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Curr Med Res Opin. 2004 Aug;20 Suppl 1:S5-26.

The CORE Diabetes Model: Projecting long-term clinical outcomes, costs and cost-effectiveness of interventions in diabetes mellitus (types 1 and 2) to support clinical and reimbursement decision-making.

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CORE--Center for Outcomes Research, Basel, Switzerland.



We have developed an Internet-based, interactive computer model to determine the long-term health outcomes and economic consequences of implementing different treatment policies or interventions in type 1 and type 2 diabetes mellitus. The model projects outcomes for populations, taking into account baseline cohort characteristics and past history of complications, current and future diabetes management and concomitant medications, screening strategies and changes in physiological parameters over time. The development of complications, life expectancy, quality-adjusted life expectancy and total costs within populations can be calculated.


The model is based on a series of sub-models that simulate important complications of diabetes (cardiovascular disease, eye disease, hypoglycaemia, nephropathy, neuropathy, foot ulcer, amputation, stroke, ketoacidosis, lactic acidosis and mortality). Each sub-model is a Markov model using Monte Carlo simulation incorporating time, state, time-in state, and diabetes type-dependent probabilities derived from published sources. Analyses can be performed on cohorts with type 1 or type 2 diabetes. Cohorts, defined in terms of age, gender, baseline risk factors and pre-existing complications, can be modified or new cohorts defined by the user. Economic and clinical data in the model can be edited, thus ensuring adaptability by allowing the inclusion of new data as they become available; creation of country- or provider-specific versions of the model; and allowing the investigation of new hypotheses.


The CORE Diabetes Model allows the calculation of long-term outcomes, based on the best data currently available. Diabetes management strategies can be compared in different patient populations in a variety of realistic clinical settings, allowing the identification of efficient diabetes management strategies.

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