Determining optimal screening policies using decision trees and spreadsheets

Area of Science:

• Health economics
• Public health screening
• Decision analysis

Background:

• Screening programs aim to detect diseases early but incur significant costs.
• Determining the optimal screening proportion is crucial for cost-effective healthcare delivery.
• Existing models may not fully capture the complexities of cost-benefit analysis in screening.

Purpose of the Study:

• To identify the ideal proportion of a population to screen to achieve minimum overall costs.
• To develop a flexible model for analyzing the economic impact of varying screening levels.
• To provide a framework for exploring the sensitivity of cost-minimization to different parameters.

Main Methods:

• A decision tree approach was employed, incorporating probabilities and costs of all potential outcomes.
• Models were specified to estimate disease prevalence in both screened and non-screened populations.
• A generalized microcomputer spreadsheet implementation allowed for exploration of sensitivity and optimality.

Main Results:

• The study presents a method for calculating the cost-minimizing screening proportion.
• The spreadsheet model facilitates the analysis of various screening parameters and their impact on costs.
• A numerical example demonstrates the practical application and interpretation of the results.

Conclusions:

• The proposed decision tree approach effectively determines the optimal screening proportion for cost minimization.
• The developed model offers a valuable tool for health policy and resource allocation in screening programs.
• Sensitivity analysis is essential for understanding the robustness of optimal screening strategies under varying conditions.