高级检索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于贝叶斯时空模型黑龙江省肺癌死亡风险及其影响因素分析

郭玉珠 于钏钏 许宁 郑萍 王强

郭玉珠, 于钏钏, 许宁, 郑萍, 王强. 基于贝叶斯时空模型黑龙江省肺癌死亡风险及其影响因素分析[J]. 中国公共卫生, 2021, 37(6): 965-973. doi: 10.11847/zgggws1133288
引用本文: 郭玉珠, 于钏钏, 许宁, 郑萍, 王强. 基于贝叶斯时空模型黑龙江省肺癌死亡风险及其影响因素分析[J]. 中国公共卫生, 2021, 37(6): 965-973. doi: 10.11847/zgggws1133288
GUO Yu-zhu, YU Chuan-chuan, XU Ning, . Lung cancer mortality risk and its risk factors in Heilongjiang province: a Bayesian spatio-temporal modeling analysis[J]. Chinese Journal of Public Health, 2021, 37(6): 965-973. doi: 10.11847/zgggws1133288
Citation: GUO Yu-zhu, YU Chuan-chuan, XU Ning, . Lung cancer mortality risk and its risk factors in Heilongjiang province: a Bayesian spatio-temporal modeling analysis[J]. Chinese Journal of Public Health, 2021, 37(6): 965-973. doi: 10.11847/zgggws1133288

基于贝叶斯时空模型黑龙江省肺癌死亡风险及其影响因素分析

doi: 10.11847/zgggws1133288
基金项目: 环保公益科研专项(201509063)
详细信息
    作者简介:

    郭玉珠(1994 – ),女,山西晋中人,硕士在读,研究方向:环境流行病

    通信作者:

    王强,E-mail:wangqiang@nieh.chinacdc.cn

  • 中图分类号: R 188.2;R 188.7

Lung cancer mortality risk and its risk factors in Heilongjiang province: a Bayesian spatio-temporal modeling analysis

  • 摘要:   目的   了解黑龙江省肺癌死亡的时空风险及其影响因素,为肺癌的预防控制提供参考依据。   方法   收集黑龙江省2008 — 2017年肺癌死亡数据,利用基于集成嵌套拉普拉斯逼近(INLA)的贝叶斯时空模型估计黑龙江省肺癌标化死亡比(SMR),并分析肺癌SMR的影响因素。   结果   黑龙江省2008 — 2017年不同区县肺癌SMR时空演变模式不同,但总体肺癌死亡风险显著增长,2008年132个区县中全人口肺癌死亡高风险(RR > 1.0)区县为9个,2017年增长到69个。在全人口、男性和女性中,市级肺癌SMR多因素时空分析结果均未见 ≥ 60岁人口比例、人均年卷烟消费量(滞后20年)、PM2.5(滞后8年)、人均地区生产总值(滞后10年)、城镇采矿业就业人数比例(滞后5年)及农业人口比例(滞后20年)与黑龙江省肺癌SMR显著相关(RR值的95 % CI均包含1),但慢性阻塞性肺疾病(COPD)的年龄标化死亡率(ASMR)均与肺癌SMR呈正相关(全人口:RR = 1.10,95 % CI = 1.04~1.16;男性:RR = 1.09,95 % CI = 1.03~1.16;女性:RR = 1.10,95 % CI = 1.04~1.16)。区县级COPD的ASMR每增加1/10万,全人口肺癌SMR增加0.34 %(95 % CI = 0.22 %~0.46 %),男性SMR增加0.29 %(95 % CI = 0.19 %~0.40 %),女性SMR增加0.33 %(95 % CI = 0.20 %~0.46 %)。   结论   黑龙江省2008 — 2017年肺癌死亡风险整体上逐年增加,COPD可能是该地区肺癌死亡的重要危险因素,建议在COPD患者中开展肺癌筛查,从而有效防控肺癌。
  • 图  1  黑龙江省2008 — 2017年肺癌死亡风险时间变化趋势

    注:exp(γt+ϕt)表示肺癌SMR全局时间相对风险RR值。

    图  2  黑龙江省2008 — 2017年区县级肺癌SMR后验均值(全人口)

    注:橙色和红色区域为肺癌死亡风险显著高于全省平均水平的地区;蓝色区域为肺癌死亡风险显著低于全省平均水平的地区;白色区域为肺癌死亡风险与全省平均水平一致的地区。

    图  3  黑龙江省2008 — 2017年区县级肺癌SMR后验均值(男性)

