Innovative Technology Policy: Simulation Modelling in Healthcare

Introduction:

The healthcare industry is witnessing a paradigm shift, with simulation modelling playing a crucial role in shaping policies. This tool, which replicates real-world outcomes, is being used extensively to predict disease outcomes, assess impacts of policy changes, and estimate resource use associated with new interventions.

The Role of Simulation Modelling in Chronic Diseases:

In chronic diseases such as diabetes, simulation modelling is particularly beneficial for extrapolating long-term outcomes and costs not captured within the short durations of clinical trials. It also allows for the manipulation of various baseline parameters, which may be useful for policy makers from different jurisdictions.

Challenges with Existing Models:

Despite the well-validated results of existing models such as the Core Diabetes Model (CDM), these models are not always suitable for health technology assessment submissions in several jurisdictions, which require open-source code or special permission to use models that are not in Microsoft Excel or other high-level software.

The Introduction of the DEDUCE Model:

To aid in the economic evaluation of diabetes interventions, the DEtermination of Diabetes Utilities, Costs, and Effects (DEDUCE) model was developed. This model, built entirely within Microsoft Excel, offers transparency and the benefit of a more recent risk engine for type 2 diabetes.

Cost Implications of Diabetes Interventions:

The economic implications of diabetes interventions are significant and multifaceted. From direct costs associated with medical care and treatment to indirect costs due to productivity loss and disability, the financial burden of diabetes is substantial. Moreover, the costs associated with managing complications related to diabetes, such as cardiovascular disease and kidney disease, add to the economic strain. The DEDUCE model aids in the economic evaluation of diabetes interventions. This is offering a comprehensive view of the cost-effectiveness of various treatments and technologies.

Differences in Type 1 and Type 2 Diabetes Results:

The DEDUCE model uses different sets of risk equations to predict outcomes for people with type 1 and type 2 diabetes. For type 2 diabetes, the model uses the Risk Equations for Complications Of type 2 Diabetes (RECODe), which has been validated extensively. On the other hand, for type 1 diabetes, the model uses the Sheffield type 1 Diabetes Model equations. The model’s performance varied between the two types of diabetes, with a generally higher predictive agreement for type 2 diabetes outcomes. This highlights the need for further research and refinement of risk equations for type 1 diabetes.

Assessing the Accuracy and Validity of the DEDUCE Model:

A recent study compared the DEDUCE model’s predictions with real outcomes from various external studies and trials to evaluate its accuracy and validity. The model generally excelled in cross-validation. In several comparisons with historical external models, it ranked either first or second.

Conclusion:

The DEDUCE model is a powerful tool for predicting diabetes-related complications and evaluating the cost-effectiveness of diabetes interventions. Future work to compare these results in a new Mount Hood challenge may bolster these results, providing valuable insight into the cost-effectiveness of diabetes technologies for various stakeholders.