朱雄, 何吕芬, 匡慧慧, 李欢, 李沙, 陈海, 郑霄, 王立程, 陈如寿. 2013-2017年海南省三亚地区气象因素与类鼻疽发病的相关性分析[J]. 疾病监测, 2020, 35(2): 156-161. DOI: 10.3784/j.issn.1003-9961.2020.02.016
引用本文: 朱雄, 何吕芬, 匡慧慧, 李欢, 李沙, 陈海, 郑霄, 王立程, 陈如寿. 2013-2017年海南省三亚地区气象因素与类鼻疽发病的相关性分析[J]. 疾病监测, 2020, 35(2): 156-161. DOI: 10.3784/j.issn.1003-9961.2020.02.016
Xiong Zhu, Lyufen He, Huihui Kuang, Huan Li, Sha Li, Hai Chen, Xiao Zheng, Licheng Wang, Rushou Chen. Correlation between climatic factors and incidence of melioidosis in Sanya, Hainan[J]. Disease Surveillance, 2020, 35(2): 156-161. DOI: 10.3784/j.issn.1003-9961.2020.02.016
Citation: Xiong Zhu, Lyufen He, Huihui Kuang, Huan Li, Sha Li, Hai Chen, Xiao Zheng, Licheng Wang, Rushou Chen. Correlation between climatic factors and incidence of melioidosis in Sanya, Hainan[J]. Disease Surveillance, 2020, 35(2): 156-161. DOI: 10.3784/j.issn.1003-9961.2020.02.016

2013-2017年海南省三亚地区气象因素与类鼻疽发病的相关性分析

Correlation between climatic factors and incidence of melioidosis in Sanya, Hainan

  • 摘要:
    目的探讨气象因素变化对类鼻疽发病的影响, 为类鼻疽的防控提供依据。
    方法对海南省三亚地区2013 — 2017年类鼻疽月平均发病数据进行分析,对可能影响发病的降水量、风速、气温3项研究变量进行单因素相关分析和多元逐步回归分析。
    结果本研究收集类鼻疽患者123 例,平均年龄51.20岁,41~60岁组患者占53.66%。 男性多于女性,占81.30%。 临床表现为败血症的79例(64.23%),肺炎64例(52.03%)。 7 — 10月为高峰期,其病例数占全年的66.67%。 单因素相关分析显示,类鼻疽发病与平均降水量、风速等均有显著相关(r=0.765,P=0.004;r=0.614,P=0.034),与气温无显著相关性((r=0.358,P=0.258)。 多元逐步回归分析显示,类鼻疽发病与平均降水量显著相关(P=0.001)。
    结论三亚地区气候因素是类鼻疽发病流行的重要影响因素,降水量与发病数呈正相关,类鼻疽在降水量大的月份易流行,风速大加速类鼻疽的传播。 三亚地区全年气温波动较小,与发病数无显著相关性。

     

    Abstract:
    ObjectiveTo evaluate the influence of climatic factors on the incidence of melioidosis, and provide evidence for the prevention and control of melioidosis.
    MethodsThe average incidence data of melioidosis in Sanya of Hainan from 2013 to 2017 was analyzed, and univariate correlation analysis and multivariate stepwise regression analysis were performed on three research variables, i.e. precipitation, wind speed and air temperature, which might affect the incidence of melioidosis.
    ResultsIn this study, a total 123 patients with melioidosis were included in the analysis. The average age of the patients was 51.20 years, and those aged 41–60 years accounted for 53.66%. More cases occurred in males than in females, accounting for 81.30%. Sepsis occurred in 79 cases (64.23%) and pneumonia in 64 cases (52.03%). The incidence peak period was during July-October, and 66.67% of the cases occurred during this period. Univariate correlation analysis showed that the incidence of melioidosis was significantly correlated with mean precipitation and wind speed (r=0.765, P=0.004; r=0.614, P=0.034), and there was no significant correlation with air temperature (r=0.358, P=0.258). Multivariate stepwise regression analysis showed that the incidence of melioidosis was significantly correlated with mean precipitation and wind speed (P=0.001).
    ConclusionThe climate is an important factor affecting the incidence of melioidosis in Sanya, and the precipitation is positively correlated with the case number. When the wind speed is high, the spread of melioidosis is accelerated. The air temperature fluctuation in Sanya is small throughout the year, and has no significant correlation with the case number.

     

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