  2. HOCI, hospital-onset COVID-19 infection; IPC, infection prevention and control; PPE, personal protective equipment; SRT, sequence reporting tool.

  3. *

    Sites could select ‘Yes’ or ‘No’ for both ‘Support’ and ‘Refute’ as these were entered as separate data items.

Appendix 1—table 4
Per-sample costs of SARS-CoV-2 genome rapid and longer-turnaround sequencing.
LaboratoriesLab 1Lab 2Lab 3Lab 4Lab 5Lab 6Lab 7Lab 8Lab 9Lab 10
Rapid turnaround sequencing
Sequencing platformIllumina MiSeqNanopore MinION/ GridiONNanopore GridiONNanopore GridiONNanopore GridiONNanopore MinION/ GridiONNanopore GridiONNanopore GridiONIllumina MiSeqIllumina MiSeqMean
Batch size24242496242424249696
Equipment£45.11£26.06£19.34£4.38£12.38£24.66£11.99£11.26£5.91£6.13£16.72
Consumables£69.14£54.56£87.07£31.11£79.06£28.84£62.09£46.02£14.37£39.63£51.19
Staff£6.11£20.25£24.66£7.93£11.16£5.66£12.16£8.45£2.20£3.45£10.20
Total per-sample cost£120.36£100.87£131.07£43.43£102.60£59.17£86.23£65.73£22.48£49.21£78.11
Total cost (including overheads calculated at 20%)£144.43£121.04£157.28£52.11£123.12£71.01£103.48£78.88£26.97£59.05£93.74
Longer-turnaround sequencing
Sequencing platformIllumina MiSeqNanopore MinION/ GridiONNanopore GridiONNanopore GridiONNanopore GridiONNanopore MinION/ GridiONNanopore GridiONNanopore GridiONNanopore MinIONIllumina MiSeqMean
Batch size24242496242424962496
Equipment£40.60£22.15£17.02£3.94£11.88£22.44£11.27£2.81£2.54£5.76£14.04
Consumables£61.53£48.56£77.49£27.69£70.36£25.67£55.26£11.51£33.75£35.27£44.71
Staff£4.95£15.19£16.52£2.78£2.23£4.53£12.04£8.45£11.85£3.32£8.19
Total per-sample cost£107.08£85.89£111.03£34.41£84.48£52.65£78.56£22.77£48.13£44.34£66.94
Total cost (including overheads calculated at 20%)£128.50£103.07£133.24£41.29£101.38£63.18£94.28£27.33£57.76£53.21£80.32
Appendix 1—table 5
Incidence outcomes by study intervention phase with unadjusted IRR.
Study phaseIRR (95% CI, p-value)
BaselineLonger-turnaroundRapidLonger-turnaround vs. baselineRapid vs. baseline
All sites
n HOCI cases850373947
n IPC-defined HAIs488207576
Weekly incidence of IPC-defined HAIs per 100 inpatients, mean (median, IQR, range)* [primary outcome]1.0 (0.5, 0.0–1.4, 0.0–5.6)0.7 (0.3, 0.0–0.7, 0.0–7.6) 0.6 (0.3, 0.0–0.8, 0.0–5.3) 0.49 (0.21–1.19, 0.12)0.47 (0.21–1.08, 0.07)
n IPC-defined outbreak events12933114
Weekly incidence of IPC-defined outbreak events per 100 inpatients, mean (median, IQR, range)*0.3 (0.1, 0.0–0.4, 0.0–2.3)0.1 (0.0, 0.0–0.1, 0.0–0.9) 0.1 (0.0, 0.0–0.0, 0.0–0.9) 0.25 (0.10–0.66, 0.005)0.23 (0.10–0.54, 0.001)
  1. IPC-defined HAIs are considered to be ‘probable’ or ‘definite’ HAIs.

  2. HAI, hospital-acquired infection; HOCI, hospital-onset COVID-19 infection; IPC, infection prevention and control; IQR, interquartile range; IRR, incidence rate ratio.

  3. *

    Descriptive data over all week-long periods at all study sites.

  4. Without adjustment for proportion of current inpatients at site that are COVID-19 cases, community incidence rate, and calendar time.

  5. Not including data from the first week of each intervention period, or in the week following any break in the intervention period.

Data availability

A fully anonymised version of the dataset generated and analysed for this study is available on the UCL Research Data Repository (https://doi.org/10.5522/04/20769637.v1).

The following data sets were generated
    1. Oliver S
    2. James B
    3. Andrew C
    4. Judith B
    (2022) Dyrad Digital Repository
    COG-UK hospital-onset COVID-19 infection study dataset.
    https://doi.org/10.5522/04/20769637.v1

References

Decision letter

  1. Marc J Bonten
    Reviewing Editor; University Medical Center Utrecht, Netherlands
  2. Jos W van der Meer
    Senior Editor; Radboud University Medical Centre, Netherlands
  3. Marc J Bonten
    Reviewer; University Medical Center Utrecht, Netherlands

Our editorial process produces two outputs: (i) public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Decision letter after peer review:

Thank you for submitting your article "Effectiveness of rapid SARS-CoV-2 genome sequencing in supporting infection control for hospital-onset COVID-19 infection: multicenter, prospective study" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, including Marc J Bonten as Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Jos van der Meer as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Ben S Cooper and Mark Pritchard (Reviewer #3).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

The questions raised by the reviewers address clarifications and interpretation of data, and we invite you to respond to these.

Reviewer #1 (Recommendations for the authors):

Interpretational questions, comments

The turnaround times achieved are, definitely for the "rapid" part, much longer than was targeted, with overall 9,3% reported in time. This implies that even in the settings tested (which I consider "as good as it gets") these targets were too optimistic. Yet, there is a clear difference in reporting time between the 2 strategies, and that difference, as achieved, was not associated with a beneficial outcome on any of the measured outcome parameters. In fact, the proportion of HOCI in which an unexpected linkage was detected based upon WGS was low in both study phases. And the changes in IPC that were realized were infrequent (7.4-7.8%), and associated with short turnaround times. My interpretation would be that reporting of WGS was still too slow to act as a true intervention, and, therefore, no effects could be demonstrated, as stated "In 91.9%, (589/641, Table S2) of cases the reports were interpreted as supportive of IPC actions already taken."

Nevertheless, it was stated that "Extensive qualitative analyses[15] found high levels of acceptability for the SRT sequencing reports, which supported decision-making about IPC activity (e.g. stand down some IPC actions or continue as planned)."

I think that the authors are overly optimistic about the impact of WGS reporting in this study in the discussion:

Line 295: "Our study provides a substantial body of evidence regarding the introduction of viral WGS into hospital functioning, routine IPC practice, its potential impact on outbreak management and the challenges that need to be overcome to achieve implementation across the UK." I don't understand what is meant with "that the study provides evidence for its potential impact", as statistically significant impact is not demonstrated for any of the study outcomes.

Line 346: "Our study provides the first evidence that with faster turnaround times, viral sequences can inform ongoing IPC actions in managing nosocomial infections; results returned within {less than or equal to}5 days from sampling to result changed the actions of IPC teams in around 20% of cases." On an intention-to-treat basis there was no significant effect on the occurrence of HOCI, and it doesn't become clear from Tables S2 and S3 that IPC practices were changed importantly. In fact, according to these tables it looks as if hardly any IPC actions were changed. It is not clear from these tables if – in instances where measures were taken – more measures were taken simultaneously. That could be made more clear. For instance in Table S3 for Rapid and support: were measures taken in 5 of 70 outbreaks or in 30 of 70 (if you sum up all individual measures).

The research question remains whether even shorter turnaround times for more patients with HOCI could have an impact on transmissions and – ultimately – patient outcome. I would think that WGS could only have such an impact if IPC measures would be different for different genotypes or would be different if transmission was demonstrated. The authors could better explain why and under what circumstances they expect that WGS reporting could impact patient care.

Reviewer #2 (Recommendations for the authors):

1. The study appears to have excluded cases where linkage between cases was established on epidemiological grounds before sequencing occurred. This both makes it more difficult to see an effect of sequencing and begs the question about the intensity and comparability of IPC programs across the study hospitals. Hospitals with aggressive and capable IPC teams may have very good insight into nosocomial case sources and factors by virtue of their epidemiologic evaluations and thus have less room to benefit from sequencing.

