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Background & Aims: Nationwide organized gastric cancer (GC) screening programs have been running for decades in South Korea and Japan. This study conducted a quasi-experimental analysis to assess the population impact of these programs on GC mortality. Methods: We used the flexible synthetic control method (SCM) to estimate the effect of the screening programs on age-standardized GC mortality and other upper gastrointestinal (UGI) diseases (esophageal cancer and peptic ulcer) among people aged ≥40 years. World Health Organization mortality data and country-level covariates from the World Bank and the Global Burden of Diseases study were used for the analyses. We compared postintervention trends in outcome with the counterfactual trend of the synthetic control and estimated average postintervention rate ratios (RRs) with associated 95% confidence intervals (CIs). A series of sensitivity analyses were conducted. Results: The preintervention fits were acceptable for the analyses of South Korea and Japan's GC mortality but poor for Japan's other UGI disease mortality. The average postintervention RRs were 0.83 (95% CI, 0.71–0.96) for GC mortality and 0.72 (95% CI, 0.57–0.90) for other UGI disease mortality in South Korea. The RR reached 0.59 by the 15th year after the initiation of nationwide screening. For Japan, the average RRs were 0.97 (95% CI, 0.88–1.07) for GC mortality and 0.93 (95% CI, 0.68–1.28) for other UGI disease mortality. Sensitivity analysis reveals the result for Japan may potentially be biased. Conclusions: South Korea's nationwide GC screening has apparent benefits, whereas the Japanese program's effectiveness is uncertain. The experiences of South Korea and Japan could serve as a reference for other countries.

Background: Identification of individuals at elevated risk can improve cancer screening programmes by permitting risk-adjusted screening intensities. Previous work introduced a prognostic model using sex, age and two preceding faecal haemoglobin concentrations to predict the risk of colorectal cancer (CRC) in the next screening round. Using data of 3 screening rounds, this model attained an area under the receiver-operating-characteristic curve (AUC) of 0.78 for predicting advanced neoplasia (AN). We validated this existing logistic regression (LR) model and attempted to improve it by applying a more flexible machine-learning approach. Methods: We trained an existing LR and a newly developed random forest (RF) model using updated data from 219,257 third-round participants of the Dutch CRC screening programme until 2018. For both models, we performed two separate out-of-sample validations using 1,137,599 third-round participants after 2018 and 192,793 fourth-round participants from 2020 onwards. We evaluated the AUC and relative risks of the predicted high-risk groups for the outcomes AN and CRC. Results: For third-round participants after 2018, the AUC for predicting AN was 0.77 (95% CI: 0.76–0.77) using LR and 0.77 (95% CI: 0.77–0.77) using RF. For fourth-round participants, the AUCs were 0.73 (95% CI: 0.72–0.74) and 0.73 (95% CI: 0.72–0.74) for the LR and RF models, respectively. For both models, the 5% with the highest predicted risk had a 7-fold risk of AN compared to average, whereas the lowest 80% had a risk below the population average for third-round participants. Conclusion: The LR is a valid risk prediction method in stool-based screening programmes. Although predictive performance declined marginally, the LR model still effectively predicted risk in subsequent screening rounds. An RF did not improve CRC risk prediction compared to an LR, probably due to the limited number of available explanatory variables. The LR remains the preferred prediction tool