OBJECTIVES: The Disease Elimination Life Table Analysis (DELTA) approach allows deterministic modelling of interventions which affect the incidence of several diseases, such as smoking cessation, hormone therapy and diabetes prevention, without causing the number of health states to explode. To analyse the differences of applying the DELTA approach to an individual patient simulation model for modelling diabetes. METHODS: Five diseases are modelled; macrovascular disease (including MI's and strokes), heart failure, retinopathy, renal disease and peripheral vascular disease. The price for modelling these diseases deterministically is three assumptions concerning independence: the probability to get a disease (to die) in sub-model A is independent of the probability to get a disease (to die) in sub-model B. The same United Kingdom Prospective Diabetes Study (UKPDS) risk equations are used in the patient simulation model as in the DELTA model. Using both models estimates are obtained and compared concerning life expectancies and the time in different diseases. Probabilistic sensitivity analyses are carried out to obtain confidence intervals surrounding the estimates. RESULTS: The expected life time QALY's for females of ages 47, 52, 57, and 62 in the UKPDS and DELTA models are 20.2, 17.9, 15.7 and 13.5, and 20.1, 17.6, 15.1, and 13.2 respectively. Similarly for males the expected life time QALY's in both models are 19.0, 16.2, 13.6, and 11.1 and 18.9, 16.0, 13.3, and 10.34 respectively. The time needed for a probabilistic sensitivity analysis with 100 draws from the uncertainty distribution is approximately 10 seconds. CONCLUSION: The DELTA approach offers a flexible way to model multiple diseases at the same time. The underlying independency assumptions - which may restrict its need – do not seem to affect the outcomes in the case of modelling diabetes. The loss in terms of subtlety seem to be outweighed by the gains in clarity and computational speed.