Cost-Effectiveness of Stroke Unit Care Followed by Early
Supported Discharge
Ömer Saka, MD, MSc; Victoria Serra, MSc; Yevgeniy Samyshkin, MSc; Alistair McGuire, PhD;
Charles C.D.A. Wolfe, PhD
Downloaded from http://stroke.ahajournals.org/ by guest on June 18, 2017
Background and Purpose—Stroke places a significant burden on the economy in England and Wales with the overall
societal costs estimated at £7 billion per annum. There is evidence that both stroke units (SUs) and early supported
discharge (ESD) are effective in treating patients with stroke. This study assesses the cost-effectiveness of the
combination of these 2 strategies and compares it with the care provided in SU without ESD and in a general medical
ward without ESD. The objective of this study was to model the long-term (10 years) cost-effectiveness of SU care
followed by ESD.
Methods—The study design was cost-effectiveness modeling. The study took place in SUs in the coverage area of the
South London Stroke Register, UK. The modeled population was incident ischemic stroke cases (N⫽844) observed
between 2001 and 2006. SU care followed by ESD was compared with SU care without ESD and general medical ward
care without ESD. Main outcome measures were health service and societal costs and cost per quality-adjusted life-year
gained.
Results—Using the cost-effectiveness threshold of £30 000, as commonly used in the UK, SU care followed by ESD is the
cost-effective strategy compared with the other 2 options. The incremental cost-effectiveness ratio of SU care followed
by ESD is £10 661 compared with the general medical ward without ESD care and £17 721 compared with the SU
without ESD.
Conclusion—SU care followed by ESD is both an effective and a cost-effective strategy with the main gains in years of
life saved. (Stroke. 2009;40:000-000.)
Key Words: cost 䡲 cost-effectiveness 䡲 economics 䡲 stroke 䡲 stroke unit
S
troke places a significant burden on the economy in
England and Wales with the overall societal costs of
stroke estimated at £7 billion per annum. Direct care accounts
for 40% of the total costs, informal care for another 35%, and
indirect costs for approximately 25%.1
The treatment of patients with stroke in stroke units (SUs)
is becoming a standard treatment approach.2 National clinical
guidelines for stroke recommend, “Stroke services should be
organized so that patients are admitted under the care of a
specialist team for their acute care and rehabilitation.”3 SU
defined by the Stroke Unit Trialists’ Collaboration includes
the availability of a consultant physician with responsibility
for stroke, formal links with patient and caregiver organizations, multidisciplinary meetings at least weekly to plan,
provision of information to patients about stroke, and continuing education programs for staff. A minimum of 4 of
these criteria has to be met for a treatment unit to be classified
as a SU. SUs differentiate between acute treatment and
rehabilitation. Acute SUs are the care units that admit patients
at the acute stage of treatment and follow-up thereafter.
Rehabilitation SUs augmented the acute treatment phase with
initial follow-up in the hospital. Combined SUs include both
of the components.4 – 6 SU management has been shown to
reduce the relative risk of 5-year mortality by 40%7 and is
reported to reduce the risk of recurrence.8
The National Sentinel Stroke Audit has shown an increase
in the proportion of hospitals in England with stroke units
from 79% in 2004 to 91% in 2006 as well as an increase in
the size of the units themselves.9 Despite the growth in such
care in the United Kingdom, generally there is little health
economics research on the cost and cost-effectiveness of
SUs.10 –12 Existing studies suggest that SU care is more
expensive than the alternatives and there is only one study on
the cost-effectiveness of SUs, which suggests that such
increased costs are justified by the improved outcomes and
this results in favorable incremental cost-effectiveness ratios
for SU care.12
Despite the recorded benefits of SUs dealing with acute
treatment, there is less conclusive information available on
the effective management of discharge and follow-up after
Received February 21, 2008; final revision received May 21, 2008; accepted May 27, 2008.
From the Division of Health and Social Care Research (O.S., C.C.D.A.W.), King’s College, London, UK; London School of Economics and
Political Science, LSE Health and Social Care (V.S., A.M.), London, UK; IMS Health (Y.S.), Health Economics and Outcomes Research, London, UK.
Correspondence to Ömer Saka, MD, MSc, King’s College, London, Division of Health and Social Care Research, 42 Weston Street, Capital House,
SE1 3QD, London, UK. E-mail [email protected]
© 2008 American Heart Association, Inc.
