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R E S E A R C H A R T I C L E Open Access
Results of a pilot randomised controlled trial to
measure the clinical and cost effectiveness of
peer support in increasing hope and quality of
life in mental health patients discharged from
hospital in the UK
, Chris Flood
, Julie Rowe
, Jody Quigley
, Susan Henry
, Cerdic Hall
, Richard Evans
and Len Bowers
Background: Mental health patients can feel anxious about losing the support of staff and patients when
discharged from hospital and often discontinue treatment, experience relapse and readmission to hospital, and
sometimes attempt suicide. The benefits of peer support in mental health services have been identified in a
number of studies with some suggesting clinical and economic gains in patients being discharged.
Methods: This pilot randomised controlled trial with economic evaluation aimed to explore whether peer support
in addition to usual aftercare for patients during the transition from hospital to home would increase hope, reduce
loneliness, improve quality of life and show cost effectiveness compared with patients receiving usual aftercare
only, with follow-up at one and three-months post-discharge.
Results: A total of 46 service users were recruited to the study; 23 receiving peer support and 23 in the
care-as-usual arm. While this pilot trial found no statistically significant benefits for peer support on the primary or
secondary outcome measures, there is an indication that hope may be further increased in those in receipt of peer
support. The total cost per case for the peer support arm of the study was £2154 compared to £1922 for the
control arm. The mean difference between costs was minimal and not statistically significant. However, further
analyses demonstrated that peer support has a reasonably high probability of being more cost effective for a
modest positive change in the measure of hopelessness. Challenges faced in recruitment and follow-up are
explored alongside limitations in the delivery of peer support.
Conclusions: The findings suggest there is merit in conducting further research on peer support in the transition
from hospital to home consideration should be applied to the nature of the patient population to whom support is
offered; the length and frequency of support provided; and the contact between peer supporters and mental
health staff. There is no conclusive evidence to support the cost effectiveness of providing peer support, but
neither was it proven a costly intervention to deliver. The findings support an argument for a larger scale trial of
peer support as an adjunct to existing services.
Trial registration: Current Controlled Trials ISRCTN74852771
Keywords: Peer support, Mental health, Discharge, Hope, Loneliness, Quality of life, Economic evaluation, Suicide
* Correspondence: email@example.com
School of Health Sciences, City University London, Northampton Square,
London EC1V 0HB, UK
Full list of author information is available at the end of the article
© 2014 Simpson et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication
waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise
Simpson et al. BMC Psychiatry 2014, 14:30
Mental health patients recently discharged from hospital
often fail to continue with treatment , relapse and are
readmitted . In the UK, between 20% and 40% of psy-
chiatric patients were reported to be re-admitted within
six months of discharge with the peak period occurring
within the first month . Suicide is also a risk with
around 8% of all community suicides occurring in the
two weeks following discharge . Research interviews
with service users on mental health units found that
over half said they felt anxious about being discharged
. In hospital, they felt safe and supported by staff and
appreciated the company of other patients and were
concerned about coping outside. One suggestion to im-
prove post-discharge outcomes was the addition of peer
support by fellow service users alongside existing after-
care services .
Formal peer support (PS) consists of social and emo-
tional support provided by people with experience of ill-
ness to others sharing a similar condition to bring about
a desired social or personal change . It has been sug-
gested that peer support workers (PSWs), who have
achieved significant recovery of their own, offer accept-
ance, respect, empathy, support, companionship, hope
and share experiences and ideas about how to cope with
mental illness .
Peer support programmes have been developed and
incorporated into state run services in the USA  and
fledging services are operating in Scotland and England
[9,10]. A broad review of the international literature
on PS found evidence supporting its effectiveness in
empowering service users, aiding recovery and in a small
number of studies, reducing re-admissions . Most
studies reported potential benefits both for service users
and PSWs, and may increase hope, improve social net-
works and aid recovery [12-15].
A recent Cochrane Review assessed the effects of
employing consumers of mental health services as pro-
viders of services in roles that included peer support,
coaching, advocacy, case management and outreach or
crisis worker . Five trials involving 581 people com-
pared consumer-providers to professionals in similar
roles and found no significant differences across a wide
range of measures; consumer providers were as effective
as professionals. There was a small reduction in crisis
and emergency service use for clients receiving care in-
volving consumer-providers. Consumers who provided
mental health services did so differently than profes-
sionals; they spent more time face-to-face with clients,
and less time in the office, on the telephone, with clients’
friends and family, or at provider agencies. Six trials in-
volving 2215 people compared mental health services
with or without the addition of consumer-providers.
Again, there were no significant differences across vari-
ous measures between groups with consumer-providers
as an adjunct to professional-led care and those receiv-
ing usual care from health professionals alone. The qual-
ity of studies was moderate, most undertaken in the
USA and cost effectiveness was not considered.
In Canada and Scotland, trials of a transitional
discharge model in which support was provided at
discharge jointly by ward staff and PSWs, reported re-
ductions in re-admissions and use of emergency ser-
vices, lower costs and increased satisfaction [17-19]. In
Australia, a three-month pilot study of PS aimed at re-
ducing hospital admissions reported significant reduc-
tions in admissions and re-admissions, less use of
emergency services and associated cost savings . No
trials have been conducted in the UK into the effective-
ness of peer support as an intervention for patients at
the transition point of being discharged from hospital.
PS is considered to be potentially cost effective be-
cause of the possible reduction in hospital admissions
and other resource use. The clinical effectiveness and
cost effectiveness of peer support is an important re-
search question. There are potential benefits for the
wellbeing of patients at a difficult transitional stage and
potential costs savings for the health care system that
can then allow resources to be used elsewhere in the
Aims and objectives
In this pilot study, we aimed to:
1. Investigate the effect of peer support on feelings of
hope and loneliness, quality of life and service use in
mental health patients following discharge from
2. Evaluate the economic consequences of the
3. Inform the design of a definitive RCT by:
a. providing realistic estimates of recruitment and
b. assessing the feasibility of the methods of
randomisation including acceptability to
c. providing information on the limitations of
d. providing data to inform future sample size
calculations, intra-class correlations and
descriptive statistics on the baseline performance
of the final outcome measures.
It was hypothesised that peer support would have a
positive effect on the patients’feelings of hope, reduce
their loneliness and improve quality of life with an effect
Simpson et al. BMC Psychiatry 2014, 14:30 Page 2 of 14
on service use, including reduced re-hospitalisation, and
decrease the risk of suicide. The economic evaluation
also sought to explore the relative cost-effectiveness of
peer support services compared with care as usual, com-
paring service use data and associated costs collected at
three month follow-up. Such information is important
as it enables policy and decision makers to compare the
costs incurred and services used by recipients of the
peer support intervention as compared to those receiv-
ing standard care. The study also aimed to conduct a
cost utility analysis.