2. The investigators report that nosocomial infection was not suspected in ~7% of HOCI cases. Please contextualize by stating what % of HOCI cases were suspected to be nosocomial.

3. It would be helpful to describe in the main body of the manuscript the kinds of actions that were expected of IPC programs and how they might decrease hospital-acquired infections.

4. Please clarify the criteria for higher performer sites. On the face of it, SRT in ~40% of cases and median time from diagnostic sample to SRT of up to 8 days doesn't seem like high performance relative to the study goals. Were there other aspects to these sites that differentiated them besides sequencing speed? Did they have larger or more experienced or more aggressive infection control teams just as they appear to have more effective sequencing teams?

5. Was there a standardized protocol for how IPC teams were to respond to sequencing data? Without this it's possible that opportunities existed to reduce transmission based on sequencing reports that were not taken.

6. Did study sequencing and SRTs incorporate healthcare worker test results? If so, please describe testing requirements for healthcare workers. Did they get serial testing or only in response to symptoms or exposures? If cluster detection did not include healthcare worker test results then this could lead to substantial underdetection of nosocomial events as well as obscure to IPCs the source of nosocomial cases and thus how best to contain them.

7. Please comment on the median incubation period for hospital-onset cases. With α and subsequent variants, incubation periods have become quite short thus progressively increasing the importance of speed in sequencing to have a potential impact on infections.

8. In our hospital experience, the highest value from sequencing has not been control of live outbreaks but rather insights gained regarding the robustness of global infection control policies (is a surgical mask protective, do AGPs increase risk, what is the risk of transmission between people in shared rooms, etc.). Hospitals that choose to act on these insights will make structural changes to their IPC programs (e.g. switch from surgical mask to respirators, change testing practices around AGPs, decrease use of shared rooms, increase ventilation within rooms, etc.) that ideally will not just be cluster-specific but lasting changes. This means that the incremental value of sequencing may decrease over time to the extent that one puts these successive changes into place. But sequencing itself was invaluable in providing the insights that led to these changes. In addition, these insights and policy changes are arguably less sensitive to turnaround time – the insights themselves are key but the responses transcend any one cluster and thus are less time sensitive.

Reviewer #3 (Recommendations for the authors):

1. The potential effect of the intervention depends on infection prevention measures that would have been taken in the absence of sequencing, and how hospitals' infection prevention and control teams respond to the sequencing results. There is therefore considerable potential for variability in the effect of the intervention between hospitals. From the methods it is not clear how analyses accounted for the effect of differences between hospitals. More generally, there is a lack of transparency as to precisely what the modelling assumptions were. It is important to either specify the models used unambiguosly using appropriate notation (including details on link functions and all random effects) in the methods or to provide the full STATA code used for the analysis (ideally, of course, both of these things should be done). It also important to specify which analyses were pre-specified in a statistical analysis plan and which were unplanned.

2. What the authors refer to as the "per protocol" sensitivity analysis in fact seems to be an unplanned sub-group analysis. Given that none of the trusts completely adhered to the protocol referring to this as a "per protocol" analysis seems misleading.

3. It is also important to see the difference between hospitals displayed in the results. Taking Figure 2 as an example, as the results are currently presented it is impossible to tell whether trends with increased time to result represent trends within each hospital, or a difference between those hospitals that tend to produce rapid results compared to those that tend to produce less rapid results. It should be a simple matter to produce a graph showing unaggregated results from each hospital together with the fitted model (and prediction intervals).

4. As the authors discuss, this trial took place during a period of significant variation in covid-19 prevalence, both in the community and among hospital patients. They report having adjusted their results for this. It would be useful to see both unadjusted and adjusted results to show the impact of this adjustment.

5. One hospital did not participate in the longer turnaround arm as this was considered inferior to their standard practice. Please describe current levels of whole genome use in these hospitals so that it is explicit how the intervention differs from the baseline conditions.

6. Please re-check the odds ratio on line 222. The rapid sequencing has a greater proportion of finding it helpful so the odds ratio should be greater than 1. If this is correct due to adjustment, please report this as adjusted OR and add details of adjustment.

7. It is important that the discussion starts with a clear statement that for the two primary outcomes no benefit of either intervention was shown and that this was true both in the main analysis and in the sensitivity analysis.

8. Line 274 "Among sites with the most effective implementation of the sequencing intervention we showed that feedback within 5 days of diagnosis allowed for maximal impact on IPC actions". This appears to be an unplanned subgroup analysis of a secondary outcome, and as such needs to come with suitable caveats.

9. Line 317 " may reflect a shorter incubation time for the Α variant compared to earlier variants" While there has been speculation about shorter incubation times for the Α variant, we are not aware of strong evidence that supports this, and recent analysis suggests similar generation time for the Α variant and earlier variants (see, for example, https://doi.org/10.1101/2021.12.08.21267454 which has now been accepted for publication in eLife).

10. Line 345. "Our study provides the first evidence that with faster turnaround

times, viral sequences can inform ongoing IPC actions in managing nosocomial infections; " While generally the paper strikes an appropriate note of cautious optimism for the approach despite the negative results in this trial, and it is understandable that the authors might want to emphasize the more positive aspects of the results when considering implications for sequencing to inform IPC measures, there is a danger that this statement (and the whole paragraph) is interpreted as attempting to put a positive spin on an essentially negative result. Thus, while it is reasonable to mention that in a minority of cases IPC actions were informed by viral sequencing data, it is equally important to emphasize that there was no evidence those measures had a positive impact on patient outcomes (it is surely equally possible that IPC actions informed by sequencing had a negative impact). Furthermore, it needs to be emphasized that in the main (planned) analysis there was no difference in the number of IPC actions informed by the sequencing results in rapid and longer-turnaround arms. It would be helpful to add here an explicit acknowledgement of the uncertain benefits and resource implications in this paragraph along with a discussion of how we might robustly evaluate the effectiveness (and cost-effectiveness) of WGS methods for IPC in the future, and in particular, emphasising what we learn from this trial.

11. Lines 596-602 Two planned secondary outcomes are not reported because they were either not thought to be useful or were insufficiently complete. While it seems reasonable to not conduct formal analysis for these reasons, surely these outcomes should still be reported somewhere given they were specified in the protocol and data does seem to have been obtained for them. Clearly, if researchers are permitted to selectively choose which of the planned outcomes to report there is large potential for bias.

Additional comments

1. line 528-9 "Site A observed 0 HOCI cases " – presumably this is just for longer-turnaround phase, as A appears on the graph in the rapid phase.

2. Figures S3 and S4: solid and dashed lines are not defined. Also, shading might be clearer than the dashed lines.

3. In Table 3, a number of outcomes are reported as 0.0 per 100 patients. It would be more helpful to display these in a unit that allows easy display of at least one significant figure, e.g. per 10 000.

4. Some figures use the colours red and green as the only way to distinguish between baseline and rapid periods. The additional distinction might help readers who are unable to distinguish between these colours.

5. The results have a subsection titled health economics. This is limited to laboratory costs, and has no reference to changes in outcomes or costs or savings associated with changing practice on the wards. We suggest changing the title to costs of tests.

6. Lab 9 appears to have had larger batch sizes and a significant cost saving associated with rapid testing. Is this due to the difference in prevalence at the different times they were following each protocol, or is there a difference in practice that could be applied more widely?

7. It is interesting to note how much more frequently teams reported finding the report useful compared to how often they reported using it to change practice. Was any information collected on this? Were they reporting reassurance from the results or were they useful in some other way?

8. Please define IRR on first use in the text and abstract.

9. Abstract and line 200 "We did not detect a statistically significant change".

Recommendations in the statistical literature (e.g. consensus statement from the ASA or https://www.bmj.com/content/322/7280/226.1) advise against dichotomising results based on arbitrary p-values. While clearly many researchers do continue to use the language of "statistical significance" despite such recommendations, we would at least suggest the authors consider avoiding what many consider to be outmoded practice.

10. We were not able to open either the pdf or MS Word versions of the Reporting Standards Document "CONSORT Checklist".

[Editors’ note: further revisions were suggested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "Effectiveness of rapid SARS-CoV-2 genome sequencing in supporting infection control for hospital-onset COVID-19 infection: multicenter, prospective study" for further consideration by eLife. Your revised article has been evaluated by Jos van der Meer (Senior Editor) and a Reviewing Editor.

