In China, the incidence rates of major gynecological cancers have increased consistently over the past decade. Spatial epidemiological analyses are crucial for informing precision prevention strategies through visual risk mapping. However, previous studies, primarily based on residential registry data, often overlook migrant populations, potentially introducing selection bias. We conducted a countrywide, district/county-level spatial analysis of cervical, uterine corpus, and ovarian cancer incidence in China, utilizing Bayesian model-derived estimates that adjusted for internal migration. Global and local Moran's I statistics were employed to detect and visualize significant spatial clustering patterns, specifically high-high (HH) clusters (areas with high incidence surrounded by other high-incidence areas) and low-low (LL) clusters (areas with low incidence surrounded by other low-incidence areas). Significant positive spatial autocorrelation was detected for the three cancers(P < 0.000001). For cervical cancer, 836 districts/counties showed HH clustering (predominantly in central and southeastern coastal regions), while 1013 displayed LL clustering (concentrated in northeastern, northern, and western China). For uterine corpus cancer, 899 districts and counties formed HH clusters, notably in northeastern, northern, and southeastern coastal areas, while 982 districts and counties showed LL clusters, primarily in central and southwestern regions. For ovarian cancer, 794 districts and counties demonstrated HH clustering, with concentrations in northeastern, northern, and southeastern coastal zones, while 857 districts and counties exhibited LL clustering, primarily distributed across eastern, central-southern, and southwestern China. As the first countrywide spatial study to incorporate migration-adjusted data, our findings reveal marked geographic disparities in gynecological cancer incidence in China. These results underscore the necessity for region-specific prevention strategies and highlight that resource allocation must account for population mobility. This study provides a replicable framework for other regions facing similar migration-related health challenges.
Xue et al. (Sat,) studied this question.