Economic Evaluation of a General Hospital Unit for Older People with Delirium and Dementia (TEAM Randomised Controlled Trial)
Economic Evaluation of a General Hospital Unit for Older People with Delirium and Dementia (TEAM Randomised Controlled Trial)
Lukasz Tanajewski 0 1
Matthew Franklin 0 1
Georgios Gkountouras 0 1
Vladislav Berdunov 0 1
Rowan H. Harwood 0 1
Sarah E. Goldberg 0 1
Lucy E. Bradshaw 0 1
John R. F. Gladman 0 1
Rachel A. Elliott 0 1
0 Editor: Terence J Quinn, University of Glasgow , UNITED KINGDOM
1 1 School of Pharmacy, University of Nottingham , Nottingham , United Kingdom , 2 Health Care of Older People, Nottingham University Hospitals NHS Trust, Queens Medical Centre , Nottingham , United Kingdom , 3 School of Health Sciences, University of Nottingham , Nottingham , United Kingdom , 4 Division of Rehabilitation and Ageing, University of Nottingham , Nottingham , United Kingdom
One in three hospital acute medical admissions is of an older person with cognitive
impairment. Their outcomes are poor and the quality of their care in hospital has been
criticised. A specialist unit to care for older people with delirium and dementia (the Medical and
Mental Health Unit, MMHU) was developed and then tested in a randomised controlled trial where it delivered significantly higher quality of, and satisfaction with, care, but no significant benefits in terms of health status outcomes at three months.
To examine the cost-effectiveness of the MMHU for older people with delirium and dementia in general hospitals, compared with standard care.
Funding: This work was supported by National
Institute for Health Research (NIHR) under its
Programme Grants for Applied Research funding
scheme (RP-PG-0407-10147), https://ccfrms.nihr.ac.
uk/, Principal investigator: JG. After approval to fund
the programme, the funders had no role in study
design, data collection and analysis, decision to
publish, or preparation of the manuscript.
Six hundred participants aged over 65 admitted for acute medical care, identified on admis
sion as cognitively impaired, were randomised to the MMHU or to standard care on acute
geriatric or general medical wards. Cost per quality adjusted life year (QALY) gained, at
3month follow-up, was assessed in trial-based economic evaluation (599/600 participants,
intervention: 309). Multiple imputation and complete-case sample analyses were employed
to deal with missing QALY data (55%).
The total adjusted health and social care costs, including direct costs of the intervention, at
3 months was £7714 and £7862 for MMHU and standard care groups, respectively
Competing Interests: The authors have declared
that no competing interests exist.
(difference -£149 (95% confidence interval [CI]: -298, 4)). The difference in QALYs gained
was 0.001 (95% CI: -0.006, 0.008). The probability that the intervention was dominant was
58%, and the probability that it was cost-saving with QALY loss was 39%. At £20,000/QALY
threshold, the probability of cost-effectiveness was 94%, falling to 59% when cost-saving
QALY loss cases were excluded.
The MMHU was strongly cost-effective using usual criteria, although considerably less so when the less acceptable situation with QALY loss and cost savings were excluded. Nevertheless, this model of care is worthy of further evaluation.
About 50% of people over the age of 65 in general hospitals have delirium, dementia or both,
representing one in three hospital acute medical admissions. [
] Various models have been
proposed to provide for their particular needs. [
] The National Dementia Strategy for
England promotes old age liaison psychiatry services, [
] although it is unclear of what such
services should comprise, how they facilitate high quality care, and there is no firm evidence of
their cost-effectiveness. [
] We developed an alternative model—a specialist unit in a general
hospital to care for people with delirium and dementia (the Medical and Mental Health Unit
] Its development aimed to reflect best practice in dementia and delirium care
taking into account the published literature, [
]  and expert opinion from clinicians
working in the field. It was tested in a randomised controlled trial (Trial of an Elderly Acute care
Medical and mental health unit (TEAM)), [
] which showed that the quality of care was
higher, as judged by direct observation and carer satisfaction, but benefits in health status
outcomes at three months were small and not statistically significant . There are no other robust
studies of these types of specialist units and the cost and economic implications of this model
of care are not yet known.
This analysis compared the costs and cost-effectiveness of the MMHU with those of
standard care, from the perspective of the National Health Service and publically funded personal
social care. The trial-based economic evaluation is reported in accordance with the CHEERS
Statement (S1 Appendix).
Medical and Mental Health Unit and standard care wards
An existing 28-bed acute geriatric medical ward, including its ward-based staff, was converted
to a specialist unit, MMHU, where only older patients with cognitive impairment were
admitted. Five main areas of enhancement (described in depth elsewhere [
]) were: 1) Additional
specialist mental health staff were employed (mental health nurses and occupational therapist
along with additional support from physiotherapy, speech and language therapy, psychiatry
and geriatric medicine), including three healthcare assistants working as activity coordinators;
2) Staff training in recognition and management of delirium and dementia and the delivery of
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person-centred dementia care; 3) A programme of organised therapeutic and diversionary
activities; 4) The environment was made more appropriate for people with cognitive
impairment; 5) A proactive and inclusive approach to family carers was promoted.
Standard care wards included five acute geriatric medical wards, and six general (internal)
medical wards. Practice on geriatric medical wards was based on the principles of
comprehensive geriatric assessment, [
] and staff had general experience in the management of delirium
and dementia. Mental health support was provided, on request, from visiting psychiatrists.
There was no dedicated old age liaison psychiatry service at that time. None of the MMHU
enhancements listed and described above was routine on standard care wards.
