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LewerD, etal. J Epidemiol Community Health 2021;0:1–8. doi:10.1136/jech-2020-215204
Original research
Hospital readmission among people experiencing
homelessness in England: a cohort study of 2772
matched homeless and housedinpatients
Dan Lewer ,1,2,3 Dee Menezes,1 Michelle Cornes,4 Ruth M Blackburn,1
Richard Byng,5 Michael Clark,6 Spiros Denaxas,1,7 Hannah Evans,1 James Fuller,4
Nigel Hewett,8 Alan Kilmister,4 Serena April Luchenski,1 Jill Manthorpe,4
Martin McKee ,9 Joanne Neale,10 Alistair Story,11 Michela Tinelli,6
Martin Whiteford,12 Fatima Wurie,3 Alexei Yavlinsky,1 Andrew Hayward,2,3
Robert Aldridge1
To cite: LewerD, MenezesD,
CornesM, etal. J Epidemiol
Community Health Epub
ahead of print: [please
include Day Month Year].
doi:10.1136/jech-2020-
215204
►Additional material is
published online only. To view
please visit the journal online
(http:// dx. doi. org/ 10. 1136/
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For numbered affiliations see
end of article.
Correspondence to
Dan Lewer, Institute of
Epidemiology and Healthcare,
University College London,
London, UK; d. lewer@ ucl. ac. uk
The analysis presented in this
paper was outlined in our
previously published study
protocol.
Received 23 July 2020
Revised 6 November 2020
Accepted 4 December 2020
© Author(s) (or their
employer(s)) 2021. Re- use
permitted under CC BY.
Published by BMJ.
ABSTRACT
Background Inpatients experiencing homelessness
are often discharged to unstable accommodation or the
street, which may increase the risk of readmission.
Methods We conducted a cohort study of 2772
homeless patients discharged after an emergency
admission at 78 hospitals across England between
November 2013 and November 2016. For each
individual, we selected a housed patient who lived in a
socioeconomically deprived area, matched on age, sex,
hospital, and year of discharge. Counts of emergency
readmissions, planned readmissions, and Accident and
Emergency (A&E) visits post- discharge were derived from
national hospital databases, with a median of 2.8 years
of follow- up. We estimated the cumulative incidence of
readmission over 12 months, and used negative binomial
regression to estimate rate ratios.
Results After adjusting for health measured at the
index admission, homeless patients had 2.49 (95% CI
2.29 to 2.70) times the rate of emergency readmission,
0.60 (95% CI 0.53 to 0.68) times the rate of planned
readmission and 2.57 (95% CI 2.41 to 2.73) times the
rate of A&E visits compared with housed patients. The
12- month risk of emergency readmission was higher for
homeless patients (61%, 95% CI 59% to 64%) than
housed patients (33%, 95% CI 30% to 36%); and the
risk of planned readmission was lower for homeless
patients (17%, 95% CI 14% to 19%) than for housed
patients (30%, 95% CI 28% to 32%). While the risk
of emergency readmission varied with the reason for
admission for housed patients, for example being
higher for admissions due to cancers than for those
due to accidents, the risk was high across all causes for
homeless patients.
Conclusions Hospital patients experiencing
homelessness have high rates of emergency readmission
that are not explained by health. This highlights the need
for discharge arrangements that address their health,
housing and social care needs.
INTRODUCTION
Homelessness is an enduring social problem that is
associated with poor health outcomes, with cohort
studies showing mortality rates of three to six times
the general population.1–4 Although the size and
structure of the homeless population are difficult to
estimate, data in England suggest steep increases in
recent years. The number of people sleeping rough
identified by official counts increased from 1353 in
2010 to 4266 in 2019,5 with actual numbers likely
to be greater than this. The same period also saw
a steep increase in hospital attendances for people
with ‘no fixed abode’.6
Leaving hospital is often a traumatic experience
for people without a fixed address, and surveys
suggest that 30%–70% of homeless inpatients are
‘discharged to the street’ (ie, sleeping rough imme-
diately after discharge).7 8 Recovery after a hospital
admission may be spent sleeping rough or in insecure
accommodation such as hostels or sofa- surfing. In
addition, access to ongoing community healthcare
may be poor. Qualitative research has identified
barriers including stigmatisation when accessing
health services, primary healthcare practitioners
refusing to register homeless people, and priorities
that compete with health such as arranging accom-
modation.9–11 As a result, outcomes after hospital
discharge may be poor, and studies in the USA have
shown that homeless inpatients are more likely to
be readmitted than housed inpatients.12–15
In response to concerns about poor discharge
arrangements for homeless inpatients, the UK
government set up the ‘Homeless Hospital
Discharge Fund’,16 which funded partnerships of
National Health Service (NHS) and non- profit
organisations to develop methods of supported
discharge. These schemes operated between 2013
and 2016 and used a range of models. Most
included a housing specialist who helped patients
access community health and housing services. In
some schemes, discharge was managed by a multi-
disciplinary team including general practitioners
(GPs), nurses, therapists, and housing workers.
