Asian Journal of Probability and Statistics

Global diabetes incidence shows substantial heterogeneity across regions and over time, which complicates reliable modelling, comparisons, and policy planning. This study presents an uncertainty-decomposed generalized linear mixed model (UD-GLMM) framework that extends classical mixed-effects modelling by partitioning the response into three interpretable components: signal (Truth), uncertainty (Indeterminacy), and contradiction or noise (Falsity). The analysis used data from the NCD-RisC Global Diabetes Repository for 1990--2022, comprising 528 region‒year observations across eight global regions and stratified by sex. The proposed framework was compared with conventional generalized linear model (GLM), generalized mixed model (GMM), and generalized linear mixed model (GLMM) approaches. Model performance was assessed via the Akaike information criterion, Bayesian information criterion, mean absolute error, root mean squared error, cross-validation, and alternative data-partitioning schemes. The UD-GLMM produced stable and improved predictive performance, with the 80--10--10 partition yielding MAE = 0.00557, RMSE = 0.00736, R-squared = approximately 0.85, Theil's U = 0.00178, and concordance above 0.92. Lagged prevalence was the principal predictor of current prevalence, indicating strong temporal persistence, whereas lagged treatment coverage showed a weak overall association but moderated the relationship between past and current prevalence. Mediation analysis indicated that approximately 10% of the temporal effect was transmitted through treatment coverage, whereas most of the effect remained direct. Overall, the findings suggest that uncertainty decomposition can strengthen the interpretability of mixed-effects epidemiological modelling by distinguishing reliable signals from indeterminate and contradictory variations. The framework provides a transparent comparative approach for global diabetes surveillance and for identifying where prediction certainty, regional heterogeneity, and treatment-related dynamics require careful interpretation.