A randomised controlled trial, Trial of an Elderly Acute care Medical and mental health unit
(TEAM), was conducted. [
] The trial protocol (S1 Protocol) was published, [
] and the full
report on the trial, including recruitment flow chart, is available elsewhere as an open-access
The protocol for the TEAM study was given a favourable opinion by the Nottingham 1
Research Ethics Committee (reference 10/H0403/1). Recruitment of patient participants
followed the requirements of the English Mental Capacity Act (2005) and was approved by the
research ethics committee. After allocation to a ward, research staff identified patients who had
been randomised, discussed the study with them and assessed them for capacity to give consent
to take part in the study. Those with capacity who were willing to participate were asked to give
written consent. Most potential participants lacked capacity, in which case an informal carer
was asked to give written agreement to participation. If there was no available carer, the nurse
in charge of the ward was asked to act as a "professional consultee".
Patients were recruited who had been admitted for acute medical care to a single large
teaching hospital. Participants were aged over 65, and identified by admissions unit physicians as
being ‘confused’. We used the term ‘confused’ as there is considerable overlap between
delirium and dementia in this population,  and dementia is often undiagnosed in the community
and hospital. [
] Suitable patients identified on the hospital’s medical admissions unit were
entered onto a computerised screening log and, if a bed was available on the MMHU,
randomised 1:1 between the MMHU and standard care in a permuted block design, stratified for
previous care home residence. Randomised patients who were readmitted were assigned their
original allocation. Regardless of allocation, patients had access to standard medical and mental
health services, rehabilitation, intermediate and social care. Baseline clinical data was collected
from the patient, family members, or other informal or professional carers by interview with a
researcher. Outcomes were ascertained at interviews at home 90 days (±7 days) after
randomisation by researchers not involved in recruitment or baseline data collection, and blind to
Between July 2010 and December 2011, 310 patients were recruited from the MMHU and
290 from standard care. One patient in the MMHU arm was lost to follow-up (moved away
from the geographical area). A professional consultee was involved in the recruitment of 30
MMHU and 31 standard care participants, as allowed by English mental capacity law when a
patient lacking mental capacity has no family or friends to advocate for them. Baseline
characteristics of the population and clinical effectiveness outcomes have been reported previously.
] In short, there was no statistically significant difference between settings in the trial primary
outcome, days spent at home (median 51 vs 45 days; 95% confidence interval [CI] for
difference -12 to 24; p = 0.3); median index hospital stay was 11 vs 11 days, mortality 22% vs 25%
(95% CI for difference: -9%, 4%), readmission 32% vs 35% (95% CI for difference: -10%, 5%),
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and new care home admission 20% vs 28% (95% CI for difference: -16%, 0), for the MMHU
and standard care, respectively. Participants on the MMHU spent significantly more time with
positive mood or engagement (79% vs 68%; 95% CI for difference: 2%, 20%; p = 0.03), and
experienced more staff interactions that addressed emotional and psychological needs (median
4 vs 1 per observation; p<0.001). More family carers were satisfied with care (overall 91% vs
83%; 95% CI for difference: 2%, 15%; p = 0.004), and severe dissatisfaction was reduced (5% vs
10%; 95% CI for difference: -10%, 0; p = 0.05). [
The health outcome for the cost-effectiveness analysis was quality-adjusted life year (QALY)
gained, constructing utility values from the 3-level EuroQol-5D (EQ-5D-3L) [
] with societal
] We used EQ-5D utility measure in this economic evaluation because of its
relevance for UK policy makers, particularly the National Institute for Health and Care Excellence
] Patient-reported EQ-5D valuations at baseline and 90-day follow up (measuring
health state on a scale in which 0 and 1 represent death and full health, respectively) were
applied to estimate QALYs gained, assuming baseline utility until date of death for a patient
dead at follow up. Hence, a patient’s QALYs gained were calculated as the area under curve
using linear interpolation between EQ-5D measurement points, and health outcome was
summarised into a single index. Due to the nature of the population studied, 55% of participants
had missing data for self-reported EQ-5D. Other health status variables [
] were used to
impute values in these cases, including proxy completed EQ-5D, dementia-related
quality-oflife at follow up (DEMQOL [
]), behavioural and psychological symptoms
(Neuro-Psychiatric Inventory (NPI) [
]), and dependency in personal activities of daily living (Barthel ADL
Costs of delivering the MMHU intervention. The MMHU intervention cost was
calculated as the additional MMHU staffing cost compared with standard care on a general or
geriatric ward–additional staff employed on MMHU and associated costs are presented in Table 1.
Staff salary levels were based on salary levels from NHS pay scales 2011/12. [
] In order to
estimate the cost of staff involved in direct patient care, as opposed to other activities such as
general management and training, salary costs were adjusted by the proportion of time spent
on patient care on the ward. For instance, the occupational therapist, mental health nurse and
consultant spent two-thirds of their time on direct patient care so their total annual cost was
multiplied by 0.67. The total additional staffing cost was allocated on an individual patient
basis (for patients recruited to the MMHU arm of the trial), assuming 100% bed occupancy on
MMHU (28 beds), by multiplying the per-bed-day additional MMHU cost by the individual
patient’s length of stay on MMHU–calculations are presented in Table 1.
Health and social care resource-use data. Most health and social care services now use
electronic administrative record systems to record patient care. [
] Approvals were gained to
obtain electronic administrative record systems data from hospitals, social care, general
practices (GP), ambulance services, and mental healthcare. Data were collected for three months
post-hospital admission and one year pre-admission (July 2009 –March 2012). Based on our
previous research, [
] extensive fieldwork was completed with the included agencies to derive
parameters covering resource-use (details in S2 Appendix).
