Cost-Benefit Analysis Framework for Assessing Surgery Waiting Lists in Data-Scarce Settings: The Case of Egypt

OBJECTIVES: Elective surgery waiting lists increase mortality, surgery costs, and productivity loss for patients and caregivers. In data-scarce settings, evaluating interventions to reduce waiting times is challenging. This study aims to develop a simple, comprehensive cost-benefit analysis framework to assess the effectiveness of reducing elective surgery waiting times, focusing on Egypt.

METHODS: A Targeted literature review on economic evaluations related to reducing waiting times was conducted. Then, we developed a draft framework for the model and analysis, and it was validated by modelling experts. Another focus group discussion was conducted to finalize and validate the framework. The model structure depends on the available local data, in addition to internationally published data for some input parameters that were localized to the local country settings.

RESULTS: A cost-benefit model was created to suit data-scarce settings. Key parameters included costs, health state utilities, productivity loss, and population metrics. The model included three patient cohorts: who did not wait for surgery or waited for only a short time, who died while in the waiting list for the surgery , and who died due to missing the surgery or missed the surgery due to ineligibility related to the delay. The model estimates direct, indirect costs -of both patients and caregivers- and Quality-Adjusted Life Years (QALYs).The framework was then applied to evaluate five types of surgeries in Egypt, using both public healthcare payer and societal perspectives.

CONCLUSIONS: The model's design allows it to capture the value of reducing waiting times across different types of surgeries, even in data-scarce settings. By avoiding a focus on disease-specific models, this approach ensures versatility. The model can provide efficient answers to inform evidence-based decision-making about reducing waiting times for various surgeries, regardless of the specific context or available data.