Journal of Shandong University (Health Sciences) ›› 2023, Vol. 61 ›› Issue (4): 95-102.doi: 10.6040/j.issn.1671-7554.0.2022.0919

• 公共卫生与管理学 • Previous Articles    

Exploring the environmental influencing factors of lung cancer incidence based on geographically and temporally weighted regression model

CHENG Chuanlong1, HAN Chuang1, FANG Qidi1, LIU Ying1, YANG Shuxia2, CUI Feng2, LIU Jingjing2, LI Xiujun1   

  1. 1. Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, Shandong, China;
    2. Zibo Center for Disease Control and Prevention, Zibo 255026, Shandong, China
  • Published:2023-04-11

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Abstract: Objective To analyze the spatial and temporal distribution of lung cancer incidence in Zibo City, and to explore the environmental influencing factors of lung cancer incidence based on geographically and temporally weighted regression(GTWR)model. Methods The incidence data of lung cancer in Zibo City from 2015 to 2019 were collected, and the spatio-temporal epidemiological characteristics were described. GTWR model was used to explore the relationship between lung cancer incidence and environmental factors, and the fitting effect was compared with the traditional model. Results The incidence of lung cancer in Zibo City showed an increasing trend from 2015 to 2019, with an average annual incidence of 67.43/100,000. The spatial distribution of lung cancer incidence was clustered in Zibo City, with low incidence in the central region and high incidence in the surrounding and southern regions. GTWR model results showed that environmental factors in different spatial locations had different effects on lung cancer incidence. Economic development level might affect the relationship between air pollution and lung cancer incidence, and the influence of air pollution on lung cancer incidence was stronger in areas with low economic development level. Compared with traditional model, GTWR model had better fitting effect. Conclusion There is a spatial and temporal correlation between lung cancer incidence and environmental factors in Zibo City. Local medical resources should be allocated rationally. Besides paying attention to the areas with serious air pollution, lung cancer prevention and treatment should also be carried out in the areas with low economic development level to improve the local medical service level.

Key words: Lung cancer, Environmental factors, Geographically and temporally weighted regression

CLC Number: 