Pilot randomised controlled trial (RCT) with economic
evaluation comparing peer support in addition to care as
usual with usual care alone following discharge, with
follow-up at one month and three months post-discharge.
PSWs to provide peer support for four weeks to patients
discharged from four mental health wards. Initial contact
was made while the patient was an inpatient with dis-
charge expected within the next two to three weeks
(total contact time six weeks). Peer support would be in
addition to care as usual (CAU).
Patients in the control condition received CAU arrange-
ments from community mental health services.
Diagnosed mental illness; approaching discharge/ex-
tended leave; age 18–65.
Considered a serious risk to others; alcohol or drug
dependent or primary diagnosis of substance use; serious
personality disorder; pregnant or caring for children.
Sample size and power calculation
The Beck Hopelessness Scale (BHS) is the main outcome
measure . A power calculation was undertaken,
based on the results of a study of 400 randomly selected
adults from the general population that reported a mean
of 4.45 with a standard deviation of 3.09 . Using
these norms, we calculated that a sample size of 39 in
each group (total sample 78) would have 80% power to
detect a difference in means of −2.000 (the difference
between a Group 1 mean of 4.450 and a Group 2 mean
of 6.450) assuming that the common standard deviation
is 3.090 using a two group t-test with a 0.050 two-tailed
significance level [23,24].
Previous research reported an average 60 patients per
month discharged from the four wards in inner London,
England due to take part in this study . Allowing for
patients swiftly discharged from the ward, exclusion of
those not meeting inclusion criteria, and others that de-
clined to take part, we aimed to recruit 110 service users
into the trial over seven months at a rate of approximately
16 per month (26% of the predicted discharges). With 55
patients in each arm of the trial, we allowed for a 10 per
cent drop-out rate before the end of the intervention and
again before the three-month follow-up, providing 45
users in each arm, a total of 90 participants.
Recruitment and follow-up: A total of 46 patients were
recruited to the study; 23 receiving peer support and 23
in the care-as-usual arm (see Figure 1). At one-month
(T1) follow-up, 26 participants (56.5%) completed data
collection (14 PS; 12 CAU); and at three-month (T2)
follow-up, 15 (36.2% of the total sample; 57.7% T1 com-
pleters) completed data collection (6 PS; 9 CAU).
Randomisation was by ‘distance’randomization using a
computer programme with no information about the
recruited individual . ‘Block’rather than simple
randomization was employed to ensure equal-sized treat-
ment groups .
Data collection and instrumentation
Data was collected at baseline (T0), one month following
discharge (T1) and three months following discharge
(T2). The following instruments were employed:
a) Patient Data Pro-forma: Demographics, diagnosis
and history via case note audit.
b) Beck Hopelessness Scale (BHS)  is a 20-item
scale for measuring negative attitudes about the
future based on three dimensions of hopelessness. It
is the main outcome measure as it was hypothesised
that PS would increase hope and the scale is
established as a reliable measure of suicide risk in
psychiatric patients in hospital and outpatient
settings [28,29] and with high internal and
test-retest reliability [21,30].
c) UCLA Loneliness Scale (V3) . Developed
to assess subjective feelings of loneliness or
social isolation and modified to increase
reliability and validity, the scale is the most
widely used measure in studies of loneliness and
social isolation with scores predicting a variety
of mental and physical health outcomes .
Addition of PS is hypothesised to lead to an
increase in social networks and reduce loneliness
Simpson et al. BMC Psychiatry 2014, 14:30 Page 3 of 14
d) EuroQol (EQ-5D) Quality of Life Questionnaire is a
non-disease-specific instrument for describing and
valuing health-related quality of life, used in eco-
nomic evaluation , including alongside RCTs
. The EQ-5D descriptive system comprises five
dimensions: mobility, self-care, usual activities, pain/
discomfort and anxiety/depression. Each dimension
is scored: no problems (1); some problems (2);
severe problems (3). The EQ VAS records the
respondent’s self-rated health on a vertical, visual
analogue scale where the endpoints are labelled
‘Best’and ‘Worst’‘imaginable health state’.This
information can be used as a quantitative measure
of health outcome as judged by the individual
respondents. A higher score represents a better
perceived health state.
e) Client Service Receipt Inventory (CSRI) was used to
collect service use for all service users and in turn
calculate costs . Data about health and social
services resource use, including hospital admission,
was collected from patients, care co-ordinators and
clinical records. A data collection pro forma was
used to record services provided and number of
contacts with each, for all services including peer
f) Peer Support Activity Diaries, maintained by PSWs
to record the time spent and activities undertaken in
support of each user, as an indicator of the type and
level of support provided.
The idea for a ‘buddy system’or peer support on dis-
charge was suggested by the service user researcher on a
previous study . Design of the study, ethics applica-
tion, recruitment and selection of PSWs, progress up-
dates and results were all discussed with members of a
service user and carer research advisory group (SUGAR).
Three service users and a carer were also members of
the Project Advisory Group.
PSWs were recruited (n = 16), with eight successfully
completing training and working as PSWs. They were
provided with supervision and support for the duration
of the study. Potential study participants expected to be
discharged within two-to-three weeks were identified by
the Peer Support Coordinator (PSC) in discussion with
ward staff and invited to take part in the study. Partici-
pants completed a consent form and baseline measures
administered by the research assistant. Each participant
Assessed for eligibility (n=134)
Not meeting inclusion criteria (n=25)
Declined to participate (n=46)
Included in analysis (n=6)
Lost to follow-up (no contact) (n=6)
Relapsed and re-admitted (n=1)
Died (unrelated to study) (n=1)
Included in analysis (n=14)
Lost to follow-up (no contact) (n=4)
Lost to follow-up (withdrew) (n=1)
Relapsed and re-admitted (n=1)
Allocated to peer support (n=23)
Received allocated intervention (n=20)
Did not receive allocated intervention
(delayed discharged) (n=3)
Included in analysis (n=12)
Lost to follow-up (no contact) (n=5)
Lost to follow-up (withdrew) (n=2)
Relapsed and re-admitted (n=3)
Allocated to care as usual (n=23)
Received allocated intervention (n=22)
Did not receive allocated intervention
(delayed discharge) (n=1)
Included in analysis (n=9)
Lost to follow-up (no contact) (n=2)
Lost to follow-up (withdrew) (n=1)
3 month follow-up
1 month follow-up
Figure 1 CONSORT flow diagram.