The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:

In an ideal world, it would be nice if there was more framing of the work in the abstract, intro, and discussion to highlight that the study took place during an earlier phase of the pandemic when there was less insight into viral transmission, gaps in public health teaching about how best to prevent nosocomial transmission of SARS-CoV-2, and dire shortages of PPE, personnel, beds, and lab capacity. These are all important caveats that luckily are less acute now but strongly impact the interpretation of the study. I wish these caveats were pronounced more prominently in the paper in order to defend against the overly simplistic conclusion that WGS does not aid infection control of SARS-CoV-2.

https://doi.org/10.7554/eLife.78427.sa1

Author response

Reviewer #1 (Recommendations for the authors):

Interpretational questions, comments

The turnaround times achieved are, definitely for the "rapid" part, much longer than was targeted, with overall 9,3% reported in time. This implies that even in the settings tested (which I consider "as good as it gets") these targets were too optimistic. Yet, there is a clear difference in reporting time between the 2 strategies, and that difference, as achieved, was not associated with a beneficial outcome on any of the measured outcome parameters. In fact, the proportion of HOCI in which an unexpected linkage was detected based upon WGS was low in both study phases. And the changes in IPC that were realized were infrequent (7.4-7.8%), and associated with short turnaround times. My interpretation would be that reporting of WGS was still too slow to act as a true intervention, and, therefore, no effects could be demonstrated.

We have added a further explicit acknowledgments that the target turnaround times were not met in the Results:

“The median turnaround time from diagnostic sampling for reports returned was 5 days in the rapid phase and 13 days in the longer-turnaround phase, substantially longer than the targets of 48 hours and 5-10 days, respectively. A detailed breakdown of reporting turnaround times is reported separately[15].”,

and in the Discussion

“The study sites varied in their ability to process sequence and meta-data and generate and distribute reports in a timely manner (Figure 1), and the targeted turnaround times for reporting were not achieved at any of the sites for the majority of HOCIs in either the ‘rapid’ or ‘longer turnaround’ phases.”

We have also added a comment that improved turnaround times might lead to a greater impact on IPC practice:

“It may therefore be more achievable to develop effective systems for rapid viral WGS and feedback for endemic respiratory viruses at lower and more consistent levels, and more timely reporting of results might be associated with greater impact on IPC actions.”

Nevertheless, it was stated that "Extensive qualitative analyses[15] found high levels of acceptability for the SRT sequencing reports, which supported decision-making about IPC activity (e.g. stand down some IPC actions or continue as planned)."

With regards to the high acceptability of the intervention for IPC teams, we have noted in the existing text describing the qualitative analyses that “The SRT did provide new and valued insights into transmission events, outbreaks and wider hospital functioning but mainly acted to offer confirmation and reassurance to IPC teams”. As such, high acceptability of the intervention does not conflict with the high proportion of cases in which reports were considered supportive of IPC actions already taken.

Please note that we have also added a reference to a paper specifically describing qualitative analysis of the acceptability of whole genome sequencing for infection control associated with this study, which is now available on medRxiv:

Flowers P, McLeod J, Mapp F, Stirrup O, Blackstone J, Snell L, et al. How acceptable is rapid whole genome sequencing for infectious disease management in hospitals? Perspectives of those involved in managing nosocomial SARS-CoV-2. medRxiv 2022:2022.2006.2015.22276423.

This work is separate to the qualitative process evaluation for the present study (which is not yet available as a preprint):

Mapp F, Flowers P, Blackstone J, Stirrup O, Copas A, Breuer J. Understanding the effectiveness of rapid SARS-CoV-2 genome sequencing in supporting infection control teams through qualitative process evaluation: the COG-UK hospital-onset COVID-19 infection study. Submitted 2022.

I think that the authors are overly optimistic about the impact of WGS reporting in this study in the discussion:

Line 295: "Our study provides a substantial body of evidence regarding the introduction of viral WGS into hospital functioning, routine IPC practice, its potential impact on outbreak management and the challenges that need to be overcome to achieve implementation across the UK." I don't understand what is meant with "that the study provides evidence for its potential impact", as statistically significant impact is not demonstrated for any of the study outcomes.

As the sequencing intervention was qualitatively reported to be useful for the majority of cases in which SRT reports were returned and as there was an impact on IPC actions in some HOCI cases, we feel that it is reasonable to discuss a ‘potential impact on outbreak management’ despite the fact that we did not demonstrate a reduction in the incidence of HAIs during the study intervention phases (particularly as we know that the capacity of IPC teams to respond was breached during peaks in SARS-CoV-2 cases). However, to moderate the tone of this statement, we have edited ‘impact on’ to ‘usage for’ here.

Line 346: "Our study provides the first evidence that with faster turnaround times, viral sequences can inform ongoing IPC actions in managing nosocomial infections; results returned within {less than or equal to}5 days from sampling to result changed the actions of IPC teams in around 20% of cases." On an intention-to-treat basis there was no significant effect on the occurrence of HOCI, and it doesn't become clear from Tables S2 and S3 that IPC practices were changed

Tables S2 and S3 only include pre-specified options that were listed on reporting forms developed during the summer of 2020. However, we have now clarified in the Methods that

“There was considered to be an impact on IPC actions if this was recorded for any of a number of pre-defined outcomes, or if it was stated that the report had effected any change to IPC practice on that ward or elsewhere within the hospital”,

and added in the Results ‘among the options included within study reporting forms’. As noted in the Methods, a full description of outcome variable coding is provided in the Supplementary Appendix.

It is not clear from these tables if – in instances where measures were taken – more measures were taken simultaneously. That could be made more clear. For instance in Table S3 for Rapid and support: were measures taken in 5 of 70 outbreaks or in 30 of 70 (if you sum up all individual measures).

The proportion of HOCI cases with any impact on IPC actions recorded is summarised in Tables 2 (for all cases) and S1 (for the first case within each defined outbreak). This has now been flagged in the footnotes for Tables S2 and S3.

The research question remains whether even shorter turnaround times for more patients with HOCI could have an impact on transmissions and – ultimately – patient outcome.

We agree with this point, and have added the following sentence at the end of the Discussion

“It remains to be demonstrated that viral sequencing can have a direct impact on clinical outcomes such as the incidence of HAIs, and further prospective studies with refined implementation of similar interventions are required to address this”.

I would think that WGS could only have such an impact if IPC measures would be different for different genotypes or would be different if transmission was demonstrated. The authors could better explain why and under what circumstances they expect that WGS reporting could impact patient care.

We have added the following to the Discussion, particularly with reference to development of an improved reporting system with more detailed patient movement data and transmission network analysis:

“Implementation of an improved tool with these features might help to better identify routes of transmission within a hospital that could be interrupted, e.g. through changes to the management of ward transfers for patients, isolation policies or identification of areas within the hospital linked to high risk of transmission.”

We also now note in the Discussion that

“In the planning of an equivalent study now, there would be a greater focus on adjustments to ventilation, air filtration and respirator usage.”

Reviewer #2 (Recommendations for the authors):

1. The study appears to have excluded cases where linkage between cases was established on epidemiological grounds before sequencing occurred. This both makes it more difficult to see an effect of sequencing and begs the question about the intensity and comparability of IPC programs across the study hospitals. Hospitals with aggressive and capable IPC teams may have very good insight into nosocomial case sources and factors by virtue of their epidemiologic evaluations and thus have less room to benefit from sequencing.

We did not exclude any HOCI cases on the basis that linkage had already been observed prior to sequencing. However, the Reviewer is correct that for the second primary outcome “identification of linkage to individuals within an outbreak of SARS-CoV-2 nosocomial transmission using sequencing … that was not identified by pre-sequencing IPC evaluation”, there may have been ‘less room for improvement’ with more thorough initial IPC investigation. We have clarified the evaluation of this outcome with the following new sentence in the Methods

‘The second outcome used all observed HOCI cases as the denominator, and so represented the proportion of cases in which sequencing provided information regarding potential transmission routes where none had been previously uncovered.’

2. The investigators report that nosocomial infection was not suspected in ~7% of HOCI cases. Please contextualize by stating what % of HOCI cases were suspected to be nosocomial.