  • R122
[1] 李翔, 高申. 1990—2019年中国居民肺癌发病、患病和死亡趋势分析[J]. 中国慢性病预防与控制, 2021, 29(11): 821-826. LI Xiang, GAO Shen. Trend analysis of the incidence, morbidity and mortality of lung cancer in China from 1990 to 2019[J]. Chinese Journal of Prevention and Control of Chronic Diseases, 2021, 29(11): 821-826.
[2] 路友华, 王炳翔, 王家林, 等. 中国居民1990—2019年肺癌及其危险因素疾病负担变化趋势分析[J]. 中国公共卫生, 2022, 38(5): 513-517. LU Youhua, WANG Bingxiang, WANG Jialin, et al. Changing trend in disease burden of lung cancer and its risk factors among Chinese residents, 1990—2019[J]. Chinese Journal of Public Health, 2022, 38(5): 513-517.
[3] 孙鑫, 覃文进, 刘理礼, 等. 肺癌发病危险因素最新研究进展[J]. 现代肿瘤医学, 2022, 30(4): 753-756. SUN Xin, QIN Wenjin, LIU Lili, et al. Recent advances of research on risk factors of lung cancer[J]. Journal of Modern Oncology, 2022, 30(4): 753-756.
[4] 辛雯艳,黄磊,闫贻忠. 2005—2014年中国老年人肺癌发病的时间趋势分析[J]. 现代预防医学, 2020, 47(12): 2113-2116. XIN Wenyan, HUANG Lei, YAN Yizhong, et al. Analysis on the time trend of lung cancer incidence in the elderly Chinese between 2005 and 2014[J]. Modern Preventive Medicine, 2020, 47(12): 2113-2116.
[5] 丁贤彬, 唐文革, 吕晓燕, 等. 人口老龄化对重庆市肺癌发病影响的定量分析[J]. 中国肿瘤, 2017, 26(6): 442-446. DING Xianbin, TANG Wenge, LYU Xiaoyan, et al. Impact of aging on incidence of lung cancer in Chongqing Municipality[J]. China Cancer, 2017, 26(6): 442-446.
[6] 魏海涛, 刘岩, 田智慧, 等. 河南省肺癌空间分布格局及环境因素影响[J]. 地理学报, 2022, 77(1): 245-258. WEI Haitao, LIU Yan, TIAN Zhihui, et al. Spatial distribution pattern and environmental impact of lung cancer in Henan[J]. Acta Geographica Sinica, 2022, 77(1): 245-258.
[7] 俞建, 俞洁, 应倩, 等. 浙江省基于卫星遥感数据的气溶胶光学厚度与肺癌的相关性[J]. 肿瘤防治研究, 2020, 47(10): 776-781. YU Jian, YU Jie, YING Qian, et al. Association of aerosol optical depth based on satellite remote sensing data and lung cancer in Zhejiang Province[J]. Cancer Research on Prevention and Treatment, 2020, 47(10): 776-781.
[8] Xing DF, Xu CD, Liao XY, et al. Spatial association between outdoor air pollution and lung cancer incidence in China[J]. BMC Public Health, 2019, 19(1): 1377.
[9] Huang B, Wu B, Barry M. Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices[J]. Int J Geogr Inf Sci, 2010, 24(3): 383-401.
[10] Wu B, Li R, Huang B. A geographically and temporally weighted autoregressive model with application to housing prices[J]. Int J Geogr Inf Sci, 2014, 28(5): 1186-1204.
[11] 刘芳盈, 刘晓利, 张殿平, 等. 2012—2014年淄博市肺癌死亡的空间分析[J]. 预防医学论坛, 2017, 23(8): 575-578. LIU Fangying, LIU Xiaoli, ZHANG Dianping, et al. Spatial analysis on lung cancer mortality, Zibo City, 2012—2014[J]. Preventive Medicine Tribune, 2017, 23(8): 575-578.
[12] Wei J, Li Z, Lyapustin A, et al. Reconstructing 1-km-resolution high-quality PM2.5 data records from 2000 to 2018 in China: spatiotemporal variations and policy implications[J]. Remote Sens Environ, 2021, 252: 112128.
[13] Wei J, Li Z, Cribb M, et al. Improved 1 km resolution PM2.5 estimates across China using enhanced space-time extremely randomized trees[J]. Atmos Chem Phys, 2020, 20(6): 3273-3289.
[14] 张立贤, 任浙豪, 陈斌, 等. 中国长时间序列逐年人造夜间灯光数据集(1984—2020). 国家青藏高原科学数据中心, 2021[EB/OL].(2022-04-18)[2022-07-01]. https://data.tpdc.ac.cn/zh-hans/data/e755f1ba-9cd1-4e43-98ca-cd081b5a0b3e/?q=.
[15] 徐新良. 中国GDP空间分布公里网格数据集. 资源环境科学数据注册与出版系统, 2017[EB/OL]. [2022-07-01]. https://www.resdc.cn/DOI/DOI.aspx?DOIID=32.
[16] Bondarenko M, Kerr D, Sorichetta A, et al. Estimates of 2020 total number of people per grid square, adjusted to match the corresponding UNPD 2020 estimates and broken down by gender and age groupings, produced using Built-Settlement Growth Model(BSGM)outputs. WorldPop, 2020 [EB/OL].(2020-11-30)[2022-07-01]. https://hub.worldpop.org/geodata/summary?id=50346.
[17] 张松林, 张昆. 全局空间自相关Moran指数和G系数对比研究[J]. 中山大学学报(自然科学版), 2007, 46(4): 93-97. ZHANG Songlin, ZHANG Kun. Comparison between general Morans index and Getis-Ord General G of spatial autocorrelation[J]. Acta Scientiarum Naturalium Universitatis Sunyatseni, 2007, 46(4): 93-97.
[18] 马雄威. 线性回归方程中多重共线性诊断方法及其实证分析[J]. 华中农业大学学报(社会科学版), 2008(2): 78-81, 85. doi: 10.13300/j.cnki.hnwkxb.2008.02.005. MA Xiongwei. Diagnosis and empirical analysis on multicollinearity in linear regression model[J]. Journal of Huazhong Agricultural University(Social Sciences Edition), 2008(2): 78-81, 85. doi:10.13300/j.cnki.hnwkxb.2008.02.005.