Hospital care data (day-case, inpatient, outpatient and intensive care) were obtained from
two patient administration systems for patients that attended five hospitals in the Nottingham
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0.67 spent on ward
100% on ward
0.67 spent on ward
100% on ward
100% on ward
100% on ward
0.67 spent on ward
100% on ward
Adjusted total annual
368 (95% CI: 334, 410)
NB: all figures presented are rounded to 0 decimal places.
aAnnual salary based on proportion of time employed for working on MMHU; annual staffing and salary information from ward proposal, based on 2011/12
FY NHS pay scales mid-point salary levels; consultant salary was based on threshold 6 of pay scale MC58 for 2011/12 FY.
bSalary on-costs taken from PSSRU 2011/12.
cTotal cost adjusted based on time spent on the MMHU during the trial period–time spent by professional on training staff and management not included
in ward time adjustment.
dCalculated as: £255790.55/365.25 = £700.32.
eCalculated, assuming 100% occupancy (28 beds), as: £700.32/28 = £25.01.
fCalculated as mean per-patient MMHU additional cost for participants recruited to the MMHU arm of the trial (309 patients in the full sample CEA), for
whom mean length of stay on MMHU was 14.73 days (95% confidence interval [CI]: 13.35, 16.37): £25.01 14.73 = £368.45. MMHU intervention cost is
calculated on an individual patient basis, by multiplying per-bed-day MMHU additional cost (£25.01) by the patient’s length of stay on MMHU. Mean
perpatient intervention costs for the complete-case CEA dataset is presented in Table 4.
area. Primary care resource-use data were obtained from Nottingham City and
Nottinghamshire County GP practices. Of 107 GP practices serving our cohort, data were obtained from 72
practices (468/599 participants), coming from five electronic administrative record systems:
SystmOne, 220 (47%); EMIS LV, 196 (42%); EMIS Web, 34 (7%); Synergy, 13 (3%); and EMIS
PCS, 5 (1%), and were anonymised at the GP practice. Ambulance service resource-use was
extracted from the Caller Aided Despatch (CAD) IT service. The CAD system was
cross-referenced with paper-based Patient Record Forms to identify participants (using participant name
and place of pick-up). Data from mental healthcare services for older people were provided by
the Nottinghamshire Healthcare Trust data via the RiO system. [
] Social care services within
two different catchment areas (City and County), operating two different electronic systems,
were identified. Services consisted of contacts and assessments, and care plans. Care plans
included home, day, residential and telecare, housing and meals at home services.
Patient-level cost. Unit costs for primary care services were applied based on time taken
to perform each task using time assumptions obtained from PSSRU 2011/12, [
literature, or expert opinion, and mid-point yearly salary estimations taken from the NHS
“Agenda for Change” pay rates. [
] Unit costs of hospital care were applied using national
reference costs according to Healthcare Resource Group (HRG) case-mix. Inpatient spell costs
were adjusted for length-of-stay using standard excess bed day costs. Unit costs for other
services were obtained from PSSRU, standard Department of Health costs and other reference
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costs for the 2011/12 financial year. The detailed costing methods are described elsewhere, [
the sources of unit costs are presented in S2 Appendix and the HRG codes used to derive costs
are presented in S3 Appendix. Due to the high number of different parameters and unit costs
used to calculate patient-level cost (an example of which is provided in S3 Appendix for the
codes used to assign unit costs to hospital resource-use), only a brief overview of the other
costs are described below.
Unit costs were combined with resource-use to generate patient-level costs. Patient-level
costs from all health and social care services incurred during the trial were calculated for all
trial participants who remained in the study at 90-day follow-up (patients who died during the
study were not classed as ‘withdrawn’).
The economic evaluation adopted a health and social services perspective. The incremental
cost-effectiveness ratio (ICER) generated by the MMHU over standard care was calculated
using the following equation:
ICER ¼ QALYMMHU
where CostMMHU (CostSC) and QALYMMHU (QALYSC) are mean cost and QALYs gained in the
MMHU (standard care) group, respectively. Patient cost and QALYs were adjusted by baseline
characteristics using regression methods, including one year pre-admission healthcare costs as
a covariate when modelling costs. Pairwise bootstrapping with replacement was employed for
adjusted patient costs and QALYs, using 5000 replications. The resultant incremental costs and
outcomes were plotted on a cost-effectiveness plane. [
] To investigate uncertainty around
the ICER, cost-effectiveness acceptability curves (CEACs) [
] based on ceiling ratios were
constructed. These (standard) CEACs represent probability of cost-effectiveness for a given
willingness to pay (WTP) for QALY gain, equal to willingness to accept (WTA) QALY loss,
that is, WTA/WTP ratio equal to 1. 
Sensitivity analysis to capture WTA/WTP disparity was conducted. Probability of
costeffectiveness for a £20,000 WTP threshold in relation to WTA/WTP ratio was investigated,
] to account for the notion that QALY gains at additional cost (WTP) may be more
acceptable for decision makers, when compared to cost savings and QALY losses (WTA), as
suggested in the health and behavioral economics literature. [
] Conservatively, WTA/WTP
ratio between 1 and infinity [
], corresponding to WTA threshold between £20,000 and
infinity for accepting QALY loss, respectively (SW quadrant of cost-effectiveness plane), was
assumed in the sensitivity analysis. Namely, the WTA/WTP ratio, r, r 1, reflected the
proportion that, paying £20,000 maximum for QALY gain (NE quadrant of cost-effectiveness plane),
QALY loss was accepted for minimum r £20,000 (SW quadrant of cost-effectiveness plane).