Some had accompanying intermediate care facili-
ties providing accommodation and clinical support.
Data collected by the schemes give a detailed insight
into the outcomes of homeless inpatients after
discharge from hospital.
Our previous analysis of linked death records for
people attending the homeless hospital discharge
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2LewerD, etal. J Epidemiol Community Health 2021;0:1–8. doi:10.1136/jech-2020-215204
Original research
schemes found that deaths after discharge were related to a
wide range of physical health problems, with alcohol, drugs or
suicide (sometimes considered the main health problems in this
population) the primary cause for only one- third of deaths.17 In
this present analysis we compare the risk of hospital readmis-
sion among homeless inpatients with housed inpatients living in
socioeconomically deprived areas.
METHODS
We conducted a retrospective cohort study of the rate of hospital
readmission after an emergency hospital admission, comparing
homeless patients with housed patients living in socioeconomi-
cally deprived areas.
Data source
The data were collected as part of an evaluation of hospital
discharge schemes established through the Homeless Hospital
Discharge Fund.16 We worked with 17 schemes covering
78 hospitals across England between November 2013 and
November 2016. Schemes did not adopt a common definition
of homelessness and had various ways of identifying eligible
patients. Some relied on referrals from clinicians and others did
ward rounds. Services primarily focused on people living on the
streets or in night shelters and hostels for single homeless people,
but may also have worked with people who meet broader defi-
nitions of homelessness such as people who are sofa- surfing, or
at risk of losing an existing tenancy. Teams helped plan discharge
and helped patients access housing, intermediate care, and other
services that were available locally. Full details of the datasets are
available in a prepublished protocol.18
Data cleaning and linkage
We collected patient identifiers from the 17 homeless discharge
schemes. We excluded patients under the age of 18 years and
those with insufficient data for record linkage. We sent the iden-
tifiers to NHS Digital for deterministic linkage19 with national
hospital and mortality databases (‘Hospital Episode Statistics’
and the Office for National Statistics mortality database respec-
tively). Linked data were available until 31 March 2018. We
considered the ‘index’ admission as the first time a homeless
patient was seen by a hospital discharge service. The admission
dates provided by the homeless discharge schemes sometimes
varied from those recorded in the national hospital database,
and we used an algorithm to select the index admission (see
online supplemental information). For comparison purposes,
we requested data on hospital episodes and deaths for a sample
of housed inpatients admitted to the same 78 hospitals between
November 2013 and November 2016, with a home address in
the most deprived quintile of neighbourhoods based on the Index
of Multiple Deprivation,20 and who were not seen by a homeless
discharge scheme. We processed the hospital episode data into
‘spells’ (or ‘admissions’), because English hospital data are struc-
tured such that a single hospital spell is sometimes divided into
several episodes of care led by different doctors or departments.
A spell represents a continuous period of time in hospital.
Matching
For this analysis, we only included patients who were admitted
in an emergency. Planned admissions were rare in the homeless
group (8% of all admissions) and often represent healthcare
that can be conducted on a single day, such as dialysis or phys-
ical rehabilitation, and readmission may not be related to poor
discharge arrangements or ongoing care. We used a matched
design to compare homeless and housed patients. For each
homeless patient we selected at random one housed patient of
the same sex and age group (using age groups 18–24, 25–34
and then 10- year age groups) who was discharged alive from the
same hospital in the same year. We have published our matching
algorithm at https:// github. com/ danlewer/ homeless- discharge/.
