International cost-effectiveness analysis evaluating endoscopic screening for gastric cancer for populations with low and high risk

Background Gastric cancer remains one of the 3 most common causes of cancer death worldwide. Understanding the health and economic factors that affect screening cost-effectiveness in different countries will help address when and where it makes most sense to screen for gastric cancer. Methods We performed a cost-effectiveness analysis using a Markov model to compare screening and surveillance strategies for gastric cancer in Brazil, France, Japan, Nigeria, and the United States. Primary outcome was the incremental cost-effectiveness ratio. We then performed a sensitivity analysis to determine how each variable affected the overall model. Results In all countries, the most cost-effective strategies, measured by incremental cost-effectiveness ratio relative to no screening, were screening every 10 years, surveillance of high- and low-risk patients every 5 and 10 years, respectively, and screening every 5 years. Only Japan had at least one cost-effective screening strategy. The most important variables across different screening strategies and countries were starting age of screening, cost of endoscopy, and baseline probability of local gastric cancer at time of diagnosis. Conclusions Our model suggests that screening for gastric cancer is cost-effective in countries with higher incidence and lower costs of screening, but screening may still be a viable option in high-risk populations within low incidence countries.

Automated summary

This study analyzed the cost-effectiveness of screening and surveillance strategies for gastric cancer in Brazil, France, Japan, Nigeria, and the United States. The most cost-effective strategies were identified as screening every 10 years, surveillance every 5 and 10 years for high- and low-risk patients, and screening every 5 years. Starting age of screening, cost of endoscopy, and baseline probability of local gastric cancer were found to be important variables affecting the overall model.