The analyses were performed using STATA version 12 [
] and Microsoft Excel 2010.
Missing data. In the case of 90-day (trial) cost data, only primary care data were missing
(131/599 (21.9%) patients). One year pre-admission healthcare cost was missing for inpatient
care (2/599 (0.3%)) and for primary care (155/599 (25.9%)).
Missing data for patient-reported EQ-5D are: 195/599 (32.6%) baseline EQ-5D, 209/599
(34.9%) follow-up EQ-5D, resulting in QALYs obtained for 272/599 (45.4%) patients,
including 62 (MMHU: 30) dead at follow up.
No statistically significant differences in the proportions of missing EQ-5D and QALYs
values between MMHU and standard care groups were observed: 92/309 (29.8%) vs. 103/290
(35.5%), p = 0.13, for baseline EQ-5D; 113/309 (36.6%) vs 96/290 (33.1%), p = 0.37, for
follow6 / 20
up EQ-5D; and 170/309 (55.0%) vs 157/290 (54.1%), p = 0.83, for QALYs. Furthermore, for
primary care cost and for other health measurement variables of interest, the differences in the
proportions of missing values between arms were non-significant; the percentage of missing
values in the two arms was similar apart from proxy completed EQ-5D and follow-up Barthel
ADL index (see Table A in S4 Appendix).
Missing values for cost, EQ-5D, and for other variables, were assumed to be missing at
random (MAR). Given no imbalance in proportions of missing values between randomised groups
(as shown above and in Table A in S4 Appendix) and predictors of missing values for EQ-5D
(and for other health status variables) identified among variables with complete data (age,
number of medical conditions, and permanent care home residence at baseline—see Tables
B-F in S4 Appendix for details) the MAR assumption seemed to be plausible. Hence, the
multiple imputation approach was applied to deal with missing data in cost-effectiveness analysis.
Missing values for cost, EQ-5D, and for other variables of interest, were imputed using
multiple imputation by chained equations (MICE), [
] incorporating the set of variables: age and
sex; proxy-EQ-5D, NPI, Barthel ADL score, number of medical conditions—at baseline and
follow-up; DEMQOL at follow up; as well as primary, inpatient, day-case, and outpatient care
(trial and one year pre-admission) costs, social care (trial) costs, and permanent care home
residence at baseline. To avoid bias, all variables included in the models for adjusted costs and
QALYs in cost-effectiveness analysis were incorporated in the imputation. [
particular, since we imputed missing values of the models covariates, model outcomes (costs and
follow-up EQ-5D determining QALYs) were included in the imputation model as well. [
Additionally, by having the predictors of missing values for EQ-5D (and for other health status
variables) in the imputation model (age, number of medical conditions, and care home
residence at baseline) potential bias was reduced (MAR assumption was more plausible) and the
standard errors in the adjustment multiply imputed models were minimised. [
One hundred imputed datasets were generated; based on the rule of thumb that the number
of imputations was higher than the percentage of patients with at least one variable in the
imputation model missing, equal to 94% [
] (percentages of missing values are at baseline and
follow up, respectively: 33% and 56% (proxy EQ-5D), 1% and 15% (Barthel ADL), 53% and
25% (NPI), and 41% (DEMQOL, follow-up collected only)).
An alternative approach to deal with missing data, complete-case cost-effectiveness analysis,
was applied. That is, 209/599 (34.9%) patients with complete QALY and trial cost data (210
patients), for whom one-year pre-admission secondary care cost data were also complete, were
included in a complete-case cost-effectiveness analysis. In this approach, one year
pre-admission secondary care cost and other covariates with complete data in the sub-sample of 209
patients were considered for the models for adjusted cost and QALYs. Due to the choice of
adjustment covariates being the predictors of missing EQ-5D data at follow up, the MAR
assumption was also sufficient to reduce bias in cost-effectiveness estimates. Hence, the
unadjusted estimates were provided under missing completely at random (MCAR) assumption,
while the MAR assumption was sufficient to justify complete-case adjusted cost-effectiveness
Full-sample (using imputed values) cost-effectiveness analysis. In the full-sample CEA,
imputed missing primary care and EQ-5D data were incorporated into the generation of
incremental costs and QALYs. Unadjusted costs in MMHU and standard care, as well as
incremental costs by services were analysed, handling uncertainty by non-parametric bootstrapping.
In the adjusted CEA, other variables with imputed missing values (one year pre-admission
primary care cost, NPI and Barthel ADL score) were also used to adjust cost and QALYs for
baseline characteristics. Finally, adjusted cost was estimated controlling for age, sex, EQ-5D
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utility index and permanent care home residence at baseline, and one year pre-admission
healthcare cost. Adjusted QALYs were estimated controlling for age, sex, and baseline
EQ5D utility index, permanent care home residence, number of medical conditions, NPI, and
Barthel ADL score. The adjustment models for both cost and QALYs included age, sex,
EQ5D index and permanent care home residence at baseline, as the explanatory variables which
were predicted a priori to be the possible factors affecting both resource use and QALYs in
the trial follow-up. Moreover, QALYs were controlled for baseline EQ-5D index as
recommended for trial-based cost-utility analysis, [
] age and care home residence were found to
be the predictors of missing QALYs values (see Table B-C in S4 Appendix), and block
randomization was stratified for previous residence in a care home, which justified inclusion of
these covariates in the adjustment models. Additionally, one year pre-admission healthcare
cost was expected to be a strong predictor of trial resource use and costs. Baseline Barthel
ADL, NPI, and number of medical conditions were included as the potential predictors of
physical and mental health state at follow-up, and so QALYs were adjusted for these
covariates. Baseline patient characteristics by trial arm, included in the adjusted CEA, are reported
in Table A in S5 Appendix.