We used this design to allow definition of an ‘index’ admission
for housed patients, while avoiding potential biases in choosing
an index date that may result from other approaches (such
as selecting an admission at random, which is likely to select
patients during periods of poor health). The derivation of the
study cohort is shown in figure 1.
Outcomes
Outcomes were the counts of planned hospital readmissions,
emergency hospital readmissions, and Accident and Emergency
(A&E) visits, as defined in the study protocol.18
Covariates
We defined the number of comorbidities as the number of
ICD-10 (10th revision of the International Statistical Classifica-
tion of Diseases and Related Health Problems) chapters that were
present in the primary diagnosis field for hospital admissions in
the 4 years prior to the index date. We used 4 years because all
participants had at least 4 years of prior data). We only counted
comorbidities from ICD-10 chapters 2–14 and 17,21 excluding
chapters such as infections where an admission may not repre-
sent a long- term condition. We defined the reason for index
admission using the ICD-10 chapter of the primary diagnosis,
grouping chapters accounting for fewer than 100 index admis-
sions as ‘other’ (see table 1 for a list of ICD-10 chapters included
in the analysis). For descriptive purposes, we also reported
whether discharge from the index admission was against medical
advice, and whether patients died during follow- up.
Statistical analysis
We estimated the probability of patients having at least one of
each outcome (emergency readmission, planned readmission and
A&E visit) in the 12 months after discharge, with each outcome
stratified by the ICD-10 chapter of the index admission. We used
the Kaplan- Meier method to estimate cumulative incidence at 12
months, because some participants had less than 12 months of
follow- up, for example due to death. The SEs of the cumulative
incidence were clustered by hospital to account for differences
in clinical practice.
To estimate rate ratios comparing homeless and housed
patients, we used a mixed negative binomial model for each
outcome, with homeless/housed status as the main independent
variable; adjustment for the matching variables age, sex, year of
discharge and a random intercept for hospital site; and an offset
for the log time- at- risk. We then additionally adjusted for the
count of comorbidities and the reason for admission. We used
negative binomial models because the outcomes were dispersed
(ie, the variance of the count of readmissions was greater than
the mean).
Analysis was conducted using R V.3.6.2.
RESULTS
A total of 3894 homeless patients were supported by the 17
specialist discharge services. Of these patients, 3309 were
admitted in an emergency and eligible for inclusion. We matched
2772 (84%) of these patients to a unique housed patient. The
remaining 537 could not be matched as there was no remaining
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LewerD, etal. J Epidemiol Community Health 2021;0:1–8. doi:10.1136/jech-2020-215204
Original research
eligible housed patient in our data. Unmatched homeless patients
were slightly younger and more likely to be men than matched
patients (online supplemental information).
Homeless patients had more comorbidities than housed
patients and were more likely to be admitted for mental health
problems or ‘external’ causes (including accidents). Table 1
shows the characteristics of homeless patients and matched
housed patients.
We plotted all readmissions and A&E visits for every patient
in the study (figure 2). Visual inspection of this plot suggested
that emergency readmissions and A&E visits occurred more
frequently for patients experiencing homelessness at the index
admission, while there was a similar rate of planned readmis-
sions. There were more series of repeat planned admissions
within the group experiencing homelessness (visible on the plot
as solid horizontal lines). Consequently, we conducted an explor-
atory analysis where we defined a ‘series’ as weekly (or more
frequent) planned admissions for at least 8 consecutive weeks.
In the homeless group, there were 16 such series involving 9
patients. There were 1201 admissions within these series, of
which 949/1201 (79%) had a primary diagnosis of renal failure
and most had procedure codes for dialysis. This means that one-
third (1201/3400; 35%) of the planned admissions in the home-
less cohort related to these series. In the housed group, there
were 11 series involving 7 patients. There were 571 admissions
in these series and 174 (30%) had a primary diagnosis of renal
failure. This means that one in eight (571/4769; 12%) of the
planned admissions in the housed cohort related to these series.
