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www.thelancet.com/microbe Published online July 14, 2021 https://doi.org/10.1016/S2666-5247(21)00128-2
1
Articles
Epidemiology of Mycobacterium abscessus in England:
an observational study
Samuel Lipworth, Natasha Hough, Natasha Weston, Berit Muller-Pebody, Nick Phin, Richard Myers, Stephen Chapman, William Flight,
Eliza Alexander, E Grace Smith, Esther Robinson, Tim E A Peto, Derrick W Crook, A Sarah Walker, Susan Hopkins, David W Eyre*,
Timothy M Walker*
Summary
Background Mycobacterium abscessus has emerged as a significant clinical concern following reports that it is readily
transmissible in health-care settings between patients with cystic fibrosis. We linked routinely collected whole-genome
sequencing and health-care usage data with the aim of investigating the extent to which such transmission explains
acquisition in patients with and without cystic fibrosis in England.
Methods In this retrospective observational study, we analysed consecutive M abscessus whole-genome sequencing
data from England (beginning of February, 2015, to Nov 14, 2019) to identify genomically similar isolates. Linkage to
a national health-care usage database was used to investigate possible contacts between patients. Multivariable
regression analysis was done to investigate factors associated with acquisition of a genomically clustered strain
(genomic distance <25 single nucleotide polymorphisms [SNPs]).
Findings 2297 isolates from 906 patients underwent whole-genome sequencing as part of the routine Public Health
England diagnostic service. Of 14 genomic clusters containing isolates from ten or more patients, all but one contained
patients with cystic fibrosis and patients without cystic fibrosis. Patients with cystic fibrosis were equally likely to have
clustered isolates (258 [60%] of 431 patients) as those without cystic fibrosis (322 [63%] of 513 patients; p=0·38). High-
density phylogenetic clusters were randomly distributed over a wide geographical area. Most isolates with a closest
genetic neighbour consistent with potential transmission had no identifiable relevant epidemiological contacts.
Having a clustered isolate was independently associated with increasing age (adjusted odds ratio 1·14 per 10 years,
95% CI 1·04–1·26), but not time spent as an hospital inpatient or outpatient. We identified two sibling pairs with
cystic fibrosis with genetically highly divergent isolates and one pair with closely related isolates, and 25 uninfected
presumed household contacts with cystic fibrosis.
Interpretation Previously identified widely disseminated dominant clones of M abscessus are not restricted to
patients with cystic fibrosis and occur in other chronic respiratory diseases. Although our analysis showed a small
number of cases where person-to-person transmission could not be excluded, it did not support this being a major
mechanism for M abscessus dissemination at a national level in England. Overall, these data should reassure
patients and clinicians that the risk of acquisition from other patients in health-care settings is relatively low and
motivate future research eorts to focus on identifying routes of acquisition outside of the cystic fibrosis health-
care-associated niche.
Funding The National Institute for Health Research, Health Data Research UK, The Wellcome Trust, The Medical
Research Council, and Public Health England.
Copyright © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.
Lancet Microbe 2021
Published Online
July 14, 2021
https://doi.org/10.1016/
S2666-5247(21)00128-2
*Joint senior authors
Nuffield Department of
Medicine (S Lipworth MBBS,
Prof T EA Peto FRCP,
Prof D W Crook FRCP,
Prof A S Walker PhD) and
Big Data Institute, Nuffield
Department of Population
Health (D W Eyre DPhil),
University of Oxford, Oxford,
UK; Oxford University
Hospitals NHS Foundation
Trust, Oxford, UK (S Lipworth,
N Hough MBChB,
S Chapman FRCP, W Flight PhD,
Prof T E A Peto, Prof D W Crook,
D W Eyre, T M Walker DPhil);
National Mycobacterial
Reference Service-Central and
North, Public Health England,
Public Health Laboratory,
Birmingham, UK
(N Weston MBChB,
Prof E G Smith FRCPath,
E Robinson FRCPath); and
Tuberculosis, Acute
Respiratory, Gastrointestinal,
Emerging and Zoonotic
Infections and Travel Migrant
Health Division, National
Infection Service
(B Muller-Pebody PhD,
N Phin FFPH, R Myers PhD,
S Hopkins FRCP) and
National Mycobacterial
Reference Service-South
(E Alexander PhD), Public Health
England, London, UK;
NIHR
Oxford Biomedical Research
Centre, John Radcliffe Hospital,
Oxford, UK (Prof T E A Peto,
Prof D W Crook, Prof A S Walker);
Oxford University Clinical
Research Unit, Ho Chi Minh
City, Viet Nam (T M Walker)
Correspondence to:
Dr Samuel Lipworth,
Nuffield Department of
Medicine, University of
Oxford, Oxford OX3 7BN, UK
samuel.lipworth@ndm.ox.ac.uk
Introduction
Mycobacterium abscessus pulmonary disease can be devas-
tating in patients with cystic fibrosis. This pathogen is highly
antibiotic-resistant and treatment is challenging. M abscessus
pulmonary disease (caused by one of three subspecies:
massiliense, abscessus, and bolletii) can be progressive and
incurable and is a relative con traindication to lung trans-
plantation. In common with other non-tuberculous myco-
bacteria, acquisition of M abscessus was until recently
considered only to occur from the environment, especially
from contaminated water sources.1,2
Several studies in cohorts of patients with cystic fibrosis
have described genomically almost identical isolates from
patients with potential opportunities for cross-infection.3–5
A large global study showed multiple internationally
distributed dominant clades which accounted for most
infections in patients with cystic fibrosis.3 The authors
hypothesised that widespread recent transmission, most
likely indirect person-to-person transmission through
environmental contamination (eg, via fomites or aerosols)
in health-care settings and other shared venues was
the most likely explanation. Based on these studies,
international guidelines suggest that person-to-person
transmission might be an important mechanism for
M abscessus acquisition in patients with cystic fibrosis.6,7
However, smaller reports have not substantiated this
Articles
2
www.thelancet.com/microbe Published online July 14, 2021 https://doi.org/10.1016/S2666-5247(21)00128-2
See Online for appendix
hypothesis.8–10 No studies have investigated the genomic
epidemiology of M abscessus from patients without cystic
fibrosis.