Simpson et al. BMC Psychiatry 2014, 14:30 Page 4 of 14
was then randomly assigned to either the experimental
(peer support and usual aftercare) or care as usual
(CAU) condition and informed. Patients allocated to
peer support were then introduced to a PSW by the PSC
prior to discharge in order to negotiate peer support
lasting a maximum of four weeks following discharge.
Data collection and analysis
Coded, anonymous data on patient demographics, diag-
nosis and history were recorded in SPSSv17 and checked
for errors and omissions. In line with recommendations
for pilot trials  the analysis provides means, medians
and confidence interval estimations to inform further
studies. Relatively small datasets due to lower than ex-
pected recruitment and follow-up combined with
skewed data determined the use of non-parametric tests
to explore scores on the outcome measures.
Costs of services used by each participant were esti-
mated from the quantities of each type of resource used
multiplied by the unit cost. Unit costs of resources was
derived from routine sources locally where possible, and
from national sources judged representative of local
costs. Costs associated with the different resources and
services used are reported as part of this economic
evaluation. To calculate more specifically the costs, we
looked at resource use for primary care, which included
an examination of total drugs used, visits to general
practitioners, dentists and physiotherapy. Secondary care
resource use included collecting data for attendance at
emergency departments for non-psychiatric care. Mental
health service resource use included multidisciplinary
staff from community, home treatment crisis resolution
and assertive outreach teams. Costs for the year 2010
were used .
Included as part of the resource use measurements
were the peer support worker visits and contacts based
on PSW records kept during each peer support period.
Participants’use of counsellors, therapist visits, housing
support worker visits, visits to community mental health
centres, day hospital attendances and use of telephone
crisis lines were also recorded. Resources associated with
psychiatric admissions were recorded and totalled with
all the previously identified resources, to calculate a total
cost to the health service overall. Resources used and
costs to the criminal justice system, with the latter being
based on police contact and police doctor contact, were
A total cost per case per patient was calculated based
on all of the above which in turn allowed for an average
cost per case per trial arm for treatment and the control
group, presented with rates of significance of difference
A secondary cost utility analysis was performed com-
bining the cost data with the Beck Hopelessness Scale
(BHS) data and EuroQol (EQ5D) data. As part of this
analysis, non-parametric bootstrapping  was used to
develop confidence intervals around the incremental
cost effectiveness ratio based on costs and effects. This
process also generates acceptability curves to illustrate
the uncertainty associated with the estimate of costs and
effects combined and estimates of affordability given po-
tentially different decision maker cost thresholds.
A comparison of patient-related factors was undertaken
between intervention and control groups to identify any
baseline differences between groups (see Table 1). There
were no statistically significant differences between age
in the PS condition (M = 34.13, SD = 10.27) and the
care as usual (CAU) condition (M = 36.36, SD = 10.15),
t(44) = −.0742, P = 0.462. The distribution of gender was
equal in each group (χ2(1) = 1.627, P = 0.202) and both
groups contained participants from a wide range of eth-
nic backgrounds with a broadly similar spread across
groups. The two groups were also similar regarding
housing status (predominantly council/housing associ-
ation/supported accommodation or no fixed abode).
Those in the peer support group appeared slightly more
likely to live alone but this was uncertain given the high
level of missing data for this category.
Around two-thirds of the patients in both groups were
‘informal’patients, rather than detained under the Mental
Health Act. The primary diagnoses in both groups were
largely spread around a range of psychotic disorders,
although there were a higher proportion of people with
depression in the peer support cohort (not significant).
Around half of the patients in both arms reported several
admissions in the previous three years.
PSW activities including face-to-face contact, phone
contact, attempted contacts and liaison with mental
health and other staff are summarised in Table 2.
Results on key outcome measures
Beck’s Hopelessness Scale (BHS)
Higher scores on the BHS denote higher levels of hope-
lessness. There was no significant difference on BHS
scores at baseline between the Peer Support (PS) and
care-as-usual (CAU) arms (U = 177, P = 0.083). We com-
pared the baseline (T0) scores on the BHS between those
who subsequently completed the BHS at one-month
follow-up (T1) and those who did not, using the Mann
Whitney UTest. The mean rank BHS T0 score was higher
for the T1 completers than in the T1 non completers, but
was not statistically significant (U = 204.5, P = 0.277).
Differences in the BHS scores between three data
points within each condition were compared using the
Friedman Test. For both PS and CAU the mean rank
scores reduce between T0 and T2, as participants
Simpson et al. BMC Psychiatry 2014, 14:30 Page 5 of 14
become more hopeful, with a larger drop for those in
the PS condition, although this fell short of significance
χ2(2) = 5.810, P = 0.055. For those in the CAU condition
there was no statistical difference in hopelessness be-
tween baseline and follow up at one month nor at
3 months χ2(2) = 0.276, P = 0.871. Finally, there was no
statistical difference in hopelessness at T1 between the
PS condition and CAU using the Mann Whitney Utest
(U = 70.5, P = 0.494).
UCLA Loneliness Scale V3 (UCLA)
Higher scores on the UCLA denote higher levels of
loneliness. We compared the baseline (T0) scores on the
UCLA between those who subsequently completed the
UCLA at one-month follow-up (T1) and those who did
not, using the Mann Whitney UTest. Whilst the mean
rank UCLA T0 score was higher for the T1 completers
than in the T1 non completers, this was not statistically
significant (U = 236.5, P = 0.724). We then compared the
differences in the UCLA scores between three data
points within each condition using the Friedman Test.
Loneliness scores increased in the PS arm at T1, then
decreased overall at T2, whereas for CAU the mean rank
loneliness scores increased at both T1 and T2. However,
there was no statistical difference in loneliness between
baseline and follow up at one month nor at three
months for those in the PS condition χ2(2) = 0.609, P =
0.738 or for those in the CAU arm χ2(2) = 2.250, P =
0.325. Finally, there was no statistical difference in
UCLA scores at T1 between the PS condition and CAU
using the Mann Whitney Utest (U = 68, P = 0.432).
EQ5D EuroQoL Quality of Life Scale (EQ5D)
There was no significant difference between T1completers
and T1non completers on the baseline EQ-5D quality of
life total dimensions score (U =238.00, P = 0.732), or the
baseline health status VAS scores (U =207.00, P = 0.305).
