motivational enhancement therapy with and without cognitive behaviour therapy for type 1 diabetes:...

Article: Treatment

Motivational enhancement therapy with and without

cognitive behaviour therapy for Type 1 diabetes:

economic evaluation from a randomized controlled trial

A. Patel, E. Maissi, H.-C. Chang, I. Rodrigues*, M. Smith, S. Thomas†, T. Chalder, U. Schmidt,J. Treasure and K. Ismail

Institute of Psychiatry, King’s College London, *Guy’s, King’s and St Thomas’ School of Medicine and †Diabetes Centre, St Thomas’ Hospital, London, UK

Accepted 10 November 2010

Abstract

Aims To assess the cost-effectiveness of motivational enhancement therapy and cognitive behaviour therapy for poorly

controlled Type 1 diabetes.

Methods Within-trial prospective economic evaluation from (i) health and social care and (ii) societal perspectives. Three

hundred and forty-four adults with Type 1 diabetes for at least 2 years and persistent, suboptimal glycaemic control were

recruited to a three-arm multi-centre randomized controlled trial in London and Manchester, UK. They were randomized to (i)

usual care plus four sessions of motivational enhancement therapy (ii) usual care plus four sessions of motivational enhancement

therapy and eight sessions of cognitive behaviour therapy or (iii) usual care alone. Outcomes were (i) costs, (ii) Quality-Adjusted

Life Year gains measured by the EuroQol 5-dimensional health state index and the 36-item Short Form and (iii) diabetes control

measured by change in HbA1c level at 1 year.

Results Both intervention groups had significantly higher mean health and social care costs (+ £535 for motivational

enhancement therapy and + £790 for combined motivational enhancement and cognitive behavioural therapy ), but not societal

costs compared with the usual-care group. There were no differences in Quality Adjusted Life Years. There was a significantly

greater HbA1c improvement in the combined motivational enhancement and cognitive behavioural therapy group (+ 0.45%;

incremental cost-effectiveness ratio = £1756), but the not in the motivational enhancement therapy group. Cost-effectiveness

acceptability curves suggested that both interventions had low probabilities of cost-effectiveness based on Quality Adjusted Life

Years (but high based on HbA1c improvements). Imputing missing costs and outcomes confirmed these findings.

Conclusions Neither therapy was undisputedly cost-effective compared with usual care alone, but conclusions vary depending

on the relative importance of clinical and quality-of-life outcomes.

Diabet. Med. 28, 470–479 (2011)

Keywords cost, cost-effectiveness, diabetes, psychological therapy

Abbreviations CBT, cognitive behaviour therapy; EQ5D, EuroQol 5-dimensional health state index; MET,

motivational enhancement therapy; QALY, quality-adjusted life year

Introduction

Diabetes and its complications incur substantial costs for health

services [1,2], patients and wider society as a result of typically

early onset (Type 1 diabetes), increasing prevalence (Type 2),

chronicity and premature mortality. Providing cost-effective

treatment and long-term management approaches is particularly

important for those at high risk of complications and those

with suboptimal glycaemic control and difficulties in adhering

to their self-care regimen. Self-care difficulties are often asso-

ciated with psychological problems, which may be improved

by psychological treatments. Although the effectiveness of

psychological therapies to improve glycaemic control in such

individuals has shown promise in Type 2 diabetes [3] and

adolescents with Type 1 diabetes [4], effectiveness and

Correspondence to: Dr Anita Patel, Health Service and Population Research

Department, Institute of Psychiatry, King’s College London, De Crespigny

Park, London SE5 8AF, UK. E-mail: [email protected]

(Clinical Trials Registry No. ISRCTN 77044517)

DIABETICMedicine

DOI:10.1111/j.1464-5491.2010.03198.x

ª 2011 The Authors.470 Diabetic Medicine ª 2011 Diabetes UK

cost-effectiveness in adults with Type 1 diabetes has not been

established. We recently reported a three-arm, multi-centre,

randomized controlled trial which compared motivational

enhancement therapy (MET) with and without cognitive

behaviour therapy (CBT) for adults with Type 1 diabetes

against usual care [5]. We found that motivational enhancement

therapy with cognitive behaviour therapy resulted in significant

improvement in HbA1c levels over 1 year, but motivational

enhancement therapy alone did not. We now report a

comprehensive economic evaluation from that trial.