A clear understanding of cross-infection risk is crucial
to protect patients with cystic fibrosis in health-care
facilities. Although nosocomial transmission needs to be
minimised, interventions might also cause financial
and operational challenges and psychological harm for
patients. Over the past 6 years, Public Health England
(PHE) has implemented whole-genome sequencing
(WGS) to replace existing reference laboratory techniques
for all mycobacteria, producing a near-complete dataset
of all sequenced M abscessus clinical isolates in England
from patients with and without cystic fibrosis. These data
have been linked to routinely collected health-care usage
datasets, presenting an opportunity to investigate person-
to-person transmission of M abscessus on a national scale
across all patient groups.
Methods
Study design and sample collection
In this observational study, we did a retrospective analysis
of routinely collected M abscessus WGS data in England.
All mycobacterial isolates are sent to one of two PHE
reference laboratories in London and Birmingham for
WGS. Routine sequencing began in Birmingham for the
Midlands at the beginning of February, 2015, and for
the north of England October, 2016; it began in London
for the south of England in January, 2018. All isolates
from these time points until Nov 14, 2019, are included,
in addition to all available isolates sequenced before the
routine service began (n=10). Historical records of
unsequenced isolates (patient details and collection date)
were available from November, 1997, from Birmingham
and from November, 2001, from London.
This study was done as a public health investigation
with internal approval from PHE and therefore ethics
committee approval was not required.
Sequencing and bioinformatics
WGS was done by PHE as part of the routine clinical
service on an Illumina Miseq instrument (San Diego, CA,
USA) as previously described.11 Reads were mapped to a
reference genome (National Center for Biotechnology
Information reference sequence NC_010397·1) using
the PHE bioinformatics pipeline v1.0.2 and sequences
were compared using recombination-adjusted (using
ClonalFrameML v1.11),12 maximum likelihood phylogenies
(IQTree v1.6.12);13 single nucleotide polymorphism (SNP)
distances; and time-scaled phylogenies and molecular
clock estimation (BEAST v1.10.4; appendix p 2).14 Within-
patient diversity was estimated using all sequenced isolates
for patients for whom more than one of these were
available. We also did a sensitivity analysis to determine
the eect of choice of reference (appendix p 3). Clusters of
isolates potentially consistent with recent transmission
Research in context
Evidence before this study
We searched PubMed for studies published from database
inception to Dec 1, 2020, with no language restrictions, using
the terms “Mycobacterium abscessus”, “transmission”, and
“whole genome sequencing”. All studies published to date have
focussed on the molecular epidemiology of M abscessus in
patients with cystic fibrosis. The largest study to date identified
multiple internationally distributed dominant clones that were
responsible for most infections in patients with cystic fibrosis.
Several studies have found evidence of highly genomically
related strains among patients attending the same cystic
fibrosis centre, raising concern about cross-infection. However,
three smaller studies have identified genomic clusters of
isolates from cystic fibrosis patients who have no
epidemiological connections and concluded there was no
evidence of cross-transmission. Although some uncertainty
exists in the literature, the predominating interpretation of
the available data is that there is a substantial risk of cross-
transmission, something reflected in international guidelines.
Added value of this study
The dataset in this study (unlike most previous studies)
is unselected and sequential, including isolates from all
patients (irrespective of underlying diagnosis) over a 5-year
period. By linking the largest genomic dataset assembled to
date with a national health-care usage database, we have
unprecedented ability to resolve potential transmission
events. We show that M abscessus isolates from patients with
cystic fibrosis are often highly genomically similar to those
with other, or no, chronic respiratory disease. These clusters
are widely geographically distributed and, in keeping with
this observation, we show that most patients with similar
isolates have no identifiable epidemiological links. We found
a low risk of household transmission, further implying that
the risk of transmission associated with short-term
nosocomial exposure is likely to be low, a finding
supported by our regression analysis.