Neither was there any statistical difference in quality of life
between baseline and follow up at one month nor at
three months for those in the PS condition χ
(2) = 0.667,
P= 0.717 or for those receiving CAU χ
(2) = 2.800,
A similar test comparing the differences in the EQ
VAS scores between three data points within each con-
dition revealed a statistically significant difference to-
wards improvement in self-reported general health,
Table 1 Participant demographic and baseline
characteristics by group
Characteristics Peer support
(n = 23)
Female 7 (30.4 %) 3 (13.0 %)
Male 16 (69.6 %) 20 (87.0 %)
Age - mean
34.13 (10.27) 23.36 (10.15)
Age - range 20-55 22-57
Ethnicity Black African 7 2
Black Caribbean 1 7
Black other 5 2
Mixed race 4 1
White UK/Irish 5 7
White other 1 1
Chinese 0 1
Bangladeshi 0 1
Living status Alone 12 (52.1 %) 14 (60.9 %)
With others 6 6
Missing 5 3
8 (34.8 %) 8 (34.8 %)
Supported 5 2
Hostel 0 2
Own home 1 3
Rented home 1 3
Family home 2 0
Missing 0 1
MHA status Informal 15 (65.2 %) 14 (60.9 %)
Formal 7 9
Missing 1 0
8 (34.8 %) 7 (30.4 %)
Depression 6 2
Psychosis 2 1
Schizophrenia 1 3
Unconfirmed 1 1
None 11 9
1 to 3 10 11
Missing 2 3
Table 1 Participant demographic and baseline
characteristics by group (Continued)
Total admissions None 4 6
1 to 3 11 10
4+ 7 7
Missing 1 0
Simpson et al. BMC Psychiatry 2014, 14:30 Page 6 of 14
depending on time period, for the CAU condition χ
10.640, P = 0.005. No statistical differences in self-reported
general health was evident for the PS condition χ
0.667, P = 0.717.
To compare differences in the EQ VAS between the
three time points within the CAU cohort, post-hoc ana-
lysis with Wilcoxon Signed-Rank Tests was conducted
with a Bonferroni correction applied, resulting in a sig-
nificance level set at P < 0.017. Median (IQR) perceived
effort levels for care-as-usual for the T0, T1 and T2 were
0.66 (0.23 to 0.86), 0.72 (0.57 to 0.95) and 0.81 (0.62 to
1.00), respectively. There were no significant differences
between T0 and T1 scores (Z = −1.362, P = 0.173) or be-
tween T1 and T2 general health scores (Z = −1.826, P =
0.068) although the latter was approaching significance.
However, there was a statistically significant increase in
self-reported general health status from baseline to T2
(Z = −2.366, P = 0.018).
There was no statistical difference at T1 for EQ-5D
quality of life between the PS condition and CAU using
the Mann Whitney Utest (U = 71, P = 0.977) or for the
EQ-5D health status VAS scores (U = 71, p = 0.977).
Between discharge and one-month follow-up, one pa-
tient receiving PS was readmitted to hospital, compared
with three in the CAU arm. Another patient in the PS
arm was readmitted before three-month follow-up and
one person in this arm died from an unrelated medical
Resource use and cost outcomes
The total cost per case for the PS arm of the study was
£2154 compared to £1922 for the control arm as can be
seen in Table 3. The mean difference between costs in
the trial arms was minimal and not statistically signifi-
cant. There were no significant differences in costs for
primary care, secondary care, mental health services,
and criminal justice or for total costs to the health
service system when costed overall. Total drugs resource
use and costs, part of the primary care overall costs, ini-
tially showed a borderline level of significance. However,
having established a borderline leveI of significance for
drug costs, the baseline data was checked to see if there
were any differences in resource use between the groups
after randomisation. This was found to be the case with
a significant difference in costs for drug use noted
already at baseline between trial arms. These analyses
was based on the assumption that missing cost data for
participants truly was a zero cost. As there was poten-
tially great uncertainty around this assumption, another
analysis was conducted where missing cost data for par-
ticipants was handled using mean imputation methods
for missing data. As a result of conducting this analysis
the average cost per case of the Peer Support Work arm
was £5103 compared to £3321 for the control arm. The
mean difference between costs in the trial arms was
again not statistically significant.
Results of cost utility analysis using cost data and clinical
Initially using point estimates (such as means) from the
cost and effect distributions to provide estimates of the
cost and effect of the alternative treatments in the pri-
mary analysis is valid. However some health economists
have warned against separate and sequential hypothesis
tests on differences in effects and costs, where there is
no statistically significant difference in either, as in this
study, which can lead to a superficial observation of the
data and an acceptance of a Cost Minimisation Analysis
. This could allow researchers to fall into the poten-
tial trap of a type II error (a failure to reject the null hy-
pothesis of no difference when in fact a difference does
exist). Instead it is suggested that the goal of economic
evaluation should be more than hypothesis testing and
cost minimisation and should estimate parameters of
uncertainty . In other words economic evaluation
should estimate the incremental cost effectiveness, with
Table 2 PSW activity
Total time per
N Valid 21 21 21 21 21 21 21 21
Missing 2 2 2 2 2 2 2 2
Mean 5.6190 50.9005 8.7143 6.4710 2.9048 9.2729 .8571 11.2290
Median 5.0000 50.0000 7.0000 6.0000 1.0000 1.0000 .0000 .0000
Minimum 1.00 15.00 .00 .00 .00 .00 .00 .00
Maximum 15.00 112.50 30.00 13.88 13.00 100.00 7.00 180.00
Percentiles 25 2.5000 28.9300 3.0000 3.5650 .0000 .0000 .0000 .0000
50 5.0000 50.0000 7.0000 6.0000 1.0000 1.0000 .0000 .0000
75 8.0000 63.2350 11.5000 10.0000 6.0000 4.1250 1.5000 5.3350
Simpson et al. BMC Psychiatry 2014, 14:30 Page 7 of 14
an analytic focus on the estimation of the joint density
of cost and effect differences. This, they assert, allows
for the quantification of uncertainty, using confidence
intervals surrounding the incremental cost-effectiveness
ratio (differences in costs between arms divided by the
differences in effect between arms) with the presentation
of such data as cost-effectiveness acceptability curves.