Patients and methods

The economic evaluation was undertaken from health and social

care, and societal, perspectives over 1 year. Individual-level

economic data were collected within a three-arm, multi-centre

randomized controlled trial, full details of which are described by

Ismail et al. [5]. To summarize, 344 adults (18–65 years) with

Type 1 diabetes for at least 2 years and persistent, suboptimal

glycaemic control were recruited from five hospitals in south-east

London and three hospitals in Greater Manchester, England.

Ethics review and approval was obtained from all relevant

committees.

Interventions

Participants were randomized to receive either (i) four sessions

of a diabetes-specific manualized motivational enhancement

therapy over 2 months (MET) in addition to their usual diabetes

care, (ii) four sessions of motivational enhancement therapy

(over 2 months) followed by eight sessions of manualized

cognitive behaviour therapy (MET + CBT) (over an additional

4 months) in addition to their usual diabetes care or (iii) usual

diabetes care alone.

The first motivational enhancement therapy session was a

standardized computerized self-assessment of diabetes-relevant

behaviours, followed by feedbackandan assessment of the rating

of the level of importance, confidence and readiness to change.

Remaining sessions covered obstacles to, and options for,

changing behaviours, emphasizing participants’ own problem-

solving skills andcollaborative formulationofa changeplan.The

therapy was supplemented with homework writing tasks to

increase motivation.

The cognitive behaviour therapy manual covered developing a

collaborative individualized programme, with each session

structured around agenda setting, homework planning and

feedback. Strategies included: normalizing dietary, exercise,

diabetes-related self-monitoring and lifestyle-related behaviours;

anxiety, worry and stress management; challenging diabetes-

specific negative automatic thoughts; improving impulse control;

behavioural experiments; activity scheduling; strategies for

eliciting social support; and assertiveness training.

Both interventions were delivered in diabetes clinics separate

from usual clinic visits by six trained nurses assessed for

competency.

Data collection

Individual-level economic data related to impacts attributable to

diabetes or related illness were collected using an adapted Client

ServiceReceipt Inventory (CSRI) [6]. It includedquestionsabout:

socio-demographic and socio-economic profile; time off work;

use of health and social care resources; informal care and any

time carers tookoff work to provide such care; and out-of-pocket

expenses. It was administered retrospectively at three assessment

points: by face-to-face interview at baseline (covering the

previous 3-month period), and by telephone interview at

6 months and 12 months after randomization (covering the

previous 6-month periods).

Additionally, economic impacts of attending a typical

intervention session were measured using a self-complete

questionnaire at participants’ last therapy session (or posted for

self-completion if they did not attend all sessions) to avoid

revealing randomization allocation during follow-up interviews.

This covered: time taken to attend a session; whether they took

time off work to do so and, if so, in which way (annual leave, sick

leave, unpaid leave, made up the time or other arrangement); lost

pay; and travel costs.

Health-related quality life was measured using the 36-item

Short Form [7] for the purpose of estimating quality-adjusted life

years (QALYs). As general population utility weights were not

yet available for this measure when this study was designed, we

additionally included the EuroQol 5-dimensional health state

index (EQ5D) [8]. Both were administered at baseline and

12 months.

Costs

Each participant’s resource-use data were multiplied by

appropriate unit costs (see also Supporting Information,

Appendix S1) to calculate costs for each assessment period.

Follow-up costs were summed to calculate total 1-year costs and

are reported in pounds sterling (£) at 2005 ⁄ 2006 prices.

Discounting was unnecessary as all costs were related to a

1-year period.

Total health and social care costs consisted of: hospital

inpatient and outpatient services, primary care services, other

community-based services, social services, medications, insulin-

related equipment, other equipment and adaptations and

intervention costs. Total societal costs consisted of the same

plus: informal care; participants’ and families’ out-of-pocket

expenses (including travel to intervention sessions); lost

productivity because of work absence; and lost productivity,

lost leisure time and lost pay because of attending intervention

sessions.

