[Applications of microsimulation model for cost-effectiveness analysis on screening in epidemiology]

Area of Science:

• Epidemiology
• Health Economics
• Biostatistics

Background:

• Traditional models like Markov may not efficiently address complex diseases.
• Heterogeneous individual characteristics and disease histories pose challenges in epidemiological cost-effectiveness analysis.
• Accurate parameter estimation and model transparency are crucial for public health decision-making.

Purpose of the Study:

• To introduce the principles, implementation, and analytic methods of microsimulation models.
• To discuss the application of microsimulation in cost-effectiveness analysis for screening programs.
• To highlight critical considerations for developing and interpreting microsimulation models in public health.

Main Methods:

• Microsimulation modeling using individual participant data to estimate population transition probabilities.
• Application of the model to a case study on microalbuminuria screening for chronic kidney disease in the US.
• Detailed discussion of model development, analysis, and results interpretation within the case study.

Main Results:

• Microsimulation models effectively handle individual heterogeneity and dynamic disease processes.
• The model provides insights into complex health problems that simpler models cannot address.
• The case study demonstrated the practical application and critical points of using microsimulation for screening cost-effectiveness.

Conclusions:

• Microsimulation models offer a robust approach for cost-effectiveness analysis of screening programs, especially for complex conditions.
• Future research should prioritize parameter accuracy, model transparency, and adherence to reporting standards for evidence-informed public health decisions.
• This approach enhances the understanding of disease dynamics and individual risk factors in epidemiological studies.