    注:橙色和红色区域为肺癌死亡风险显著高于全省平均水平的地区;蓝色区域为肺癌死亡风险显著低于全省平均水平的地区;白色区域为肺癌死亡风险与全省平均水平一致的地区。

    图  4  黑龙江省2008 — 2017年区县级肺癌SMR后验均值(女性)

    注:橙色和红色区域为肺癌死亡风险显著高于全省平均水平的地区;蓝色区域为肺癌死亡风险显著低于全省平均水平的地区;白色区域为肺癌死亡风险与全省平均水平一致的地区。

    表  1  贝叶斯时空模型

    模型公式时空交互作用项
    A$\log{\theta }_{it}=\alpha + {\mu }_{i} + {\nu }_{i} + \left(\beta + {\delta }_{i}\right)×t$${\delta }_{i}×t$
    B$ \log{\theta }_{it}=\alpha + {\mu }_{i} + {\nu }_{i} + {\gamma }_{t} + {\phi }_{t} $
    C$ \log{\theta }_{it}=\alpha + {\mu }_{i} + {\nu }_{i} + {\gamma }_{t} + {\phi }_{t} + {\delta }_{it} $$ {{\delta }_{it}: \nu }_{i} \otimes {\phi }_{t} $
    D$ \log{\theta }_{it}=\alpha + {\mu }_{i} + {\nu }_{i} + {\gamma }_{t} + {\phi }_{t} + {\delta }_{it} $$ {\delta }_{it}:{\nu }_{i} \otimes {\gamma }_{t} $
    E$ \log{\theta }_{it}=\alpha + {\mu }_{i} + {\nu }_{i} + {\gamma }_{t} + {\phi }_{t} + {\delta }_{it} $$ {{\delta }_{it}: \mu }_{i} \otimes {\phi }_{t} $
    F$ \log{\theta }_{it}=\alpha + {\mu }_{i} + {\nu }_{i} + {\gamma }_{t} + {\phi }_{t} + {\delta }_{it} $$ {{\delta }_{it}: \mu }_{i} \otimes {\gamma }_{t} $
    下载: 导出CSV

    表  2  黑龙江省2008 — 2017年区县级肺癌SMR全局Moran′s I指数

    年份全人口男性女性
    Moran′s I P Moran′s I P Moran′s I P
    2008 0.156 0.002 0.137 0.005 0.168 0.001
    2009 0.161 0.003 0.154 0.002 0.112 0.016
    2010 0.119 0.011 0.110 0.016 0.129 0.007
    2011 0.174 0.001 0.166 0.001 0.133 0.006
    2012 0.137 0.004 0.117 0.010 0.142 0.003
    2013 0.037 0.193 0.016 0.329 0.034 0.214
    2014 0.234 < 0.001 0.231 < 0.001 0.189 < 0.001
    2015 0.054 0.133 – 0.068 0.863 0.227 < 0.001
    2016 0.042 0.187 0.014 0.352 0.037 0.213
    2017 0.174 < 0.001 0.145 0.003 0.150 0.002
    下载: 导出CSV

    表  3  黑龙江省区县级全人口肺癌SMR时空分析模型评价指标

    模型$ \bar{D} $pDDICDICcWAICLS
    A 16608 250 16858 16937 18303 9232
    B 19457 138 19595 19611 20569 10236
    C 9909 1120 11029 25119 10847 6254
    D 9863 968 10831 15903 10700 5897
    E 9992 1111 11103 25427 10995 6367
    F 9943 968 10911 16335 10841 6007
      注:$ \bar{D} $表示偏差后验均值之和,用于度量模型拟合度;pD表示有效参数个数,用于度量模型复杂度。
    下载: 导出CSV