Motivational enhancement therapy and cognitive behaviourtherapy unit costs

After identifying time and material inputs, including training and

supervision, directly associated with an average session of each

DIABETICMedicineOriginal article

ª 2011 The Authors.Diabetic Medicine ª 2011 Diabetes UK 471

therapy, we estimated motivational enhancement therapy and

cognitive behaviour therapy unit costs (from the healthcare

perspective) as an average cost per session ⁄ per person for each

intervention, assuming resource inputs for one session of each

intervention did not significantly vary between sessions or

persons (Table 1).

One 50-min motivational enhancement therapy session cost

£49 and £48 per session, including and excluding training,

respectively. Despite having equivalent patient contact time,

cognitive behaviour therapy involved more training and

supervision inputs than motivational enhancement therapy, so

respective costs for cognitive behaviour therapy were £81 and

£73.

Individual-level intervention costs were calculated by

multiplying these unit costs by the number of each type of

therapy session attended.

Outcome measures

We linked costs with the trial’s primary outcome measure,

diabetes control as measured by change in HbA1c level between

baseline and 12 months, and QALYs. Although relevant36-item

Short Form utility weights were available by the end of the study,

we additionally calculated EQ5D-based QALYs to maximize

potential for comparisonswithother evidence.Utilityweights for

eachmeasure [9,10]wereattached tohealth states atbaselineand

12 months to calculate QALYs using the total area under the

curve approach with linear interpolation between assessment

points (and baseline adjustment for comparisons) [11].

Analyses

We analysed data using SPSS for Windows release 12.0.1 (SPSS

Inc., Chicago, IL, USA;1989–2001), Stata 8.2 for Windows

(StataCorp., College Station, TX, USA; 1985–2004) and Stata

for Windows 10.1 (StataCorp; 1985–2008).

Costs and outcomes are presented as mean values with

standard deviations. Mean differences and 95% CIs were

obtained by non-parametric bootstrap regressions (1000 repeti-

tions) to account for the non-normal distribution commonly

found in economic data. Although the three groups were

expected to be balanced at baseline, baseline costs and

outcomes could be predictors of follow-up costs. To provide

more relevant treatment-effect estimates [12], the regression on

costs included covariates for the baseline value for the same cost

category and baseline HbA1c. Similarly, regressions on outcomes

included covariates for the baseline value of the same outcome.

All groups were compared against each other in turn, keeping

participants in the randomization group to which they were

assigned regardless of the number ⁄ type of intervention sessions

they attended.

Collecting Client Service Receipt Inventory data by interview,

rather than self-complete, resulted inminimal itemnon-response.

In the few instances of missing items, values were imputed to

enable estimation of cost subtotals ⁄ totals. Where there was

indication of resource use (e.g. duration of contact was provided,

but number of contacts was missing), the mean value for other

users of that resource in the same randomization group at the

same assessment point was assumed. Where there was no such

indication of use, it was assumed not and a zero cost was

allocated for that resource. Where medication name was missing,

but other information (e.g. dose) indicated use, an average

national prescription cost was assumed. Where medication name

was provided without usage quantity, an average national

prescription cost for that particular medication was assumed.

Client Service Receipt Inventory non-responders at 6-month

and ⁄ or 12-month follow-up were excluded from analyses

because both assessments were needed to compute 1-year costs.

Table 1 Motivational enhancement therapy (MET) and cognitive behaviour therapy (CBT) resource inputs and unit costs (£, 2005 ⁄ 2006 prices)

Resources MET unit cost CBT unit cost

Delivery to patient Therapist* contact and non-contact time 24 26

Therapist supervision Therapist and supervisor� contact and non-contact time 22 46

Therapist training Therapist and trainer� contact and non-contact time 1 8

Materials Manual, information sheets, Accu-Test CD-ROM, tape

recorder� and tapes�1 1

Other inputs Therapist time to chase non-attenders 1 < 1

Total cost per 50-min session

(cost assuming 20% higher attendance rate)

49 (46) 81 (73)

Total cost per 50-min session, excluding

training costs (cost assuming 20% higher

attendance rate)

48 (45) 73 (66)

*Therapist salary and on-costs [10] based on a nurse on the mid-point of National Health Service Agenda for Change (NHS AfC) Band 6

(£0.39 per min).

