Purpose: To establish methods to incorporate human behaviour modelling into a healthcare simulation model of diabetes and examine what value is added to the results of a cost-effectiveness analysis by including psychological and behavioural predictors.
Method: The behavioural modelling was informed by questionnaire data collected as part of the National Institute for Health Research programme on the Dose Adjustment For Normal Eating (DAFNE) diabetes educational intervention. Psychological, clinical and demographic data were collected from DAFNE patients at baseline and at 3-, 6- and 12-month follow-up. Statistical methods were developed to investigate whether specific patient characteristics predict those who do well or poorly after attending the DAFNE educational intervention. Structural equation modelling (SEM) was used to investigate the causal links between psychological variables and the key outcome variable (HbA1c) over the first 6 months. Piecewise growth modelling (PGM) was used to analyse the nonlinear change in HbA1c over the 12-month follow-up period. PGM allowed estimation of mean rates of change and patient-level variability in change and exploration of key correlates of change. The results of the behavioural modelling are embedded within a patient-level simulation model of the long-term progression of type 1 diabetes and the cost-effectiveness of DAFNE. The simulation model uses annual risk of developing diabetic complications to predict mortality, morbidity, costs and quality-adjusted life-years. Patient-level risks are estimated from a number of predictive clinical and demographic factors with HbA1c as the key driver of the model. The integrated framework of behavioural and cost-effectiveness modelling is used to explore the relationship between heterogeneity in patients’ behavioural responses to DAFNE and the cost-effectiveness of the DAFNE intervention.
Result: Initial results from SEM and PGM analyses suggest a number of factors which differentially affect change in HbA1c over time. We present a comparison of the cost-effectiveness of DAFNE treatment for a number of subgroups defined both in terms of baseline HbA1c and psychological covariates.
Conclusion: The framework appears to be successful in integrating behavioural and clinical outcomes to inform the cost-effectiveness model. The benefits and barriers to incorporating variability in patients’ behavioural response to treatment into a cost-effectiveness model are highlighted. Finally we discuss the implications of this study for selection of variables and sample size required for future studies integrating psychological, clinical and cost-effectiveness data.
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