    表  4  黑龙江省市级肺癌SMR影响因素单因素分析

    因素全人口男性女性
    RR 95 % CIRR 95 % CIRR 95 % CI
    COPD的ASMR(1/10万) 1.0078 1.0035~1.0120 1.0074 1.0028~1.0120 1.0076 1.0033~1.0119
    ≥ 60岁人口比例(%) 1.0113 0.9842~1.0389 1.0119 0.9825~1.0419 1.0100 0.9813~1.0394
    农业人口比例(滞后20年,%) 1.0003 0.9985~1.0021 0.9998 0.9979~1.0017 1.0008 0.9988~1.0028
    人均年卷烟消费量(滞后20年,支) 1.0000 0.9998~1.0002 1.0000 0.9998~1.0002 1.0000 0.9998~1.0003
    人均地区生产总值(滞后10年,元) 1.0000 1.0000~1.0000 1.0000 1.0000~1.0000 1.0000 1.0000~1.0000
    城镇采矿业就业人数比例(滞后5年,%) 0.9991 0.9967~1.0014 0.9992 0.9967~1.0018 0.9988 0.9962~1.0014
    PM2.5(滞后8年,μg/m3 1.0039 0.9949~1.0130 1.0033 0.9938~1.0126 1.0053 0.9953~1.0155
    PM10(μg/m3 1.0007 0.9976~1.0039 1.0005 0.9971~1.0040 1.0007 0.9973~1.0041
    人均公共绿地面积(滞后7年,m2 0.9995 0.9917~1.0072 1.0025 0.9938~1.0114 0.9966 0.9873~1.0059
    城市燃气普及率(滞后7年,%) 0.9999 0.9984~1.0015 1.0004 0.9987~1.0022 0.9994 0.9976~1.0012
    下载: 导出CSV

