Skip to main content

ORIGINAL RESEARCH article

Front. Oncol.
Sec. Cancer Epidemiology and Prevention
Volume 14 - 2024 | doi: 10.3389/fonc.2024.1297405
This article is part of the Research Topic The Future of Cancer Surveillance Research View all 13 articles

The trend of lymphoma incidence in China from 2005 to 2017 and prediction to 2035: a log-linear regression and Bayesian age-period-cohort analysis

Provisionally accepted
Kangqian Lin Kangqian Lin 1*Jianjiang Shao Jianjiang Shao 1*Yuting Cao Yuting Cao 2*Lijun Lu Lijun Lu 2*Peng Lei Peng Lei 2*Xiaohong Chen Xiaohong Chen 2*Mengwei Tong Mengwei Tong 2*Yaping Lu Yaping Lu 2*Yizhong Yan Yizhong Yan 1*Lei Zhang Lei Zhang 3*Xin Pan Xin Pan 2,4*Weixia Nong Weixia Nong 2,4*
  • 1 Department of Preventive Medicine, School of Medicine, Shihezi University, Shihezi, Xinjiang Uyghur Region, China
  • 2 Department of Hematology, First Affiliated Hospital, School of Medicine, Shihezi University, Shihezi, Xinjiang Uyghur Region, China
  • 3 Clinical Laboratory, First Affiliated Hospital, School of Medicine, Shihezi University, Shihezi, Xinjiang Uyghur Region, China
  • 4 National Hematology Clinical Research Center Xinjiang Production and Construction Corps Branch Center, Shihezi, Xinjiang Uyghur Region, China

The final, formatted version of the article will be published soon.

    Objective: To explore the incidence characteristics and trend prediction of lymphoma from 2005 to 2035, and to provide data basis for the prevention and control of lymphoma in China.The data on lymphoma incidence in China from 2005 to 2017 were obtained from the Chinese Cancer Registry Annual Report. The Joinpoint regression model was used to calculate annual percentage change (APC) and average annual percentage change (AAPC) to reflect time trends. Age-period-cohort models were conducted to estimate age, period, and cohort effects on the lymphoma incidence. A Bayesian age-period-cohort model was used to predict lymphoma incidence trends from 2018 to 2035.Result: From 2005 to 2017, the incidence of lymphoma was 6.26/100,000, the age-standardized incidence rate (ASIR) was 4.11/100,000, and increasing with the AAPC of 1.4% (95% Confidence Interval (CI) : 0.3%, 2.5%). The ASIR was higher in males and urban areas than those in females and rural areas, respectively. The age effect showed that the incidence risk of lymphoma increased with age. In the period effect, the incidence risk of lymphoma in rural areas decreased first and then increased with 2010 as the cut-off point. The overall risk of lymphoma incidence was higher in the cohort before the 1970-1974 birth cohort than in the cohort after. From 2018 to 2035, the lymphoma incidence of males, females, and urban areas will show an upward trend.From 2005 to 2017, the incidence of lymphoma showed an increasing trend, and was different in regions, genders, and ages in China. And it will show an upward trend from 2018 to 2035. These results are helpful for the formulation and adjustment of lymphoma prevention, control, and management strategies, and have important reference significance for the treatment of lymphoma in China.

    Keywords: Lymphoma, Incidence, Joinpoint regression model, age-period-cohort model, Bayesian age-period-cohort model

    Received: 20 Sep 2023; Accepted: 29 Apr 2024.

    Copyright: © 2024 Lin, Shao, Cao, Lu, Lei, Chen, Tong, Lu, Yan, Zhang, Pan and Nong. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Kangqian Lin, Department of Preventive Medicine, School of Medicine, Shihezi University, Shihezi, 832011, Xinjiang Uyghur Region, China
    Jianjiang Shao, Department of Preventive Medicine, School of Medicine, Shihezi University, Shihezi, 832011, Xinjiang Uyghur Region, China
    Yuting Cao, Department of Hematology, First Affiliated Hospital, School of Medicine, Shihezi University, Shihezi, 832008, Xinjiang Uyghur Region, China
    Lijun Lu, Department of Hematology, First Affiliated Hospital, School of Medicine, Shihezi University, Shihezi, 832008, Xinjiang Uyghur Region, China
    Peng Lei, Department of Hematology, First Affiliated Hospital, School of Medicine, Shihezi University, Shihezi, 832008, Xinjiang Uyghur Region, China
    Xiaohong Chen, Department of Hematology, First Affiliated Hospital, School of Medicine, Shihezi University, Shihezi, 832008, Xinjiang Uyghur Region, China
    Mengwei Tong, Department of Hematology, First Affiliated Hospital, School of Medicine, Shihezi University, Shihezi, 832008, Xinjiang Uyghur Region, China
    Yaping Lu, Department of Hematology, First Affiliated Hospital, School of Medicine, Shihezi University, Shihezi, 832008, Xinjiang Uyghur Region, China
    Yizhong Yan, Department of Preventive Medicine, School of Medicine, Shihezi University, Shihezi, 832011, Xinjiang Uyghur Region, China
    Lei Zhang, Clinical Laboratory, First Affiliated Hospital, School of Medicine, Shihezi University, Shihezi, 832008, Xinjiang Uyghur Region, China
    Xin Pan, Department of Hematology, First Affiliated Hospital, School of Medicine, Shihezi University, Shihezi, 832008, Xinjiang Uyghur Region, China
    Weixia Nong, Department of Hematology, First Affiliated Hospital, School of Medicine, Shihezi University, Shihezi, 832008, Xinjiang Uyghur Region, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.