1. Accounting for Psychological Determinants of Treatment Response in Health Economic
Simulation Models of Behavioural Interventions
A Case Study in Type 1 Diabetes
Jen Kruger1, Alan Brennan1, Praveen Thokala1, Debbie Cooke2, Rod Bond3 and Simon Heller4
1Health Economics and Decision Science, ScHARR, University of Sheffield, UK., 2Department of Epidemiology & Public Health, University College London, UK.,
3School of Psychology, University of Sussex, UK., 4Academic Unit of Diabetes, Endocrinology and Metabolism, University of Sheffield, UK.
Health economic modelling has paid limited attention to incorporating the
effects patients’ psychological characteristics can have on the
effectiveness of a treatment. In attempting to represent the real world this
is a substantial limitation, particularly when modelling diseases that involve
a large element of self-care or when evaluating interventions that aim to
change health behaviours.
The objective of this study was to test the feasibility of incorporating
psychological prediction models of treatment response within an economic
model of a diabetes structured education programme: Dose Adjustment
For Normal Eating (DAFNE).
Introduction
Data from the National Institute for Health Research (NIHR) DAFNE
Research Programme were used to support all analyses*. Three
regression models were used to investigate the relationships between
patients’ baseline psychological characteristics (e.g. beliefs about
diabetes, confidence in performing self-care behaviours, fear of
hypoglycaemia) and their 12-month blood glucose (% HbA1c) response to
DAFNE. The regression prediction models were integrated with a patient-
level simulation model of type 1 diabetes (Sheffield Type 1 Diabetes
Model) to evaluate the cost-effectiveness of two new policies:
1. Providing DAFNE only to predicted responders
2. Offering a follow-up intervention to predicted non-responders
Response was defined as a reduction in HbA1c of 0.5% or more. Both new
policies were compared with current practice of providing DAFNE to all
adults with type 1 diabetes and not offering a follow-up intervention.
The model estimated costs and quality-adjusted life-years (QALYs) over a
50-year time horizon from a UK National Health Service (NHS)
perspective. Deterministic sensitivity analyses were conducted.
Methodology
By collecting data on psychological variables for a subgroup
of patients before an intervention, we can construct
predictive models of treatment response to behavioural
interventions and incorporate these into health economic
simulation models to investigate more complex treatment
policies. Further research using this methodology is
indicated.
Conclusions
• Psychological predictors of treatment response were
successfully integrated with the health economic
simulation model and allowed new treatment policies to
be evaluated.
• The results suggest that providing DAFNE only to
predicted responders is dominated by current practice
(incremental costs ranged from £297 to £616 and
incremental QALYs from –0.112 to –0.209) (see Figure
1).
• This result was insensitive to the psychological
prediction model used and to the majority of sensitivity
analysis assumptions tested (sensitivity analysis results
not shown).
• The results suggest that providing a follow-up
intervention to predicted non-responders dominates
current practice (see Figure 2).
• This result was sensitive to model assumptions
regarding the treatment benefit of the follow-up
intervention (see Figure 2).
Results
• The psychological prediction models had low predictive
power for HbA1c change, suggesting alternative
predictor variables or model functional forms may be
required.
• The results of this study demonstrate that
improvements can be made to the way we model the
cost-effectiveness of interventions in disease areas
where patients’ psychological and behavioural
characteristics are important.
• The next phase of development of the Sheffield Type 1
Diabetes Model is to fully capture parameter
uncertainty in a full probabilistic sensitivity analysis.
Discussion
* This study was funded by the NIHR. This poster presents independent research commissioned by the
NIHR under the Programme for Applied Research. The views expressed in this poster are those of the
authors and not necessarily those of the NHS, the NIHR or the Department of Health.
Figure 1 The cost-effectiveness of providing DAFNE only to predicted
responders vs. current practice
Figure 2 The cost-effectiveness of providing a follow-up intervention
costing the same as the original DAFNE intervention vs. current practice
-£700
-£600
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-£100
£0
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-0.30 -0.20 -0.10 0.00 0.10 0.20 0.30
Averageincrementaldiscountedcosts
Average incremental discounted QALYs
Prediction model A
Prediction model B
Prediction model C
Prediction model A,
-0.25% HbA1c
benefit
Prediction model A,
-0.5% HbA1c benefit
Prediction model A,
-1% HbA1c benefit
Prediction model B,
-0.25% HbA1c
benefit
Prediction model B,
-0.5% HbA1c benefit
Prediction model B,
-1% HbA1c benefit
-£600
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£0
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-0.04 -0.03 -0.02 -0.01 0.00 0.01 0.02 0.03 0.04
Averagediscountedincrementalcosts
Average discounted incremental QALYs
ICER =
£20,000/QALY
Contact: J. Kruger
Postal address: ScHARR, Regents Court, 30 Regent Street, Sheffield S1
4DA, United Kingdom.
Email: j.kruger@shef.ac.uk
Website: www.shef.ac.uk/heds
Contact