Stroke is available at http://stroke.ahajournals.org
DOI: 10.1161/STROKEAHA.108.518043
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February 2009
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acute SU care. The Royal College of Physicians Stroke
guidelines state that “Specialist stroke services should be
available in the community as a part of an integrated system
of care to facilitate early supported discharge.”3 The development of early supported discharge (ESD) offers an effective care pathway in which less disabled patients can be
discharged early to undergo further rehabilitation at home.
This strategy has the obvious benefit of shortening the length
of hospital stay13 and to reduce long-term dependency and
risk of further disability after 6 months. It also results in fewer
admissions to institutional care, particularly where there are
coordinated services across the acute and rehabilitation management of patients with mild to moderate disability.14,15 Six
months after stroke, the proportion of such patients that are
dead or in an institution is significantly lower for those who
have undergone properly managed ESD.16 Furthermore, patients who received ESD have higher recorded independence
scores than those receiving conventional care.17,18 In summarizing such evidence, a Cochrane Review evaluating the
effectiveness of ESD for patients with stroke concluded that
appropriately resourced ESD services could reduce long-term
dependency and admission to institutional care as well as
reduce the length of hospital stay without causing any adverse
effects on the mood or subjective health status of patients or
caregivers.19
A systematic review looking at the economic evidence on
rehabilitation concluded that there was some evidence that
the mean total cost of rehabilitation per patient in a SU is
comparable to care provided in a conventional hospital ward;
however, there was no evidence that ESD services do
significantly reduce costs compared with the costs arising
from the usual care of patients with stroke. There is
insufficient evidence on the cost of community-based
rehabilitation compared with commonly provided care.20
There are 2 other studies looking at the effects of ESD
strategy together with the costs.21,22 However, thus far, no
study has attempted to model and calculate the long-term
cost-effectiveness of ESD strategies as compared with
conventional discharge strategies.
The aim of this study is to evaluate the long-term (10-year)
cost-effectiveness of SU care followed by ESD (SUESD)
compared with SU care and treatment in a general medical
ward with no ESD (SUNESD and GWNESD, respectively).
To do so, data were obtained from the South London Stroke
Register (SLSR) in England and from a randomized, controlled trial of an ESD scheme for patients with stroke.23
Methods
Model
To establish the cost-effectiveness of SUESD, a Markov health state
transition model24 was developed. The decision analytic model was
constructed using Treeage Pro 2007 software (Treeage Software;
Williamstown, Mass).
Data on the service use and health outcomes of a group of patients
with stroke registered in the SLSR were used.25,26 The SLSR is an
ongoing population-based register in a defined area corresponding to
22 wards of the Boroughs of Lambeth and Southwark. Data were
used from November 2001 to January 2006 (Table 1), which
corresponds to the period when a SU was opened at the only hospital
in the SLSR area. The SU has 4 acute beds and 23 rehabilitation beds
Table 1.
Service Provision Characteristics, SLSR25
Characteristics
SU (n⫽571)
GMW (n⫽581)
Age
64.2
63.3
Gender, female
47.3%
48.7%
Limitation in activities of daily
living, mean BI score (SD)
11.27 (7.82)
10.3 (8.2)
Home
402
356
Institutional care (nursing
home, residential home)
156
75
Discharge destination
Deceased before discharge
32
23
Length of stay, mean (SD)
32.3 (34.2)
35.3 (44.9)
admitting only patients with stroke. In addition, data from a local
ESD trial were used to assess the effectiveness of ESD and the
resources used in the process23 (Table 2).
The health state transition model simulated the care pathways after
stroke, starting from the diagnosis of acute stroke and admission to
inpatient care. The model structure allows for either the conventional
path of discharge from inpatient care and follow-up or ESD and
follows the disease progression and costs of care for 10 years
(Figure). Patients with acute stroke can be referred to as: (1) SUESD;
(2) SUNESD; or (3) GMWNESD.
Outcome of Care
Postdischarge patients were assumed to be either residing at home or
in institutional care (nursing home, residential home). The model did
not allow for recurrent strokes given the lack of data on the
occurrence of recurrent strokes. Given that the majority of recurrences occur during the first year after the initial stroke27 and SU
treatment actually reduces the incidence of recurrences,8 this is a
conservative assumption.
