Gastric cancer (GC) is the fourth most common type of cancer and the second leading cause of cancer-related deaths worldwide. To detect the spatial trends of GC risk based on hospital-diagnosed patients, this study presented a selection probability model and integrated it into the Bayesian spatial statistical model. Then, the spatial pattern of GC risk in Shanxi Province in north central China was estimated. In addition, factors influencing GC were investigated mainly using the Bayesian Lasso model. The spatial variability of GC risk in Shanxi has the conspicuous feature of being ‘high in the south and low in the north’. The highest GC relative risk was 1.291 (95% highest posterior density: 0.789–4.002). The univariable analysis and Bayesian Lasso regression results showed that a diverse dietary structure and increased consumption of beef and cow milk were significantly (p ≤ 0.08) and in high probability (greater than 68%) negatively associated with GC risk. Pork production per capita has a positive correlation with GC risk. Moreover, four geographic factors, namely, temperature, terrain, vegetation cover, and precipitation, showed significant (i68%) negatively associated with GC risk. Pork production per capita has a positive correlation with GC risk. Moreover, four geographic factors, namely, temperature, terrain, vegetation cover, and precipitation, showed significant (p < 0.05) associations with GC risk based on univariable analysis, and associated with GC risks in high probability (greater than 60%) inferred from Bayesian Lasso regression model.
PREDICTIVE MODEL SELECTION CRITERIA FOR BAYESIAN LASSO REGRESSION