All included HOCI cases were suspected by IPC teams to be nosocomial infections, although there will have been some uncertainty for the 899 (41.4%) indeterminate cases diagnosed 3-7 days after admission. We have clarified interpretation of the ~7% values in the Results:

‘Nosocomial linkage to other individual cases, where initial IPC investigation had not correctly identified any such linkage, was identified in 6.7% and 6.8% of all HOCI cases in the rapid and longer-turnaround phases, respectively’.

3. It would be helpful to describe in the main body of the manuscript the kinds of actions that were expected of IPC programs and how they might decrease hospital-acquired infections.

We have added the following to the Results ‘Guidance regarding IPC actions was not specified as part of this study. Sites were expected to follow current national guidelines, which evolved throughout the course of the pandemic.’ However, we have now mentioned within the main text of the Methods some of the pre-defined changes to IPC actions recorded on study forms:

‘There was considered to be an impact on IPC actions if this was recorded for any of a number of pre-defined outcomes (e.g. enhanced cleaning, visitor and staffing restrictions, provision of personal protective equipment), or if it was stated that the report had effected any change to IPC practice on that ward or elsewhere within the hospital.’

As noted in response to the public comments above, we have also added the following in the Discussion:

“Our qualitative analyses also found that the capacity of sites … was breached by the volume of HOCI cases in combination with the finite personnel resources and limited physical space for isolation that was available” and “Planning this study … during the early stages of a novel viral pandemic was challenging… In the planning of an equivalent study now.. It would also be possible to be more prescriptive and standardised regarding the recommended changes to IPC practice in response to sequencing findings”.

4. Please clarify the criteria for higher performer sites. On the face of it, SRT in ~40% of cases and median time from diagnostic sample to SRT of up to 8 days doesn't seem like high performance relative to the study goals.

We have added the following to the Results:

“The criteria for this analysis were decided after data collection but prior to data analysis, as per the statistical analysis plan. However, we acknowledge that the ‘higher performing sites’ did not meet the target turnaround time for reporting in the rapid phase; criteria were therefore set to split the sites into upper and lower 50% based on level of implementation”.

As stated in the Results, the ‘higher performing sites’ “…returned ≥40% of SRTs within a median time from diagnostic sample of ≤8 days within their rapid phase”. These criteria were used to select the sites in the bottom right corner of the ‘Rapid phase’ plot in Figure 1.

Were there other aspects to these sites that differentiated them besides sequencing speed? Did they have larger or more experienced or more aggressive infection control teams just as they appear to have more effective sequencing teams?

Yes, there were differences between the sites in their staffing and organisation of IPC teams, but these were not formally investigated. Some discussion regarding how this impacted on implementation of the HOCI intervention will be included in the full qualitative process evaluation, for example: routinisation of discussion of sequencing reports, dynamic leadership, effective and rapid sharing of sequencing reports. We have now switched to analysis of a fully anonymised data without site names for data governance reasons and are unable to link the study data back to individual sites.

5. Was there a standardized protocol for how IPC teams were to respond to sequencing data? Without this it's possible that opportunities existed to reduce transmission based on sequencing reports that were not taken.

Please see response to comment #3 above. We agree that opportunities to reduce transmission will have been missed due to imperfect understanding of the virus and optimal strategies, limited staff and PPE and constraints associated with aging buildings and chronic underinvestment in the health service.

In the planning of the study, there was considerable debate about guidance for IPC teams. Given the heterogeneity of the sites and their resources it was felt that it was best left for sites to generate local solutions. The sites were not in a position to routinely follow systematic guidance for IPC reactions to sequencing data in winter 2020/2021.

6. Did study sequencing and SRTs incorporate healthcare worker test results? If so, please describe testing requirements for healthcare workers. Did they get serial testing or only in response to symptoms or exposures? If cluster detection did not include healthcare worker test results then this could lead to substantial underdetection of nosocomial events as well as obscure to IPCs the source of nosocomial cases and thus how best to contain them.

We have added the following text to the Methods:

‘Sequencing data from healthcare workers (HCW) could be utilised in the SRT system, and this was implemented by 8/14 sites. Whether this was done depended on availability of HCW samples for each lab as staff testing was generally managed separately to patient testing. HCW testing protocols followed national guidelines’.

We have also added the following to the Discussion:

‘HCW sequencing data could not be incorporated at all sites dues to logistical and data management and access constraints’.

7. Please comment on the median incubation period for hospital-onset cases. With α and subsequent variants, incubation periods have become quite short thus progressively increasing the importance of speed in sequencing to have a potential impact on infections.

We had mentioned the potentially lower incubation time of the Α variant in the Discussion, but we have now also added the following:

‘Variants with shorter incubation times would lead to a greater importance for the rapidity of feedback in informing adjustments to IPC actions’.

8. In our hospital experience, the highest value from sequencing has not been control of live outbreaks but rather insights gained regarding the robustness of global infection control policies (is a surgical mask protective, do AGPs increase risk, what is the risk of transmission between people in shared rooms, etc.). Hospitals that choose to act on these insights will make structural changes to their IPC programs (e.g. switch from surgical mask to respirators, change testing practices around AGPs, decrease use of shared rooms, increase ventilation within rooms, etc.) that ideally will not just be cluster-specific but lasting changes. This means that the incremental value of sequencing may decrease over time to the extent that one puts these successive changes into place. But sequencing itself was invaluable in providing the insights that led to these changes. In addition, these insights and policy changes are arguably less sensitive to turnaround time – the insights themselves are key but the responses transcend any one cluster and thus are less time sensitive.

Thank you for flagging this. We agree with the points raised, and have added the following to the Discussion:

‘As well as acute changes to IPC actions, there is the potential for routine pathogen sequencing to allow prospective IPC practice and policies to be refined. This could enable a longer-term reduction in the incidence of nosocomial infection at any given site, and such effects would be less dependent on turnaround time of sequencing in any given case. However, the capacity of sites to make such informed adjustments to IPC practice were limited during peaks in incidence of SARS-CoV-2 over the timescale of the present study’.

Reviewer #3 (Recommendations for the authors):

1. The potential effect of the intervention depends on infection prevention measures that would have been taken in the absence of sequencing, and how hospitals' infection prevention and control teams respond to the sequencing results.

We have added to the Methods ‘Guidance regarding IPC actions was not specified as part of this study. Sites were expected to follow current national guidelines, which evolved throughout the course of the pandemic’, and also relevant text to the Results:

“Planning this study … during the early stages of a novel viral pandemic was challenging… In the planning of an equivalent study now.. It would also be possible to be more prescriptive and standardised regarding the recommended changes to IPC practice in response to sequencing findings”.

We do not feel that it would have been possible to be prescriptive regarding IPC actions in the summer of 2020, particularly given differences between sites in IPC organisation and available resources.

There is therefore considerable potential for variability in the effect of the intervention between hospitals. From the methods it is not clear how analyses accounted for the effect of differences between hospitals.

The analysis models included site-phase random effect terms, which allow for variability in the effect of the intervention. However, we were aiming to find evidence of an overall reduction in the incidence of nosocomial infections across the sites. Analyses did not directly adjust for differences between hospitals in IPC actions.

More generally, there is a lack of transparency as to precisely what the modelling assumptions were. It is important to either specify the models used nambiguously using appropriate notation (including details on link functions and all random effects) in the methods or to provide the full STATA code used for the analysis (ideally, of course, both of these things should be done).

We feel that the original text of the manuscript does give an appropriate degree of detail regarding the models used for statistical analysis. However, we have added further clarification to the Appendices as requested within a new subsection

‘Further details for incidence model specification’ which includes indicative Stata code. We have also added to the Statistical Analysis subsection of the main text that such models ‘..in this context correspond to Poisson regression with an additional overdispersion parameter’.

It also important to specify which analyses were pre-specified in a statistical analysis plan and which were unplanned.

For the most part the analyses presented were prespecified in the SAP. However, we have clarified in the text that the results in Figure 2 go beyond the evaluation specified in the SAP, and we have ensured that the ‘Changes with respect to the statistical analysis plan (SAP)’ subsection in the Appendix is fully comprehensive.

