基于多中心调查数据和空间统计模型的全国结核分枝杆菌潜伏感染率估算

doi:10.19982/j.issn.1000-6621.20210661

中国防痨杂志 ›› 2022, Vol. 44 ›› Issue (1): 54-59.doi: 10.19982/j.issn.1000-6621.20210661

基于多中心调查数据和空间统计模型的全国结核分枝杆菌潜伏感染率估算

高磊¹, 张慧², 胡茂桂³, 徐成东³, 夏愔愔², 李涛², 陈伟², 何翼君¹, 曹雪芳¹, 辛赫男¹, 张浩然¹, 赵雁林², 王劲峰³(), 成诗明⁴, 金奇¹(), 刘剑君²()

¹中国医学科学院病原生物学研究所,北京 100730
²中国疾病预防控制中心,北京 102206
³中国科学院地理科学与资源研究所,北京 100101
⁴中国防痨协会,北京 100710

收稿日期:2021-11-20 出版日期:2022-01-10 发布日期:2021-12-29
通信作者: 王劲峰,金奇,刘剑君 E-mail:wangjf@lreis.ac.cn;jinqi@ipbcams.ac.cn;liujj@chinacdc.cn
基金资助:
“十二五”国家科技重大专项(2013ZX10003004-002);“十三五”国家科技重大专项(2017ZX10201302002);中国医学科学院医学与健康科技创新工程项目(2021-I2M-1-037)

Estimation of the national burden on latent tuberculosis infection based a multi-center epidemiological survey and the space statistics model

GAO Lei¹, ZHANG Hui², HU Mao-gui³, XU Cheng-dong³, XIA Yin-yin², LI Tao², CHEN Wei², HE Yi-jun¹, CAO Xue-fang¹, XIN He-nan¹, ZHANG Hao-ran¹, ZHAO Yan-lin², WANG Jin-feng³(), CHENG Shi-ming⁴, JIN Qi¹(), LIU Jian-jun²()

¹Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
²Chinese Center for Disease Control and Prevention, Beijing 102206, China
³Union Medical Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
⁴Chinese Antituberculosis Association, Beijing 100710, China

Received:2021-11-20 Online:2022-01-10 Published:2021-12-29
Contact: WANG Jin-feng,JIN Qi,LIU Jian-jun E-mail:wangjf@lreis.ac.cn;jinqi@ipbcams.ac.cn;liujj@chinacdc.cn
Supported by:
National Science and Technology Major Project during the Twelfth Five-year Plan Period(2013ZX10003004-002);National Science and Technology Major Project during the Thirteenth Five-year Plan Period(2017ZX10201302002);CAMS Innovation Fund for Medical Sciences(2021-I2M-1-037)

摘要/Abstract

摘要：

目的: 估算全国结核分枝杆菌潜伏感染率,为我国结核病综合防控策略提供依据。方法: 综合2013年多中心的基于γ-干扰素释放试验的结核分枝杆菌潜伏感染流行病学调查数据和2013—2019年全国各县(区)肺结核报告发病率等多源数据,基于三位一体空间统计框架利用纠偏空间统计推断模型(Biased Sample Hospital-based Area Disease Estimation,B-SHADE)估算全国结核分枝杆菌潜伏感染率。利用交叉验证法和两个新增研究现场的调查数据进行精度验证。结果: 2013年,我国5岁及以上人群结核分枝杆菌潜伏感染率为18.08%(95%CI:13.73%~22.42%);15岁及以上人群结核分枝杆菌潜伏感染率为20.34%(95%CI:15.63%~25.06%),呈现出随着年龄增长而升高的趋势,男性潜伏感染率[24.02%(95%CI:18.27%~29.77%)]高于女性[16.91%(95%CI:12.13%~21.70%)]。B-SHADE模型对两个新增验证点估计结果的平均绝对误差为0.95%。结论: 基于多中心的流行病学调查结果和B-SHADE的全国结核分枝杆菌潜伏感染率估算填补了近年来相关全国性数据的空缺,将为我国适时加强结核分枝杆菌潜伏感染重点人群预防干预、完善“预防为主”的防控策略提供数据依据。

关键词: 分枝杆菌,结核, 传染病潜伏期, 感染, 预防和防护用药, 模型,统计学

Abstract:

