Association analysis between air pollution and lung cancer burden and prediction of disease burden based on PCA-GAM in China

MA Qianqian; CHEN Baozhan; TAN Zhongke; WANG Zhenbo

doi:10.16462/j.cnki.zhjbkz.2024.09.008

Volume 28 Issue 9

Sep. 2024

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MA Qianqian, CHEN Baozhan, TAN Zhongke, WANG Zhenbo. Association analysis between air pollution and lung cancer burden and prediction of disease burden based on PCA-GAM in China[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(9): 1037-1042. doi: 10.16462/j.cnki.zhjbkz.2024.09.008

Citation:

MA Qianqian, CHEN Baozhan, TAN Zhongke, WANG Zhenbo. Association analysis between air pollution and lung cancer burden and prediction of disease burden based on PCA-GAM in China[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(9): 1037-1042. doi: 10.16462/j.cnki.zhjbkz.2024.09.008

Citation:

MA Qianqian, CHEN Baozhan, TAN Zhongke, WANG Zhenbo. Association analysis between air pollution and lung cancer burden and prediction of disease burden based on PCA-GAM in China[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(9): 1037-1042. doi: 10.16462/j.cnki.zhjbkz.2024.09.008

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Association analysis between air pollution and lung cancer burden and prediction of disease burden based on PCA-GAM in China

doi: 10.16462/j.cnki.zhjbkz.2024.09.008

MA Qianqian^{1, 2
,
,},
CHEN Baozhan^{1, 2},
TAN Zhongke¹,
WANG Zhenbo^{1, 2}

1.
National Engineering Laboratory for Internet Medical Systems and Applications, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
2.
Telemedicine Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China

Funds:

National Support Programme for Postdoctrol Researchers of China GZC20232414

Jonit Construction Project of the Henan Province Medical Science and Technology LHGJ20210323

More Information

Corresponding author: MA Qianqian, E-mail: qian_ma91green@163.com
Received Date: 2024-02-01
Rev Recd Date: 2024-06-17

Available Online: 2024-10-24

Publish Date: 2024-09-10

Abstract

Abstract

Objective To explore the time series characteristics of the disease burden of lung cancer and air pollution in China, and to analyze their correlation and predict the disease burden of lung cancer, and provide a reference for lung cancer prevention and control. Methods The data of disease burden such as the incidence and death of lung cancer in China from 1990 to 2019, and air pollution data from 1970 to 2015 were collected. Grey correlation analysis and generalized additive model were used to study the association between air pollution and lung cancer disease burden, and to predict the lung cancer disease burden from 2020 to 2022. Results From 1990 to 2019, the compound annual growth rate of the incidence of lung cancer in China was 3.48%, and the compound annual growth rate of the mortality rate was 3.15%. The lag times of PM_2.5, PM₁₀, BC, OC, SO₂, NH₃, NOx, CO, and NMVOC sequences most correlated with the incidence of lung cancer were 19 years, 19 years, 19 years, 19 years, 13 years, 20 years, 10 years, 19 years, 4 years. While the lag times of the sequences with the greatest correlation between the above air pollutants and lung cancer mortality were 19 years, 19 years, 19 years, 19 years, 12 years, 12 years, 10 years, 19 years, and 4 years, respectively. Nonparametric effect analysis showed a curvilinear correlation between pollutants such as PM_2.5 and the disease burden of lung cancer. It is predicted that the incidence rate of lung cancer in China from 2020 to 2022 will be 59.547/100 000, 59.838/100 000 and 58.026/100 000 respectively; The mortality rates of lung cancer will be 53.465/100 000, 54.106/100 000, and 53.458/100 000, respectively. Conclusions There is a hysteresis effect on the incidence and death of lung cancer caused by air pollution, and they are curvilinearly positively correlated. The disease burden of lung cancer in China has shown a fluctuated trend from 2020 to 2022.
- Lung cancer,
- Disease burden,
- Air pollution,
- Grey relation analysis,
- Generalized additive model,
- Principal component analysis

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References(18)

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