�MET supervisor ⁄ trainer costs [10] were based on a clinical psychiatrist on the mid-point of NHS AfC Band 8A (£0.75 per min); CBT

supervisor ⁄ trainer salary and on-costs [10] were based on a senior CBT therapist on the mid-point of NHS AfC Band 8A (£0.75 per min) and

a junior CBT therapist on the low-point of NHS AfC Band 8A (£0.55 per min).

�Office Depot Business Solutions Catalogue, 2007.

DIABETICMedicine Cost-effectiveness of psychological therapies for Type 1 diabetes • A. Patel et al.


Similarly, outcome calculations required both baseline and

12 months’ values, so participants with either of these missing

were also excluded. Examinations of cost data on their own are

based on available cases regardless of availability of outcomes

data, and vice versa for examinations of outcomes data.

However, cost-effectiveness and cost-utility analyses only

include participants with both cost and relevant outcome data.

Sensitivity analyses

To explore the potential impact of excluding cases with missing

data, we examined characteristics of included and excluded

cases. We also imputed missing 1-year costs and outcomes using

the multiple imputation procedure in Stata for Windows 10.1.

Cost imputations were based on variables expected to predict

follow-up costs: randomization group, baseline age, sex, baseline

HbA1c, baseline value for the same cost category and the number

of therapy sessions attended. Predictor variables for outcome

imputations were the same, except that they included the

baseline value of the same outcome rather than cost. We then

compared mean cost and outcome differences for this imputed

full sample.

Cost-effectiveness and cost utility

With two cost perspectives, three outcomes and three arms,

there were 18 cost–outcome combinations to examine. We

calculated incremental cost-effectiveness ratios (ICERs; mean

cost difference divided by mean outcome difference) for any

combination which showed both significantly higher costs and

outcomes in one group compared with another.

Given difficulties around estimating confidence intervals for

incremental cost-effectiveness ratios, we explored uncertainty

using cost-effectiveness acceptability curves (CEACs) based on

the net-benefit approach [13]. These represent the probability

that one intervention is cost-effective compared with another,

accounting for hypothetical monetary values that policymakers

may place on point improvements in each outcome.

Cost-effectiveness acceptability curves were constructed by first

calculating a series of net benefits for each individual, using the

following formula, wherek represents the monetary value placed

on one additional unit of outcome: net benefit = (k · outcome)

– cost.

We examined k values ranging £0 to £45 000 (in £2500

increments) to cover the £20 000–£30 000 per QALY gain

threshold range currently specified for National Institute for

Health and Clinical Excellence (NICE) decision making in

England and Wales [14]. We then calculated between-group

differences in mean net benefits (for each value of k) using series

of bootstrapped linear regressions (1000 repetitions), which

included covariates for the baseline value of the same cost

category and outcome. We then counted the proportion of times

one group had a greater net benefit than another (for each value

of k) and plotted these proportions (or probabilities) for all 18

cost–outcome combinations.

Results

Participant characteristics and response rates

Of participants, 216 ⁄ 344 (62.8%) had both 6- and 12-month

cost data, i.e. data required for the calculation of 1-year costs.

Those with cost data were on average 2 years older and had

better mean HbA1c levels (9.13% ⁄ 76 mmol ⁄ mol vs. 9.32% ⁄78 mmol ⁄ mol) at 12 months compared with the full study

sample, although differences were not explored statistically

(Table 2).The samplewas further reduced forcost-effectiveness ⁄cost-utility analyses. Of particular note, EQ5D-based analyses

included only 50.4% of the MET group and 36-item Short

Form-based analyses included only 48.8% of the usual-care

group.

Resource use

Resource use was broadly comparable between groups at all

assessments (not tested statistically to avoid multiple testing).

Table 2 Sample characteristics

Full sample

(n = 344)

Subsample with

1-year cost data

(n = 216)

Subsample with

1-year cost data

and HbA1c data

(n = 207)

Subsample with

1-year cost data

and SF-36-based

QALY gain data

(n = 189)

Subsample with

1-year cost data

and EQ5D-based

QALY gain data

(n = 185)

Valid no. Valid no. Valid no. Valid no. Valid no.