This was achieved in this study by repeat re-sampling
from the costs and effectiveness data using non-
parametric bootstrapping to generate a distribution of
mean costs and effects for the two treatment conditions
. These distributions were used to plot the cost
Table 3 Cost per patient by treatment arm (£ Sterling 2010 prices)
PSW arm Control arm
(N = 6) (N = 9)
Total cost Mean difference
Type of cost Detail of costs Mean (SD) % Mean (SD) % (PSW - control) (95% CI) P
Primary care 126 703 6 317 423 16 191 418 −37 0.11
Total drugs 38 117 2 160 257 8 122 252 −7 0.07
General practitioner 88 211 4 147 187 8 58 186 −70 0.38
Dentist 0 0 0 4 15 0 4 11 −3 0.31
Physiotherapist 0 0 0 6 27 0 0 0 19 0.00
Accident & emergency
for non psychiatric care
Mental health service 2005 4930 93 1622 3016 84 −382 2236 −3000 0.77
CRHTT psychiatric visits 20 87 1 0 0 0 −20 19 −59 0.53
CRHTT nurse visits 6 27 0 24 83 1 18 58 −22 0.38
CRHTT OT visits 0 0 0 19 82 1 19 57 −19 0.31
CMHT psychiatric visits 106 299 5 33 83 2 72 647 −71 0.32
CMHT nurse visits 18 77 1 9 40 0 −831−48 0.68
CMHT OT visits 10 34 0 39 89 2 29 72 −15 0.20
AOT social worker visits 0 0 0 39 164 2 39 114 −37 0.31
AOT OT visits 0 0 0 3 14 0 3 10 −3 0.31
Psychologist visits 0 0 0 68 230 4 68 174 −39 0.21
Other doctor visits 7 29 0 0 0 0 −76−20 0.34
Drug & alcohol advisor visits 16 59 1 0 0 0 −16 10 −43 0.26
Peer support worker visits 9 6 0 0 0 0 −9−6−12 0.00
Other counselor/therapist visits 0 0 0 18 54 1 18 −7 43 0.16
Care co-ordinator 6 27 0 10 41 1 4 26 −19 0.75
Housing support worker 8 33 0 16 67 1 8 43 −26 0.63
Community mental health
18 78 1 0 0 0 18 17 −53 0.34
205 750 10 338 840 18 133 647 −381 0.61
Telephone crisis line calls 0 0 0 6 18 0 6 15 −2 0.14
Psychiatric admission 1648 4981 77 965 3001 50 −684 1950 −3318 0.61
Total cost to
2136 4919 99 1922 3046 100 −213 2408 −2835 0.87
Total cost to criminal
18 60 1 0 0 0 −18 9 −45 0.22
Police contact 4 19 0 0 0 0 −44−13 0.34
Police doctor 13 58 1 0 0 0 −13 13 −40 0.34
Total cost per case 2154 4919 100 1922 3046 100 −231 −2853 2390 0.86
Simpson et al. BMC Psychiatry 2014, 14:30 Page 8 of 14
effectiveness acceptability curves, which show the prob-
ability that Peer Support could be more cost effective
compared with standard care for a range of maximum
monetary values (ceiling ratios, λ) that a decision maker
might be willing to pay . These acceptability curves
illustrate the uncertainty associated with the estimate of
costs and effects as a result of sampling variation. They
were developed as a way of overcoming the statistical
difficulties in calculating confidence intervals around in-
cremental cost effectiveness ratios [42,43].
The incremental cost effectiveness ratio (ICER) for
utility based on an improvement change in Becks Hope-
lessness Scale was 12,555. This means that for a 0.02
utility improvement, decision makers might be expected
to pay £231.
Figure 2 shows the methods described above in order
to produce non-parametric bootstrapping to generate a
distribution of mean costs and effects for the two treat-
ment conditions using Becks Hopelessness Scale as a
measure of effect.
Figure 3 shows the probability of PS being cost effect-
ive given different thresholds for expenditure (using a
positive change in Becks Hopelessness Scale as a meas-
ure of clinical effectiveness). The ICER for utility based
on a change in the EQ5D Scale was -£1,158. This means
that despite a utility loss of on average of 0.20 decision
makers would still be paying £231 on average.
Figure 4 shows the methods described above in order
to produce non-parametric bootstrapping to generate a
distribution of mean costs and effects for the two treat-
ments using EQ5D as the outcome measure.
Figure 5 shows the probability of PS being cost effect-
ive given different thresholds for expenditure (using
change in EQ5D as a measure of clinical effectiveness).
The cost-effectiveness of PS compared to CAU, using
the EQ5D as a measure of utility cannot be demonstrated
in this study. This is represented in Figure 4 where the
replicates of costs and effects can be seen to fall across all
the quadrants on the cost effectiveness plane. Ideally a
study depicting cost effectiveness would show most repli-
cates falling in the south east quadrant where costs are
lower and the effect of the intervention is greater. In con-
trast the cost effectiveness plane in Figure 2 shows a distri-
bution of 1000 replicates of cost and effects for PS and
care as usual using a change in Beck’s Hopelessness Scale
to determine utility scores. This shows a larger number of
replicates in the south east quadrant and signifies the po-
tential for the data to show lower costs and higher effects
associated with the intervention.
The cost effectiveness acceptability curve in Figure 3
shows that the probability of PS being cost effective
given different thresholds for expenditure (using the
Becks Hopelessness Scale as a measure of effect),
reaches 57% at a given point and exceeds 55% for most
of the different potential cost values a decision maker
would be willing to spend on the intervention. This was
irrespective of how the missing data was handled which
may suggest that this data was missing at random and
not particularly sensitive to a trend within it.
Other sensitivity analyses
A re-analysis of the data to exclude potentially expensive
non psychiatric drugs was also performed though again
this did not make any difference to the overall costs for
drugs. Given questions around the cost of providing a PS
service, four further scenarios were considered for sensi-
tivity analysis, which were: 1. Conflating costs to a higher
overall rate of £540 for the whole period irrespective of
Figure 2 Cost effectiveness plane showing distribution of 1000 replicates of cost and effects for peer support and care as usual using
a change in Becks Hopelessness Scale to determine utility scores.
Simpson et al. BMC Psychiatry 2014, 14:30 Page 9 of 14
hours contributed (as opposed to just paying PSWs ex-
penses as they arose), thus reflecting the total sum of
money available to pay service users fulfilling the PS func-
tion. 2. Basing costs associated with PSWs on an overall
average number of hours. This would be based on the mi-
nutes recorded as part of the study for all PSWs and then
averaged as an intervention cost across all of the partici-
pants in the intervention arm. 3. Using the minimum
wage for 2010 (£5.80 per hour) as a proxy for cost rather
than expenses. 4. Using the top of a United Kingdom Na-
tional Health Service Agenda for Change band 3 as a staff
cost . This would represent a proxy cost for unquali-
fied staff in the event of a PSW being paid as a salaried
member of staff. Overall, running different scenarios for
calculating costs around the PSW role did not make any
difference to the intervention in terms of its cost or cost-
effectiveness. For any of the above scenarios the cost per
case for PSWs remains low and in fact hardly differs.
Boot strapping and cost effectiveness acceptability
curves were also generated for the cost data that was
missing using the mean imputation method, though
these did not show any difference in distributions to the
previous analyses, with the cost effectiveness acceptabil-
ity curve being unchanged.
Forty-six service users were successfully recruited to the
study against a target of 110; 6.3 per cent of the total
number of discharges (n = 734) and 34.3 per cent of the
134 patients approached following prior exclusion of
Figure 3 Probability of PS being cost effective given different thresholds for expenditure (using a positive change in Beck’s
Hopelessness Scale as a measure of clinical effectiveness).
