杨胜雄, 潘春柳, 李军, 汪俊华, 冯军, 杨俊, 黄延, 陈洪, 张彬兵, 毛永佳. 2010-2019年贵阳市肺结核患者流行病学特征分析及趋势预测[J]. 疾病监测, 2022, 37(6): 750-754. DOI: 10.3784/jbjc.202112090631
引用本文: 杨胜雄, 潘春柳, 李军, 汪俊华, 冯军, 杨俊, 黄延, 陈洪, 张彬兵, 毛永佳. 2010-2019年贵阳市肺结核患者流行病学特征分析及趋势预测[J]. 疾病监测, 2022, 37(6): 750-754. DOI: 10.3784/jbjc.202112090631
Yang Shengxiong, Pan Chunliu, Li Jun, Wang Junhua, Feng Jun, Yang Jun, Huang Yan, Chen Hong, Zhang Binbing, Mao Yongjia. Epidemiological characteristics of pulmonary tuberculosis cases and incidence trend in Guiyang, 2010−2019[J]. Disease Surveillance, 2022, 37(6): 750-754. DOI: 10.3784/jbjc.202112090631
Citation: Yang Shengxiong, Pan Chunliu, Li Jun, Wang Junhua, Feng Jun, Yang Jun, Huang Yan, Chen Hong, Zhang Binbing, Mao Yongjia. Epidemiological characteristics of pulmonary tuberculosis cases and incidence trend in Guiyang, 2010−2019[J]. Disease Surveillance, 2022, 37(6): 750-754. DOI: 10.3784/jbjc.202112090631

2010-2019年贵阳市肺结核患者流行病学特征分析及趋势预测

Epidemiological characteristics of pulmonary tuberculosis cases and incidence trend in Guiyang, 2010−2019

  • 摘要:
      目的   分析贵州省贵阳市2010—2019年登记的肺结核患者流行病学特征并预测流行趋势,为制定肺结核防控措施提供参考依据。
      方法   运用描述性流行病学方法分析2010—2019年贵阳市肺结核疫情数据,肺结核患者空间核密度图使用ArcMap 10.2软件绘制,通过MATLAB建立GM(1,1)模型预测2021—2022年肺结核流行趋势。
      结果   2010—2019年贵阳市肺结核登记率整体呈下降趋势(趋势χ2=698.560,P<0.001);男女性肺结核登记比为1.72∶1;年龄主要集中在20~64岁;50~、65~、80~岁年龄组肺结核登记人数构成比呈上升趋势;病原学阳性率在2019年超过50%;职业以农民为主,占60.84%;3—7月是高发期,从2013年开始第二个“小高峰”从11月前移至9月;地区分布不均衡,观山湖区、开阳县、南明区肺结核登记数构成比呈现上升趋势;应用GM(1, 1)模型预测2021—2022年贵阳市肺结核登记数分别为2 881.946例、2 788.933例。
      结论   应加强贵阳市重点人群、重点地区的防控和主动筛查,加大肺结核的发现力度;GM(1, 1)模型的预测效果较好,适用于贵阳市肺结核流行趋势的预测。

     

    Abstract:
      Objective  To understand the epidemiological characteristics of registered pulmonary tuberculosis (TB) cases from 2010 to 2019 and predict the incidence trend from 2021 to 2022 in Guiyang, and provide evidence for the local prevention and control of pulmonary TB.
      Methods  Descriptive epidemiological methods were used to analyze the pulmonary TB incidence data in Guiyang from 2010 to 2019. The spatial kernel density map of pulmonary TB patients was drawn using ArcMap 10.2, and the GM (1, 1) model was established by MATLAB to predict the pulmonary TB incidence trend from 2021 to 2022.
      Results  From 2010 to 2019, the pulmonary TB registration rate in Guiyang showed a downward trend (trend χ2=698.560, P<0.001). The ratio of male to female of TB cases was 1.72∶1. The age of the cases ranged from 20 years to 64 years. The proportion of pulmonary TB registration rates in age groups >50 , >65 and >80 years showed upward trends. The positive rate of etiological test was over 50% in 2019. Most cases were farmers, accounting for 60.84%. The annual incidence peak was during March - July, and the sub incidence peak has shifted from November to September since 2013. The area distribution was uneven, and the proportion of pulmonary TB registered in Guanshanhu, Kaiyang and Nanming districts (county) showed upward trends. GM (1, 1) model predicted that the registered case numbers of pulmonary TB in Guiyang in 2021 and 2022 would be 2881.946 and 2788.933, respectively.
      Conclusion  The prevention and control of pulmonary TB in key population and key areas in Guiyang should be strengthened. The prediction effect of GM (1, 1) model is well, which is suitable for predicting the incidence trend of pulmonary TB in Guiyang.

     

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