1. Epidemiology and Global Health
  2. Microbiology and Infectious Disease

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
https://doi.org/10.7554/eLife.78427
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Cite this article
  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
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9 figures, 8 tables and 3 additional files

Figures

Figure 1
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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.

Figure 2
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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).

Appendix 1—figure 1
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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
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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
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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
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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
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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
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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
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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.

Tables

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.

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].

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 events–40133––
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 events–1467––
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.

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 events–41135176
n/N (%) of HOCI cases part of outbreak event–292/373 (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 (%)
  Yes–20/35 (57.1)51/82 (62.2)71/117 (60.7)
  No–9/35 (25.7)15/82 (18.3)24/117 (20.5)
  Unsure–6/35 (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.

Additional files

Supplementary file 1

List of COG-UK HOCI investigators.

https://cdn.elifesciences.org/articles/78427/elife-78427-supp1-v2.docx
Supplementary file 2

Member list for the COVID-19 Genomics UK (COG-UK) consortium.

https://cdn.elifesciences.org/articles/78427/elife-78427-supp2-v2.docx
MDAR checklist
https://cdn.elifesciences.org/articles/78427/elife-78427-mdarchecklist1-v2.docx

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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