Figure 4 Cost effectiveness plane showing distribution of 1000 replicates of cost and effects for PS and care as usual using the EQ5D.
Simpson et al. BMC Psychiatry 2014, 14:30 Page 10 of 14
those that did not match our inclusion criteria. Recruit-
ment was affected by a number of factors. Significant de-
lays in obtaining criminal records bureau (CRB) checks
for the PSWs led to limited availability of PSWs at the
start of the study so recruitment was stalled. Delayed
discharges to a number of patients recruited to the study
meant that PSWs were ‘tied-up’with patients still on
wards so delaying the recruitment of more patients.
Additionally, the study took place in an inner London
borough renowned for both its high level of morbidity
and a patient population with significant forensic histor-
ies. Consequently, a high proportion of patients did not
meet inclusion criteria, particularly around perceived se-
vere risk to others (as the PSWs would be lone workers).
Additionally a number of patients declined to take part
unless they could be guaranteed peer support.
Follow-up rates were disappointing which may again
reflect the complex and transient nature of the patient
population. Some may have withdrawn in disappoint-
ment at being allocated to care-as-usual. More pro-
active and creative measures will need to be employed in
future studies [45,46].
No significant differences were found between those
receiving peer support and those receiving care-as-usual
on two of the three main outcome measures of hopeless-
ness and loneliness. However, hope increased in both
conditions with a near significant change on Beck’s
Hopelessness Scale in those receiving PS. No significant
differences between time points or conditions was de-
tected on the EQ-5D quality of life measure, although
the EQ VAS general health state scores suggested a sig-
nificant difference in general health scores between base-
line and three-month follow-up for the care-as-usual
arm. No immediate explanation for this is available.
Fewer readmissions were reported in the PS arm of
the study but no conclusions can be drawn from such a
small sample and short follow-up period. Face-to-face
contact was made by PSWs about once a week with add-
itional support provided via frequent telephone contact.
Studies with different patient groups have reported the
successful use of telephone peer support [47,48] and this
could be explored further. Future studies could test a
more directed or manualised approach with PSWs de-
tailed to make a minimum number of contacts com-
pared with a more flexible patient-empowered approach
in line with a recovery focus . Fairly limited time was
spent in contact with mental health staff and other agen-
cies. This perhaps reflects the patient and community
focus of the intervention but may also echo the indiffer-
ence shown by many ward staff, as reported by the
PSWs on some wards.
This study showed that providing PS is not an expen-
sive intervention and it may be possible to demonstrate
cost effectiveness in a larger study. This pilot suggests
that although Peer Support has not shown cost effective-
ness from a statistical perspective, decision makers could
well be willing to pay an extra £231 per person over a
three month post discharge period (based on the analysis
of resources used), to see a modest improvement change
in Beck’s measure of hopelessness. There are also posi-
tive wider economic implications for the Peer Support
Workers themselves, such as employment and its associ-
ated indirect benefits. Employment is a major factor in
promoting the social inclusion of people with mental ill-
ness . This project, though a modest pilot, did pro-
vide a ‘route to employment’for people with a history of
mental illness, in a deprived area where only 8% of
people with severe mental illness are in paid work .
This study did not explore this and future research may
wish to develop this focus.
Previous research suggests that a PS intervention
would be successful and potentially cost-effective ,
Figure 5 Probability of PS being cost effective given different thresholds for expenditure (using change in EQ5D as a measure of
Simpson et al. BMC Psychiatry 2014, 14:30 Page 11 of 14
with claimed savings of half a million Canadian dollars
with a care model designed to assist individuals with pro-
longed psychiatric hospitalization to successfully integrate
back into the community. However, the authors do not
disclose how the costing methodology was deployed or
how these cost savings were calculated. A thorough cost-
effectiveness analysis as evidenced in this paper was not
carried out, so their claims of overall cost-savings and
cost-effectiveness should be viewed with caution.
One earlier study seeking to examine the cost effect-
iveness of PS at discharge  was not shown to be sta-
tistically significant, whilst another more recent study
 did not use prospective RCT methodology but ra-
ther a far weaker estimation of potential savings. More
recent research in 2013 by the Centre for Mental Health
in the UK again highlights that there has been relatively
little high quality research into the cost effectiveness of
peer support . The report makes the case for peer
support being good value for money, especially when the
potential for PSWs to reduce psychiatric inpatient bed
use, either by preventing admissions or by shortening
lengths of stay. The report goes on to argue that because
of the very high cost of inpatient care, savings that result
from even small changes in bed use may be sufficient to
outweigh the costs of employing peer workers. Ultim-
ately the report authors argue that the financial benefits
of employing peer support workers do exceed the costs.
The authors do however concede that the evidence for
their findings is very limited in both quantity and qual-
ity, and unlike the results reported in this paper do not
arise from trial based data. Asserting the potential for
value for money is not the same as showing statistical
evidence of cost effectiveness from a properly conducted
cost effectiveness study. Cost effectiveness analyses are
best designed as part of a randomised controlled trial
(RCT) with the rigour that comes from such a method-
ology with the ability to most likely detect a true statis-
tical cost effect and change. RCTs overall have a lower
susceptibility to biased or unfounded random results.
A solid cost effectiveness study of a decent size, demon-
strating cost effectiveness of PSWs deployed at discharge
has yet to be established, though this pilot provides some
very informative data towards the development of a larger
study. The authors of this paper would agree with the
Centre of Mental Report (52) in supporting a continued
interest in the employment of properly trained and sup-
ported peer workers in mental health teams, whilst
researching more rigorously their effects.
Limitations and difficulties
Caution needs to be applied to any interpretation of
these results given the specific geographical location of
the study, the small sample size recruited and the even
smaller numbers completing follow-up measures. As a
result, this pilot was simply not powered sufficently to de-
tect an effect. Reviews of clinical evaluations have shown
how a substantial proportion of studies reporting ‘nega-
tive’results had insufficient power to detect important
differences in treatment effect [40,53]. Overall, this rein-
forces the argument for a larger trial whereby the impact
of outliers (and the risk of missing data) will be minimised.
This will be especially the case with the cost data where a
larger study would have less dramatic overall effects aris-
ing from high or zero cost participants.
Missing data for the EQ5D and Becks Hopelessness
Scale sometimes exceeded 50%, which resulted in a reli-
ance on mean imputation to calculate all costs. Arguably
the use of mean imputation was justifiable given that the
alternative in such circumstances might be to default to
complete case analysis. However such methods run the
risk of insufficient numbers to analyse and as such, are a
poorer alternative method and ‘can yield biased esti-
There were difficulties in recruiting to the study lead-
ing to smaller numbers than the researchers had hoped
for. Additionally there was a need to exclude a high
number of service users in an inner city environment
who had a history of offending and who could not be re-
cruited as participants due to the potential risk to the
PSWs who would be lone workers in the community.