The main outcome for the model was the combination of death and
activities of daily living score as measured by the Barthel Index
(BI)28 (mild: BI score 15 to 20; moderate: BI score 10 to 14; severe:
BI score 0 to 9). BI index scores were expressed in health-related
quality-of-life values to calculate the quality-adjusted life-years
(QALYs) gained from the model using the conversion method
developed by Van Exel et al.29 The transitions in the model were
among 3 disability states: mild, moderate, or severe. The most
disabled patients older than age 85 were assumed to remain severe
for the rest of their lives (Supplemental Figure I, available online at
http://stroke.ahajournals.org). The 1-year outcomes from the ESD
trial were extrapolated for 10 years and were used as transition
probabilities obtained from that trial23 (Table 3).
SLSR data were used to calculate the average length of hospital
stay for patients with stroke in an SU and general medical ward
(GMW), the discharge location of patients, and also to identify the
disability levels of patients at discharge. The disability levels of the
patients at the end of the first year were obtained from the ESD
trial.23 Data on resource use and severity levels of ESD and non-ESD
patients were obtained from Beech et al.13
Costs
Costs were analyzed from a societal perspective, not including the
transportation costs for outpatients to the point of care (Table 4).
Table 2.
Population Characteristics, ESD Trial23
Characteristics
ESD (n⫽167)
Conventional Care
(Non-ESD; n⫽164)
Limitation in activities of
daily living, mean BI score
(SD)
11.27 (7.82)
10.3 (8.2)
Length of stay, mean (SD)
34 (34)
42 (41)
Saka et al
CEA of Stroke Unit Care and Early Supported Discharge
Discharged home
Discharged patients with
mild, moderate or severe
disabilities
Patients who
are alive at
the end of the
cycle
Discharged to
institutional care
Stroke Unit and
ESD - SUESD
3
Patients who died
in hospital
Discharged home
Discharged patients with
mild, moderate or severe
disabilities
Discharged to
institutional care
Stroke Unit no ESD
SUNESD
Stroke
Patients who died
in hospital
Discharged home
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General Medical
Ward
no ESD – GWNESD
Discharged patients with
mild, moderate or severe
disabilities
Patients who
are not
alive at the
end
of the cycle
Discharged to
institutional care
Patients who died
in hospital
Figure. Description of the model.
Indirect cost estimates were based on income loss due to mortality
and/or morbidity were calculated using the data obtained from the
Office for National Statistics on mortality30 and the mean earnings of
UK workers in different age bands for 2003.31 For both the mortality
and morbidity calculations, it is assumed that people aged older than
65 years are retired. The rate of economic productivity and the
current unemployment figure as published in the Annual Abstract of
Statistics were used to estimate friction costs.32 The income loss of
stroke-related morbidity was then estimated by multiplying the
number of certified days off work from stroke with the income per
day.33
The direct cost estimates were based on the costs of hospital stay
and follow-up care where appropriate. The relevant detailed National
Health Service service costs were obtained from the Guy’s & St
Thomas’ Foundation Trust, Financial Performance Report, 2004/
2005 (unpublished data) using a full costing approach with apportioned indirect and overhead costs. The cost of an inpatient stay was
calculated using the average length of stay for stroke as documented
by the SLSR and the ESD trial23 for patients with different severity
levels and multiplied by the per-diem cost of hospital stay. The
per-diem cost of inpatient stays included cost of the hospital bed
(including nursing services) and the cost of time of physicians and
therapists. The hourly costs of specialists were calculated using the
salary schedules of specialists obtained from the stroke unit at Guy’s
& St Thomas’ NHS Foundation Trust as well as the per-day cost of
hospital stay (including nursing services; Guy’s & St. Thomas’
Foundation Trust, Financial Performance Report, 2004/2005, unpub-
Table 3. Health-Related Quality-of-Life Values According to
BI Scores
BI Score
Health-Related
Quality-of-Life Values
Range
Mild
15–20
0.69
0.55–0.8
Moderate
10–14
0.38
0.33–0.44
0–9
0.046
Classification
Severe
0–0.14
lished data). The amount of time spent by physicians, physiotherapists, occupational therapists, and speech and language therapists per
patient per diem is taken from De Wit et al.34
Unit costs and resource use patterns of community-based health
and social service items and the unit cost of outpatient contact with
therapists and physicians were also taken from Beech et al.13 The
resource use data on outpatient services provided by therapists and
physicians for each severity group was obtained from the ESD
trial.23 The cost of institutional care was represented by the cost of
nursing home care, because the majority of patients classified as
Table 4.
