Evidence map›Paper›PMID 33313577›Full record

Observational studyThe Lancet. Microbe2020

Definition of a genetic relatedness cutoff to exclude recent transmission of meticillin-resistant

Francesc Coll, Kathy E Raven, Gwenan M Knight, Beth Blane, Ewan M Harrison, Danielle Leek, David A Enoch, Nicholas M Brown, Julian Parkhill, Sharon J Peacock

Abstract readObservational Study
In one paragraph

Observational study in The Lancet. Microbe, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 87 papers.

0numbers the graph read from it
0cells of the map it votes in
87citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

87 citing papers in PubMed.

  1. Trial
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  14. Prediction of genetic relatedness ofAntimicrobial agents and chemotherapy · 2026
    Article
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  20. Enhanced Spread of Carbapenem-ResistantInfection and drug resistance · 2026
    Article

27 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Francesc CollDepartment of Infection Biology, Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, UK.
Kathy E RavenDepartment of Medicine, University of Cambridge, Cambridge, UK.
Gwenan M KnightDepartment of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK.
Beth BlaneDepartment of Medicine, University of Cambridge, Cambridge, UK.
Ewan M HarrisonDepartment of Medicine, University of Cambridge, Cambridge, UK.
Danielle LeekDepartment of Medicine, University of Cambridge, Cambridge, UK.
David A EnochPublic Health England, Cambridge, UK.
Nicholas M BrownPublic Health England, Cambridge, UK.
Julian ParkhillDepartment of Veterinary Medicine, University of Cambridge, Cambridge, UK.
Sharon J PeacockDepartment of Medicine, University of Cambridge, Cambridge, UK.

Funding

Department of HealthMedical Research Council MR/S00291X/1Wellcome TrustWellcome Trust 098051Wellcome Trust 201344/Z/16/Z
6 · The paper itself

Abstract

backgroundWhole-genome sequencing (WGS) can be used in genomic epidemiology investigations to confirm or refute outbreaks of bacterial pathogens, and to support targeted and efficient infection control interventions. We aimed to define a genetic relatedness cutoff, quantified as a number of single-nucleotide polymorphisms (SNP), for meticillin-resistant

methodsWe did a retrospective genomic and epidemiological analysis of MRSA data from two prospective observational cohort studies in the UK to establish SNP cutoffs for genetic relatedness, above which recent transmission was unlikely. We used three separate approaches to calculate these thresholds. First, we applied a linear mixed model to estimate the

findingsIn the linear mixed model, the estimated substitution rate was roughly 5 whole-genome SNPs (wgSNPs) or 3 core-genome SNPs (cgSNPs) per genome per year, and the 95th percentile within-host diversity was 19 wgSNPs or 10 cgSNPs. The combined SNP cutoffs for detection of MRSA transmission within 6 months per this model were thus 24 wgSNPs or 13 cgSNPs. The simulated transmission model suggested that cutoffs of 17 wgSNPs or 12 cgSNPs would detect 95% of MRSA transmission events within the same timeframe. Finally, in the second cohort, cutoffs of 22 wgSNPs or 11 cgSNPs captured 95% of epidemiologically linked cases within 6 months.

interpretationOn the basis of our results, we propose conservative cutoffs of 25 wgSNPs or 15 cgSNPS above which transmission of MRSA within the previous 6 months can be ruled out. These cutoffs could potentially be used as part of a genomic sequencing approach to the management of outbreaks of MRSA in conjunction with traditional epidemiological techniques.

fundingUK Department of Health, Wellcome Trust, UK National Institute for Health Research.

Indexed as

Methicillin-Resistant Staphylococcus aureusStaphylococcal InfectionsGenomicsHumansMethicillinRetrospective StudiesStaphylococcus aureusMethicillin

Identifiers

PMID33313577
PMCPMC7721685

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.