Computer simulation models of pre-diabetes populations: a systematic review protocol.

Area of Science:

• Health economics
• Epidemiology
• Biostatistics

Background:

• Prediabetes significantly increases the risk of developing type 2 diabetes, necessitating effective preventive strategies.
• Cost-effectiveness analyses are crucial for implementing interventions in prediabetes populations.
• Decision models are vital for forecasting long-term outcomes and costs associated with prediabetes.

Purpose of the Study:

• To identify and critically appraise recent computer simulation and decision models for prediabetes.
• To evaluate the quality, data inputs, and validation of existing models.
• To identify knowledge gaps and challenges in model-based economic evaluations for prediabetes.

Main Methods:

• Systematic review of studies published between 2000 and 2016.
• Searches conducted in MEDLINE, Embase, EconLit, and NHS Economic Evaluation Database.
• Data extraction and quality assessment using predefined pro forma and checklist by two independent reviewers.

Main Results:

• Narrative synthesis of identified studies focusing on model structure, quality, and validation.
• Assessment of models evaluating interventions, risk stratification, and screening in prediabetes.
• Discussion of the strengths and limitations of current modeling approaches.

Conclusions:

• There is a need for robust, validated decision models to support evidence-based implementation and reimbursement of prediabetes interventions.
• Future research should address identified knowledge gaps to enhance the credibility and utility of economic evaluations in prediabetes care.
• Improved modeling is essential for optimizing public health strategies and resource allocation in diabetes prevention.