The 12- month risk of emergency readmission was 61% (95%
CI 59% to 64%) for homeless patients and 33% (95% CI 30% to
36%) for housed patients; for planned readmission it was 17%
(95% CI 14% to 19%) for homeless patients and 30% (95% CI
28% to 32%) for housed patients; and for A&E visits it was 94%
(95% CI 93% to 95%) for homeless patients and 84% (95%
CI 81% to 86%) for housed patients (figure 3). Among housed
patients, the risk of emergency readmission varied substantially
according to the cause of the index admission. For example,
patients admitted with a primary cause of cancer had a 56%
(95% CI 45% to 68%) risk of emergency readmission over the
following 12 months, compared with 25% (95% CI 20% to
30%) for patients admitted following an accident. In contrast,
the 12- month risk of an emergency readmission was greater
than 50% regardless of the reason for the index admission, with
limited variation.
After adjusting for comorbidities and the ICD-10 chapter of
the index admission, patients experiencing homelessness had
2.49 (95% CI 2.29 to 2.70) times the rate of emergency read-
missions, 0.60 (95% CI 0.53 to 0.68) times the rate of planned
readmissions and 2.57 (95% CI 2.41 to 2.73) times the rate
of A&E visits (table 2). As an exploratory sensitivity analysis
(not prespecified), we fit the model for planned readmissions
excluding ‘series’ of admissions (defined above), which gave
a rate ratio of 0.51 (95% CI 0.45 to 0.57) when adjusting for
matching variables only, and a fully adjusted ratio of 0.55 (95%
CI 0.49 to 0.62), that is, a wider relative difference between
homeless and housed patients after removing series of planned
admissions.
DISCUSSION
Hospital patients who are experiencing homelessness have
high rates of emergency readmission and A&E visits after
discharge. Following an acute illness, most patients are
expected to recover in their own home, and the rate of read-
mission is relatively low. Our results show this is not the case
for homeless patients.
Figure 1 Derivation of the study cohort. HES, Hospital Episode Statistics; NHS. National Health Service.
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Original research
Similar to our study, studies of hospital admissions among
homeless populations in North America have reported diverse
physical and mental health problems on admission and high
rates of emergency readmission.8 12–15 Results are difficult to
compare directly due to differences in the populations, health-
care systems, and study methodologies. A study in the USA found
that 17% of inpatients with hospital records indicating homeless-
ness were readmitted within 90 days; only slightly higher than
14% of housed patients.15 Another study, also using homeless-
ness recorded in routine hospital data, found that homelessness
was associated with 1.4 times the odds of readmission.13 These
studies both found modest differences in readmission risk. Use
of routinely captured data to identify homelessness may have led
to inclusion of people experiencing less severe forms of home-
lessness, such as unstable or temporary housing. In contrast,
two other studies in the USA measured hospital readmission for
patients who live in homeless shelters, and are likely to have
longer histories of homelessness and sleeping rough.12 13 These
studies found that readmission risk was approximately three
times that of housed patients; more similar to our estimates. It is
also important to remember that the housed comparison group
in our study live in areas of high socioeconomic deprivation.
The difference in readmission risk between homeless individ-
uals and the general population is likely to be wider, because
socioeconomic deprivation is associated with morbidity and
readmission.22
As far as we are aware, ours is the largest study of the
outcomes of homeless patients after discharge from hospital
in the UK. National data regarding inpatients with ‘no fixed
abode’ show that the median age was 43, three- quarters were
men and 9 out of 10 were admitted in an emergency.23 Our
sample has similar characteristics, supporting generalisability
between our results and homeless patients discharged from
hospital nationally.