Objective: To estimate the prevalence of latent tuberculosis infection (LTBI) in China, which would provide essential evidence for improving the comprehensive strategy of tuberculosis (TB) control. Methods: The data of multi-center LTBI epidemiological survey in 2013 and the nationwide incidence of reported TB by counties between 2013-2019 were collected, and the prevalence of LTBI was estimated by using the Biased Sample Hospital-based Area Disease Estimation (B-SHADE). The accuracy was verified by using cross validation method with the survey data of two additional study sites. Results: In 2013, the prevalence of LTBI in people 5 years old and above was 18.08% (95%CI: 13.73%-22.42%) and in people 15 years old and above was 20.34% (95%CI: 15.63%-25.06%), respectively. The LTBI prevalence showed a trend of increasing with age and it was significantly higher in men (24.02% (95%CI: 18.27%-29.77%)) than that in women (16.91% (95%CI: 12.13%-21.70%)) at 15 years old and above group. The average absolute error of the estimations of two additional verification sites by B-SHADE model was found to be 0.95%. Conclusion: The prevalence of LTBI estimated by using the results of multi-center epidemiological survey and B-SHADE method makes up the lack of relevant data in recent years, which will provide evidence for China to timely strengthen LTBI management in target populations and to improve the prevention based TB control strategy.

Key words: Mycobacterium tuberculosis, Infectious disease incubation period, Infection, Protective agents, Models,statistical

中图分类号:

R521
C32

高磊, 张慧, 胡茂桂, 徐成东, 夏愔愔, 李涛, 陈伟, 何翼君, 曹雪芳, 辛赫男, 张浩然, 赵雁林, 王劲峰, 成诗明, 金奇, 刘剑君. 基于多中心调查数据和空间统计模型的全国结核分枝杆菌潜伏感染率估算[J]. 中国防痨杂志, 2022, 44(1): 54-59. doi: 10.19982/j.issn.1000-6621.20210661

GAO Lei, ZHANG Hui, HU Mao-gui, XU Cheng-dong, XIA Yin-yin, LI Tao, CHEN Wei, HE Yi-jun, CAO Xue-fang, XIN He-nan, ZHANG Hao-ran, ZHAO Yan-lin, WANG Jin-feng, CHENG Shi-ming, JIN Qi, LIU Jian-jun. Estimation of the national burden on latent tuberculosis infection based a multi-center epidemiological survey and the space statistics model[J]. Chinese Journal of Antituberculosis, 2022, 44(1): 54-59. doi: 10.19982/j.issn.1000-6621.20210661

