Evidence map›Paper›PMID 42755586›Full record

ArticleFrontiers in oncology2026

Spatial-temporal epidemiological characteristics of thyroid cancer mortality in China cancer registration areas: analysis based on multiple models.

Jiaqi Yang, Zhenhao Zheng, Jianjiang Shao, Yueze Gong, Yu Zhang, Han Wang, Yunhua Hu, Hao Liu, Wen Liu, Yizhong Yan

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Article in Frontiers in oncology, 2026. 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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5 · Who and what money

Authors and funding

10 authors.

Jiaqi Yang *Shihezi University School of Public Health, Shihezi, Xinjiang, China.
Zhenhao Zheng *Department of Ultrasound, the First Affiliated Hospital of Shihezi University, Shihezi, Xinjiang, China.
Jianjiang ShaoShihezi University School of Public Health, Shihezi, Xinjiang, China.
Yueze GongShihezi University School of Clinical Medical, Shihezi, Xinjiang, China.
Yu ZhangShihezi University School of Public Health, Shihezi, Xinjiang, China.
Han WangShihezi University School of Clinical Medical, Shihezi, Xinjiang, China.
Yunhua HuShihezi University School of Public Health, Shihezi, Xinjiang, China.
Hao LiuDepartment of Gastroenterology, the First Affiliated Hospital of Shihezi University, Shihezi, Xinjiang, China.
Wen LiuDepartment of Ultrasound, the First Affiliated Hospital of Shihezi University, Shihezi, Xinjiang, China.
Yizhong YanShihezi University School of Public Health, Shihezi, Xinjiang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study analyzed spatial and temporal patterns of thyroid cancer mortality in China cancer registration areas and predicted future trends, thereby providing a scientific basis for the formulation of prevention and control strategies for thyroid cancer. Methods: Thyroid cancer death data from 2005 to 2018 were obtained from the China Cancer Registry Annual Report from 2008 to 2021. Joinpoint regression model was used to describe the temporal trends. Age-period-cohort model was used to analyze the age, period and cohort effects on thyroid cancer mortality. The Bayesian age-period-cohort model was used to predict the age-standardized mortality rate (ASMR) of thyroid cancer. Spatial autocorrelation was used to analyze the spatial distribution characteristics of thyroid cancer mortality and identify high-risk areas. Results: The ASMR of thyroid cancer in China cancer registration areas from 2005 to 2018 was 0.42 per 100,000 and exhibited an increasing trend, with an average annual percent change of 2.1% (95% CI: 1.1%, 3.2%). The upward trend in urban areas and among females was more pronounced than that in rural areas and among males. In terms of age effects, thyroid cancer mortality risk exhibited a W-shaped pattern in the overall population, as well as in urban and rural areas and among females, whereas males showed a pattern of an initial decline followed by an increase with age. The period effects demonstrated increasing trends in the overall population, urban areas, and among males and females. In contrast, the period effect in rural areas initially decreased and subsequently increased, with a turning point in 2010. The cohort effects generally showed a decreasing-increasing-decreasing pattern across the overall population, urban and rural areas, and among males and females. From 2019 to 2030, the ASMR of thyroid cancer in China cancer registration areas will continue to rise. The thyroid cancer mortality showed spatial aggregation, of which Xinjiang, Tibet, and Jilin were high-high aggregation areas. Conclusion: Thyroid cancer mortality in China cancer registration areas showed an increasing trend from 2005 to 2018, and it will continue to rise in 2019-2030. Attention to thyroid cancer should not be relaxed, especially in Xinjiang, Tibet, and Jilin.

Indexed as

cancer registrydeath characteristicsprediction analysisspatial autocorrelationthyroid cancer

Identifiers

PMID42755586
PMCPMC13581727

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