Regression techniques, employing a generalised linear model (GLM), were applied to adjust
costs and QALYs by baseline characteristics. The appropriate distributional family type for the
variance function was chosen by using the modified Park test; [
] Pregibon link and modified
Hosmer-Lemeshow tests were used to diagnose any misspecification of the link function. [
Regression models and diagnostic tests were calculated on each imputed dataset, to obtain
adjusted cost and QALYs averaged across 100 imputations (Rubin’s rules), and to find the
optimal GLMs for both costs and QALYs (considering the worst test results across imputations).
Gamma distribution family and log link were chosen for costs, and normal family distribution
and power link 0.25 were chosen for QALYs. [
] Adjusted patient cost and QALYs, calculated
using the recycled prediction method, [
] were used to generate cost-effectiveness planes and
cost-effectiveness acceptability curves (CEACs) on each imputed dataset; the full sample
CEAC was obtained from the probability of cost-effectiveness for a given ceiling ratio, averaged
across 100 imputations.
Complete-case cost-effectiveness analysis (alternative approach). In the complete-case
CEA, comprising 209/599 (34.9%) patients with complete QALY and trial cost data, for whom
one year pre-admission secondary care cost data were also complete, unadjusted costs in
MMHU and standard care were analysed, handling uncertainty by non-parametric
Adjusted cost was estimated controlling for age, sex, utility and permanent care home
residence at baseline, and one year pre-admission secondary and tertiary care cost (pre-admission
primary care costs are omitted here). Adjusted QALYs were estimated controlling for age, sex,
and baseline utility, permanent care home residence, number of medical conditions, delirium
at admission (defined by a score of at least 18/46 on the delirium rating scale (DRS-R-98 [
and severe cognitive impairment (Mini-Mental State Examination (MMSE) [
], MMSE 9).
The reasons for inclusion of these baseline characteristics in the adjustment models were
similar to the full-sample cost-effectiveness analysis. However, only covariates with complete data
in the sub-sample of 209 patients were considered for the models for adjusted cost and QALYs.
In particular, one year pre-admission secondary and tertiary care cost, permanent care home
residence, and number of medical conditions were included as covariates in the models. To
control QALYs for mental health status at baseline, DRS and MMSE variables were used, for
which data for 2 and 1 participants, respectively, were missing in the complete-case CEA
subsample (patients with missing DRS and MMSE baseline values were assumed to have delirium
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and severe cognitive impairment at admission). Baseline patient characteristics by trial arm,
included in the adjusted complete-case CEA, are reported in Table B in S5 Appendix.
A diagnostic process was used to find the optimal GLMs for both costs and QALYs (the
same tests as for the full-sample analysis). Gamma distribution family and power link 0.95
were chosen for costs, normal distribution family and power link 0.6 were chosen for QALYs.
Recycled prediction method to generate adjusted patient cost and QALY was applied.[
Per-bed-day MMHU additional cost was £25. In the full sample, mean length of stay on
MMHU was 15 days (95% confidence interval [CI]: 13, 16), and the mean per-patient cost of
delivering the intervention (mean per-patient MMHU additional cost) was £368 (95% CI: 334,
410)–calculations are presented in Table 1.
Full-sample (using imputed data) cost-effectiveness analysis
In the full-sample cost-effectiveness analysis (CEA), 599 (MMHU: 309) participants were
analysed at 90-day follow-up, at which point 139 (MMHU: 68) were dead. In the unadjusted
analysis, comparing the MMHU to standard care, the cost of inpatient care was non-significantly
lower (-£434, 95% CI: -1199, 357), social care cost was non-significantly lower (-£194, 95% CI:
-657, 301), and the cost of care (primary, secondary, tertiary and social care) was
non-significantly lower (-£690, 95% CI: -1571, 246), resulting in incremental total cost of -£322 (95% CI:
-1219, 621). The difference in QALYs gained was non-significant (0.008, 95% CI: -0.005,
0.020). In the adjusted CEA, the total cost for the MMHU was lower by -£149 (95% CI: -298,
4), with QALYs gained difference equal to 0.001 (95% CI: -0.006, 0.008), and a 58% probability
of the MMHU being dominant (cost-saving with QALY benefit) and a 94% probability of
costeffectiveness (at a £20,000/QALY threshold). The probability of the MMHU being cost-saving
with QALY loss (SW quadrant) was 39% (Tables 2 and 3, Figs 1 and 2).
Probability of cost-effectiveness for £20,000 WTP threshold in relation to WTA/WTP ratio
is presented in Fig 3 (full-sample). It is shown that this probability goes down from 94%
(WTA/WTP ratio equal to 1, as assumed in Fig 2 for the full-sample CEAC) to 86% for the
ratio equal to 2, to 73% for the ratio equal to 5, approaching 59% for the infinite ratio (infinite
WTA threshold—interpreted as non-acceptance of QALY loss for any amount of money
Complete-case cost-effectiveness analysis
In the subgroup of 209 (MMHU: 109) patients with complete QALY and resource-use data,
including 49 (MMHU: 24) patients dead at follow up, comparing MMHU to standard care,
the total cost was non-significantly lower (-£402, 95% CI: -2227, 1297) and the difference in
QALYs gained was non-significant (0.007, 95% CI: -0.013, 0.027). In the adjusted CEA, the
total cost for MMHU was lower (-£206, 95% CI: -591, 153) with no QALYs gained difference
(0.000, 95% CI: -0.011, 0.011) and a 47% probability of the MMHU being dominant, and a
81% probability of cost-effectiveness (at a £20,000/QALY threshold). The probability of the
MMHU being cost-saving with QALY loss (SW quadrant) was 40%. (Table 4, Figs 2 and 4)
Probability of cost-effectiveness for £20,000 WTP threshold in relation to WTA/WTP ratio
is presented in Fig 3 (complete-case). This probability goes down from 81% (WTA/WTP ratio
equal to 1, as assumed in Fig 2 for the complete-case CEAC) to 74% for ratio equal to 2, to 63%
for ratio equal to 5, approaching 48% for infinite ratio (infinite WTA threshold). (Fig 3)
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MMHU (309 patients) Standard carea(290 patients) Incremental cost / QALYs gained
aGeriatric ward (204 patients) and general ward (86 patients).
