OBJECTIVES We have developed a model to evaluate type-2 diabetes prevention interventions. The model allows flexible layering of multiple interventions in order to determine the optimal combination of strategies for maximal cost-effectiveness. Our objective was to demonstrate the utility of the model for analysis of multiple interventions in different population sub-groups. METHODS A model of type-2 diabetes prevention was developed using a micro-simulation framework. Individual patients have simulated trajectories of metabolic factors such as BMI, HbA1c, systolic blood pressure and total cholesterol, and can be diagnosed with diabetes, cardiovascular disease, cancer, osteoarthritis or microvascular complications of diabetes over the course of a lifetime. Interventions targeting metabolic factors influence the likelihood of patients developing ill health and dying prematurely. Five interventions were chosen for layering analyses: soft drinks taxation; retail policy; a workplace intervention; an educational intervention aimed at deprived individuals; and a diabetes screening programme followed by intensive intervention for high risk individuals. Eight different intervention combinations were modelled, using the assumption of either additive, synergistic or antagonistic effects in individuals subject to multiple interventions. RESULTS All interventions generated cost-savings and QALY gains, with the screening intervention performing particularly well, followed by the soft drinks tax. Combining interventions results in roughly additive effects; this holds whether the layering is additive, synergistic or antagonistic due to the relatively low proportions of individuals subject to multiple interventions. Certain pairs of interventions were found to be almost as cost-effective as combining all five interventions, in particular, a soft drinks tax combined with a screening programme is likely to be one of the most cost-effective options. CONCLUSIONS The SPHR Diabetes Model is a useful tool for analysing the cost-effectiveness of different diabetes prevention interventions, either singly or in combination. This will enable development of intervention strategies tailored to the needs of the target population.