Table 1 Characteristics of hospital patient experiencing homelessness, compared with housed patients living in socioeconomically deprived areas
Variable Level Homeless, n (%) Housed, n (%)
Total 2772 (100) 2772 (100)
Age at index admission (matched) Mean (SD) 44.21 (14.15) 44.34 (14.56)
Median (IQR) 43.64 (33.37–53.75) 43.67 (33.05–53.51)
Sex (matched) Female 768 (28) 768 (28)
Male 2004 (72) 2004 (72)
Year of index admission (matched) 2013 76 (3) 76 (3)
2014 769 (28) 769 (28)
2015 948 (34) 948 (34)
2016 979 (35) 979 (35)
Number of comorbidities, based on prior hospital admissions
(ICD-10 chapters 2–14 and 17)
0 926 (33) 1238 (45)
1 769 (28) 784 (28)
2 541 (20) 424 (15)
3 307 (11) 183 (7)
4+ 229 (8) 143 (5)
Mean (SD) 1.38 (1.41) 1.02 (1.24)
Median (IQR) 1 (0–2) 1 (0–2)
ICD-10 chapter of index admission Accidents and other external 695 (25) 452 (16)
Digestive diseases 223 (8) 298 (11)
Circulatory diseases 226 (8) 228 (8)
Mental health 347 (13) 90 (3)
Respiratory diseases 189 (7) 236 (9)
Skin problems 206 (7) 106 (4)
Genitourinary diseases 89 (3) 210 (8)
Musculoskeletal problems 112 (4) 127 (5)
Infections 75 (3) 78 (3)
Cancers 63 (2) 80 (3)
Other* 547 (20) 867 (31)
Discharge method for index admission Self (without clinical consent) 253 (9) 100 (4)
With clinical consent 2519 (91) 2672 (96)
Years of follow- up after index admission 6753 6987
Emergency readmissions (rate per 1000 person- years) 12 472 (1847) 4525 (648)
Planned readmissions (rate per 1000 person- years) 3400 (503) 4769 (683)
A&E visits (rate per 1000 person- years) 43 808 (6487) 14 186 (2030)
Died during the study (%) 451 (16) 311 (11)
*‘Other’ includes ICD-10 chapters (II) diseases of the blood and blood- forming organs; (IV) endocrine, nutritional and metabolic diseases; (VI) diseases of the nervous system; (VII) diseases of
the eye; (VIII) diseases of the ear.
A&E, Accident and Emergency; ICD-10, 10th revision of the International Statistical Classification of Diseases and Related Health Problems.
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LewerD, etal. J Epidemiol Community Health 2021;0:1–8. doi:10.1136/jech-2020-215204
Original research
In our protocol18 we planned to analyse data from a ‘control’
group of homeless patients who were not seen by a specialist
discharge scheme and were instead identified through linkage
to an external service that supports people experiencing
homelessness. This control group was intended to allow esti-
mation of the effect of the specialist discharge schemes on
readmission rates. However, we subsequently found problems
with these data; most importantly that we could not confirm
whether patients were homeless at the point of hospital admis-
sion (unlike those included in this analysis, whom specialist
discharge teams identified as homeless). We therefore limited
the present analysis to a comparison of readmission for home-
less and housed patients. This means that our analysis primarily
provides insight into readmissions for homeless people after
hospital discharge, rather than an evaluation of the discharge
schemes.
We did not account for competing risks in our analysis. Death
is likely to be a competing risk for hospital readmission, on the
assumption that patients who died would have an increased
risk of readmission if they had survived. Homeless patients in
our study had a higher risk of death (16% of homeless patients
died, compared with 11% of housed patients) and the effect
of competing risks is therefore likely to be that rate ratios are
slightly understated. We did not account for competing risks to
maximise the simplicity of the analysis, and because competing
risks are unlikely to have an important bearing on the results.
Figure 2 Plot of hospital spells and A&E visits after discharge, with patients arranged in rows and ordered by follow- up duration. Dots indicate
readmission dates. Patients with no readmissions are represented as an empty (white) row. A&E, Accident and Emergency.
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Original research
We found that homeless inpatients had a lower rate of planned
readmissions than housed inpatients. Our post- hoc visual inspec-
tion of the readmissions data (figure 2) suggested that the distri-
bution of planned readmissions may be different in the two
groups, with more ‘series’ of planned admissions for homeless
patients. We found that these series related to a small number of
individuals with large numbers of admissions, mainly for renal
failure and dialysis. After removing these individuals from both
groups, the difference between homeless and housed patients
was wider. This low rate of planned care reflects barriers that
have been observed in qualitative research,9–11 and suggests that
long- term conditions are often managed in crises. The relatively
large number of homeless patients with renal failure was not
expected and is an avenue for further research. It may represent
different patterns of healthcare use (for example dialysis being
undertaken by housed patients in other settings), or a higher risk
of renal failure related to use of drugs or alcohol, or cardiovas-
cular disease. The importance of renal failure in terms of the
quantity of healthcare would not have been identified from the
index admission alone and reflects the strengths of using longi-
tudinal data. Nephrologists in the USA have previously observed
the difficulty of providing dialysis to homeless patients with end-
stage renal disease.24
A range of intermediate care services has been developed in
England for older people. These services are not easily acces-
sible to homeless patients younger than 55 years who may
also be frail or in need of rehabilitation or palliative care.25 26
The effectiveness of these services can be limited by shortages
of longer term care, leading to intermediate care becoming
‘blocked’27 and contributing to bed shortages. In this context it
is unsurprising that people experiencing homelessness, many of
whom are middle- aged and do not meet eligibility criteria for
services for older people, struggle to access existing intermediate
care provision. Our results show a need for community ‘step-
down’ services that provide ongoing care. Observational studies
have found that step- down services are associated with reduced
readmissions,28–31 and a trial of GP- led management of discharge
in the UK found reduced street homelessness and improved
quality of life.32
Hospital patients who are experiencing homelessness have
extreme rates of emergency readmission after discharge,
reflecting poor housing and ongoing community care that is
designed around the needs of people who have stable housing.