图/表 5

图1

图2

图3

表1

表2

参考文献 21

[1]	Houben RM, Dodd PJ. The Global Burden of Latent Tuberculosis Infection: A Re-estimation Using Mathematical Modelling. PLoS Med, 2016, 13(10):e1002152. doi: 10.1371/journal.pmed.1002152. doi: 10.1371/journal.pmed.1002152
[2]	Hartman-Adams H, Clark K, Juckett G. Update on latent tuberculosis infection. Am Fam Physician, 2014, 89(11):889-896. pmid: 25077395
[3]	World Health Organization. Guidelines on the management of latent tuberculosis infection. Geneva: World Health Organization, 2015.
[4]	World Health Organization. WHO consolidated guidelines on tuberculosis: tuberculosis preventive treatment: Module 1: prevention. Geneva: World Health Organization, 2020.
[5]	World Health Organization. Global tuberculosis report 2020. Geneva: World Health Organization, 2020.
[6]	全国结核病流行病学抽样调查技术指导组, 全国结核病流行病学抽样调查办公室. 2000年全国结核病流行病学抽样调查报告. 中国防痨杂志, 2002, 24(2):3-46.
[7]	Gao L, Lu W, Bai L, et al. Latent tuberculosis infection in rural China: baseline results of a population-based, multicentre, prospective cohort study. Lancet Infect Dis, 2015, 15(3):310-319. doi: 10.1016/s1473-3099(14)71085-0. doi: 10.1016/s1473-3099(14)71085-0 URL
[8]	Wang JF, Gao BB, Stein A. The spatial statistic trinity: A generic framework for spatial sampling and inference. Environmental Modelling & Software, 2020, 134:104835. doi: 10.1016/j.neuroimage.2012.04.046. doi: 10.1016/j.neuroimage.2012.04.046
[9]	Wang JF, Zhang TL, Fu BJ. A measure of spatial stratified heterogeneity. Ecological Indicators, 2016, 67:250-256. doi: 10.1016/j.ecolind.2016.02.052. doi: 10.1016/j.ecolind.2016.02.052 URL
[10]	Hu MG, Wang JF, Zhao Y, et al. A B-SHADE based best linear unbiased estimation tool for biased samples. Environmental Modelling & Software, 2013, 48:93-97. doi: 10.1016/j.envsoft.2013.06.011. doi: 10.1016/j.envsoft.2013.06.011
[11]	Xu C, Wang J, Hu M, et al. Interpolation of Missing Temperature Data at Meteorological Stations Using P-BSHADE. Journal of Climate, 2013, 26(19):7452-7463. doi: 10.1175/JCLI-D-12-00633.1. doi: 10.1175/JCLI-D-12-00633.1 URL
[12]	Huang DC, Wang JF, Huang JX, et al. Towards Identifying and Reducing the Bias of Disease Information Extracted from Search Engine Data. PLoS Comput Biol, 2016, 12(6):e1004876. doi: 10.1371/journal.pcbi.1004876. doi: 10.1371/journal.pcbi.1004876
[13]	Wang JF, Reis BY, Hu MG, et al. Area disease estimation based on sentinel hospital records. PLoS One, 2011, 6(8):e23428. doi: 10.1371/journal.pone.0023428. doi: 10.1371/journal.pone.0023428
[14]	Wang JF, Xu CD, Hu MG, et al. Global land surface air temperature dynamics since 1880. International Journal of Climatology, 2018, 38:e466-e474. doi: 10.1002/joc.5384. doi: 10.1002/joc.5384 URL
[15]	Wang JF, Xu CD, Hu MG, et al. A new estimate of the China temperature anomaly series and uncertainty assessment in 1900—2006. Journal of Geophysical Research: Atmospheres, 2014, 119(1):1-9. doi: 10.1002/jgrd.v119.1 URL
[16]	Dye C, Scheele S, Dolin P, et al. Consensus statement. Global burden of tuberculosis: estimated incidence, prevalence, and mortality by country. WHO Global Surveillance and Monitoring Project. JAMA, 1999, 282(7):677-686. doi: 10.1001/jama.282.7.677. doi: 10.1001/jama.282.7.677 URL
[17]	Collins JM, Hunter M, Gordon W, et al. False-Positive Tuberculin Skin Test Results Among Low-Risk Healthcare Workers Following Implementation of Fifty-Dose Vials of Purified Protein Derivative. Infect Control Hosp Epidemiol, 2018, 39(6):750-752. doi: 10.1017/ice.2018.59. doi: 10.1017/ice.2018.59 URL
[18]	Li H, Xin H, Qian S, et al. Testing of tuberculosis infection among Chinese adolescents born after terminating the Bacillus Calmette-Guérin booster vaccination: subgroup analysis of a population-based cross-sectional study. Front Med, 2017, 11(4):528-535. doi: 10.1007/s11684-017-0573-0. doi: 10.1007/s11684-017-0573-0 URL
[19]	Newton SM, Brent AJ, Anderson S, et al. Paediatric tuberculosis. Lancet Infect Dis, 2008, 8(8):498-510. doi: 10.1016/S1473-3099(08)70182-8. doi: 10.1016/S1473-3099(08)70182-8 URL
[20]	中华人民共和国国家卫生和计划生育委员会办公厅, 中华人民共和国教育部办公厅 . 关于印发学校结核病防控工作规范(2017 版)的通知. 国卫办疾控发〔2017〕22号. 2017-06-26.
[21]	中华人民共和国国家卫生健康委员会办公厅. 国家卫生健康委办公厅关于印发中国结核病预防控制工作技术规范(2020年版)的通知. 国卫办疾控函〔2020〕279号. 2020-04-02.

年龄组	全人群	男性	女性
≥5岁	18.08 (13.73~22.42)	21.42 (16.26~26.59)	15.31 (10.81~19.81)
≥15岁	20.34 (15.63~25.06)	24.02 (18.27~29.77)	16.91 (12.13~21.70)
5~14岁	2.54 (0.48~4.60)	2.36 (0.81~3.91)	2.51 (0.00~5.84)
15~39岁	10.86 (6.62~15.11)	12.42 (6.33~18.51)	9.86 (6.18~13.55)
40~59岁	22.46 (17.81~27.11)	28.01 (22.48~33.54)	18.23 (12.90~23.55)
≥60岁	38.36 (31.25~45.46)	44.94 (36.22~53.65)	32.18 (23.10~41.26)

验证点	县(区)1	县(区)2	平均绝对误差
江苏省丹阳市、河南省中牟县	4.59	2.42	3.51
江苏省丹阳市、湖南省湘潭县	3.44	3.03	3.23
江苏省丹阳市、广东省英德市	4.31	0.40	2.35
江苏省丹阳市、重庆市南川区	4.60	2.21	3.40
江苏省丹阳市、甘肃省陇西县	4.56	1.05	2.80
河南省中牟县、湖南省湘潭县	0.10	4.66	2.38
河南省中牟县、广东省英德市	0.42	0.57	0.49
河南省中牟县、重庆市南川区	0.02	0.26	0.14
河南省中牟县、甘肃省陇西县	0.12	0.62	0.37
湖南省湘潭县、广东省英德市	5.39	0.48	2.94
湖南省湘潭县、重庆市南川区	5.32	0.04	2.68
湖南省湘潭县、甘肃省陇西县	5.51	2.22	3.87
广东省英德市、重庆市南川区	1.09	0.81	0.95
广东省英德市、甘肃省陇西县	0.19	1.96	1.07
重庆市南川区、甘肃省陇西县	0.24	1.83	1.03
平均绝对误差	2.66	1.50	2.08

