{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T14:11:14Z","timestamp":1785161474185,"version":"3.55.0"},"reference-count":28,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T00:00:00Z","timestamp":1768953600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82073492"],"award-info":[{"award-number":["82073492"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>China\u2019s internal migration poses major challenges to cancer surveillance systems that rely on household-registered populations (HRP). Excluding migrants can lead to biased incidence estimates and misinformed public health planning.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study estimated the prostate cancer incidence among the resident population (RP) using a Bayesian integrated nested Laplace approximation with stochastic partial differential equation (INLA-SPDE) model, incorporating inter-provincial migrant weights, and explored spatial clustering.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The findings revealed a substantial interprovincial migrant population of 73,459,708 individuals, based on data from the 2016 China Migrants Dynamic Survey conducted by the Migrant Population Service Center, National Health Commission of China. With Shanghai and Beijing showing relatively high difference proportions of 40.7 and 37.9%, respectively. Nationally, the differences in estimated incidence between RP and HRP were substantial, ranging from 1.1\/100,000 in Guizhou (HRP 5.4\/100,000, RP 6.5\/100,000) to \u22129.9\/100,000 in Shanghai (HRP 27.6\/100,000, RP 17.7\/100,000). The analysis estimated that the provinces with the largest differences between incident cases among RP and HRP were Guangdong (469 cases, 9.7% relative to HRP cases) and Jiangsu (305 cases, 6.2% relative to HRP cases). Inflow provinces tended to have their cases underestimated and their incidence overestimated, whereas the opposite pattern was observed in outflow provinces. Incidence exhibits significant spatial clustering, with higher incidence in eastern coastal metropolitan areas and lower incidence in central-western regions and northeastern parts.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Migration substantially influences prostate cancer incidence surveillance in China. Incorporating migrant-adjusted estimates provides a more accurate representation of disease burden, supports equitable allocation of healthcare resources, and offers methodological guidance for improving cancer registration systems in regions with high population mobility.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2025.1735390","type":"journal-article","created":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T06:41:49Z","timestamp":1768977709000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Migration-adjusted prostate cancer incidence in China: a population-based epidemiological analysis"],"prefix":"10.3389","volume":"13","author":[{"given":"Yu","family":"Zhang","sequence":"first","affiliation":[{"name":"Chinese Center for Endemic Disease Control, Harbin Medical University","place":["Harbin, China"]},{"name":"NHC Key Laboratory of Etiology and Epidemiology, Harbin Medical 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for Endemic Disease Control, Harbin Medical University","place":["Harbin, China"]},{"name":"NHC Key Laboratory of Etiology and Epidemiology, Harbin Medical University","place":["Harbin, China"]},{"name":"Joint Key Laboratory of Endemic Diseases (Harbin Medical University, Guizhou Medical University, Xi\u2019an Jiaotong University)","place":["Harbin, China"]},{"name":"School of Public Health, Qiqihar Medical University","place":["Qiqihar, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyu","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Harbin Medical University Cancer Hospital","place":["Harbin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liangfang","family":"Xue","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Harbin Medical University Cancer Hospital","place":["Harbin, 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