2. What the authors refer to as the "per protocol" sensitivity analysis in fact seems to be an unplanned sub-group analysis. Given that none of the trusts completely adhered to the protocol referring to this as a "per protocol" analysis seems misleading.

We have added the following clarification in the ‘Changes with respect to the statistical analysis plan’ section of the Appendix:

“It was stated in the SAP that “We will conduct sensitivity analyses excluding study sites and/or periods with suboptimal implementation of the trial intervention, both in terms of overall population sequencing coverage for HOCIs and the turnaround time for sequence reports being returned to IPC teams. The exact criteria for this will be decided amongst the study team before any analysis has been conducted”. This forms the basis for the ‘per protocol’ analyses presented. It was not possible to prespecify the exact criteria. After data collection for the study had been completed it became clear that none of the sites had met target turnaround times for sequence reporting in the intervention phases, and so it was decided to set criteria to select the 50% ‘higher performing’ sites.”

We feel that ‘per protocol’ is a useful concise term to describe this sensitivity analysis, but have added inverted commas throughout and have now noted in the Results that:

“The criteria for this analysis were decided after data collection but prior to data analysis, as per the statistical analysis plan (SAP). However, we acknowledge that the ‘higher performing sites’ did not meet the target turnaround time for reporting in the rapid phase; criteria were therefore set to split the sites into upper and lower 50% based on level of implementation.”

3. It is also important to see the difference between hospitals displayed in the results. Taking Figure 2 as an example, as the results are currently presented it is impossible to tell whether trends with increased time to result represent trends within each hospital, or a difference between those hospitals that tend to produce rapid results compared to those that tend to produce less rapid results. It should be a simple matter to produce a graph showing unaggregated results from each hospital together with the fitted model (and prediction intervals).

We have added these plots as requested and added a comment in the Results:

‘However, we note that many of the HOCI cases with SRT returned within 5 days were from a single study site, and some sites did not seem to have clearly differentiated ‘useful’ SRT reports when completing data collection (Figures S2 and S3)’.

4. As the authors discuss, this trial took place during a period of significant variation in covid-19 prevalence, both in the community and among hospital patients. They report having adjusted their results for this. It would be useful to see both unadjusted and adjusted results to show the impact of this adjustment.

We do not feel that unadjusted IRR estimates are meaningful given the lack of randomisation, and the clear dependence of the outcomes on the adjustment variables. However, we have now added these values for completeness in Appendix 1—table 5 and noted this additional deviation from the SAP.

5. One hospital did not participate in the longer turnaround arm as this was considered inferior to their standard practice. Please describe current levels of whole genome use in these hospitals so that it is explicit how the intervention differs from the baseline conditions.

We have added a clarification to the Methods: ‘, comprising outbreak sequencing with weekly meetings to discuss phylogenetic analyses; they nonetheless completed the baseline phase of the study without use of the SRT or automated feedback to IPC teams on all HOCI cases’.

6. Please re-check the odds ratio on line 222. The rapid sequencing has a greater proportion of finding it helpful so the odds ratio should be greater than 1. If this is correct due to adjustment, please report this as adjusted OR and add details of adjustment.

Thank you for flagging this, we have added a clarification: ‘although this association was reversed on analysis within the multi-level mode specified,’ and have also now specified in the Methods that mixed effect logistic regression models were also used for ‘…the ‘usefulness’ of SRT reports’.

7. It is important that the discussion starts with a clear statement that for the two primary outcomes no benefit of either intervention was shown and that this was true both in the main analysis and in the sensitivity analysis

We have added a more direct statement at the opening of the Discussion as requested:

‘We did not demonstrate a direct impact of sequencing on the primary outcome of the incidence of HAIs, either on full analysis or when restricted to the higher performing sites, and the overall proportion of cases with nosocomial transmission linkage identified using sequencing that had been missed by IPC investigation was <10% in the intervention phases’.

8. Line 274 "Among sites with the most effective implementation of the sequencing intervention we showed that feedback within 5 days of diagnosis allowed for maximal impact on IPC actions". This appears to be an unplanned subgroup analysis of a secondary outcome, and as such needs to come with suitable caveats.

As noted above, this exploratory investigation was not entirely unplanned. However, we appreciate this point and have added ‘…post hoc exploratory investigation…’ here.

9. Line 317 " may reflect a shorter incubation time for the Α variant compared to earlier variants" While there has been speculation about shorter incubation times for the Α variant, we are not aware of strong evidence that supports this, and recent analysis suggests similar generation time for the Α variant and earlier variants (see, for example, https://doi.org/10.1101/2021.12.08.21267454 which has now been accepted for publication in eLife).

Thank you for flagging this work, we have added this as a reference at the relevant point in the Discussion: ‘(although this remains uncertain[31])’.

10. Line 345. "Our study provides the first evidence that with faster turnaround

times, viral sequences can inform ongoing IPC actions in managing nosocomial infections; " While generally the paper strikes an appropriate note of cautious optimism for the approach despite the negative results in this trial, and it is understandable that the authors might want to emphasize the more positive aspects of the results when considering implications for sequencing to inform IPC measures, there is a danger that this statement (and the whole paragraph) is interpreted as attempting to put a positive spin on an essentially negative result. Thus, while it is reasonable to mention that in a minority of cases IPC actions were informed by viral sequencing data, it is equally important to emphasize that there was no evidence those measures had a positive impact on patient outcomes (it is surely equally possible that IPC actions informed by sequencing had a negative impact). Furthermore, it needs to be emphasized that in the main (planned) analysis there was no difference in the number of IPC actions informed by the sequencing results in rapid and longer-turnaround arms. It would be helpful to add here an explicit acknowledgement of the uncertain benefits and resource implications in this paragraph along with a discussion of how we might robustly evaluate the effectiveness (and cost-effectiveness) of WGS methods for IPC in the future, and in particular, emphasising what we learn from this trial.

We have added some caveats here to the text as requested:

‘We did not demonstrate an effect of our sequencing intervention on our primary outcome of the incidence of HAIs, and there were challenges in the implementation of the intervention. However, our study provides the first prospective evidence that with faster turnaround times, viral sequences can inform ongoing IPC actions in managing nosocomial infections; on post hoc exploratory analysis, results returned within ≤5 days from sampling to result changed the actions of IPC teams in around 20% of cases’.

Given these reminders of the limitations of our results, we feel that this ‘positive’ paragraph is reasonable in the context of the Discussion as a whole, particularly with the changes to the first and last paragraphs in this revised version.

11. Lines 596-602 Two planned secondary outcomes are not reported because they were either not thought to be useful or were insufficiently complete. While it seems reasonable to not conduct formal analysis for these reasons, surely these outcomes should still be reported somewhere given they were specified in the protocol and data does seem to have been obtained for them. Clearly, if researchers are permitted to selectively choose which of the planned outcomes to report there is large potential for bias.

The available descriptive data for these outcomes are summarised in Tables 2 and 3. We have also clarified at the relevant point in the Appendices that the decision to drop formal analysis was made prior to any statistical analysis being conducted, and that data collection for HCW absence ceased during the conduct of the study, reducing the potential for bias through selective reporting. We feel that dropping these outcomes is reasonable given the complexity and pandemic context of the study, and that we have been transparent in our reporting.

Additional comments

1. line 528-9 "Site A observed 0 HOCI cases " – presumably this is just for longer-turnaround phase, as A appears on the graph in the rapid phase.

Yes, this sentence is regarding the longer-turnaround phase ‘Site N did not have a longer-turnaround phase, Site A observed 0 HOCI cases …in this phase’.

2. Figures S3 and S4: solid and dashed lines are not defined. Also, shading might be clearer than the dashed lines.

We have added the following to the captions for each of these figures: ‘The associations for each covariable indicated by model parameter point estimates are shown as solid lines, with 95%CIs shown as dashed lines’. The use of dashed or dotted lines is conventional for plotting 95%CIs.

3. In Table 3, a number of outcomes are reported as 0.0 per 100 patients. It would be more helpful to display these in a unit that allows easy display of at least one significant figure, e.g. per 10 000.

We have changed the units for outbreak events to ‘weekly incidence per 1000 inpatients’ to improve the display.

4. Some figures use the colours red and green as the only way to distinguish between baseline and rapid periods. The additional distinction might help readers who are unable to distinguish between these colours.