[19] Kletting P, Glatting G. Model selection for time-activity curves: the corrected Akaike information criterion and the F-test[J]. Z Med Phys, 2009, 19(3): 200-206.
[20] Huang J, Deng Y, Tin MS, et al. Distribution, risk factors, and temporal trends for lung cancer incidence and mortality: a global analysis[J]. Chest, 2022, 161(4): 1101-1111.
[21] 董婷, 成姝雯, 胥馨尹, 等. 2009—2017年四川省肿瘤登记地区肺癌发病和死亡流行特征分析[J]. 中国肿瘤, 2022, 31(6): 437-441. DONG Ting, CHENG Shuwen, XU Xinyin, et al. Characteristics of lung cancer incidence and mortality in Sichuan cancer registration areas from 2009 to 2017[J]. China Cancer, 2022, 31(6): 437-441.
[22] 刘茵,陈琼,郭兰伟,等. 2017年河南省肺癌流行现状及2010—2017年趋势分析[J]. 中国肿瘤, 2022, 31(2): 112-118. LIU Yin, CHEN Qiong, GUO Lanwei, et al. Incidence and mortality of lung vancer in Henan Province in 2017 and trend from 2010 to 2017[J]. China Cancer, 2022, 31(2): 112-118.
[23] Galffy G, Vastag A, Bogos K, et al. Significant regional differences in lung cancer incidence in hungary: epidemiological study between 2011 and 2016[J]. Pathol Oncol Res, 2021, 27: 1609916. doi: 10.3389/pore.2021.1609916.
[24] Maiti A, Zhang Q, Sannigrahi S, et al. Exploring spatiotemporal effects of the driving factors on COVID-19 incidences in the contiguous United States[J]. Sustain Cities Soc, 2021, 68: 102784. doi: 10.1016/j.scs.2021.102784.
[25] Wu S, Du Z, Wang Y, et al. Modeling spatially anisotropic nonstationary processes in coastal environments based on a directional geographically neural network weighted regression[J]. Sci Total Environ, 2020, 709: 136097. doi: 10.1016/j.scitotenv.2019.136097.
[26] Sifriyani S, Rasjid M, Rosadi D, et al. Spatial-temporal epidemiology of COVID-19 using a geographically and temporally weighted regression model[J]. Symmetry, 2022, 14(4): 742.
[27] Guo B, Wang Y, Pei L, et al. Determining the effects of socioeconomic and environmental determinants on chronic obstructive pulmonary disease(COPD)mortality using geographically and temporally weighted regression model across Xian during 2014-2016[J]. Sci Total Environ, 2021, 756:143869. doi: 10.1016/j.scitotenv.2020.143869.
[28] Wang Y, Guo B, Pei L, et al. The influence of socioeconomic and environmental determinants on acute myocardial infarction(AMI)mortality from the spatial epidemiological perspective[J]. Environ Sci Pollut Res Int, 2022, 29(42): 63494-63511.
[29] Cao Q, Rui G, Liang Y. Study on PM2.5 pollution and the mortality due to lung cancer in China based on geographic weighted regression model[J]. BMC Public Health, 2018, 18(1): 925.
[30] 白羽, 倪洋,曾强. 基于队列研究的空气污染中PM2.5与肺癌关联性的Meta分析[J]. 公共卫生与预防医学, 2020, 31(4): 5-8. BAI Yu, NI Yang, ZENG Qiang. A meta-analysis on association between PM2.5 exposure and lung cancer based on cohort studies[J]. Journal of Public Health and Preventive Medicine, 2020, 31(4): 5-8.
[31] Coleman NC, Burnett RT, Ezzati M, et al. Fine particulate matter exposure and cancer incidence: analysis of SEER cancer registry data from 1992-2016[J]. Environ Health Perspect, 2020, 128(10): 107004.
[32] You W, Ruhli FJ, Henneberg RJ, et al. Greater family size is associated with less cancer risk: an ecological analysis of 178 countries[J]. BMC Cancer, 2018, 18(1): 924.
[33] Wang SC, Sung WW, Kao YL, et al. The gender difference and mortality-to-incidence ratio relate to health care disparities in bladder cancer: national estimates from 33 countries[J]. Sci Rep, 2017, 7(1): 4360.
[34] Ouyang W, Gao B, Cheng H, et al. Exposure inequality assessment for PM2.5 and the potential association with environmental health in Beijing[J]. Sci Total Environ, 2018, 635: 769-778. doi: 10.1016/j.scitotenv.2018.04.190.
[35] Zhang H, Tripathi NK. Geospatial hot spot analysis of lung cancer patients correlated to fine particulate matter(PM2.5)and industrial wind in Eastern Thailand[J]. J Clean Prod, 2018, 170: 407-424.
[36] Yang Z, Zheng R, Zhang S, et al. Comparison of cancer incidence and mortality in three GDP per capita levels in China, 2013[J]. Chin J Cancer Res, 2017, 29(5): 385-394.
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