Figure 3 The 12- month risk of readmission, stratified by the ICD-10 chapter of index admission. A&E, Accident and Emergency; ICD-10, 10th
revision of the International Statistical Classification of Diseases and Related Health Problems.
Table 2 Incidence rate ratios of readmissions and A&E visits,
comparing homeless patients with housed patients living in
socioeconomically deprived areas (results of negative binomial
regression)
Adjusted on matching
variables
Further adjusted for
comorbidities and ICD-10
chapter of index admission
Emergency
readmissions
2.92 (2.67–3.19) p<0.001 2.49 (2.29–2.70) p<0.001
Planned
readmissions
0.63 (0.55–0.72) p<0.001 0.60 (0.53–0.68) p<0.001
A&E visits 3.06 (2.86–3.27) p<0.001 2.57 (2.41–2.73) p<0.001
A&E, Accident and Emergency; ICD-10, 10th revision of the International Statistical
Classification of Diseases and Related Health Problems.
Box 1 Interpretation by an expert with lived experience
For many people who are street homeless, hospital is an
inhospitable, if not hostile environment. A single visit, or
even street lore alone, can be enough to cause one to make
inventive efforts to disguise one’s homelessness in order to
receive less visceral and judgemental handling. Certainly, it is
not an experience anyone would rush to embrace, hence the
(potentially fatal) avoidance and delay before seeking treatment.
Conversely, particularly for some of us with poor mental health,
a hospital represents a building with ‘indoor’ comforts and
facilities like heat, light and hot water and crucially, a place
populated by people who are perceived to have a duty to play
nicely. Perhaps this cohort of ‘regulars’ is partly responsible for
the medical profession’s distaste of us as a whole. In any case,
there are deep differences in how people who are homeless
approach and are received by healthcare services, as opposed
to those who are housed. This may explain the results under
Discussion. If hospital Trusts were to adopt a less ‘gatekeeping’
approach to homeless patients, try not to refer to us as
bed- blockers, at least not in our hearing, and provide timely
treatments for our multiple morbidities, the costs of our care
could be reduced dramatically. (JF)
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Original research
Ultimately, the inequality between housed and homeless people
can only be addressed by preventing homelessness (Box 1).
Author affiliations
1Institute of Health Informatics, University College London, London, UK
2Collaborative Centre for Inclusion Health, University College London, London, UK
3Institute of Epidemiology and Health Care, University College London, London, UK
4NIHR Policy Research Unit in Health and Social Care Workforce, King’s College
London, London, UK
5Community and Primary Care Research Group, University of Plymouth, Plymouth, UK
6Care Policy and Evaluation Centre, The London School of Economics and Political
Science, London, UK
7Alan Turing Institute, British Library, London, UK
8Pathway Charity, London, UK
9Department of Health Services Research and Policy, London School of Hygiene &
Tropical Medicine, London, UK
10National Addiction Centre, Institute of Psychiatry, Psychology & Neuroscience,
King’s College London, London, UK
11Find & Treat, University College London Hospitals NHS Foundation Trust, London,
UK
12Department of Nursing & Community Health, Glasgow Caledonian University,
Glasgow, UK
Twitter Dan Lewer @danlewer, Martin McKee @martinmckee and Robert Aldridge
@rob_aldridge
Contributors MCo, AH and RA—conceptualisation. DL, DM, RMB, HE and RA—
methodology. DL and DM—software. DL—formal analysis. DM, RMB, HE, AY and
RA—data curation. DL and JF—writing (original draft). DL, DM, MCo, RMB, RB,
MC, SD, HE, JF, NH, AK, SAL, JM, MM, JN, AS, MT, MW, FW, AY, AH and RA—writing
(review and editing). DL—visualisation. MCo, AH and RA—supervision. DM and
RA—project administration. MCo, AH and RA—funding acquisition.