Implications of all the available evidence
Earlier studies, which only analysed isolates from patients
with cystic fibrosis, suggested genomic clusters were
propagated by (probably indirect) transmission among
patients with cystic fibrosis in health-care facilities. Our study
challenges this interpretation by showing that genomic
clusters of M abscessus are widely geographically dispersed
and shared across all patient groups. Short-term nosocomial
exposure with normal infection control procedures is likely to
carry a low risk of person-to-person transmission. Future
efforts to protect patients from infection should focus on
identifying potential locally or nationally distributed vectors.
Articles
www.thelancet.com/microbe Published online July 14, 2021 https://doi.org/10.1016/S2666-5247(21)00128-2
3
For the Hospital Episode
Statistics database see https://
digital.nhs.uk/data-and-
information/data-tools-and-
services/data-services/
hospital-episode-statistics
For TreeGubbins see https://
github.com/simonrharris/tree_
gubbins
For Nomenclature of Territorial
Units for Statistics see https://
ec.europa.eu/eurostat/web/nuts/
correspondence-tables/
postcodes-and-nuts
were identified using the previously defined genomic
threshold of fewer than 25 SNPs.4
Epidemiological linkage
Laboratory records were linked to the national Hospital
Episode Statistics (HES) database using patient-specific
identifiers; data were extracted detailing health-care
contact, clinical procedures, and diagnostic codes
(appendix p 3). Underlying diagnoses were identified
using codes submitted before the date of first isolation
of M abscessus. Respiratory diagnoses were assigned
hierarchically (appendix p 7). For epidemiological
analysis, we assumed that isolates submitted to the
reference laboratories before WGS was introduced
would belong to the same clusters as subsequently
sequenced isolates from the same patients.
We examined whether epidemiological contact with
another patient (defined as attendance to the same unit
on the same day or shared postcode district [approximately
2066 in England] or primary health-care practice in
the year before acquisition) was associated with the
acquisition of genetically similar isolates. We adopted the
model of Bryant and colleagues,4 assuming that patients
could become infected with M abscessus up to 1 year
before first isolation and remained potentially infectious
from this point onwards. We used the date of first
isolation of M abscessus (whether sequenced or not) for
the epidemiological analysis and considered the first
isolate per cluster per patient (ie, if a patient had isolates
in multiple clusters the first from each was included).
To identify possible household contacts we searched
the health-care usage database (HES) to identify patients
with cystic fibrosis who lived at the same postcode as a
cystic fibrosis patient in our dataset in the year in which
M abscessus was first isolated from them. Although
the mean number of households within a postcode is
approximately 15, the population prevalence of cystic
fibrosis is such that most pairs of individuals sharing the
same full postcode and both with cystic fibrosis would be
expected to be in the same household. Where these pairs
were also both present in our laboratory records (hence
had both had M abscessus isolated) and shared a surname,
we defined these as siblings. One possible sibling pair
was identified in which individuals shared a surname
but were not household contacts in the year of acquisition
(but had been previously).
Geospatial analysis
For each patient, the postcode (typically shared by
approximately 15 properties) closest in time to their first
M abscessus isolate (whether sequenced or not) was used.
We identified high-density phylogenetic clusters using
TreeGubbins and for each calculated the ratio of the
median genetic distance within and between geographical
areas (Nomenclature of Territorial Units for Statistics
[NUTS] regions identified using patient postcodes;
appendix p 3). We did a permutation test15 to determine
whether observed values were compatible with the
null hypothesis of no regional clustering of
isolates (appendix p 3). We additionally calculated the
Pearson correlation between distance to nearest genomic
neighbour (in SNPs) and geographical distance
(measured using the Harversine distance) between
patient’s postcodes. We generated a phylogenetic tree
including all isolates from this and three previous
studies.3,9,16
Statistical analysis
Descriptive statistical analysis of laboratory and HES
data were done using R v3.4.3, as were all other analyses
unless otherwise stated. Proportions of clustered isolates
between subgroups (patients with vs without cystic
fibrosis overall, patients with cystic fibrosis vs with
bronchiectasis from 2014, patients with cystic fibrosis
and bronchiectasis vs all other patients, patients with
cystic fibrosis with a fist isolate after 2014 or 2015 vs
before these years) were compared using Fisher’s exact
test. To determine whether the observed proportion of
clusters containing only patients with cystic fibrosis was
greater than that expected by chance alone, a permutation
test was done by randomising the diagnostic labels
1000 times and recalculating the number of cystic
fibrosis-only clusters (appendix p 2). For patients
with cystic fibrosis, we additionally analysed whether the
median genomic distance to the nearest patient
with cystic fibrosis was smaller than the median genomic
distance to the nearest patient without cystic fibrosis
using a Wilcoxon rank-sum test. Univariate and multi-
variable regression analysis were done to investigate
factors (health-care exposures, demographic or socio-
economic and clinical variables) associated with
acquisition of a genomically clustered strain (ie, genomic
distance from another genome <25 SNPs).4 For each
factor included in the models, the odds ratio (OR; or
adjusted OR [aOR] where relevant) and 95% CIs were
calculated. Only the first isolate per patient was used. As
it was unclear what relevant exposures might be, we
considered this analysis to be exploratory and initially
included all demographic, clinical, and socio economic
factors available. To ensure that only variables making a
significant contribution were included, the final model
was fitted by backwards elimination using the Akaike
Information Criteria, allowing for potential inte ractions
and non-linearity (appendix p 4). We additionally
did a secondary regression analysis using the same
methodology to investigate factors associated with
acquiring a clustered isolate in patients with cystic
fibrosis. For all statistical tests we considered p<0·05 as
the threshold for significance.