Unit Cost and Resource Use Items
Cost Component
SU per patient
GMW per patient
CT
MRI
Echocardiography
Outpatient drugs
Cost of stay in a nursing home
Cost of stay in a residential home
Cost of stay in a sheltered home
Cost of caregiver
Minimum wage
Average income
Resource component
Time available per specialist
Physician
Unit Cost
Unit
Reference
£165
£115
£85
£400
£101
£35.6
£570
£513
£234
£9
$5.52
£90
Resource Use
Per diem
Per diem
Per test
Per test
Per test
Per month
Per week
Per week
Per week
Per hour
Per hour
Per day
Unit
*
*
*
*
*
SLSR
35
35
35
31
31
31
Reference
3.31
Physiotherapist
7.35
Speech and language therapist
1.35
Occupational therapist
4.06
Hours
per
Hours
per
Hours
per
Hours
per
per week
patient
per week
patient
per week
patient
per week
patient
34
34
34
34
*Guy’s & St Thomas’ Foundation Trust, Financial Performance Report,
2004/2005, unpublished data.
4
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February 2009
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residing in an institutional care center were living in a nursing home.
Cost of care at home and at an institutional care center were assumed
to be the same across patients with different disability levels.35 All
costs were adjusted to 2005 to 2006 prices36 with an annual discount
rate of 3.5% applied to both costs and effects.37
Incremental cost-effectiveness ratios (ICERs) were calculated as
cost per QALY to assess the cost-effectiveness of the different
strategies. Univariate deterministic and stochastic sensitivity analyses were performed to illuminate the importance of the assumptions
made for the baseline case and to test the robustness of the model.38
Parameters analyzed for the univariate sensitivity analysis included
the cost multipliers, probabilities of death, hospital length of stay,
distribution of patients across the functional outcome groups, cost of
medication management, outpatient physician visits and durable
equipment, health usefulness weights, and discount rates. The effect
of varying individual parameters was examined using plausible
ranges of values from the literature, 95% CIs, or by varying estimates
by 20% in each direction.
Deterministic sensitivity analysis is useful for understanding key
parameters that determine cost-effectiveness and for examining the
range of a variable, which results in the ICER falling below a certain
threshold level. However, the likelihood of achieving an ICER that
falls below that threshold value cannot be inferred from such
analysis.39 Available information may be used to perform a Monte
Carlo simulation and estimate the probability of cost-effectiveness.
For the probabilistic sensitivity analysis, a Monte Carlo simulation
method was used to vary model parameters simultaneously using
distributions.40 The parameters varied are the cost, length of stay,
and health-related quality-of-life variables. It was assumed that
parameter estimates followed a lognormal distribution for cost
multipliers and for length of stay in the hospital. A normal distribution was assumed for the QALY values. The Monte Carlo simulation
was run 10 000 times to achieve stability of results. The sensitivity
analyses were applied to all 3 arms of the decision model.
Results
The GMWNESD option was used as the base case for the
initial run of the model. When SU options were compared with
the GMW option, they were more cost-effective with an ICER
of £10 661 and £11 615 per QALY for SUNESD and SUESD
options, respectively (Table 5). A comparison of the 2 SU
strategies showed that SUESD is the most cost-effective with an
ICER of £17 721 (Table 5). All ICER values fall below the
National Institute for Health and Clinical Excellence’s costeffectiveness threshold level of £30 000 per QALY gained.41
A univariate sensitivity analysis of the base case scenario
was conducted by varying the model variables used in the
calculations by 20% below and above their default value. The
only exception to this was the discount rate, which was varied
between 0% and 10% after the conventional use of discount
rates.42 We used all the variables in the one-way sensitivity
analysis (Supplemental Figures I and II).
Discount rates and health-related quality-of-life value of a
mild stroke had the greatest impact on the ICER values when 2
competing strategies were compared at any one time; however,
the ICER values never went over the £30 000 threshold. Variation of the cost per day for both the SU and the GMWs also had
an impact on the results. Variations in the length of stay
appeared to have some effect on the ICER values as well.
The comparison of SUESD with GWNESD produced
robust results. ICER values never exceeded £30 000 and
stayed well within the limits (maximum ICER £14 200). The
ICERs did not exceed £30 000 when SUESD and SUNESD
were compared either; however, highest ICER value was
closer to the National Institute for Health and Clinical
Excellence threshold (£25 000). In the comparison of SUESD
with SUNESD, outcomes and different length of stay variables had a greater impact on the results. Health-related
quality-of-life measures for patients with mild disabilities had
some impact on the ICER values in this comparison.
