Cost-utility Analysis of a Novel Oral Cancer Screening Model in Thailand

Purpose: Oral cancer rates have risen globally at an alarming rate. This is notable in Thailand, where oral cancer screening is crucial to reduce the government’s health care burden. To this end, this study aimed to generate a novel oral cancer screening model for early detection and diagnosis, and to analyze its cost-eff ectiveness compared with a non-screening scenario. Methods: We designed a model with three main screening processes, S1, S2, and S3, each involving an individual set of procedures, the last being a biopsy for defi nitive diagnosis. A simulation model was formulated to represent the screening scenario and was used to assess the costs and the subsequent quality-adjusted life years (QALYs). An incremental costeff ectiveness ratio (ICER) was also calculated based on a non-screening scenario. Results: This novel model proved to have an 88% coverage level in oral cancer screening. The ICER calculation was 176,312.65 THB/QALY which was 0.80 times relative to gross domestic product (GDP) per capita in 2022. Conclusion: This novel oral cancer screening shows the highest coverage level and the largest volume of targeted inhabitants that can be reached in Thailand which will be ultimately cost-eff ective compared with late diagnosis. Therefore, this model can be employed as a government policy for the more budget-effi cient early detection and treatment of oral cancer. Key points: i.Development of a novel oral cancer screening model tailored for Thailand, addressing rising incidence rates and the need for early detection to reduce healthcare burdens. ii.Comprehensive cost-utility analysis demonstrating the model’s high coverage, cost-eff ectiveness, and potential to revolutionize oral cancer screening policies in Thailand. iii.Contradiction of previous fi ndings regarding the cost-ineff ectiveness of screening through improvements in screening fl ow, increased cancer incidence rates, and adjustments to cost-eff ectiveness thresholds, ultimately supporting the model’s eff ectiveness and feasibility.