Role of the funding source
The funder of the study had no role in study design, data
collection, data analysis, data interpretation, or writing of
the report.
Articles
4
www.thelancet.com/microbe Published online July 14, 2021 https://doi.org/10.1016/S2666-5247(21)00128-2
Results
2431 isolates were sequenced by the reference
laboratory; full linkage was achieved for 2297 isolates
from 906 patients (appendix p 8). The most common
sample type was sputum (1997 [87%] of 2297) followed
by bronchoalveolar lavage (135 [6%] of 2297). The most
common non-respiratory sample types were blood
(n=19 [<1%]), tissue of unknown source (n=14 [<1%]),
and cutaneous biopsies (n=10 [<1%]; appendix p 17).
The most common primary respiratory diagnosis was
cystic fibrosis (408 [45%] of 906). Another 296 (33%)
patients had a dierent chronic respiratory diagnosis
and 202 (22%) had no documented respiratory
diagnosis. These diagnostic groups were reflected in a
bimodal age distribution (median age of patients with
cystic fibrosis 21 [IQR 16–27] years vs patients without
cystic fibrosis 65 [ 49–74] years; appendix p 18).
Patients with cystic fibrosis had a higher number of
isolates sequenced (median 2 [IQR 1–4] vs 1 [1–3];
p<0·0001).
We adopted the previously reported threshold of fewer
than 25 SNPs for inferring possible recent transmission.4
We estimated a molecular clock of 1·1 SNPs per genome
per year (95% highest posterior density interval 0·9–1·4),
consistent with the mean time to most recent common
ancestor for two clustered strains being approximately
10·9 years. We found that this threshold represented
more than 95% of within-patient diversity (median time
between first and last sequenced isolates 246 days
[IQR 77–570]) in the same subspecies, which is consistent
with previous studies (appendix p 9).3,4
Retaining the first genome per patient per cluster, there
were 703 M abscessus subspecies abscessus, 52 M abscessus
subspecies bolletii, and 189 M abscessus subspecies
massiliense isolates (figure 1). 560 (62%) of 906 patients
had one or more isolate that was part of a genomic cluster
(n=115 clusters, median size 2 [range 2–54]); 364 (40%)
patients had non-clustered isolates. 32 (4%) patients had
multiple isolates that fell into more than one cluster or
had at least one clustered and one non-clustered isolate.
Figure 1: Phylogeny of 944 Mycobacterium abscessus isolates
The phylogenetic tree includes one unique isolate per patient per cluster (36 were excluded due to missing data). The inner ring shows the presence or absence of a
diagnosis of cystic fibrosis. The outer ring shows the geographical region of England in which the patient lives.
Diagnosis
Cystic fibrosis
Other
Region
East Midlands
East of England
London
Northeast
Northwest
Southeast
Southwest
Wales
West Midlands
Yorkshire and Humber
M abscessus subspecies
Subspecies abscessus
Subspecies massiliense
Subspecies bolletii
Articles
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5
Patients with cystic fibrosis were no more likely than
patients without cystic fibrosis to have a clustered isolate
(258 [60%] of 431 vs 322 [63%] of 513; p=0·38). Comparing
isolates from patients with cystic fibrosis whose first isolate
occurred from 2014 (who likely underwent enhanced
infection control and cohorting) with those from patients
without cystic fibrosis but with bronchiectasis from the
same period revealed no dierence in the proportion
which were clustered (227 [64%] of 354 vs 103 [69%] of 150;
p=0·36). In some centres, cystic fibrosis and bronchiectasis
services are co-located and both groups might have been
subject to enhanced infection control procedures. We
therefore repeated this analysis to compare patients with
cystic fibrosis and bronchiectasis with all other patients;
again, there was no dierence (330 [65%] of 504 vs
204 [62%] of 327; p=0·37). Furthermore, when we looked
only at patients with cystic fibrosis, the proportion with a
clustered isolate was higher in those who acquired their
first isolate after 2014 versus those who acquired it before
then (227 [64%] of 354 vs 31 [40%] of 77; exact p<0·0002)
and in those who acquired their first isolate after
2015 versus those who acquired it before then (218 [69%]
of 318 vs 40 [43%] of 93; p<0·0003). High proportions
of clustered patients were observed in non-sputum-
producing respiratory phenotypes (eg, 20 [59%] of
34 patients with asthma, 9 [90%] of 10 patients with lung
cancer, and 17 [81%] of patients with interstitial lung
disease. Notably, four samples from cutaneous biopsies
clustered with other patients with a variety of diagnostic
codes (including cystic fibrosis or non-cystic fibrosis
bronchiectasis; figure 2; appendix p 10).