The timing of the outcomes and effects had a large
influence on the results in both the comparisons as reflected
in the sensitivity of the results to discount rates. This result
was expected because high and low values for discounting
costs and effects (no discounting versus 10% per annum)
were simulated. Neither of the variables mentioned had a
significant enough impact on the results to either allow one
strategy to dominate the other or increase the cost per QALY
to levels higher than £30 000.
Finally, we carried out a probabilistic sensitivity analysis
of health-related quality-of-life measure and length of stay
variables. We did not include the cost variables because we
had no data on the distribution of unit costs.
When we compared SUESD with GWNESD, we found
that only 1.8% of the time the willingness to pay the threshold
of £30 000 is exceeded, and the ICER stayed under this level
97.1% of the time. A total of 1.1% of the time, the SUESD
scenario dominated the GWNESD option (Supplemental
Table I, available online at http://stroke.ahajournals.org).
Comparison of SUNESD and GWNESD similarly produced
robust results with either SUNESD strategy dominating or the
ICER being below the threshold levels 95.2% of the time.
The results appeared to be robust when SUESD was
compared with SUNESD as well. SUESD option dominated
SUNESD 3.6% of the time and the ICER value was below the
threshold 96.4% of the time. Therefore, the willing-to-pay
threshold levels were never exceeded by the incremental
cost-effectiveness ratios.
Table 5.
A 10-Year Projection of Cost-Effectiveness of Treatment Options
Strategies
Average Cost per Patient
in 10 Years, £
Incremental
Cost, £
Effectiveness QALY Gained
per Patient
Incremental
Effectiveness
Cost per
QALY, £
Incremental
Cost-Effectiveness, £
40 500
45 500
46 900
5000
6400
1.679
2.151
2.230
0.472
0.550
24 095
21 150
21 020
10 661
11 615
45 700
47 300
1400
2.151
2.230
0.079
21 268
21 220
17 721
GWNESD as the base case scenario
GMWNESD
SUNESD
SUESD
SUNESD as the base case scenario
SUNESD
SUESD
Saka et al
CEA of Stroke Unit Care and Early Supported Discharge
Discussion
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This is the first study that modeled the long-term costeffectiveness of UK SUs and ESD policies with care in a
GMW and with conventional discharge policies using unbiased data collected by the SLSR. The study demonstrates that
integrated provision of SU care followed by ESD is likely to
lead to improvements in treatment outcomes in a costeffective manner. This was demonstrated through a comparison of the SUESD option with SUNESD and GWNESD
alternatives. Our analysis suggests that SUESD offers the best
results in terms of effectiveness with an additional cost,
which is within accepted reasonable cost-effectiveness levels
the majority of the time.37 The GWNESD strategy, although
less expensive than the other 2, appeared the least effective of
all 3 strategies, which confirms the trial evidence on SUs.
When both SUESD and SUNESD strategies were compared with the GWNESD option, the results were favorable
for the SU care options. Both univariate and probabilistic
sensitivity analysis results confirmed this conclusion.
The results appeared to be also cost-effective when
SUESD was compared with SUNESD, the ICER value being
£17 721. When a univariate sensitivity analysis was performed varying parameter values by 20%, ICER values did
not exceed the £30 000 per QALY National Institute for
Health and Clinical Excellence threshold. The results of the
probabilistic sensitivity analysis gave rise to a wider range of
outcomes, however, possibly due to the wide distribution of
values used for the health-related quality-of-life variables.
Also, for some ESD cases in this probabilistic sensitivity
analysis, the length of stay values were comparable to the
non-ESD patients despite the fact that mean length of stay for
ESD is 8 days lower than for conventional care. Another
factor that could have an impact on these cost-effectiveness
results could be that the average cost per day for SU and
GMW inpatient stays were used for all types of patient, although
the cost per day for a more severe patient would be higher than
for a milder patient. This might have increased the costs of the
less severe cases and increased the costs for SUESD strategy.
There are a number of issues, which might have an impact
on our results. First, the ESD trial we used, although it is the
largest trial carried out on ESD to date, is a 1-year follow-up
study mainly looking at resource use and is limited in its
approach to measuring outcomes. In particular, functional
ability scores had to be converted to the preferred outcome
measure, QALYs. It was also necessary to extrapolate the
results of the trial to 10 years poststroke, which inherently
assumed that the advantages provided by the ESD in the first
year were persistent. The few long-term SU studies, although
they have small sample sizes, suggest that the positive impact
of SUs on the outcome of care is sustainable for 5 to 10
years.43,44 Therefore, we believe that this is a fair assumption
given the lack of long-term data on ESD.
