The estimated incidence of uterine corpus cancers among permanent residents in mainland China using Bayesian spatial modeling

Cancer registration in mainland China traditionally focuses on household-registered residents (HRR) and does not include the migrant population among permanent residents (PR), leading to significant selection bias. Estimating incidence among permanent residents provides a less biased and more representative measure of the true incidence. We developed a Bayesian Integrated Nested Laplace Approximation with Stochastic Partial Differential Equation model, incorporating inter-provincial migrant population weights to estimate uterine corpus cancer incidence among permanent residents. The findings revealed a substantial interprovincial migrant population of 67,509,881 individuals, with Shanghai and Beijing showing relatively high difference proportions of 39.6% and 37.0%, respectively. Nationally, the differences in estimated uterine corpus cancer incidence between female PR and HRR were marginal, ranging from 0.2/100,000 in Qinghai to - 0.4/100,000 in Shanghai. The analysis estimated that the provinces with the largest differences between incident cases among female PR and HRR were Henan (- 899 cases, 15.7%) and Guangdong (630 cases, 13.7%). This research holds significant implications for countries relying on HRR-based cancer registration system, particularly those contending with substantial migrant populations. The estimated differences in uterine corpus cancer incidence between PR and HRR provide crucial data support for optimizing prevention strategies and enabling precise allocation of regional healthcare resources.