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

Lin Lei, Anyan Huang, Weicong Cai, Ling Liang, Yirong Wang, Fangjiang Liu and Ji Peng
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Lin Lei: Department of Cancer Control and Prevention, Shenzhen Center for Chronic Disease Control, Shenzhen 518020, China
Anyan Huang: Department of Cancer Control and Prevention, Shenzhen Center for Chronic Disease Control, Shenzhen 518020, China
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

IJERPH, 2020, vol. 18, issue 1, 1-12

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.

Keywords: cancer registry; incidence rate; lung cancer; spatial autocorrelation; temporal trend (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
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