Mean age (years) 344 36 216 38 207 38 189 38 185 38

Number male (%) 344 136 (39.5) 216 87 (40.3) 207 83 (40.1) 189 74 (39.2) 185 72 (38.9)

Number female (%) 344 208 (60.5) 216 129 (59.7) 207 124 (59.9) 189 115 (60.8) 185 113 (61.1)

Mean HbA1c at baseline,

% (sd; mmol ⁄ mol)

344 9.63 (1.15; 81) 216 9.60 (1.17; 81) 207 9.58 (1.17; 81) 189 9.52 (1.09; 80) 185 9.52 (1.08; 80)

Mean HbA1c at 12 months,

% (sd; mmol ⁄ mol)

305 9.32 (1.52; 78) 207 9.13 (1.35; 76) 207 9.13 (1.35; 76) 186 9.03 (1.29; 75) 182 9.03 (1.32; 75)

EQ5D, EuroQol 5-dimensional health state index; QALY, quality-adjusted life year; SF-36, 36-item Short Form.



Participants were high users of hospital-based specialist diabetes

services, general practitioner surgery services, chiropody and

opticians (Table 3) and used few other community-based

services.

Costs

Mean intervention costs were £195 for the MET group and £660

for the MET + CBT group (Table 4). While specific follow-ups

showed no cost differences, total 1-year health and social care

costs were higher for both intervention groups compared with

the usual-care group (but did not differ between the two

intervention groups). There were no differences in costs unre-

lated to the interventions, suggesting that the additional costs of

the interventions were neither fully offset by savings elsewhere

nor led to additional costs. Patient ⁄ family and lost productivity

costs, which were small compared with health and social care

costs, did not differ between the groups. Total societal costs

showed no differences, although confidence intervals do suggest

a tendency towards higher costs for both intervention arms

compared with usual care.

Outcomes

There was no significant difference in HbA1c improvement

between the MET and usual-care groups or between the MET

and MET + CBT groups, but the MET + CBT group showed a

significantly greater improvement of 0.45% compared with the

usual-care group (Table 5).

Neither the EQ5D nor the 36-item Short Form suggested

differences in mean QALYs. Despite some quantitative dif-

ferences in results derived from the two measures (with the

EQ5D indicating greater mean total QALYs per group and thus

slightly greater mean differences), both suggested the same

direction of difference.

Sensitivity analyses

The available case analyses are likely to be representative of the

full study sample because imputing missing costs and outcomes

produced similar findings (Tables 4 and 5). The only notable

difference following imputation was a very small QALY

disadvantage (–0.0001 QALYs), rather than advantage (0.003

QALYs) for the MET + CBT group compared with usual care.

This would alter cost-effectiveness conclusions based on that

particular comparison, but the meaningfulness of this is unclear

given the small size and lack of statistical significance of these

differences.

Cost utility and cost-effectiveness

Only one cost–outcome combination showed significant dif-

ferences in both costs and outcomes: the MET + CBT group had

higher health and social care costs (+ £790) and greater HbA1c

improvement (+ 0.45 points) compared with the usual-care Tab

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group, translating into an incremental cost-effectiveness ratio of

£1756. Further incremental cost-effectiveness ratio calculations

were unnecessary as all other cost–outcome combinations

suggested either ‘neutrality’ (no significant differences in costs

and outcomes) or ‘dominance’ (one treatment option intuitively

preferable because of lower costs and better outcomes). Informal

incremental cost-effectiveness ratio calculations for the ‘neutral’

scenarios showed that, compared with usual care, motivational

enhancement therapy plus cognitive behaviour therapy gener-

ally had lower incremental cost-effectiveness ratios than

motivational enhancement therapy alone based on HbA1c

improvements, but higher incremental cost-effectiveness ratios

based on QALYs.

Ata thresholdof£25 000peradditionalQALY(themid-point

of the range specified by the National Institute for Health

and Clinical Excellence), probabilities of cost-effectiveness

for motivational enhancement therapy and motivational

enhancement therapy plus motivational enhancement therapy

compared with usual care and compared with each other were

low (0.33 maximum) from both perspectives (Fig. 1). Cost-

effectiveness acceptability curves based on the 36-item Short

Form and EQ5D were similar.

Probabilities of cost-effectiveness based on HbA1c improve-

ments were high for both interventions, but the implications of

this are unclear given that there is currently no policy-based cost-

effectiveness threshold based on HbA1c.