Thank you for this comment, we have switched these figures to a colour-blind friendly palette.

5. The results have a subsection titled health economics. This is limited to laboratory costs, and has no reference to changes in outcomes or costs or savings associated with changing practice on the wards. We suggest changing the title to costs of tests.

Thank you for your comment. The scope of the economic evaluation was to evaluate the economic effects of SARS-CoV-2 genome sequencing in supporting infection control teams and not the health benefits of the intervention. Therefore, the outcome of the intervention would be rather the benefit of rapid/slow return of the sequencing report expressed as potential reduction in resource utilisation and costs. A paper presenting the methodology and findings is under preparation. Here, we presented only the cost of the intervention. We will remove the “Health economic findings” heading and re-name it “Cost of SARS-CoV-2 genome sequencing”.

6. Lab 9 appears to have had larger batch sizes and a significant cost saving associated with rapid testing. Is this due to the difference in prevalence at the different times they were following each protocol, or is there a difference in practice that could be applied more widely?

Thank you for your comment. The costing analysis was based entirely on information provided by laboratories. Through the various phases of the study, the situation changed drastically, for example depending upon sample numbers and the required turnaround the pathway adopted was adapted by each laboratory. Generally, costs can vary significantly depending on run size, kits/reagents, staff, with small batches working out more expensive per sample. Lab 9 did use different sequencing platforms in intervention phases: Illumina MiSeq sequencing with 96 samples per run in rapid phase when there was a higher throughput; for lower throughput, as the sample numbers declined, they switched to Nanopore ONP in slower turnaround phase.

7. It is interesting to note how much more frequently teams reported finding the report useful compared to how often they reported using it to change practice. Was any information collected on this? Were they reporting reassurance from the results or were they useful in some other way?

Yes, we feel that reassurance regarding previous conclusions and actions is likely to main explanation here. This is consistent with SRT reports being reported to be consistent with prior IPC actions in >90% of HOCI cases (Appendix 1—table 2). ‘Reassurance’ is also mentioned in our brief summary of the qualitative analysis in the Results, with the full process evaluation yet to be published.

8. Please define IRR on first use in the text and abstract.

We have now done this, thank you for flagging.

9. Abstract and line 200 "We did not detect a statistically significant change".

Recommendations in the statistical literature (e.g. consensus statement from the ASA or https://www.bmj.com/content/322/7280/226.1) advise against dichotomising results based on arbitrary p-values. While clearly many researchers do continue to use the language of "statistical significance" despite such recommendations, we would at least suggest the authors consider avoiding what many consider to be outmoded practice.

Thank you for your comment. We appreciate the sentiment, but feel that the use of this phrase is reasonable in the context of the primary outcome of a prospective study.

10. We were not able to open either the pdf or MS Word versions of the Reporting Standards Document "CONSORT Checklist".

This would seem to be an issue regarding the eLife file system.

[Editors’ note: further revisions were suggested prior to acceptance, as described below.]

In an ideal world, it would be nice if there was more framing of the work in the abstract, intro, and discussion to highlight that the study took place during an earlier phase of the pandemic when there was less insight into viral transmission, gaps in public health teaching about how best to prevent nosocomial transmission of SARS-CoV-2, and dire shortages of PPE, personnel, beds, and lab capacity. These are all important caveats that luckily are less acute now but strongly impact the interpretation of the study. I wish these caveats were pronounced more prominently in the paper in order to defend against the overly simplistic conclusion that WGS does not aid infection control of SARS-CoV-2.

Thank you for these further comments. We have made some adjustments throughout the manuscript in response to the concerns raised.

We have added the following to the Abstract of the manuscript, in order to flag the context of the study as requested:

“A total of 2170 HOCI cases were recorded from October 2020-April 2021, corresponding to a period of extreme strain on the health service, with sequence reports returned for…”

And:

“Capacity to respond effectively to insights from sequencing was breached in most sites by the volume of cases and limited resources.”

These additions mean that the Abstract is now a little over the standard 250 word count limit recommended by eLife. We feel that these additions are justified, as suggested by your comments, and that there is not any other information that could be removed from the Abstract. We hope that the Editors agree that the Abstract word limit may be extended a little in this case.

We have added the following paragraph at the end of the Introduction:

“When this study was planned in the summer of 2020 there was imperfect knowledge regarding the dominant mode of transmission of SARS-CoV-2[10] and it was not possible to predict the future course of the pandemic. In conducting this study, substantial difficulties were encountered in implementing the intervention and in responding effectively to any insights generated. As such, this report serves as a record of the challenge of conducting research within a pandemic as well as being a conventional study summary report.”

With reference:

10. Greenhalgh T, Jimenez JL, Prather KA, Tufekci Z, Fisman D, Schooley R. Ten scientific reasons in support of airborne transmission of SARS-CoV-2. The Lancet 2021; 397(10285):1603-1605.

In the Results section, we have added:

“Critically, given the context of the study within the pandemic timeline, the capacity to generate and respond to these insights effectively on a case-by-case basis was breached in most sites by the volume of HOCIs”

We feel that the Discussion did already cover a number of the issues raised by the Editors. However, in order to highlight these points more clearly to the reader, we have added the following paragraph near the start of the Discussion:

“The study was undertaken during a period of extreme strain on the NHS, with hospitals described as being “in the eye of a covid-19 storm”[19]. Sites reported that they lacked the additional resources, in terms of staff and bed space, needed to respond effectively to insights generated by sequencing. Furthermore, if the study were repeated now then IPC teams would have more evidence-backed tools at their disposal, such as increasing respirator usage. As such, we do not believe that the null result for the impact on incidence of nosocomial transmission should be taken as strong evidence for a general lack of effectiveness of viral WGS for IPC.”

With reference:

19. Oliver D. David Oliver: Could we do better on hospital acquired covid-19 in a future wave? BMJ 2021; 372:n70.

We have also made edits and added small sections of additional text at a few points elsewhere in the Discussion:

“…the challenges faced in implementing the intervention reflected the context and barriers in winter 2020–2021 in the UK”

“It would also be possible to be more prescriptive and standardised regarding the recommended changes to IPC practice in response to sequencing findings, with the potential that our improved knowledge and available tools might facilitate a measurable impact on the incidence of nosocomial transmission.”

“Our study nonetheless provides valuable evidence regarding the implementation and utility of this technology for IPC, and potentially it will have a greater positive impact on IPC practice outside of the burdens and resource constraints imposed by a pandemic”

We have added a couple of direct quotes from IPC staff, taken from our qualitative analyses, in order to further communicate the context of this study:

“Our qualitative analyses also found that the capacity of sites to react to information generated by the sequencing intervention was breached by the volume of HOCI and admitted COVID-19 cases (‘we’ve been basically deluged’ IPC staff) in combination with the finite personnel resources and limited physical space for isolation that was available (‘The trouble is when you have so many wards going down and such a high prevalence of COVID, your actions are kind of the same regardless’ IPC staff).”

We have also added a few further relevant references to the Discussion:

“In the planning of an equivalent study now, there would be a greater focus on adjustments to ventilation[32], air filtration[33] and respirator[34] usage.”

32. Allen JG, Ibrahim AM. Indoor Air Changes and Potential Implications for SARS-CoV-2 Transmission. JAMA 2021; 325(20):2112-2113.

33. Conway Morris A, Sharrocks K, Bousfield R, Kermack L, Maes M, Higginson E, et al. The Removal of Airborne Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and Other Microbial Bioaerosols by Air Filtration on Coronavirus Disease 2019 (COVID-19) Surge Units. Clinical Infectious Diseases 2021:ciab933.

