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首页> 外文期刊>The Science of the Total Environment >Effect of meteorological factors on scarlet fever incidence in Guangzhou City, Southern China, 2006-2017
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Effect of meteorological factors on scarlet fever incidence in Guangzhou City, Southern China, 2006-2017

机译:2006-2017年中国广州市气象因素对猩红热发病的影响

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摘要

Objective: To explore the relationship between meteorological factors and scarlet fever incidence from 2006 to 2017 in Guangzhou, the largest subtropical city of Southern China, and assist public health prevention and control measures.Methods: Data for weekly scarlet fever incidence and meteorological variables from 2006 to 2017 in Guangzhou were collected from the National Notifiable Disease Report System (NNDRS) and the Guangzhou Meteorological Bureau (GZMB). Distributed lag nonlinear models (DLNMs) were conducted to estimate the effect of meteorological factors on weekly scarlet fever incidence in Guangzhou.Results: We observed nonlinear effects of temperature, relative humidity, and wind velocity. The risk was the highest when the weekly mean temperature was 31 degrees C during lag week 14, yielding a relative risk (RR) of 1.48 (95% CI: 1.01-2.17). When relative humidity was 43.5% during lag week 0, the RR was 1.49 (95% CI: 1.04-2.12); the highest RR (1.55, 95% CI: 1.20-1.99) was reached when relative humidity was 93.5% during lag week 20. When wind velocity was 4.4 m/s during lag week 13, the RR was highest at 3.41 (95% CI: 1.57-7.44). Positive correlations were observed among weekly temperature ranges and atmospheric pressure with scarlet fever incidence, while a negative correlation was detected with aggregate rainfall. The cumulative extreme effect of meteorological variables on scarlet fever incidence was statistically significant, except for the high effect of wind velocity.Conclusion: Weekly mean temperature, relative humidity, and wind velocity had double-trough effects on scarlet fever incidence; high weekly temperature range, high atmospheric pressure, and low aggregate rain fall were risk factors for scarlet fever morbidity. Our findings provided preliminary, but fundamental, information that may be useful for a better understanding of epidemic trends of scarlet fever and for developing an early warning system. Laboratory surveillance for scarlet fever should be strengthened in the future. (C) 2019 Elsevier B.V. All rights reserved.
机译:目的:探讨华南最大的亚热带城市广州市2006-2017年猩红热发病与气象因素的关系,并采取公共卫生预防和控制措施。方法:2006年每周猩红热发病与气象变量的数据到2017年广州市的数据来自国家法定疾病报告系统(NNDRS)和广州市气象局(GZMB)。运用分布滞后非线性模型(DLNMs)评估气象因素对广州每周猩红热发病率的影响。结果:我们观察到温度,相对湿度和风速的非线性影响。在第14周的滞后周中,当每周平均温度为31摄氏度时,风险最高,产生的相对风险(RR)为1.48(95%CI:1.01-2.17)。在滞后第0周相对湿度为43.5%时,RR为1.49(95%CI:1.04-2.12);在滞后20周相对湿度为93.5%时达到最高RR(1.55,95%CI:1.20-1.99)。在滞后13周风速为4.4 m / s时RR最高为3.41(95%CI) :1.57-7.44)。每周温度范围和大气压力与猩红热发生率之间呈正相关,而与总降雨呈负相关。气象变量对猩红热发生的累积极端影响具有统计学意义,除了风速的影响较大。结论:每周平均温度,相对湿度和风速对猩红热的发生有双谷效应;每周高温范围高,大气压高和降雨总量低是猩红热发病的危险因素。我们的发现提供了初步但基本的信息,可能有助于更好地了解猩红热的流行趋势并建立预警系统。将来应加强实验室对猩红热的监测。 (C)2019 Elsevier B.V.保留所有权利。

著录项

  • 来源
    《The Science of the Total Environment》 |2019年第1期|227-235|共9页
  • 作者单位

    Guangzhou Ctr Dis Control & Prevent, Dept Infect Dis Control & Prevent, Baiyun Dist Qi Rd, Guangzhou 510440, Guangdong, Peoples R China;

    Guangzhou Ctr Dis Control & Prevent, Dept Infect Dis Control & Prevent, Baiyun Dist Qi Rd, Guangzhou 510440, Guangdong, Peoples R China;

    Guangzhou Ctr Dis Control & Prevent, Dept Infect Dis Control & Prevent, Baiyun Dist Qi Rd, Guangzhou 510440, Guangdong, Peoples R China;

    Guangzhou Ctr Dis Control & Prevent, Dept Infect Dis Control & Prevent, Baiyun Dist Qi Rd, Guangzhou 510440, Guangdong, Peoples R China;

    Guangzhou Ctr Dis Control & Prevent, Dept Infect Dis Control & Prevent, Baiyun Dist Qi Rd, Guangzhou 510440, Guangdong, Peoples R China;

    Guangzhou Ctr Dis Control & Prevent, Dept Infect Dis Control & Prevent, Baiyun Dist Qi Rd, Guangzhou 510440, Guangdong, Peoples R China;

    Guangzhou Ctr Dis Control & Prevent, Baiyun Dist Qi Rd, Guangzhou 510440, Guangdong, Peoples R China;

    Guangzhou Ctr Dis Control & Prevent, Baiyun Dist Qi Rd, Guangzhou 510440, Guangdong, Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Distributed lag nonlinear models; Meteorological factors; Scarlet fever;

    机译:分布式滞后非线性模型气象因素猩红热;

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