Discussion

Among several good-quality economic evaluations of various

approaches to management of diabetes and its complications

[15,16], there are none of psychological treatments for

improving glycaemic control in Type 1 diabetes. This

comprehensive, randomized controlled trial-based economic

evaluation suggests that neither motivational enhancement

therapy plus cognitive behaviour therapy nor motivational

enhancement therapy alone is an undisputedly cost-effectiveTab

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HbA1c point improvement, health and social care perspectiveHbA1c point improvement, societal perspectiveQALY gain (SF-36), health and social care perspectiveQALY gain (SF-36), societal perspectiveQALY gain (EQ5D), health and social care perspectiveQALY gain (EQ5D), societal perspective

FIGURE 1 Cost-effectiveness acceptability curves: MET + CBT vs. usual

care. CBT, cognitive behaviour therapy; EQ5D, EuroQol 5-dimensional

health state index; MET, motivational enhancement therapy; QALY,

quality-adjusted life year; SF-36, 36-item Short Form.



treatment approach compared with usual care alone over 1 year.

Both cost perspectives suggested similar conclusions, thus

avoiding potential dilemmas of trade-offs between different

stakeholders.

Conclusions vary between clinical and quality-of-life

outcomes. There is evidence to suggest that, if small HbA1c

improvements are sustained for a reasonable duration, they can

reduce future complications [17,18] and confer significant

healthcare cost savings within a relatively short time [19].

Quality-of-life measures can detect differences among diabetes

subgroups (e.g. UK Prospective Diabetes Study [20]), so we

cannot rule out the possibility that our measures were too

insensitive. However, reaching similar conclusions using two

quality-of-life measures (or with the Diabetes Quality-of-Life

measure [5]) supports the robustness of our conclusions. It also

suggests that providing psychological therapies to this group had

no negative effect on the measured quality-of-life domains. It is

likely that quality-of-life improvements occur in the longer term

and our time horizon was too short to detect these. For example,

a Type 2 diabetes study suggested that a 1% reduction in HbA1c

is equivalent to a 0.4 QALY gain (and £108 cost saving) over

20 years [21]. Another study examining the longer term cost-

effectiveness of professional-directed and patient-centred

diabetes guideline implementation strategies suggested that a

0.2% and 0.3% reduction in HbA1c at 1 year for each group,

respectively, translated into lifetime QALY gains of 0.29 and

0.59, respectively [22]. Different quantifications of QALYs

between measures (which is not unusual—see Grieve et al. [23]

and McCrone et al. [24]) raises important issues about choice of

health-related quality-of-life measures in economic evaluations

and the comparability of variably produced QALYs for

policymaking.

Both interventions increased health and social care costs with

no apparent cost offsets. They paradoxically aimed to increase

diabetes resource use for a difficult-to-treat group, in which

conventional intensive multidisciplinary diabetes care had

already failed to improve glycaemic control; for example, both

therapies included topics aboutmakingbestuseofdiabetes teams

and assertively seeking help to optimize self care. So, while

increased costs elsewhere may have reflected improved self care,

use of needs-appropriate services and investments in long-term

societal gains, such effects were either absent or too subtle to

affect overall care costs.

This study had some limitations. Firstly, while any data

collection biases are likely to be equal across the groups, we

cannot discount the possibility of double-counting if

intervention group participants mistakenly reported therapy

sessions as part of their usual diabetes care, in which case

healthcare costs for those two groups may be overstated and

any potential savings concealed. More generally, the reliability

of self-reported resource use over a 6-month period is unclear.

However, the multi-site nature of this study, broad perspective

(attributable to the breadth of diabetes impacts) and lack of

comprehensive computerized medical records necessitated this

approach. Using shorter recall periods would have required

more frequent assessments (which were infeasible because of

resource constraints as we used interviews to maximize quality)

or assessment gaps. The latter risked finding artificial cost

differences simply because of timing variations in routine

annual patient care reviews—temporary ‘flurries’ of healthcare

activity can occur around these reviews and thus lead to cost

variations depending on inclusion ⁄ exclusion of such periods.

Secondly, longer time horizons may be necessary to identify

relevant longer-term outcomes; for example, quality-of-life

gains may be more evident alongside reductions in future

complications. Finally, the sample size for the economic

evaluation was disappointing despite all reasonable attempts

at follow-up. The imputation method used in the sensitivity

analyses assumed that data were missing at random but, at this

level of missing data, we cannot rule out the possibility of non-

random causes for dropout (e.g. less improvement in outcomes,

negative views of therapy), which may have differently

impacted on both costs and outcomes. The sensitivity

analyses may thus present more optimistic scenarios than

ones which would be presented under more informed cost and

outcome imputations.