bPrimary care cost and QALY imputed using Multiple imputation by chained equation (MICE). Multiple imputation model applying predictive mean
matching (pmm) for costs and utilities, and ordered logit (ologit) for Barthel ADL scores, DEMQOL, and NPI; 100 imputations generated.
cHealthcare (inpatient, day-case, outpatient, EMAS, MHT, critical care, primary care) and social care cost.
dAdjusted by age, sex, utility and permanent care home residence at baseline, and one year pre-admission healthcare cost care cost. A GLM model
(family–gamma, link–log) was applied, as it was found to be optimal upon diagnostic procedure on each imputation (the worst test results across
imputations were: Park test for gamma family, p-value = 0.05, Pregibon link test, p-value = 0.36, Hosmer-Lemeshow test, p-value = 0.11).
eAdjusted by age, sex, and baseline utility, permanent care home residence, number of medical conditions, NPI, and Barthel ADL. A GLM model (family–
normal, link–power 0.25) was applied, as it was found to be optimal upon diagnostic procedure on each imputation (the worst test results across
imputations were: Park test for normal family, p-value = 0.02, Pregibon link test, p-value = 0.50, Hosmer-Lemeshow test, p-value = 0.07, with Park test
pvalue being higher than 0.05 for 95% imputations and with average Park test p-value across imputations equal to 0.41).
-690 (-1571, 246)
-517 (-660, -374)
368 (334, 410)
-322 (-1219, 621)
-149 (-298, 4)
0.008 (-0.005, 0.020)
0.001 (-0.006, 0.008)
Summary of results
The specialist unit for people with delirium and dementia did not demonstrate convincing
benefits in health status over usual hospital care, as no significant effect on QALY gain was
observed. However, the results did show a trend towards cost savings and a high probability of
MMHU (309 patients)
Standard carea(290 patients)
Incremental cost / QALYs gained
NB: the cost of the intervention is not included in these cost estimates. The cost of the intervention is presented in Table 2.
aGeriatric ward (204 patients) and general ward (86 patients).
bPrimary care cost imputed using Multiple imputation by chained equation (MICE). Multiple imputation model applying predictive mean matching (pmm);
100 imputations generated.
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Fig 1. Cost-effectiveness plane–pairwise bootstrapping (adjusted analysis, full-sample imputed analysis). Bootstrapped incremental costs and
QALYs were obtained for each imputation (5000 replications), and these were used in the full-sample cost-effectiveness analysis. Consequently, a
costeffectiveness plane should be drawn for 100 imputations (which would be impossible to present (100 5000 = 500 000 points)). Hence, to approximate and
illustrate the cost-effectiveness plane for the full-sample imputed analysis, 100 replications randomly chosen from each imputation were plotted in this figure
(100 100 = 10 000 points). The red square represents the point estimate: 0.001 QALY and -£149.
cost-effectiveness (94%) from a combined health and social care perspective, when usual
criteria were applied. When excluding the cases in which there were cost savings but worse
outcomes (QALY loss), the probability of cost-effectiveness fell to 59%.
The strengths of this study were that it was conducted as part of a RCT rather than a less robust
design, resource-use ascertainment was by extraction from electronic datasets rather than recall
enhancing the quality of the data and hence results, and multiple resource-use datasets were
examined to produce a more comprehensive estimate of costs than using a single and
potentially unreliable data source. The economic evaluation was conducted independently of the
clinical service and, in large part, independently of the investigators who had designed and
implemented the clinical effectiveness evaluation.
There were considerable missing data, due to the inability of frail and cognitively impaired
participants to complete EQ-5D, and a systematic difference in values for proxy compared
with self-completed EQ-5D. Hence imputation was used, incorporating proxy EQ-5D and
11 / 20
Fig 2. Cost-effectiveness acceptability curves (adjusted analyses)–full sample and complete-case analyses. Full-sample cost-effectiveness
acceptability curve is obtained from probability of cost-effectiveness for given ceiling ratio, averaged across 100 imputations. CEACs represent probability of
cost-effectiveness of MMHU for given WTP, where WTA is assumed to be equal to WTP (SW quadrant of cost-effectiveness plane, see Figs 1 and 3).
other clinical measures, to estimate the true impact of MMHU care on patients’ health status,
which could be a source of error. Employing an alternative approach omitting missing data
(complete-case analysis) showed no major differences in results; however, we did not ascertain
informal care or privately funded costs, meaning that our findings are limited to the health and
social care service perspective. Informal care costs form an important part of total costs for
people with dementia. [
] The findings represent a comparison between the MMHU and
standard care. However, 70% of standard care was situated on specialist acute geriatric medical
wards delivering comprehensive geriatric assessment, which is known to deliver better health
outcomes than general internal medical wards for frail older people (that is, an ‘active control’).
] The impact of the MMHU on health status may therefore have been understated
compared with less specialist care.
