Development of the health economic model for type 2 diabetes considering glycaemic variability

This study aimed to develop and validate a health-economic model incorporating glycaemic variability to support the economic evaluation of novel interventions.

We developed a Monte Carlo-based Markov model, primarily using data from the United Kingdom Prospective Diabetes Study. An innovative glycaemic variability module was constructed, with its key parameter being the time in range (TIR). TIR was defined as the percentage of time a patient's glucose level remained within the target range of 3.9-10.0 mmol/L. The module was parameterized based on quantitative clinical evidence. The base model underwent face, internal, and external validation, with performance assessed using the R², root mean square percentage error (RMSPE) and symmetric mean absolute percentage error (SMAPE). To evaluate the structural uncertainty introduced by the new module, we conducted an exploratory scenario analysis in which the incremental improvement in TIR was modelled from 0% to 100%.

The model showed good overall reliability. Specifically, face validity was confirmed, while internal and external validation both demonstrated R² values exceeding 0.95. For the internal and external validation, the RMSPE was 0.328 and 0.347, and the corresponding SMAPE values were 0.175 and 0.279, respectively. The exploratory scenario analysis supported the structural validity of the module. Over the first decade, the cumulative incidence of background retinopathy, microalbuminuria, peripheral vascular disease, and mortality decreased as TIR improved. However, the incidence gaps narrowed in the long run, which was consistent with clinical observations.

This study developed and validated a health-economic model incorporating glycaemic variability, which provides a potentially robust tool to inform economic evaluations of novel interventions.