34. Ferris M, Ferris R, Workman C, O'Connor E, Enoch DA, Goldesgeyme E, et al. Efficacy of FFP3 respirators for prevention of SARS-CoV-2 infection in healthcare workers. eLife 2021; 10:e71131.

https://doi.org/10.7554/eLife.78427.sa2

Article and author information

Author details

  1. Oliver Stirrup

    Institute for Global Health, University College London, London, United Kingdom
    Contribution
    Conceptualization, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8705-3281
  2. James Blackstone

    The Comprehensive Clinical Trials Unit, University College London, London, United Kingdom
    Contribution
    Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Project administration, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4335-5269
  3. Fiona Mapp

    Institute for Global Health, University College London, London, United Kingdom
    Contribution
    Conceptualization, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
  4. Alyson MacNeil

    The Comprehensive Clinical Trials Unit, University College London, London, United Kingdom
    Contribution
    Data curation, Project administration, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8409-2755
  5. Monica Panca

    The Comprehensive Clinical Trials Unit, University College London, London, United Kingdom
    Contribution
    Formal analysis, Investigation, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
  6. Alison Holmes

    Imperial College Healthcare NHS Trust, London, United Kingdom
    Contribution
    Conceptualization, Supervision, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
  7. Nicholas Machin

    Manchester University NHS Foundation Trust, Manchester, United Kingdom
    Contribution
    Supervision, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
  8. Gee Yen Shin

    University College London Hospitals NHS Foundation Trust, London, United Kingdom
    Contribution
    Supervision, Investigation, Writing – review and editing
    Competing interests
    has an unpaid role as Deputy Chair, British Medical Association London Regional Council. The author has no other competing interests to declare
  9. Tabitha Mahungu

    Royal Free London NHS Foundation Trust, London, United Kingdom
    Contribution
    Supervision, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
  10. Kordo Saeed

    University Hospital Southampton NHS Foundation Trust, Southampton, United Kingdom
    Contribution
    Supervision, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
  11. Tranprit Saluja

    Sandwell & West Birmingham Hospitals NHS Trust, Birmingham, United Kingdom
    Contribution
    Supervision, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
  12. Yusri Taha

    Department of Virology and Infectious Diseases, Newcastle-upon-Tyne Hospitals NHS Foundation Trust, Newcastle, United Kingdom
    Contribution
    Supervision, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
  13. Nikunj Mahida

    Nottingham University Hospitals NHS Trust, Nottingham, United Kingdom
    Contribution
    Supervision, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
  14. Cassie Pope

    St George’s University Hospitals NHS Foundation Trust, London, United Kingdom
    Contribution
    Supervision, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
  15. Anu Chawla

    Liverpool University Hospitals NHS Foundation Trust, Liverpool, United Kingdom
    Contribution
    Supervision, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
  16. Maria-Teresa Cutino-Moguel

    Barts Health NHS Trust, London, United Kingdom
    Contribution
    Supervision, Investigation, Writing – review and editing
    Competing interests
    received payment for anonymous interview conducted by Adkins Research Group. The author has no other competing interests to declare
  17. Asif Tamuri

    Research Computing, University College London, London, United Kingdom
    Contribution
    Software, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
  18. Rachel Williams

    Department of Genetics and Genomic Medicine, UCL Great Ormond Street Institute of Child Health, University College London, London, United Kingdom
    Contribution
    Data curation, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
  19. Alistair Darby

    Centre for Genomic Research, University of Liverpool, Liverpool, United Kingdom
    Contribution
    Data curation, Supervision, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
  20. David L Robertson

    MRC-University of Glasgow Centre For Virus Research, University of Glasgow, Glasgow, United Kingdom
    Contribution
    Supervision, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6338-0221
  21. Flavia Flaviani

    Guy's and St Thomas' Hospital NHS Foundation Trust, London, United Kingdom
    Contribution
    Data curation, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4210-0451
  22. Eleni Nastouli

    University College London Hospitals NHS Foundation Trust, London, United Kingdom
    Contribution
    Supervision, Investigation, Writing – review and editing
    Competing interests
    holds grants by NIHR, EPSRC, MRC-UKRI , H2020, ViiV Healthcare, Pfizer and Amfar, and has received grants to attend meetings from H2020 and ViiV Healthcare
  23. Samuel Robson

    Centre for Enzyme Innovation and School of Pharmacy and Biomedical Science, University of Portsmouth, Portsmouth, United Kingdom
    Contribution
    Data curation, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
  24. Darren Smith

    Department of Applied Sciences, Northumbria University, Newcastle-upon-Tyne, United Kingdom
    Contribution
    Data curation, Investigation, Writing – review and editing
    Competing interests
    holds the following grants that are not specifically for the present work: COG-UK, PHE test and trace funded the sequencing aspect. HOCI funded a technician to support sequencing during study period. The author has no other competing interests to declare
  25. Matthew Loose

    School of Life Sciences, University of Nottingham, Nottingham, United Kingdom
    Contribution
    Data curation, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
  26. Kenneth Laing

    Institute for Infection and Immunity, St George’s University of London, London, United Kingdom
    Contribution
    Data curation, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
  27. Irene Monahan

    Institute for Infection and Immunity, St George’s University of London, London, United Kingdom
    Contribution
    Data curation, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
  28. Beatrix Kele

    Barts Health NHS Trust, London, United Kingdom
    Contribution
    Data curation, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
  29. Sam Haldenby

    Centre for Genomic Research, University of Liverpool, Liverpool, United Kingdom
    Contribution
    Data curation, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
  30. Ryan George

    Manchester University NHS Foundation Trust, Manchester, United Kingdom
    Contribution
    Data curation, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
  31. Matthew Bashton

    The Hub for Biotechnology in the Built Environment, Department of Applied Sciences, Northumbria University, Newcastle, United Kingdom
    Contribution
    Data curation, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
  32. Adam A Witney

    Institute for Infection and Immunity, St George’s University of London, London, United Kingdom
    Contribution
    Data curation, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
  33. Matthew Byott

    University College London Hospitals NHS Foundation Trust, London, United Kingdom
    Contribution
    Data curation, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
  34. Francesc Coll

    Department of Infection Biology, Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
    Contribution
    Conceptualization, Writing – review and editing
    Competing interests
    received consulting fees from Next Gen Diagnostics LLC (during 2018/2019), received payment or honoria for lectures from University of Cambridge and Wellcome Genome Campus Advanced Courses, and received support for attending meeting and/or travel to meetings from European Congress of Clinical Microbiology & Infectious Diseases (ECCMID), The American Society for Microbiology (ASM), Microbiology Society, European Congress of Clinical Microbiology & Infectious Diseases (ECCMID), and the British Infection Association (BIA). The author has no other competing interests to declare
  35. Michael Chapman

    Health Data Research UK Cambridge Hub, Cambridge, United Kingdom
    Contribution
    Conceptualization, Writing – review and editing
    Competing interests
    No competing interests declared
  36. Sharon J Peacock

    Department of Medicine, University of Cambridge, Cambridge, United Kingdom
    Contribution
    Conceptualization, Funding acquisition, Writing – review and editing
    Competing interests
    received consultancy fees from Pfizer (Coronavirus External Advisory Board) and Melinta Therapeutics, received payment from SVB Leerink for a round table meeting and for Mary Strauss Distinguished Public Lecture from the Fralin Biomedical Research Institute, US, and support for attending ICPIC conference, Geneva and World Health Summit, Berlin in 2021, and hold stocks or stock options in Specific Technologies (European Union Scientific Advisory Board) and Next Gen Diagnostics (Scientific Advisory Board). SP also serves as Chair, Medical Advisory Committee, Sir Jules Thorn Charitable Trust, Board member of the Wellcome SEDRIC (Surveillance and Epidemiology of Drug Resistant Consortium), and Non-Executive Director of Cambridge University Hospitals NHS Foundation Trust. The author has no other competing interests to declare
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1718-2782
  37. COG-UK HOCI Investigators

    Contribution
    Investigation
    Competing interests
    No competing interests declared
    Additional information
    Full list of investigators and their affiliations can be found in Supplementary file 1
  38. The COVID-19 Genomics UK (COG-UK) consortium

    Contribution
    Investigation
    Competing interests
    No competing interests declared
    Additional information
    Full list of consortium members and their affiliations can be found in Supplementary file 2
  39. Joseph Hughes

    MRC-University of Glasgow Centre For Virus Research, University of Glasgow, Glasgow, United Kingdom
    Contribution
    Data curation, Investigation, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
  40. Gaia Nebbia

    Guy's and St Thomas' Hospital NHS Foundation Trust, London, United Kingdom
    Contribution
    Data curation, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
  41. David G Partridge

    Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
    Contribution
    Conceptualization, Supervision, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0417-2016
  42. Matthew Parker

    Sheffield Bioinformatics Core, University of Sheffield, Sheffield, United Kingdom
    Contribution
    Data curation, Investigation, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
  43. James Richard Price

