IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2020i1p26-d466666.html
   My bibliography  Save this article

Spatial and Temporal Analysis of Lung Cancer in Shenzhen, 2008–2018

Author

Listed:
  • Lin Lei

    (Department of Cancer Control and Prevention, Shenzhen Center for Chronic Disease Control, Shenzhen 518020, China
    These authors contributed equally to this work.)

  • Anyan Huang

    (Department of Cancer Control and Prevention, Shenzhen Center for Chronic Disease Control, Shenzhen 518020, China
    Mental Health Center, Shantou University Medical College, North Taishan Road, Shantou 515065, China
    These authors contributed equally to this work.)

  • Weicong Cai

    (Department of Cancer Control and Prevention, Shenzhen Center for Chronic Disease Control, Shenzhen 518020, China)

  • Ling Liang

    (Department of Cancer Control and Prevention, Shenzhen Center for Chronic Disease Control, Shenzhen 518020, China)

  • Yirong Wang

    (Department of Cancer Control and Prevention, Shenzhen Center for Chronic Disease Control, Shenzhen 518020, China)

  • Fangjiang Liu

    (Department of Cancer Control and Prevention, Shenzhen Center for Chronic Disease Control, Shenzhen 518020, China)

  • Ji Peng

    (Department of Cancer Control and Prevention, Shenzhen Center for Chronic Disease Control, Shenzhen 518020, China)

Abstract

Lung cancer is the most commonly diagnosed cancer in China. The incidence trend and geographical distribution of lung cancer in southern China have not been reported. The present study explored the temporal trend and spatial distribution of lung cancer incidence in Shenzhen from 2008 to 2018. The lung cancer incidence data were obtained from the registered population in the Shenzhen Cancer Registry System between 2008 and 2018. The standardized incidence rates of lung cancer were analyzed by using the joinpoint regression model. The Moran’s I method was used for spatial autocorrelation analysis and to further draw a spatial cluster map in Shenzhen. From 2008 to 2018, the average crude incidence rate of lung cancer was 27.1 (1/100,000), with an annual percentage change of 2.7% ( p < 0.05). The largest average proportion of histological type of lung cancer was determined as adenocarcinoma (69.1%), and an increasing trend was observed in females, with an average annual percentage change of 14.7%. The spatial autocorrelation analysis indicated some sites in Shenzhen as a high incidence rate spatial clustering area. Understanding the incidence patterns of lung cancer is useful for monitoring and prevention.

Suggested Citation

  • Lin Lei & Anyan Huang & Weicong Cai & Ling Liang & Yirong Wang & Fangjiang Liu & Ji Peng, 2020. "Spatial and Temporal Analysis of Lung Cancer in Shenzhen, 2008–2018," IJERPH, MDPI, vol. 18(1), pages 1-12, December.
    • Handle: RePEc:gam:jijerp:v:18:y:2020:i:1:p:26-:d:466666
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/1/26/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/18/1/26/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Khalid Al-Ahmadi & Ali Al-Zahrani, 2013. "Spatial Autocorrelation of Cancer Incidence in Saudi Arabia," IJERPH, MDPI, vol. 10(12), pages 1-22, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yusuf A. Aina & Johannes H. Van der Merwe & Habib M. Alshuwaikhat, 2014. "Spatial and Temporal Variations of Satellite-Derived Multi-Year Particulate Data of Saudi Arabia: An Exploratory Analysis," IJERPH, MDPI, vol. 11(11), pages 1-15, October.
    2. Katarína Vilinová, 2020. "Spatial Autocorrelation of Breast and Prostate Cancer in Slovakia," IJERPH, MDPI, vol. 17(12), pages 1-20, June.
    3. Daniel A. Griffith & Yongwan Chun & Monghyeon Lee, 2020. "Deeper Spatial Statistical Insights into Small Geographic Area Data Uncertainty," IJERPH, MDPI, vol. 18(1), pages 1-16, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:18:y:2020:i:1:p:26-:d:466666. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.