The EQ-5D has limitations as a preference-based generic health status measure for
calculating QALYs in frail and cognitively impaired older people with progressive conditions. Firstly,
the EQ-5D is a simple, five dimension, 3-level measure of health status which may be
insensitive to changes in health that are important in this context. [
] There is some evidence to
support the EQ-5D as a valid measure for assessing quality of life in older people, 
including people with cognitive impairment using proxies when necessary. [
] Due to the advocacy
12 / 20
Fig 3. Probability of cost-effectiveness for WTP threshold equal to £20,000 in relation to WTA/WTP ratio–full sample and complete-case analyses.
by NICE to use the EQ-5D for comparability between studies, and the lack of other, more
sensitive preference-based measures which can be used to elicit the QALY, the EQ-5D was the best
preferred option for performing this economic evaluation. At the time of planning this study
the DEMQOL, a condition specific quality of life measure for use in older people with
] did not have a valid preference-based scoring tariff. The DEMQOL may be more
sensitive for measuring condition-specific quality of life but was no different when measured in
survivors at the end of the follow up period. [
] More recently the UDEMQOL has been
developed as a preference-based version of the DEMQOL which can be used as a condition-specific
preference-based measure for eliciting the QALY. [
] The UDEMQOL can be used to
provide complimentary results for comparison with the EQ-5D [
] and should be considered
for use in future studies if further studies establish its validity in this setting. [
] Secondly, the
QALY as elicited by the EQ-5D is a unidimensional metric of change in health status over
years of life, and therefore does capture broader aspects of well-being, [
] capability [
] or the ‘spillover’ effect on carers [
] that may have been affected by the intervention.
These aspects are increasingly recognised as areas that should be accounted for when assessing
the economic outcome of trials. [
] Evidence from the TEAM trial showed that
participants on the MMHU spent significantly more time with positive mood or engagement, and
experienced more staff interactions that addressed emotional and psychological needs. [
13 / 20
aGeriatric ward (66 patients) and general ward (34 patients).
bInpatient, day-case, ambulance service (EMAS), Mental Health Trust (MHT), critical care, outpatient, primary care, and social care.
cAdjusted by age, sex, utility and permanent care home residence at baseline, and one year pre-admission secondary care cost. A GLM model (family—
gamma, power link—0.95) was applied. Park test for gamma family, p-value = 0.92, Pregibon link test, p-value = 0.39, Hosmer-Lemeshow test,
pvalue = 0.36.
dAdjusted by age, sex, and baseline utility, permanent care home residence, number of medical conditions, delirium (DRS-R-98 > 17.75) and severe
cognitive impairment (MMSE 9). A GLM model (family—normal, power link—0.6) was applied. Park test for normal family, p value = 0.07, Pregibon link
test, p-value = 0.68, Hosmer-Lemeshow test, p-value = 0.20.
Additionally, more family carers in MMHU arm were satisfied with care. For these reasons, we
believe our analysis only presents a partial assessment of the overall benefit of the intervention.
This economic evaluation was derived from trial data up to three months of follow-up,
without measuring or modelling the health and cost outcomes beyond this horizon. However, given
the fast moving changes in clinical conditions of patients, the health and cost effects of MMHU
care are likely to be limited to a short period after hospital stay, and the trial follow-up was
long enough to assess effects of the MMHU (cf. trial protocol [
] and [
]), although the trends
towards cost savings (such as from long term care) may have been stronger if we had data from
a longer period of follow up.
This is the first study of this specific model of care: no cost-effectiveness analyses of specialist
unit care for cognitively impaired frail older people have been identified. [
] However, the
patient group involved and the core processes of the MMHU were similar to the patient groups
and core processes involved in services delivering comprehensive geriatric assessment (CGA),
where a potential cost reduction compared with general medical care has been observed.
] Thus this study contributes towards, and is compatible with, a small evidence base
about the economic consequences of CGA.
The economic impact of the health and social care of older people has been rarely described
] Despite recommendations to assess opportunity costs , only half of published
studies measured costs other than secondary care, even fewer including long term or social
care costs: the eight studies reporting costs in CGA trials in a recent review only reported costs
from a hospital perspective, and so did not investigate whether costs were shifted to other areas
of health care, or to social care or informal carers. [
] Thus, this study is an important
contribution to the evidence base, particularly because around 1/3 of the cost savings observed in this
study were non-hospital costs (social and primary care). Despite the fact that cost savings
14 / 20
Fig 4. Cost-effectiveness plane–pairwise bootstrapping (adjusted analysis, complete case analysis). Red square represents point estimate 0.000
QALY and -£206.
shown were only small percentages of the total care cost occurred in the standard care arm (4%
and 2%, for unadjusted and adjusted costs, respectively), the potential cost savings for the NHS
could be large if similar specialist dementia care is implemented in the UK hospitals.
What the results mean
The value of these findings depends upon the degree to which the findings from economic
studies based on trials that were not positive for their primary outcome are judged by those
using such information, and the degree to which conventional cost-effectiveness estimates are
judged when they rely considerably upon cases in which there were cost savings but QALY
losses. Health care funders may find that cost-effectiveness findings based upon QALY gains at
additional cost (willingness to pay, WTP) are more acceptable than cost savings and QALY
losses (willingness to accept, WTA)–an issue discussed widely in the health and behavioral
economics literature (cf. [
]). Hence, we provided the sensitivity analysis to incorporate
possible WTA/WTP disparity, by estimating probability of cost-effectiveness dependent on the
value of WTA/WTP ratio. [
] Due to unknown decision makers’ preference over WTA/WTP,
the interpretation of such sensitivity analysis is limited to the extreme in which small QALY
loss is not accepted for any level of cost-savings. In this study, the interpretation is even more
difficult because of the possibility that the overall benefits of the intervention may have been
15 / 20
understated in the economic analysis. However, we conclude that there are sufficient grounds
for further development of evaluation of specialist medical and mental health units.