基于多中心调查数据和空间统计模型的全国结核分枝杆菌潜伏感染率估算

Estimation of the national burden on latent tuberculosis infection based a multi-center epidemiological survey and the space statistics model

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 5

参考文献 21

相关文章 15

编辑推荐

Metrics

本文评价

友情链接

[1]	何翼君, 曹雪芳, 高磊. 《结核菌素皮肤试验-γ干扰素释放试验两步法的操作技术规范》解读[J]. 中国防痨杂志, 2022, 44(5): 438-441.
[2]	熊姿, 谢赐福, 宋丽新, 王孝君, 田斌, 黄竹林, 徐祖辉, 张锡兴, 白丽琼. 2011—2020年长沙市MTB/HIV双重感染流行特征分析[J]. 中国防痨杂志, 2022, 44(5): 484-488.
[3]	周伟东, 王雪燕, 赵洋, 刘洁清. 胞内分枝杆菌脊柱感染误诊为脊柱结核一例[J]. 中国防痨杂志, 2022, 44(5): 522-525.
[4]	马艳, 陆伟, 高磊, 初乃惠, 周林, 成诗明. 终止结核病流行须加强结核分枝杆菌潜伏感染高危人群筛查和预防性治疗的管理[J]. 中国防痨杂志, 2022, 44(3): 209-214.
[5]	孙桂香, 王娜, 王裕康, 仲昭亿, 李晓娜, 汪求真, 刘玉峰. 2型糖尿病合并肺结核患者感染新型冠状病毒病例报告[J]. 中国防痨杂志, 2022, 44(3): 302-304.
[6]	张灿有, 陈卉, 法立峰, 张慧, 成君. 2016—2019年结核感染控制培训班培训效果评价[J]. 中国防痨杂志, 2022, 44(2): 174-180.
[7]	中国医学科学院病原生物学研究所, 中国疾病预防控制中心, 中国科学院地理科学与资源研究所. 全国结核分枝杆菌潜伏感染率估算专家共识[J]. 中国防痨杂志, 2022, 44(1): 4-8.
[8]	中国防痨协会. 高危人群结核分枝杆菌潜伏感染检测及预防性治疗专家共识[J]. 中国防痨杂志, 2021, 43(9): 874-878.
[9]	李婷, 何金戈, 夏岚, 陈闯, 肖月, 逯嘉, 王丹霞. 新型冠状病毒肺炎疫情对四川省结核病患者登记情况的影响分析[J]. 中国防痨杂志, 2021, 43(9): 929-938.
[10]	曹红, 邓晓岚, 钱冰, 张洁莹, 陈伟. 2014—2019年合肥市MTB/HIV双重感染防治情况分析[J]. 中国防痨杂志, 2021, 43(8): 803-807.
[11]	祁雪, 吴喜蓉, 郭雅洁, 王咏红, 马琦, 陈玉莹, 孙琳, 申阿东. 住院儿童结核分枝杆菌潜伏感染筛查情况分析[J]. 中国防痨杂志, 2021, 43(7): 653-658.
[12]	李京, 袁风顺, 李婷, 李运葵, 高文凤, 杨成彬, 何金戈, 杨文. 2013—2019年四川省凉山彝族自治州HIV/AIDS与结核病时空分布特征及相关性分析[J]. 中国防痨杂志, 2021, 43(7): 708-715.
[13]	郑璐瑶, 陆宇, 陈效友. 非结核分枝杆菌肺病治疗药物研发的现状与挑战[J]. 中国防痨杂志, 2021, 43(7): 729-734.
[14]	谭守勇, 袁园, 邝浩斌, 龚芳, 汪敏. 中性粒细胞/淋巴细胞比值与2型糖尿病并发肺结核患者继发肺部感染相关性[J]. 中国防痨杂志, 2021, 43(6): 602-605.
[15]	姚黎明, 董昭良, 王连波, 贾晨光, 李卓, 刘丰胜. 原发性非特异性脊柱感染与脊柱结核的临床特征分析[J]. 中国防痨杂志, 2021, 43(6): 612-618.