92 (80%) of 115 clusters contained at least one patient
with cystic fibrosis, 68 of which crossed disease strata
(ie, contained at least one cystic fibrosis patient and one
patient with a dierent or no respiratory diagnosis). We
reasoned that if cystic fibrosis communities or health-care
facilities are the primary facilitators of clonal outbreaks,
then the number of clusters containing exclusively cystic
fibrosis patients ought to be greater than would be
expected by chance. This was not the case (observed
proportion of exclusively cystic fibrosis clusters 0·21,
permuted 95% CI 0·00–0·22). For patients with cystic
fibrosis, the median genomic distance to the nearest
patient with cystic fibrosis was 24 SNPs (IQR 9–51)
whereas the median genomic distance to the nearest
patient without cystic fibrosis was 31 SNPs (8–67,
p=0·093).
Because there was a substantial change in infection
control guidelines in late 2013,17 we considered the
possibility that previously described clusters might have
gradually died out after this period and therefore be
unrelated to those described here. Bayesian dating analysis
Figure 2: Distribution of Mycobacterium abscessus isolate cluster sizes by sample type and patient diagnosis
Distribution of cluster sizes for all clusters identified using a genomic distance threshold of fewer than 25 SNPs, by diagnosis (A) and by sample types (B).
The algorithm used to assign respiratory diagnoses to patients is in the appendix (p 7).
0 30 60 90 120
0
20
40
B
Number of isolates in cluster
0
20
40
A
Number of isolates in cluster
Cluster
Diagnosis
No chronic respiratory disease
Asthma
Chronic obstructive pulmonary disease
Emphysema
Interstitial lung disease
Lung cancer
Sinusitis
Bronchiectasis
Cystic fibrosis
Sample Type
Unknown
Blood
Cutaneous tissue
Tissue unclear source
Tibia aspirate
Pus
Fluid unclear site
Peritoneal dialysis catheter
Abdominal
Swab
Pleural fluid
Bronchoalveolar lavage or bronchial wash
Sputum
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showed that all larger clusters probably emerged before
this period (figure 3). We identified 107 patients in these
clusters with previous (unsequenced) isolates pre-dating
the implementation of enhanced infection control
procedures and for the purposes of our epidemiological
analysis assumed these would mostly fall in the same
cluster as later sequenced isolates. As we analysed
epidemiological data from the date of first acquisition
(not sequencing date), if substantial person-to-person
transmission was occurring before the introduction of the
new guidelines we would have expected to have observed it.
For each measure of nosocomial contact (eg, outpatient
attendances, days in hospital, respiratory procedures) we
considered the total number of relevant exposures in the
year preceding the first recorded isolate of M abscessus
(whether sequenced or not) for each patient (table).
Multivariable models revealed evidence of increasing age
(aOR per 10 years 1·14, 95% CI 1·04–1·26) being associated
with an increased risk of being colonised with a clustered
isolate. There was some evidence of an association with
increasing morbidity (aOR 1·02, 95% CI 1·00–1·04).
When restricting only to patients with cystic fibrosis, there
was some evidence of decreased risk of having a clustered
isolate with increasing number of inpatient days (aOR per
7 days 0·94, 95% CI 0·88–1·00; p=0·045) and increasing
risk associated with greater comorbidity (adjusted OR 1·03,
95% CI 1·00–1·06; appendix 21). Notably, in both analyses
neither the number of outpatient attendances nor inpatient
days were significantly associated with the risk of having a
clustered isolate in univariate analysis.
If contact with a contaminated environment is a strong
risk factor for M abscessus transmission, then household
contacts who both have cystic fibrosis and M abscessus
infection would be expected to be colonised with the same
strain. In our dataset there were three such pairs of
siblings: two had genetically divergent strains (14 103 and
17 352 SNPs dierence), whereas one had near identical
strains (2 SNPs). There was a further possible sibling pair
with divergent strains (54 878 SNPs; appendix p 4),
although this pair were not household contacts at the point
Figure 3: Timeline of genomic clusters identified in this study
Isolates (deduplicated per cluster) are shown for patients with and without cystic fibrosis. Clusters of isolates defined using a genomic distance threshold of fewer than 25 SNPs are connected by dark grey
lines. Ligh grey lines show the time to the earliest non-sequenced isolate belonging to a member of a given cluster. The orange bars show the 95% highest posterior density interval for the inferred date of
the root for the time-scaled phylogenetic trees for larger clusters (n≥10); the mean point estimate of these dates is shown as a black dot.
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1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018201920202021
Year
Genetic cluster
Diagnosis
|
|
Cystic fibrosis
Not cystic fibrosis
Articles
www.thelancet.com/microbe Published online July 14, 2021 https://doi.org/10.1016/S2666-5247(21)00128-2
7
of acquisition. There were additionally 25 pairs of possible
household contacts who both had cystic fibrosis but one
had not had M abscessus infection. Given that we obtained
the full records of both English reference laboratories and
that none of these patients have a recorded diagnosis of
mycobacterial infection, it is unlikely that they had
experienced disease due to M abscessus.