The study was further complicated by a number of pa-
tients being visited by PSWs over an extended period of
time whilst still on the ward as their discharge was con-
These limitations should be considered in the context of
the study being a pilot, the purposes of which was to test
the feasibility of the study aims and objectives rather than
to necessarily show clinical effect or cost-effectiveness at
this stage. However there are also counter arguments jus-
tifying small sized pilots. For example when undertaking a
pilot trial there is often no prior information on which to
base a sample size. In such cases the recommendation for
pilot studies has been a sample size as little as 12 per
group; the justification for this small sample size being
that pilots are about testing feasibility and other re-
searchers can use information from the pilot to aid their
design of large studies in the future .
This pilot trial of peer support for patients discharged
from a mental health hospital found no statistically signifi-
cant clinical or cost benefits compared to those receiving
usual care. However, there is an indication that measures
of hopelessness as a reliable indicator of suicide risk may
be further decreased in those in receipt of peer support.
Lessons learned from the various challenges faced in con-
ducting the study suggest that there is merit in conducting
further research on established peer support programmes.
Simpson et al. BMC Psychiatry 2014, 14:30 Page 12 of 14
Greater consideration should be applied to the design of
the study; the nature of the patient population to whom
support is offered; the length and frequency of the support
provided; and the contact between PSWs and established
mental health staff.
The intervention was not costly to deliver, however it
is important to highlight that these results do not sup-
port, suggest or advocate that peer support workers
could or should replace nursing, social work or other
qualified mental health staff. In addition, the results have
to be considered with caution due to the small sample
size obtained. The findings provide useful information
and a justification to support a larger scale trial of peer
support as an adjunct to existing services. The authors
would advocate more research into a) the impact of PS
on service users/outcomes; b) the impact of PS roles on
PSWs; and c) the impact of PS on the professional work-
force, alongside well designed trials.
Research ethics approval was provided by East London
and The City Research Ethics Committee Alpha (Ref: 10/
H0704/9). Research governance approval was also ob-
tained from the participating NHS Trust.
The authors declare that they have no competing interests.
AS conceived, designed and coordinated the study, collected data,
interpreted the results and co-authored the manuscript. CF contributed to
design of the study, led on economic analysis and co-authored the
transcript. JR conducted the analysis and helped interpret results. JQ
collected data and helped interpret results. SH co-designed and delivered
PSW training, provided supervision and support to PSWs, liaised with ward
staff and identified potential participants. CH co-designed and delivered PSW
training and facilitated peer group support. LB contributed to design and
conduct of the study. RE contributed to design and conduct of the study.
PS contributed to the design and conduct of the study. All authors read and
approved the final manuscript.
This paper outlines independent research funded by the National Institute
for Health Research (NIHR) under its Research for Patient Benefit (RfPB)
Programme (Ref No. PB-PG-0408-16151). The views expressed are those
of the authors and not necessarily those of the NHS, the NIHR or the
Department of Health.
Thanks to all members of the Project Advisory Group; Eileen Dickinson and
Katie Williams for supervision and support; the Peer Support Workers and all
staff that supported the study.
School of Health Sciences, City University London, Northampton Square,
London EC1V 0HB, UK.
KentHealth, Centre for Health Services Studies,
University of Kent, Canterbury, Kent CT2 7NF, UK.
School of Psychological
Sciences and Health, University of Strathclyde, Graham Hills Building, 40
George Street, Glasgow G1 1QE, UK.
East London NHS Foundation Trust, 22
Commercial Street, London E1 6LPUK, UK.
Institute of Psychiatry, King’s
College, London SE5 8AF, UK.
Received: 3 October 2013 Accepted: 3 February 2014
Published: 5 February 2014
1. Qurashi I, Kapur N, Appleby L: A prospective study of non-compliance
with medication, suicidal ideation, and suicidal behaviour in recently
discharged psychiatric inpatients. Arch Suicide Res 2006, 10(1):61–67.
2. Craig TJ, Fennig S, Tanenberg-Karant M, Brome EJ: Rapid versus delayed
readmission in first-admission psychosis: quality indicators for managed
care? Ann Clin Psychiatry 2000, 12(4):233–238.
3. Glover G, Arts G, Suresh Babu K: Crisis resolution/home treatment teams and
psychiatric admission rates in England. Br J Psychiatry 2006, 18(9):441–445.
4. Meehan J, Kapur N, Hunt I, Turnbull P, Robinson J, Bickley H, Parsons R,
Flynn S, Burns J, Amos T, Shaw J, Appleby L: Suicide in mental health
in-patients and within 3 months of discharge: national clinical survey.
Br J Psychiatry 2006, 188:129–134.
5. Bowers L, Whittington R, Nolan P, Parkin D, Curtis S, Bhui K, Hackney D,
Allan T, Simpson A: Relationship between service ecology, special
observation and self-harm during acute in-patient care: city-128 study.
Br J Psychiatry 2008, 193:395–401.
6. Solomon P: Peer support/peer provided services: Underlying processes,
benefits and critical ingredients. Psychiatr Rehabil J 2004, 27(4):392–401.
7. Mead S, Hilton D, Curtis L: Peer support: a theoretical perspective. ychiatr
Rehabil J 2001, 25(2):134–141.
8. Sabin JE, Daniels N: Strengthening the consumer voice in managed
care: VII. The Georgia peer specialist program. Psychiatr Serv 2003,
9. McClean J, Biggs H, Whitehead I, Pratt R, Maxwell M: Evaluation Of The
Delivering For Mental Health Peer Support Workers Pilot Scheme. Edinburgh:
Scottish Government Social Research; 2009.
10. Faulkner A, Bassett T: A helping hand: taking peer support into the 21st
century. Ment Health Soc Incl 2012, 16(1):41–47.
11. Repper J, Carter T: A review of the literature on peer support in mental
health services. J Ment Health 2011, 20(4):392–411.
12. Sells D, Davidson L, Jewell C, Falzer P, Rowe M: The treatment relationship
in peer-based and regular case management for clients with severe
mental illness. Psychiatr Serv 2006, 57(8):1179–1184.
13. Davidson L, Chinman M, Sells D, Rowe M: Peer support among adults with
serious mental illness: a report from the field. Schizophr Bull 2006,
14. Ochocka J, Nelson G, Janzen R, Trainor J: A longitudinal study of mental
health consumer/survivor initiatives: part 3 –a qualitative study of
impacts of participation on new members. J Community Psychol 2006,
15. Salzer M, Shear SL: Identifying consumer-provider benefits in evaluations
of consumer-delivered services. Psychiatr Rehabil J 2002, 25:281–288.