Funding This study was supported by the National Institute for Health Research
(NIHR) (Project number: 13/156/10 to HS & DR). We also acknowledge the support
from the Health Data Research (HDR) UK which receives its funding from HDR UK
funded by the UK Medical Research Council, Engineering and Physical Sciences
Research Council, Economic and Social Research Council, Department of Health
and Social Care (England), Chief Scientist Office of the Scottish Government Health
and Social Care Directorates, Health and Social Care Research and Development
Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart
Foundation (BHF) and the Wellcome Trust. AH’s salary is provided by Central and
North West London NHS Community Trust. AS is funded by UCLH Foundation Trust.
DL is funded by an NIHR Doctoral Research Fellowship (DRF-2018-11- ST2-016).
JN is part- funded by the NIHR Biomedical Research Centre for Mental Health at
South London and Maudsley NHS Foundation Trust and King’s College London.
RMB is supported by a UK Research and Innovation Fellowship funded by a grant
from the Medical Research Council (MR/S003797/1). SL is funded by NIHR (ICA-
CDRF-2016-02-042). RB is supported by the NIHR Applied Research Collaboration
(ARC) South West Peninsula and JM is supported by the NIHR ARC South London.
RWA is supported by Wellcome through a Wellcome Clinical Research Career
Development Fellowship (206602). This article is based on independent research
commissioned and funded by the NIHR Health Service and Delivery Programme.
Disclaimer The views expressed in the publication are those of the authors and
not necessarily those of the NHS, the NIHR, the Wellcome Trust, the Department of
Health and Social Care, Public Health England or its arm’s length bodies or other
government departments.
Competing interests NH is medical director, and AH is a trustee of the charity
’Pathway: Healthcare for homeless people’. AS is clinical lead and manager for the
’Find and Treat’ service at University College London Hospitals.
Patient consent for publication Not required.
Ethics approval To undertake this study, collection of patient identifiers and
performed data linkage were performed without explicit consent from participants.
This research was therefore undertaken following approval (reference 16/CAG/0021)
from the Secretary of State for Health and Social Care through the Confidentiality
Advisory Group (CAG). Health Research Authority Research Ethics Committee
approval was also sought and received (REC 16/EE/0018). In addition, local R&D
approvals were obtained prior to local data collection at each of the Homeless
Hospital Discharge Fund sites.
Provenance and peer review Not commissioned; externally peer reviewed.
Data availability statement Data may be obtained from a third party and
are not publicly available. As part of the approvals and information governance
frameworks, we are unable to share the underlying data for this research study. Our
approval only allowed researchers involved in this specific project to access data for
the prespecified and approved analyses. Therefore, data collection and linkage would
have to be repeated with new approvals sought by anyone wanting access to the
underlying data used in this analysis. Application for access should be directed to the
CAG of the Health Research Authority. Information regarding the application process
and relevant links for applications are available from the CAG website.
Supplemental material This content has been supplied by the author(s). It
has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have
been peer- reviewed. Any opinions or recommendations discussed are solely those
of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and
responsibility arising from any reliance placed on the content. Where the content
includes any translated material, BMJ does not warrant the accuracy and reliability
of the translations (including but not limited to local regulations, clinical guidelines,
terminology, drug names and drug dosages), and is not responsible for any error
and/or omissions arising from translation and adaptation or otherwise.
Open access This is an open access article distributed in accordance with the
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purpose, provided the original work is properly cited, a link to the licence is given,
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licenses/ by/ 4. 0/.
ORCID iDs
DanLewer http:// orcid. org/ 0000- 0003- 3698- 7196
MartinMcKee http:// orcid. org/ 0000- 0002- 0121- 9683
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