Also, the ESD services observed in the trial were provided
by a specialized stroke team in our location, which may not
be replicated elsewhere. The difference between the effectiveness of SUESD and SUNESD is based on the differences
between the disability levels of the patients in the ESD trial.23
Beech et al, using the same trial data, report the resource use
and cost differences between ESD and no ESD strategies but
5
not the differences in effectiveness. However, the study
argues that, “When set alongside the previously reported
clinical implications of the intervention, the results indicate
that the early discharge strategy is cost-effective, with the
same achieved at lower average costs.”13 This suggests that
even if an ESD strategy does not provide a health-related
quality-of-life advantage over no ESD strategy, it should lead
to an improvement in health services if not by increasing
effectiveness, then by decreasing overall costs.
The ESD strategy analyzed is a specific ESD strategy,
which was used by the clinical trial from which the data were
drawn. Different stroke care institutions could be using different
strategies with different patterns of resource use involved, which
could in the end alter the overall effects and the costs.
The model did not account for recurrent strokes; therefore,
the inherent assumption was that the recurrence rates for
either of the treatment options were the same. However, there
is some evidence in favor of SUs in reducing recurrent
strokes.16 Therefore, this simplifying assumption in the model
could only have led to our results being more conservative
when estimating the cost-effectiveness of the SU options.
This study is the first economic analysis evaluating the
cost-effectiveness of SU care followed by ESD and provides
important evidence for understanding the economic implications of different discharge policies. The cost savings that can
be generated by the reduction in the average hospital length
of stay is partly offset by the increase in the ESD rehabilitation costs. However, the increase in costs remains within
reasonable limits when compared with the increase in effectiveness. Therefore, SUESD is a cost-effective strategy with
respect to the other 2 strategies discussed.
Conclusion
SU care followed by ESD provides a cost-effective option to
conventional care provision methods for patients with stroke.
However, future research is needed to obtain long-term
outcome data.
Acknowledgments
We thank all the patients and their families and the healthcare
professionals involved. Particular thanks go to stroke physician, Dr
Anthony Rudd, and specialist stroke nurse, Ms Gill Cluckie, for their
contributions to the model design and all the fieldworkers who have
collected data for the register since 1995.
Sources of Funding
This study was supported by the Stroke Association, London, UK,
and the Department of Health, National Institute of Health Research
(NIHR). The NIHR Comprehensive Biomedical Research Centre at
Guy’s & St Thomas’ NHS Foundation Trust (GSTFT) and King’s
College London (KCL) is one of 5 new comprehensive Biomedical
Research Centres in the United Kingdom. The Centre has a strong
focus on translational research taking advances in basic medical
research out of the laboratory and into the clinical setting, forming a
key part of the Department of Health’s new strategy for research and
development in the National Health Service. The GSTFT/KCL
Centre was awarded a total of £45 million over 5 years to build on
its excellence in translational research and develop translational
research capacity through training and education. The Biomedical
Research Centre at Guy’s & St Thomas’ NHS Foundation Trust and
King’s College London focuses on 7 research themes encompassing
Asthma & Allergy, Atherosclerosis, Cutaneous Medicine, Cancer,
Immunity and Infection, Oral Health, and Transplantation.
6
Stroke
February 2009
Disclosures
None.
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Saka et al
CEA of Stroke Unit Care and Early Supported Discharge
Figure I. Univariate sensitivity analysis
SUESD versus GMWNESD.
Downloaded from http://stroke.ahajournals.org/ by guest on June 18, 2017
Figure II. Univariate sensitivity analysis SUESD
versus SUNESD.
Table I. Results of the Probabilistic Sensitivity Analysis,
Monte Carlo Simulation, 10 Cycle Years and 10 000 Iterations
Comparison
Result
SUESD versus GWNESD
SUESD dominates
SUESD versus GWNESD
ICER under £30 000
SUESD versus GWNESD
ICER above £30 000
Total
Percentage
1.10
97
1.80
100.00
SUESD versus SUNESD
SUESD dominates
3.60
SUESD versus SUNESD
ICER under £30 000
96.40
SUESD versus SUNESD
ICER above £30 000
0.00
SUESD versus SUNESD
SUESD is dominated
0.00
Total
100.00
7
Cost-Effectiveness of Stroke Unit Care Followed by Early Supported Discharge
Ömer Saka, Victoria Serra, Yevgeniy Samyshkin, Alistair McGuire and Charles C.D.A. Wolfe
Stroke. published online November 13, 2008;
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