16. Pitt V, et al:Consumer-providers of care for adult clients of statutory
mental health services. Cochrane Database Syst Rev 2013, 3:CD004807.
17. Forchuk C, Jewell J, Schofield R, Sircelj M, Valledor T: From hospital to
community: bridging therapeutic relationships. J Psychiatr Ment
Health Nurs 1998, 5:197–202.
18. Forchuk C, Reynolds W, Sharkey S, Martin M-L, Jensen E: Transitional
discharge based on therapeutic relationships: state of the art.
Arch Psychiatr Nurs 2007, 21(2):80–86.
19. Reynolds W, Lauder W, Sharkey S, Maciver S, Veitch T, Cameron D: The
effect of transitional discharge model for psychiatric patients. J Psychiatr
Ment Health Nurs 2004, 11:82–88.
20. Lawn S, Smith A, Hunter K: Mental health peer support for hospital
avoidance and early discharge: an Australian example of consumer
driven and operated service. J Ment Health 2008, 17(5):498–508.
21. Beck AT, Steer RA: Manual For The Beck Hopelessness Scale. San Antonio:
Psychological Corporation; 1988.
22. Greene SM: Levels of measured hopelessness in the general population.
Br J Clin Psychol 1981, 20:11–14.
23. Dixon WJ, Massey FJ: Introduction to statistical analysis (4th ed). New York:
24. O’Brien RG, Muller KE: Applied Analysis of Variance in Behavioral Science. New
York: Marcel Dekker; 1983.
Longitudinal Study of Conflict and Containment on Acute Psychiatric Wards: Report
to the DH Policy Research Programme. London: City University London; 2007.
26. Moher D, Schulz KF, Altman D: The CONSORT statement: revised
recommendations for improving the quality of reports of parallel-group
randomized trials. JAMA 2000, 285:1987–1991.
Simpson et al. BMC Psychiatry 2014, 14:30 Page 13 of 14
27. Torgerson DJ, Roberts C: Understanding controlled trials. Randomisation
methods: concealment. BMJ 1999, 319:375–376.
28. McMillan D, Gilbody S, Beresford E, Neilly L: Can we predict suicide and
non-fatal self-harm with the beck hopelessness scale? a meta-analysis.
Psychol Med 2007, 37(6):769–778.
29. Beck AT, Steer RA: Clinical predictors of eventual suicide: a five to ten
year prospective study of suicide attempters. J Affect Disord 1989,
30. Holden RR, Fekken C: Test-retest reliability of the hopelessness scale and
its items in a university population. J Clin Psychol 1998, 44:40–43.
31. Russell D: The UCLA loneliness scale (version 3): reliability, validity, and
factor structure. J Pers Assess 1996, 66:20–40.
32. Brooks R, EuroQo L: The current state of play. Health Policy 1996,
33. Flood C, Mugford M, Stewart S, Harvey I, Poland F, Lloyd-Smith W:
Occupational therapy compared with social work assessment for older
people. An economic evaluation alongside the CAMELOT randomised
controlled trial. Age Ageing 2005, 34:47–52.
34. Beecham J, Knapp M: Costing Psychiatric Interventions.In
Measuring Mental Health Needs. Edited by Thornicroft. London: Gaskell;
35. Bowers L, Whittington R, Nolan P, Parkin D, Curtis S, Bhui K, Hackney D,
Allan T, Simpson A, Flood C: The City 128 Study of Observation and Outcomes
on Acute Psychiatric Wards: Report to the NHS SDO Programme. London: City
University London; 2006.
36. Lancaster GA, Dodd S, Williamson PR: Design and analysis of pilot
studies: recommendations for good practice. J Eval Clin Pract 2004,
37. Curtis L: Unit Costs of Health and Social Care 2010. Kent University:
Personal Social Services Research Unit; 2010.
38. Barber JA, Thompson SG: Analysis of cost data in randomized trials:
an application of the non-parametric bootstrap. Stat Med 2000,
39. Briggs AH, O’Brien BJ: The death of cost-minimization analysis?
Health Econ 2001, 10(2):179–184.
40. Efron B, Tibshirani R: An Introduction To The Bootstrap. New York:
Chapman and Hall; 1993.
41. Fenwick E, Claxton K, Sculpher M: Representing uncertainty: the
role of cost-effectiveness acceptability curves. Health Econ 2001,
42. Fenwick E, Byford S: A guide to cost-effectiveness acceptability curves.
Br J Psychiatry 2005, 187:106–108.
43. Fenwick E, O’Brien BJ, Briggs A: Cost-effectiveness acceptability
curves—facts, fallacies and frequently asked questions. Health Econ
44. NHS Careers Website (accessed 2012). http://www.nhscareers.nhs.uk/
45. Festinger DS, Marlowe DB, Dugosh KL, Croft JR, Arabia PL: Higher
magnitude cash payments improve research follow-up rates without
increasing drug use or perceived coercion. Drug Alcohol Dependency 2008,
46. Patel MX, Doku V, Tennakoon L: Challenges in recruitment of research
participants. Adv Psychiatr Treat 2003, 9:229–238.
47. Mental Health Foundation: Exploring Peer Support As An Approach
To Supporting Self Management: Feasibility Study Report.
48. Duquemin A: Peer Support –Options and Effectiveness. NHS Kidney
49. Scottish Recovery Network: Experts by Experience: Values Framework for
Peer Working. http://www.scottishrecovery.net/.
50. Cabinet Office: Reaching Out: An Action Plan on Social Exclusion. London:
Cabinet Office; 2006.
51. Colson A: Mental Health and Employment in North East London. London:
NHS London; 2007.
52. Trachtenberg M, Parsonage M, Shepherd G, Boardman J: Peer Support in
Mental Health Care: Is it good value for money? London: Centre of Mental
53. Freiman JA, Chalmers TC, Smith H Jr: The importance of beta, the type II
error and sample size in the design and interpretation of the
randomized control trial. Survey of 71 ‘negative’trials. N Engl J Med 1978,
54. Raghunathan TE: What do we do with missing data? Some options for
analysis of incomplete data. Annu Rev Public Health 2004, 25:99–117.
55. Julious SA: Sample size of 12 per group rule of thumb for a pilot study.
Pharm Stat 2005, 4(4):287–291.
Cite this article as: Simpson et al.:Results of a pilot randomised
controlled trial to measure the clinical and cost effectiveness of peer
support in increasing hope and quality of life in mental health patients
discharged from hospital in the UK. BMC Psychiatry 2014 14:30.
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