    表  5  黑龙江省市级肺癌SMR影响因素多因素分析(模型D)a

    因素全人口男性女性
    RR 95 % CIRR 95 % CIRR 95 % CI
    COPD的ASMR(1/10万) 1.0992 1.0408~1.1601 1.0934 1.0296~1.1599 1.0983 1.0394~1.1590
    ≥ 60岁人口比例(%) 1.0168 0.9362~1.1050 1.0263 0.9367~1.1249 1.0005 0.9152~1.0951
    农业人口比例(滞后20年,%) 1.0033 0.9667~1.0418 0.9952 0.9566~1.0361 1.0074 0.9670~1.0502
    人均年卷烟消费量(滞后20年,支) 1.0004 0.9366~1.0689 0.9996 0.9303~1.0743 1.0018 0.9345~1.0747
    人均地区生产总值(滞后10年,元) 0.9959 0.9327~1.0638 0.9874 0.9204~1.0599 1.0055 0.9411~1.0749
    城镇采矿业就业人数比例(滞后5年,%) 0.9887 0.9471~1.0320 0.9897 0.9444~1.0371 0.9850 0.9401~1.0316
    PM2.5(滞后8年,μg/m3 1.0224 0.9769~1.0716 1.0147 0.9681~1.0642 1.0328 0.9807~1.0910
      注:a 采用模型D进行多因素分析时,对COPD标化死亡率、≥ 60岁人口比例、农业人口比例(滞后20年)、人均年卷烟消费量(滞后20年)、人均地区生产总值(滞后10年)、城镇采矿业就业人数比例(滞后5年)、PM2.5(滞后8年)等7个变量进行了标准化处理,以比较不同变量的RR值大小。
    下载: 导出CSV
  • [1] Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA: A Cancer Journal for Clinicians, 2018, 68(6): 394 – 424. doi: 10.3322/caac.21492
    [2] 兰蓝, 赵飞, 蔡玥, 等. 中国居民2015年恶性肿瘤死亡率流行病学特征分析[J]. 中华流行病学杂志, 2018, 39(1): 32 – 34. doi: 10.3760/cma.j.issn.0254-6450.2018.01.006
    [3] Wang N, Mengersen K, Tong SL, et al. Global, regional, and national burden of lung cancer and its attributable risk factors, 1990 to 2017[J]. Cancer, 2020, 126(18): 4220 – 4234. doi: 10.1002/cncr.33078
    [4] Cao MM, Chen WQ. Epidemiology of lung cancer in China[J]. Thoracic Cancer, 2019, 10(1): 3 – 7. doi: 10.1111/1759-7714.12916
    [5] Liu YN, Astell-Burt T, Liu JM, et al. Spatiotemporal variations in lung cancer mortality in China between 2006 and 2012: a multi-level analysis[J]. International Journal of Environmental Research and Public Health, 2016, 13(12): 1252. doi: 10.3390/ijerph13121252
    [6] Leroux BG, Lei XY, Breslow N. Estimation of disease rates in small areas: a new mixed model for spatial dependence[M]//Halloran ME, Berry D. Statistical Models in Epidemiology, the Environment, and Clinical Trials. New York: Springer, 2000.
    [7] Jiang W, Han SW, Tsui KL, et al. Spatiotemporal surveillance methods in the presence of spatial correlation[J]. Statistics in Medicine, 2011, 30(5): 569 – 583. doi: 10.1002/sim.3877
    [8] Banerjee S, Carlin BP, Gelfand AE. Hierarchical modeling and analysis for spatial data[M]. 2nd ed. Boca Raton: Chapman and Hall/CRC, 2014.
    [9] 黑龙江省统计局, 黑龙江省第六次人口普查办公室. 黑龙江省2010年人口普查资料[M]. 北京: 中国统计出版社, 2012.
    [10] 黑龙江省1 %人口抽样调查办公室, 黑龙江省统计局人口和社会科技处. 2005年黑龙江省1 %人口抽样调查资料[M]. 北京: 中国统计出版社, 2007.
    [11] 黑龙江省统计局. 2015年黑龙江省1 %人口抽样调查资料[M]. 北京: 中国统计出版社, 2017.
    [12] 吴兴让, 奚杰, 徐兴野, 等. 绥化烟草志[M/OL]. 绥化: 鸿德印刷厂, 1999[2012 – 05 – 03]. http://fz.wanfangdata.com.cn/details/newLocalchronicle.do?Id=fz201205348.
    [13] 黑龙江省环境保护厅. 2013 年黑龙江省环境状况公报[EB/OL]. [2020 – 04 – 28]. http//www.hljdep.gov.cn/hjgl/hjjc/hjzkgb/2014/06.html.
    [14] 黑龙江省环境保护厅. 2014 年黑龙江省环境状况公报[EB/OL]. [2020 – 04 – 28]. http//www.www.hljdep.gov.cn/hjgl/hjjc/hjzkgb/2015/06/8599.html.
    [15] 孙明宇, 程佳新. 哈尔滨市环境空气质量评价与经济增长关系研究[J]. 环境科学与管理, 2017, 42(7): 42 – 45. doi: 10.3969/j.issn.1673-1212.2017.07.011
    [16] 宋涛, 王婷婷. 黑龙江省主要城市的大气污染物变化特征[J]. 黑龙江气象, 2012, 29(4): 18 – 20. doi: 10.3969/j.issn.1002-252X.2012.04.010
    [17] 袁国新, 李丽. 伊春城区“十二五”期间空气质量状况与气象条件分析[J]. 黑龙江环境通报, 2016, 40(2): 54 – 57, 66. doi: 10.3969/j.issn.1674-263X.2016.02.021
    [18] 赵坤宇, 张婉怡, 邱洪斌, 等. 2010 — 2013年佳木斯市城区大气污染物特征分析[J]. 环境与健康杂志, 2014, 31(9): 817 – 819.
    [19] 李永亮, 于亭亭. 佳木斯市环境空气质量分析评价[J]. 仪器仪表与分析监测, 2015(4): 33 – 38. doi: 10.3969/j.issn.1002-3720.2015.04.009
    [20] 王林凤, 栗艳杰, 于淑静, 等. 加格达奇区空气质量变化规律及其影响因子分析[J]. 黑龙江气象, 2014, 31(4): 21 – 23. doi: 10.3969/j.issn.1002-252X.2014.04.009
    [21] Hammer MS, van Donkelaar A, Li C, et al. Global estimates and long-term trends of fine particulate matter concentrations (1998 – 2018)[J]. Environmental Science and Technology, 2020, 54(13): 7879 – 7890. doi: 10.1021/acs.est.0c01764
