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张琪, 刘文东, 吴莹, 时影影, 郑金鑫, 周明浩. 江苏省气象因素对猩红热发病影响[J]. 中国公共卫生, 2018, 34(3): 385-389. DOI: 10.11847/zgggws1116823
引用本文: 张琪, 刘文东, 吴莹, 时影影, 郑金鑫, 周明浩. 江苏省气象因素对猩红热发病影响[J]. 中国公共卫生, 2018, 34(3): 385-389. DOI: 10.11847/zgggws1116823
Qi ZHANG, Wen-dong LIU, Ying WU, . Impact of meteorological factors on scarlet fever incidence in Jiangsu province[J]. Chinese Journal of Public Health, 2018, 34(3): 385-389. DOI: 10.11847/zgggws1116823
Citation: Qi ZHANG, Wen-dong LIU, Ying WU, . Impact of meteorological factors on scarlet fever incidence in Jiangsu province[J]. Chinese Journal of Public Health, 2018, 34(3): 385-389. DOI: 10.11847/zgggws1116823

江苏省气象因素对猩红热发病影响

Impact of meteorological factors on scarlet fever incidence in Jiangsu province

  • 摘要:
      目的  探讨江苏省气象因素对猩红热发病的影响,为猩红热的防制工作提供理论依据。
      方法  以江苏省2010年1月1日 — 2015年12月31日猩红热日发病数据及同期气象数据为基础,采用分布滞后非线性模型(DLNM)分析气象因素对猩红热发病的影响。
      结果  江苏省2010 — 2015年共报告猩红热新发病例10 886例,年均发病率为2.29/10万;其中男性新发病例6 748例,女性4 138例,男女性别比为1.63:1。相关分析结果显示,平均气温和相对湿度与猩红热发病均呈负相关(rs = – 0.140、– 0.132,均P < 0.05);平均气压和温差与猩红热发病均呈正相关(rs = 0.051、0.172,均P < 0.05)。DLNM结果显示,温度较低时猩红热发病增多,且对猩红热发病的影响时间较长,其效应在滞后10 d时依然呈正效应,而后随着温度升高,猩红热发病风险降低;气温变化较小时猩红热发病风险相对较小,呈持续时间较长的保护效应,随着气温变化加大,猩红热的发病人数不断增加,且温差越大,猩红热发病风险持续时间越长;干燥气象条件下猩红热发病风险较高且影响时间较长,当相对湿度较为舒适时猩红热的发病数减少,在环境非常潮湿时相对湿度对猩红热发病呈保护效应,且在滞后5 d时仍存在。
      结论  气象因素对猩红热流行的影响呈非线性关系,且具有一定的滞后作用。

     

    Abstract:
      Objective  To investigate the influence of meteorological factors on the incidence of scarlet fever in Jiangsu province for providing evidences to the prevention and control of scarlet fever.
      Methods  We collected data on daily meteorological variables and reported scarlet fever incidents in Jiangsu province from January 1, 2010 to December 31, 2015. Distributed lag non-liner model was applied to explore associations between meteorological factors and scarlet fever incidence.
      Results  During the 6-year period, a total of 10 886 scarlet fever cases (6 748 males and 4 138 females) were registered in Jiangsu province, with a male-to-female ratio of 1.63:1 and an average annual incidence rate of 2.29 per 100 000 population. Spearman correlation analysis indicated that daily mean temperature and relative humidity were reversely correlated with scarlet fever (rs = – 0.140, – 0.132, P < 0.05), while mean pressure and temperature range presented a positive correlation (rs = 0.051, 0.172, P < 0.05). Distributed lag non-liner model revealed that the reported cases were increased at low temperature, and this positive effect was lasted for 10 days; however with the temperature rising, the risk of scarlet fever decreased. For the temperature varying within a small range, the risk of scarlet fever incidence was at a low level and long-term protective effect was also observed; as the temperature changing in a large range, the incidence of scarlet fever increased and the effect persisted for a long time. For relative humidity, the incidence of scarlet fever increased with dry climate, and the effect was also lasted for a long time; with very humid air, the protective effect was still detected with a 5-day lag.
      Conclusion  Several meteorological factors affect scarlet fever incidence with a non-linear relationship and different lag patterns.

     

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