Evidence map›Paper›PMID 40414901›Full record

ArticleScientific reports2025

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

Shuxiu Hao, Liyuan Guo, Sihong Guo, Chen Feng, Huixin Sun, Linlin Du, Guijin Li, Cheng Wang, Yu Zhang, Cunqi Lv and 6 more

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Shuxiu Hao *Institute of Keshan Disease, Chinese Center for Endemic Disease Control, Harbin Medical University, Harbin, 150081, People's Republic of China.
Liyuan Guo *Department of Gynecological Oncology, Harbin Medical University Cancer Hospital, Harbin, 150081, People's Republic of China.
Sihong Guo *Department of Gynecological Oncology, The First Affiliated Hospital, Heilongjiang University of Chinese Medicine, Harbin, 150006, People's Republic of China.
Chen FengInstitute of Keshan Disease, Chinese Center for Endemic Disease Control, Harbin Medical University, Harbin, 150081, People's Republic of China.
Huixin SunHeilongjiang Cancer Center, Harbin Medical University Cancer Hospital, Harbin, 150081, People's Republic of China.
Linlin DuInstitute of Keshan Disease, Chinese Center for Endemic Disease Control, Harbin Medical University, Harbin, 150081, People's Republic of China.
Guijin LiInstitute of Keshan Disease, Chinese Center for Endemic Disease Control, Harbin Medical University, Harbin, 150081, People's Republic of China.
Cheng WangInstitute of Keshan Disease, Chinese Center for Endemic Disease Control, Harbin Medical University, Harbin, 150081, People's Republic of China.
Yu ZhangInstitute of Keshan Disease, Chinese Center for Endemic Disease Control, Harbin Medical University, Harbin, 150081, People's Republic of China.
Cunqi LvInstitute of Keshan Disease, Chinese Center for Endemic Disease Control, Harbin Medical University, Harbin, 150081, People's Republic of China.
Qingyu ZengInstitute of Keshan Disease, Chinese Center for Endemic Disease Control, Harbin Medical University, Harbin, 150081, People's Republic of China.
Jiacheng LiInstitute of Keshan Disease, Chinese Center for Endemic Disease Control, Harbin Medical University, Harbin, 150081, People's Republic of China.
Xinshu WangNanchang University Queen Mary School, Nanchang, 330031, People's Republic of China.
Tong WangInstitute of Keshan Disease, Chinese Center for Endemic Disease Control, Harbin Medical University, Harbin, 150081, People's Republic of China. wangtong@ems.hrbmu.edu.cn.
Liping TangDepartment of Gynecological Oncology, Harbin Medical University Cancer Hospital, Harbin, 150081, People's Republic of China. TangLP73@163.com.cn.
Qi LiDepartment of Radiation Oncology, Harbin Medical University Cancer Hospital, Harbin, 150081, People's Republic of China. liqi@ems.hrbmu.edu.cn.

Funding

National Natural Science Foundation of China 82073492
6 · The paper itself

Abstract

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.

Indexed as

Uterine NeoplasmsAdultAgedBayes TheoremChinaFemaleHumansIncidenceMaleMiddle AgedRegistriesEstimationIncidenceINLA-SPDE modelingPermanent residentsUterine corpus cancer

Identifiers

PMID40414901
PMCPMC12104362

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.