    [22] van Donkelaar A, Martin RV, Li C, et al. Regional estimates of chemical composition of fine particulate matter using a combined geoscience-statistical method with information from satellites, models, and monitors[J]. Environmental Science and Technology, 2019, 53(5): 2595 – 2611. doi: 10.1021/acs.est.8b06392
    [23] van der Loo M. Simputation: Simple Imputation[M/OL]. (2020 – 03 – 13) [2020 – 11 – 01]. https://CRAN.R-project.org/package=simputation.
    [24] Iddrisu AK. Bayesian hierarchical spatial and spatio-temporal modeling and mapping of tuberculosis in Kenya[D]. Pietermarit-zburg: University of KwaZulu-Natal, 2013.
    [25] Bernardinelli L, Clayton D, Pascutto C, et al. Bayesian analysis of space-time variation in disease risk[J]. Statistics in Medicine, 1995, 14(21/22): 2433 – 2443.
    [26] Knorr-Held L. Bayesian modelling of inseparable space-time varia-tion in disease risk[J]. Statistics in Medicine, 2000, 19(17/18): 2555 – 2567.
    [27] Watanabe S. Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory[J]. The Journal of Machine Learning Research, 2010, 11(116): 3571 – 3594.
    [28] Plummer M. Penalized loss functions for Bayesian model com-parison[J]. Biostatistics, 2008, 9(3): 523 – 539. doi: 10.1093/biostatistics/kxm049
    [29] Gneiting T, Raftery AE. Strictly proper scoring rules, prediction, and estimation[J]. Journal of the American Statistical Association, 2007, 102(477): 359 – 378. doi: 10.1198/016214506000001437
    [30] Spiegelhalter DJ, Best NG, Carlin BP, et al. Bayesian measures of model complexity and fit[J]. Journal of the Royal Statis-tical Society: Series B (Statistical Methodology), 2002, 64(4): 583 – 639. doi: 10.1111/1467-9868.00353
    [31] Dormann CF, Elith J, Bacher S, et al. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance[J]. Ecography, 2013, 36(1): 27 – 46. doi: 10.1111/j.1600-0587.2012.07348.x
    [32] Rue H, Martino S, Chopin N. Approximate Bayesian inference for latent Gaussian models by using integrated nested laplace approximations[J]. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2009, 71(2): 319 – 392. doi: 10.1111/j.1467-9868.2008.00700.x
    [33] 沈丹, 江德鹏, 蒋幼凡. 未吸烟人群慢性阻塞性肺疾病与肺癌危险关系的系统评价[J]. 重庆医科大学学报, 2016, 41(4): 360 – 363.
    [34] Brenner DR, Boffetta P, Duell EJ, et al. Previous lung diseases and lung cancer risk: a pooled analysis from the International Lung Cancer Consortium[J]. American Journal of Epidemiology, 2012, 176(7): 573 – 585. doi: 10.1093/aje/kws151
    [35] Wang H, Yang L, Zou LN, et al. Association between chronic obstructive pulmonary disease and lung cancer: a case-control study in Southern Chinese and a meta-analysis[J]. PLoS One, 2012, 7(9): e46144. doi: 10.1371/journal.pone.0046144
    [36] Brenner DR, McLaughlin JR, Hung RJ. Previous lung diseases and lung cancer risk: a systematic review and meta-analysis[J]. PLoS One, 2011, 6(3): e17479. doi: 10.1371/journal.pone.0017479
    [37] Turner MC, Chen Y, Krewski D, et al. Chronic obstructive pulmonary disease is associated with lung cancer mortality in a prospective study of never smokers[J]. American Journal of Respiratory and Critical Care Medicine, 2007, 176(3): 285 – 290. doi: 10.1164/rccm.200612-1792OC
    [38] Park HY, Kang D, Shin SH, et al. Chronic obstructive pulmonary disease and lung cancer incidence in never smokers: a cohort study[J]. Thorax, 2020, 75(6): 506 – 509. doi: 10.1136/thoraxjnl-2019-213732
    [39] Oelsner EC, Carr JJ, Enright PL, et al. Per cent emphysema is associated with respiratory and lung cancer mortality in the general population: a cohort study[J]. Thorax, 2016, 71(7): 624 – 632. doi: 10.1136/thoraxjnl-2015-207822
    [40] 王晓燕. 卷烟消费情况及代谢酶基因CYP1A1的m1位点多态性和肺癌关系的研究[D]. 杭州: 浙江大学, 2007.
    [41] IARC Working Group on the Evaluation of Carcinogenic Risks to Humans. Household use of solid fuels and high-temperature fry-ing[M]. Lyon: International Agency for Research on Cancer, 2010.