Only 47 (13%) of 356 isolates with a nearest genetic
neighbour potentially compatible with transmission
(<25 SNPs) had a plausible epidemiological contact. In
these cases, person-to-person transmission (direct or
indirect) cannot be excluded (appendix pp 11–12). Of
these, 33 were cystic fibrosis patients meaning that
33 [8%] of 408 patients with cystic fibrosis in this study
had an isolate that could have been acquired by person-
to-person transmission.
872 (96%) of 906 patients had available postcodes and
could be linked to one of nine geostatistical regions in
England (appendix p 3). Larger clusters (isolates from ten
or more patients) linked by a genomic distance of fewer
than 25 SNPs were not confined to particular geographical
regions (median 7 regions, range 5–9; figure 4,
appendix p 13). All but one larger cluster contained
patients both with and without cystic fibrosis. As SNP
clusters are based on an arbitrary threshold, we did an
additional whole-phylogeny-based test to confirm this
Patients with non-
clustered isolates (n=342)
Patients with clustered
isolates (n=519)
OR (95% CI, p value) aOR (95% CI, p value)
Sex
Female (n=427) 161 (37·7%) 266 (62·3%) ·· ··
Male (n=434) 181 (41·7%) 253 (58·3%) 0·85 (0·64–1·11, p=0·23) ··
Age, years 30 (20–63) 39 (22–69) 1·10 (1·04–1·16, p=0·0014)
per 10 years
1·14 (1·04–1·26, p=0·0071)per
10 years
Outpatient attendances 9 (4–16) 10 (5–15) 1·01 (0·99–1·02, p=0·39) ··
Inpatient days 1 (0–14) 2 (0–14) 0·99 (0·95–1·04, p=0·68)
per 7 days
··
Elixhauser score 3 (0–10) 5 (2–13) 1·03 (1·02–1·05, p<0·0003) 1·02 (1·00–1·04, p=0·058)
Respiratory procedures 0 (0–0) 0 (0–1) 1·20 (1·04–1·41, p=0·019) 1·15 (0·98–1·36, p=0·093)
Rural or urban dwelling
Hamlet (n=22) 10 (45·5%) 12 (54·5%) ·· ··
Town and fringe (n=73) 30 (41·1%) 43 (58·9%) 1·19 (0·45–3·13, p=0·72) ··
Urban (n=703) 277 (39·4%) 426 (60·6%) 1·28 (0·53–3·01, p=0·57) ··
Village (n=63) 25 (39·7%) 38 (60·3%) 1·27 (0·47–3·38, p=0·64) ··
Index of multiple deprivation decile
Most deprived 10% (n=99) 44 (44·4%) 55 (55·6%) ·· ··
More deprived 10–20% (n=102) 44 (43·1%) 58 (56·9%) 1·05 (0·60–1·84, p=0·85) ··
More deprived 20–30% (n=81) 36 (44·4%) 45 (55·6%) 1·00 (0·55–1·81, p=1·00) ··
More deprived 30–40% (n=67) 21 (31·3%) 46 (68·7%) 1·75 (0·92–3·40, p=0·091) ··
More deprived 40–50% (n=95) 37 (38·9%) 58 (61·1%) 1·25 (0·71–2·23, p=0·44) ··
Less deprived 50–60% (n=70) 26 (37·1%) 44 (62·9%) 1·35 (0·73–2·55, p=0·34) ··
Less deprived 60–70% (n=73) 25 (34·2%) 48 (65·8%) 1·54 (0·83–2·89, p=0·18) ··
Less deprived 70–80% (n=87) 30 (34·5%) 57 (65·5%) 1·52 (0·84–2·77, p=0·17) ··
Less deprived 80–90% (n=103) 46 (44·7%) 57 (55·3%) 0·99 (0·57–1·73, p=0·98) ··
Least deprived 10% (n=84) 33 (39·3%) 51 (60·7%) 1·24 (0·69–2·24, p=0·48) ··
Diagnosis
Bronchiectasis (n=146) 48 (32·9%) 98 (67·1%) ·· ··
No chronic respiratory diagnosis
(n=180)
89 (49·4%) 91 (50·6%) 0·50 (0·32–0·78, p<0·0027) 0·66 (0·41–1·07, p=0·10)
Asthma (n=34) 14 (41·2%) 20 (58·8%) 0·70 (0·33–1·53, p=0·36) 0·83 (0·38–1·84, p=0·64)
Lung cancer (n=10) 1 (10·0%) 9 (90·0%) 4·41 (0·79–82·46, p=0·17) 2·89 (0·50–54·7, p=0·33)
Cystic fibrosis (n=400) 165 (41·2%) 235 (58·8%) 0·70 (0·47–1·03, p=0·077) 1·27 (0·72–2·26, p=0·41)
Chronic obstructive pulmonary
disease (n=70)
21 (30·0%) 49 (70·0%) 1·14 (0·62–2·14, p=0·67) 1·03 (0·56–1·94, p=0·93)
Interstitial lung disease (n=21) 4 (19·0%) 17 (81·0%) 2·08 (0·72–7·53, p=0·21) 1·88 (0·64–6·89, p=0·29)
Data are n (%), median (IQR), or OR (95% CI, p value). 45 patients had one or more incomplete datapoint and were excluded from the model. Univariate estimates (ORs)
are shown for all variables, multivariable estimates (aOR) are only shown for variables included in the final model. Inpatient days, outpatient attendances, and respiratory
procedures refer to the number of these in the year before M abscessus was first isolated from the patient. OR=odds ratio. aOR=adjusted OR.