    Imperial College Healthcare NHS Trust, London, United Kingdom
    Contribution
    Conceptualization, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
  44. Christine Peters

    NHS Greater Glasgow and Clyde, Glasgow, United Kingdom
    Contribution
    Conceptualization, Supervision, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
  45. Sunando Roy

    Department of Infection, Immunity and Inflammation, UCL Great Ormond Street Institute of Child Health, University College London, London, United Kingdom
    Contribution
    Data curation, Investigation, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
  46. Luke B Snell

    Guy's and St Thomas' Hospital NHS Foundation Trust, London, United Kingdom
    Contribution
    Data curation, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
  47. Thushan I de Silva

    Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom
    Contribution
    Conceptualization, Supervision, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
  48. Emma Thomson

    MRC-University of Glasgow Centre For Virus Research, University of Glasgow, Glasgow, United Kingdom
    Contribution
    Conceptualization, Supervision, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
  49. Paul Flowers

    School of Psychological Sciences and Health, University of Strathclyde, Glasgow, United Kingdom
    Contribution
    Conceptualization, Supervision, Methodology, Writing – review and editing
    Competing interests
    is a member of the SAGE hospital onset covid working group 2020-2022. The author has no other competing interests to declare
  50. Andrew Copas

    Institute for Global Health, University College London, London, United Kingdom
    Contribution
    Conceptualization, Supervision, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
  51. Judith Breuer

    Department of Infection, Immunity and Inflammation, UCL Great Ormond Street Institute of Child Health, University College London, London, United Kingdom
    Contribution
    Conceptualization, Supervision, Funding acquisition, Investigation, Methodology, Project administration, Writing – review and editing
    For correspondence
    j.breuer@ucl.ac.uk
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8246-0534

Funding

Medical Research Council

  • Judith Breuer

National Institute for Health and Care Research (MC_PC_19027)

  • Judith Breuer

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

COG-UK is supported by funding from the Medical Research Council (MRC) part of UK Research & Innovation (UKRI), the National Institute of Health Research (NIHR) (grant code: MC_PC_19027), and Genome Research Limited, operating as the Wellcome Sanger Institute. UCLH APDU is funded by the UCLH NHS Foundation Trust and the UCLH NIHR BRC. MRC-University of Glasgow Centre for Virus Research has received funding from the Medical Research Council. SR is part-funded by the Research England’s Expanding Excellence in England (E3) Fund, and additional funding for sequencing of SARS-CoV-2 at University of Portsmouth came from the Wessex Academic Health Sciences Centre (AHSC). FC was funded by a Wellcome Trust Sir Henry Postdoctoral Fellowship. LS has received support for research from NIHR Biomedical Research Centre at Guy’s and St Thomas NHS Foundation Trust, Guy’s & St Thomas Charity and King’s Health Partners. We also acknowledge the support of the independent members of the Joint Trial Steering Committee and Data Monitoring Committee (TSC-DMC): Prof Marion Koopmans (Erasmus MC), Prof Walter Zingg (University of Geneva), Prof Colm Bergin (Trinity College Dublin), Prof Karla Hemming (University of Birmingham), and Prof Katherine Fielding (LSHTM). Also, TSC-DMC non-independent members: Prof Nick Lemoine (NIHR CRN) and Prof Sharon Peacock (COG-UK). We also thank the members of COG-UK who have directly supported the study: Dr Ewan Harrison (Cambridge University), Dr Katerina Galai (PHE), Dr Francesc Coll (LSHTM), Dr Michael Chapman (HDR-UK), Prof Thomas Connor and team (Cardiff University), and Prof Nick Loman and team (University of Birmingham). We also thank the COG-UK Consortium and the UK National Institute for Health Research Clinical Research Network (NIHR CRN).

Ethics

Clinical trial registration ClinicalTrials.gov Identifier: NCT04405934.

Human subjects: Ethical approval for the study was granted by NHS HRA (REC 20/EE/0118). The need for consent from individual participants was waived because the study involved a hospital-level intervention that did not directly affect the clinical management of individual participants once diagnosed with a SARS-COV-2 infection.

Senior Editor

  1. Jos W van der Meer, Radboud University Medical Centre, Netherlands

Reviewing Editor

  1. Marc J Bonten, University Medical Center Utrecht, Netherlands

Reviewer

  1. Marc J Bonten, University Medical Center Utrecht, Netherlands

Publication history

  1. Preprint posted: February 13, 2022 (view preprint)
  2. Received: March 11, 2022
  3. Accepted: August 25, 2022
  4. Accepted Manuscript published: September 13, 2022 (version 1)
  5. Version of Record published: October 25, 2022 (version 2)

Copyright

© 2022, Stirrup et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Oliver Stirrup
  2. James Blackstone
  3. Fiona Mapp
  4. Alyson MacNeil
  5. Monica Panca
  6. Alison Holmes
  7. Nicholas Machin
  8. Gee Yen Shin
  9. Tabitha Mahungu
  10. Kordo Saeed
  11. Tranprit Saluja
  12. Yusri Taha
  13. Nikunj Mahida
  14. Cassie Pope
  15. Anu Chawla
  16. Maria-Teresa Cutino-Moguel
  17. Asif Tamuri
  18. Rachel Williams
  19. Alistair Darby
  20. David L Robertson
  21. Flavia Flaviani
  22. Eleni Nastouli
  23. Samuel Robson
  24. Darren Smith
  25. Matthew Loose
  26. Kenneth Laing
  27. Irene Monahan
  28. Beatrix Kele
  29. Sam Haldenby
  30. Ryan George
  31. Matthew Bashton
  32. Adam A Witney
  33. Matthew Byott
  34. Francesc Coll
  35. Michael Chapman
  36. Sharon J Peacock
  37. COG-UK HOCI Investigators
  38. The COVID-19 Genomics UK (COG-UK) consortium
  39. Joseph Hughes
  40. Gaia Nebbia
  41. David G Partridge
  42. Matthew Parker
  43. James Richard Price
  44. Christine Peters
  45. Sunando Roy
  46. Luke B Snell
  47. Thushan I de Silva
  48. Emma Thomson
  49. Paul Flowers
  50. Andrew Copas
  51. Judith Breuer
(2022)
Effectiveness of rapid SARS-CoV-2 genome sequencing in supporting infection control for hospital-onset COVID-19 infection: Multicentre, prospective study
eLife 11:e78427.
https://doi.org/10.7554/eLife.78427

Further reading

    1. Epidemiology and Global Health
    2. Medicine
    3. Microbiology and Infectious Disease
    Edited by Diane M Harper et al.
    Collection

    eLife has published the following articles on SARS-CoV-2 and COVID-19.

    1. Epidemiology and Global Health
    Paolo Giorgi Rossi, Giuliano Carrozzi ... Paola Mantellini
    Research Article

    Background: In Italy, Regions have the mandate to implement population-based screening programs for breast, cervical, and colorectal cancer. From March to May 2020, a severe lockdown was imposed due to the COVID-19 pandemic by the Italian Ministry of Health, with the suspension of screening programs. This paper describes the impact of the pandemic on Italian screening activities and test coverage in 2020 overall and by socio-economic characteristics.

    Methods: The regional number of subjects invited and of screening tests performed in 2020 were compared with those in 2019. Invitation and examination coverage were also calculated. PASSI surveillance system, through telephone interviews, collects information about screening test uptake by test provider (public screening and private opportunistic). Test coverage and test uptake in the last year were computed, by educational attainment, perceived economic difficulties, and citizenship.

    Results: A reduction of subjects invited and tests performed, with differences between periods and geographic macro areas, was observed in 2020 vs. 2019. The reduction in examination coverage was larger than that in invitation coverage for all screening programs. From the second half of 2020, the trend for test coverage showed a decrease in all the macro areas for all the screening programs. Compared with the pre-pandemic period, there was a greater difference according to the level of education in the odds of having had a test last year vs. never having been screened or not being up to date with screening tests.

    Conclusions: The lockdown and the ongoing COVID-19 emergency caused an important delay in screening activities. This increased the pre-existing individual and geographical inequalities in access. The opportunistic screening did not mitigate the impact of the pandemic.

    Funding: This study was partially supported by Italian Ministry of Health - Ricerca Corrente Annual Program 2023.