    [42] Wong JYY, Downward GS, Hu W, et al. Lung cancer risk by geologic coal deposits: a case-control study of female never-smokers from Xuanwei and Fuyuan, China[J]. International Journal of Cancer, 2019, 144(12): 2918 – 2927. doi: 10.1002/ijc.32034
    [43] Wu SM, Zheng XY, You CY, et al. Household energy consump-tion in rural China: historical development, present pattern and policy implication[J]. Journal of Cleaner Production, 2019, 211: 981 – 991. doi: 10.1016/j.jclepro.2018.11.265
    [44] Hystad P, Duong M, Brauer M, et al. Health effects of household solid fuel use: findings from 11 countries within the Prospective Urban and Rural Epidemiology Study[J]. Environmental Health Perspectives, 2019, 127(5): 057003. doi: 10.1289/EHP3915
    [45] Kim C, Seow WJ, Shu XO, et al. Cooking coal use and all-cause and cause-specific mortality in a prospective cohort study of women in Shanghai, China[J]. Environ-mental Health Perspectives, 2016, 124(9): 1384 – 1389. doi: 10.1289/EHP236
    [46] Barone-Adesi F, Chapman RS, Silverman DT, et al. Risk of lung cancer associated with domestic use of coal in Xuanwei, China: retrospective cohort study[J]. BMJ, 2012, 345: e5414. doi: 10.1136/bmj.e5414
    [47] IARC Working Group on the Evaluation of Carcinogenic Risks to Humans. Outdoor air pollution[M]. Lyon: International Agency for Research on Cancer, 2016.
    [48] Raaschou-Nielsen O, Andersen ZJ, Beelen R, et al. Air pollu-tion and lung cancer incidence in 17 European cohorts: prospec-tive analyses from the European Study of Cohorts for Air Pollu-tion Effects (ESCAPE)[J]. The Lancet Oncology, 2013, 14(9): 813 – 822. doi: 10.1016/S1470-2045(13)70279-1
    [49] Lepeule J, Laden F, Dockery D, et al. Chronic exposure to fine particles and mortality: an extended follow-up of the Harvard Six Cities study from 1974 to 2009[J]. Environmental Health Perspec-tives, 2012, 120(7): 965 – 970. doi: 10.1289/ehp.1104660
    [50] Carey IM, Atkinson RW, Kent AJ, et al. Mortality associations with long-term exposure to outdoor air pollution in a national English cohort[J]. American Journal of Respiratory and Critical Care Medicine, 2013, 187(11): 1226 – 1233. doi: 10.1164/rccm.201210-1758OC
    [51] Hart JE, Garshick E, Dockery DW, et al. Long-term ambient multipollutant exposures and mortality[J]. American Journal of Respiratory and Critical care Medicine, 2011, 183(1): 73 – 78. doi: 10.1164/rccm.200912-1903OC
    [52] Cao J, Yang CX, Li JX, et al. Association between long-term exposure to outdoor air pollution and mortality in China: a cohort study[J]. Journal of Hazardous Materials, 2011, 186(2/3): 1594 – 1600.
    [53] Katanoda K, Sobue T, Satoh H, et al. An association between long-term exposure to ambient air pollution and mortality from lung cancer and respiratory diseases in Japan[J]. Journal of Epidemi-ology, 2011, 21(2): 132 – 143. doi: 10.2188/jea.JE20100098
    [54] Ugarte MD, Ibáñez B, Militino AF. Modelling risks in disease mapping[J]. Statistical Methods in Medical Research, 2006, 15(1): 21 – 35. doi: 10.1191/0962280206sm424oa
    [55] Richardson S, Thomson A, Best N, et al. Interpreting posterior relative risk estimates in disease-mapping studies[J]. Environ-mental Health Perspectives, 2004, 112(9): 1016 – 1025. doi: 10.1289/ehp.6740
    [56] 龙海, 陈佳, 黄忠峰, 等. 贵阳市中老年社区居民对慢性阻塞性肺疾病认知情况调查[J]. 中国公共卫生, 2019, 35(4): 468 – 470. doi: 10.11847/zgggws1120992
    [57] Wang C, Xu JY, Yang L, et al. Prevalence and risk factors of chronic obstructive pulmonary disease in China (the China Pulmo-nary Health [CPH] study): a national cross-sectional study[J]. The Lancet, 2018, 391(10131): 1706 – 1717. doi: 10.1016/S0140-6736(18)30841-9
  • 加载中
图(4) / 表(5)
计量
  • 文章访问数:  1086
  • HTML全文浏览量:  451
  • PDF下载量:  70
  • 被引次数: 0
出版历程
  • 接收日期:  2020-11-25
  • 网络出版日期:  2021-06-09
  • 刊出日期:  2021-06-03

目录

    /

    返回文章
    返回