Table: Multivariable predictors of acquiring a clustered Mycobacterium abscessus isolate
Articles
8
www.thelancet.com/microbe Published online July 14, 2021 https://doi.org/10.1016/S2666-5247(21)00128-2
observation. We compared ratios of genomic distances
within and between geostatistical regions for each
high-density phylogenetic cluster (appendix p 22). In all
cases the observed within-to-between region ratio was
compatible with chance. We observed no correlation
between distance to nearest genomic neighbour and
geographical proximity (Pearson coecient 0·03) and
found that distance to nearest genomic neighbour was
smaller between isolates in dierent versus the same
geographical (NUTS) regions (median 15 [IQR 4–46] vs
3272 SNPs [115–16 618]; p<0·0001). Furthermore, apart
from samples from one study,16 we observed that the
genomes of isolates obtained from previous global studies
were distributed throughout the phylogeny of English
isolates obtained in this study (appendix p 14).
Discussion
We analysed consecutive, unselected genomic data
linked to health-care records to investigate the extent of
person-to-person transmission of M abscessus in England.
Genomic clusters of M abscessus are not disease-specific;
only a minority are exclusive to cystic fibrosis. As has
previously been shown to be the case for patients with cystic
fibrosis, most patients without cystic fibrosis in England are
colonised with a clustered isolate. This situation is contrary
to what would be expected if, as currently postulated,
outbreak strains are primarily propagated in nosocomial
cystic fibrosis environments. It was particularly notable that
several patients with non-respiratory isolates (eg, skin
abscesses, bone aspirates, or peritoneal dialysis fluid) had
genomically near-identical isolates to respiratory patients.
We observed no geo graphical structure to the phylogeny
and in keeping with this, by linking genomic data to a
detailed national epidemiological database, showed that
most patients in clusters have no identifiable epidemiological
links. Previous studies sequenced selected stored isolates
exclusively from groups of patients with cystic fibrosis and
therefore do not represent the full landscape of M abscessus
epidemiology. Our findings have important implications for
future eorts to protect patients.
Current international guidelines state that “person-to-
person transmission may be an important mechanism in
the acquisition of M abscessus, at least in cystic fibrosis
patients”.6,7 A common source of clinical concern occurs
where M abscessus is isolated from several patients
attending the same clinic on the same day. In most of
such cases in this study, the genomes sequenced
were unrelated, eectively excluding person-to-person
transmission. Although we identified a small number of
cases (approximately 5% of patients) in which the genomic
relatedness of isolates and possible epidemiological
connections could be compatible with person-to-person
transmission, this number represents an upper bound
estimate of the true extent of transmission given the
integrated service design of regional cystic fibrosis
networks and the high number of epidemiological
contacts therefore expected by chance. It is hard to explain
how patients who live in geographically separate regions,
do not access the same health-care facilities, and are
unlikely to share social connections could transmit, even
indirectly, between each other. Unless there is a significant
reservoir of healthy and asymptomatic carriers in the
general population, it is unlikely that widespread person-
to-person transmission explains our observations. We
hypothesise that national dissemination via a widely
distributed, possibly water-associated exposure, could be
compatible with our data given what is known about the
environmental ecology of non-tuberculous mycobacteria
and the wide geographical distribution of clusters we
observe. A prominent example of this has been shown for
M chimaera,18 although our data would suggest that the
relevant exposure for M abscessus is more commonly
encountered in the community.
Our regression analysis identified age as being
significantly associated with the risk of acquiring a
clustered strain but there was no association with
Figure 4: Dated phylogenies and geographical distribution of Mycobacterium abscessus isolates in the two
largest clusters in this study
Clusters were identified using a genomic distance threshold of fewer than 25 SNPs. The side panel shows the
region of England in which the patient lived at the time of isolate collection. Dated phylogenies for all clusters are
shown in the appendix (p 13).
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
2015
2017
2019
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
2015
2017
2019
Region of England
East Midlands
East of England
London
Missing
Northeast
Northwest
Southeast
Southwest
Wales
West Midlands
Yorkshire and Humber
AB
Year Year
Articles
www.thelancet.com/microbe Published online July 14, 2021 https://doi.org/10.1016/S2666-5247(21)00128-2
9
increased hospital contact. Older and multi-comorbid
patients might be repeatedly exposed to clustered strains
via some unknown environmental vector whereas
non-clustered strains are sporadically found in a highly
diverse environmental pool that these patients are less
exposed to. The finding that patients with cystic fibrosis
appear to be marginally protected from acquisition of a
clustered isolate with increasing time spent as an inpatient
is likely to be spurious but might be explained by reduced
exposure if these strains are primarily community
acquired. Although we identified one pair of siblings in
whom household trans mission might have occurred, the
rarity of transmission amongst individuals with probable
household exposure suggests that transmission from
short-term nosocomial exposure would be even rarer,
which is consistent with our observations at the national
level. Nevertheless some highly infectious patients could
possibly transmit to other patients, especially in the case
of a breakdown in infection control procedures.