The further development and evaluation of this comprehensive model of care can be guided
by the results of this study. For example, this study illustrates the potential value of determining
a wider range of health and social costs to appraise the total impact of services. Given that
considerable effort was put into discharge planning, communication with families and care homes,
referral to community services, and advance care planning, it is likely that the accumulation of
multiple small incremental improvements in multiple processes and outcomes can only be
observed when multiple sources of costs across the health and social care system are taken into
Mortality was high in the population studied (25% at 90 days).[
] It is difficult to define
measurable outcomes in studies of palliative and supportive care, but patient experience and
carer satisfaction are likely to be important. The NHS Outcomes framework includes ‘a positive
experience of care’ as one of its five domains.  Tools widely used to measure health care
outcomes in economic evaluations do not appear to discriminate well in the end-of-life care
] so carer preferences should be incorporated in healthcare decision making. [
Economic evaluation of such services may need to consider broader outcomes than the QALY.
For example, a recent study has shown the advantages of multiple domain comparisons,
emphasizing transparency and better informing reimbursement and research decisions when
using this approach. [
] Therefore, considering the totality of outcomes, including patient
experience and carer satisfaction (a cost-consequences analysis), would emphasize effects that
may be more appropriate for frail older patients, often approaching the end of life. An
alternative would be a cost minimization approach. Our findings suggest that care on the specialist
unit was preferable (better quality and experience even if health status was no different). In this
case, costs can be compared to determine preference. In this study, we showed a trend towards
cost reduction in the MMHU arm, and hence a trend towards superiority.
In conclusion, further development and evaluation of specialist units in general hospitals
for people with dementia and delirium is warranted based on the fact that the unit studied here
led to better quality of care, [
] has a reasonable probability of cost-effectiveness even when
cost saving QALY losing cases are not included in the estimate of cost-effectiveness, and
showed a trend towards cost-savings when a cost minimisation approach is taken. Such units
should be seen as an important response to the challenge of managing mental health conditions
in general hospitals, in addition to liaison old age psychiatry services. Further research of
similar services should aim to find better ways of capturing health benefits in patient groups
receiving palliative and supportive care, and use multiple cost sources to assess the full cost impact.
S1 Checklist. CONSORT checklist for TEAM clinical trial manuscript. Goldberg SE,
Bradshaw LE, Kearney FC, et al. Care in specialist medical and mental health unit compared with
standard care for older people with cognitive impairment admitted to general hospital:
randomised controlled trial (NIHR TEAM trial). BMJ 2013;347 doi: 10.1136/bmj.f4132[.
S1 Appendix. CHEERS Statement for the TEAM economic evaluation study.
S2 Appendix. Summary of resource-use parameters obtained in the TEAM trial.
16 / 20
S3 Appendix. Description and breakdown of HRG codes used in costing of hospital
S4 Appendix. Missing data patterns and predictors. Proportions missing between groups for
variables of interest (Table A). Logistic regression: predictors of missing value for baseline
EQ5D, 599 observations (Table B). Logistic regression: predictors of missing value for follow-up
EQ-5D,460 observations (Table C). Logistic regression: predictors of missing value for
followup proxy EQ-5D, 460 observations (Table D). Logistic regression: predictors of missing value
for follow-up Barthel ADL, 460 observations (Table E). Logistic regression: predictors of
missing value for follow-up DEMQOL, 460 observations (Table F).
S5 Appendix. Baseline characteristics by trial arm. Baseline characteristics–covariates
included in the full-sample adjusted CEA (Table A). Baseline characteristics–covariates
included in the complete-case adjusted CEA (Table B).
S1 Protocol. Harwood R, Goldberg S, Whittamore K, et al. Evaluation of a Medical and
Mental Health Unit compared with standard care for older people whose emergency admission
to an acute general hospital is complicated by concurrent 'confusion': a controlled clinical trial.
Acronym: TEAM: Trial of an Elderly Acute care Medical and mental health unit. Trials
Professor Finbarr Martin chaired the Trial Steering Committee. We would like to thank the
NIHR Mental Health Research Network, Trent Dementia Research Network and Trent
Comprehensive Local Research Network. The authors would like to acknowledge the help of
Loraine Buck for helping recruit GP practices to participate in the study, and Rachael Taylor,
Nadia Frowd and Caroline May for visiting these practices to aid in the data extraction. We
would also like to thank the patient participants and their carers, Nottingham University
Hospitals staff, primary care trusts, general practices (unfortunately there are too many names to
list), IT services at Nottingham University Hospitals and Kingsmill Hospital (Kate Moore,
Rebecca Stevens and Samantha Cole), EMAS (Darren Coxon and Stacey Knowles), MHT
(Lawrence Flatman), Social care (Rod Madocks, Michael Christopher and Anthony Verdon)
and the patient participants in the study and their carers.
Conceived and designed the experiments: RH JG RE MF. Performed the experiments: MF SG
RH JG. Analyzed the data: LT RE MF VB GG LB. Contributed reagents/materials/analysis
tools: JG RE. Wrote the paper: RE LT MF GG VB RH LB SG JG.
17 / 20
3. Royal College of Psychiatrists (2005) Who Cares Wins. Improving the outcome for older people
admitted to the general hospital. Guidelines for the development of Liaison Mental Health Services for older
18 / 20
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