At a minimum of £48 636 per QALY gain, neither

intervention met the current cost-effectiveness threshold used

in England and Wales [14]. Longer-term cost-effectiveness,

taking account of possible reductions in complications and

future quality-of-life gains, needs to be evaluated. Other

interventions for Type 1 diabetes which have shown

significant HbA1c improvements in the short to medium term

have generally suggested good value for money in models of

long-term cost-effectiveness. For example: implementing

intensive rather than conventional diabetes management is

suggested to cost an additional $28 661 per life year gained

(lifetime model) [25]; treatment with continuous subcutaneous

insulin infusion compared with multiple daily injections is

estimated to cost £25 648 per QALY gained (lifetime model)

[26]; and a structured treatment and teaching programme

compared with standard practice is estimated to produce a 0.12

QALY gain and £2200 cost saving per patient (10-year model)

[27].

Motivational enhancement therapy plus cognitive behaviour

therapy has potential to provide additional HbA1c improvements

at a lower total additional cost and a greater probability of cost-

effectiveness, despite its greater cost. As our conclusions depend

on the outcome measure chosen, decisions regarding the

provision of nurse-delivered motivational enhancement therapy

and cognitive behaviour therapy for adults with poorly

controlled Type 1 diabetes depend on the relative importance

of these outcomes and of broader aims, such as increasing

diabetes nurse specialists’ skills (in the context of limited supply

of ⁄ access to psychological treatments) and targeting difficult-

to-treat patients.

Competing interests

Nothing to declare.



Acknowledgements

This study was presented at the 45th EASD Annual Meeting in

Vienna, Austria, September–October 2009. The UK Department

of Health’s Health Technology Assessment Programme funded

the study (http://www.nchta.gov.uk project no 01 ⁄ 17 ⁄ 05). We

would like to thank and are indebted to the following: the

participants for their time and commitment in participating in

this study; the diabetes physicians (Professor Stephanie Amiel,

King’s College Hospital; Dr Jake Powrie, Guy’s Hospital; Mrs

Judy Adcock, Lewisham Hospital; Dr Richard Savine, Mayday

Hospital; Dr Robert Davies, Manchester Royal Infirmary;

Professor Phil Wiles, North Manchester General Hospital; and

Dr Ngai Kong, Stepping Hill Hospital, Stockport) who gave

permissionandenduring support; their clinic and laboratory staff

who assisted in recruiting and following up participants; general

practitioners who assisted with data collection; Dr Kirsty

Winkley; Ms Judy Jackson (research psychologist) who did the

recruitment, follow-up and data collection in the Manchester

sites; the Trial Steering Committee members (Professor Glyn

Lewis, Dr Dennis Barnes and Dr Bianca de Stavola) and the

Data Ethics and Monitoring Committee (Professor Graham

Dunn, Professor Robert Peveler and Dr Peter Watkins) for

their intellectual guidance; to Ms Suzanne Roche (cognitive

behaviour therapy therapist, Maudsley Hospital for contributing

to the cognitive behaviour therapy training and manual

development); the nurse therapists who delivered the

treatments; the Clinical Trials Unit, Institute of Psychiatry,

King’s College London for randomization and allocation

concealment; Dr Keith Wiener (North Manchester General

Hospital) for his personal communication about glycated

haemoglobin measures; and Barbara Barrett for invaluable

assistance with the economic analyses. Finally, we would

like to especially acknowledge the contribution of Mr Phil

Dickinson (dietician, Manchester Royal Infirmary), who died

recently; he managed the diabetes database and contributed

enthusiastically to this project and will be greatly missed by his

colleagues.

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Supporting Information

Additional Supporting Information may be found in the online

version of this article:

Appendix S1. Unit cost summary (£, 2005 ⁄ 2006 prices).

Please note: Wiley-Blackwell are not responsible for the content

or functionality of any supporting materials supplied by the

authors. Any queries (other than for missing material) should be

directed to the corresponding author for the article.



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