PHE’s systematic sequencing of non-tuberculous
mycobacteria began in 2015 and after high-profile
reports in the literature of M abscessus outbreaks and
subsequent enhanced infection control procedures.4 Our
findings could therefore be interpreted as representing
evidence of the eectiveness of these measures; however,
we think this explanation is unlikely. Stringent infection
control measures and the principle of segregating
patients with cystic fibrosis from each other were in
place long before the introduction of these enhanced
procedures.19 Most new clusters are still caused by
clustered isolates. Bayesian dating analysis of larger
clusters revealed that these arose before the introduction
of enhanced infection control procedures, suggesting
that these had minimal ecacy to disrupt their
prorogation. Furthermore, if trans mission was
predominantly asso ciated with health-care settings
before the introduction of these measures and
subsequent interventions eective, we would expect the
incidence of cases in patients with cystic fibrosis to
change significantly; this has not been observed.17 We
might also expect a divergence in the epidemiology of
M abscessus between patients with and without cystic
fibrosis (particularly those with non-cystic fibrosis
bronchiectasis). No such dierence was observed.
Studies that only sequence isolates during suspected
outbreaks or isolates selected for storage risk bias. The
relatively unselected nature of our patient population is a
key strength of this study. Limitations include possible
ascertainment bias leading to over-representation of
patients with cystic fibrosis due to heightened awareness
of M abscessus infection in this community in recent
years, the non-availability of sequences for pre-2015
isolates, and our assumption that patients remain
colonised with the same strain. The use of an arbitrary
SNP threshold is an additional limitation but also
permits direct comparison with previous studies and
reflects current public health practice.
In summary, the observation in this and other studies of
widely disseminated genetically near-identical clones is
striking, but crucially these are not restricted to patients
with cystic fibrosis. It is dicult to explain how cross-
transmission could have led to the widespread geographical
dispersion of clonal lineages we have observed among
patients, the vast majority of whom have no epidemiological
links. Although it is possible that these clones are
asymptomatically carried by a much wider population than
previously thought, it seems more probable that an
as yet unidentified, widely distributed, environmental
vector might underlie M abscessus clusters in patients with
chronic respiratory disease (not just cystic fibrosis). Our
data clearly show that future studies and infection
control approaches must consider a wider focus than
exclusively a cystic fibrosis health-care-associated niche.
The identification in this study and others of possible
cross-transmission events warrants ongoing genomic
surveillance and is one of many factors justifying high
levels of infection control within facilities that treat patients
with cystic fibrosis. These data should, however, also
provide reassurance to clinicians and patients and their
families that the risk of acquisition of M abscessus from
other patients in health-care settings is low. These data also
underline the value of unselected sampling frames
when making inferences on the basis of molecular
epidemiology.
Contributors
TMW, DWE, TEAP, SL, and DWC designed the study. SL, TEAP, TMW,
SC, WF, ASW, and DWE designed the analysis; SL did the analysis. SL
wrote the first draft of the manuscript. BM-P, NP, RM, EA, EGS, ER,
DWC, and SH were involved in curation of data, access to resources,
funding acquisition, and information governance. DWE, TEAP, and
DWC supervised the project. NW and NH were involved in data
collection. SL, TMW, DWE, TEAP, DWC, ASW, NH, SC, and WF
contributed to data interpretation. SL, TMW, and DWE verified the data.
All authors commented on a draft of the manuscript and approved the
final version. All authors had full access to all the data in the study and
had final responsibility for the decision to submit for publication.
Declaration of interests
DWE declares grants from Robertson Foundation and lecture fees from
Gilead, outside the submitted work. TMW is a Wellcome Trust Clinical
Career Development Fellow (214560/Z/18/Z). All other authors declare
no competing interests.
Acknowledgments
The research was supported by the National Institute for Health
Research (NIHR) Health Protection Research Unit in Healthcare
Associated Infections and Antimicrobial Resistance (HPRU 2012–10041)
at the University of Oxford in partnership with Public Health England
(PHE) and by Oxford NIHR Biomedical Research Centre. TEAP, ASW,
and DWC are NIHR senior investigators. Computation used the Oxford
Biomedical Research Computing (BMRC) facility, a joint development
between the Wellcome Centre for Human Genetics and the Big Data
Institute supported by Health Data Research UK and the NIHR Oxford
Biomedical Research Centre. The report presents independent research
funded by NIHR. The views expressed in this publication are those of
the authors and not necessarily those of the NHS, NIHR, the
Department of Health or PHE . SL is supported by a Medical Research
Council Clinical Research Training Fellowship.
Data sharing
All sequencing data is available under NCBI project accession number
PRJEB43019. The phylogenetic tree of 2297 isolates used in the analysis
is available online.
For the phylogenetic tree see
https://doi.org/10·6084/m9.
figshare.14153219.v1
Articles
10
www.thelancet.com/microbe Published online July 14, 2021 https://doi.org/10.1016/S2666-5247(21)00128-2
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