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Incorporating psychosocial 2011 KRUGER.pdf (56.12 kB)

Incorporating psychosocial characteristics in cost-effectiveness modelling of Type 1 diabetes

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posted on 2020-02-25, 10:40 authored by J. Kruger, Alan BrennanAlan Brennan, Praveen ThokalaPraveen Thokala, P. Fitzgerald, Simon HellerSimon Heller
The Dose Adjustment for Normal Eating (DAFNE) course is a structured education programme that aims to teach individuals with Type 1 diabetes to change their self-care behaviours by estimating the carbohydrate content of food and adjusting their insulin doses accordingly in order to maintain acceptable metabolic control. DAFNE has been found to improve quality of life and glycosylated haemoglobin (HbA1c) levels in UK Type 1 diabetes patients1 and a cost-effectiveness modelling analysis concluded that DAFNE was cost-effective and would pay for itself within four years2 . As with the majority of other economic models in Type 1 diabetes, this analysis had a clinical rather than behavioural focus. The progression of diabetes and its complications was modelled, with clinical and sociodemographic factors as the model inputs, to predict costs and quality-adjusted life years (QALYs) based on the long-term incidence of diabetic complications. Despite positive clinical and cost-effectiveness outcomes from DAFNE there is variability in how patients respond, with some patients continuing to experience problems with high HbA1c levels or frequent hypoglycaemia after receiving the intervention. There is currently no method for predicting how an individual patient will respond to the DAFNE course. As part of a national DAFNE research team we are developing a new economic model of Type 1 diabetes that incorporates psychosocial and behavioural patient characteristics as predictive variables. Our study aims to account for heterogeneity in patient response to DAFNE within the model and investigate how predictions can be made prior to or shortly after the DAFNE course about how a patient’s metabolic control will be affected by the intervention.



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