摘要
目的分析东莞市厚街镇梅毒的流行病学特征,为制定梅毒防控措施提供重要的科学依据。方法对2005-2019年国家疫情信息系统报告,现住址为东莞市厚街镇的梅毒病例数据进行统计分析,建立ARIMA模型进行趋势分析与预测。结果2005-2019年厚街镇共报告4192例梅毒,梅毒发病率总体呈先升后降趋势(χ^2趋势=4.16,P=0.04);报告发病率从2005年19.07/10万增长至2015年113.95/10万后开始回落;2005-2015年梅毒发病率年平均增长率为33.11%,2015-2019年梅毒发病率年平均增长率为-8.87%,不同年龄段的性别差异有统计学意义(χ^2=456.32,P=0.00);预测2020年全镇总体呈现趋势平稳状态,5-8月为高发期,2020年厚街镇梅毒月发病率预测值的95%可信区间范围在1.83/10万~8.15/10万。结论近年厚街镇梅毒发病率下降明显;预测2020年全镇梅毒发病趋势相对平稳,应采取综合控制措施持续降低梅毒发病率。
Objective To analyze the epidemiological characteristics of syphilis in Houjie town,Dongguan city,and to provide scientific basis for the strategy development of syphilis prevention and control.Methods The data of the syphilis cases living in Houjie were downloaded from the national disease prevention and control information system.The AMIRA model was established for trend analysis and prediction.Results A total of 4192 syphilis cases were reported from 2005 to 2019,indicating the trend of increase first and decrease then(χ^2 trend=4.16,P=0.04),from 19.07/100,000 in 2005 to 113.95/100,000 in 2015,and then began to decline.The average annual growth rate of syphilis was 33.11%from 2005 to 2015 and-8.87%from 2015 to 2019.The differences were statistically significant in gender between different age groups(χ^2=456.32,P=0.00).It is predicted that the overall trend of syphilis epidemic in the town would be stable in 2020,and the morbidity peak were during May and August.The 95%confidence interval for the monthly incidence of syphilis was between 1.83/100,000 and 8.15/100,000 predicted in 2020.Conclusion The incidence of syphilis has decreased significantly in recent years.The incidence of syphilis in Houjie is predicted to be relatively stable in 2020.Comprehensive control measures should be undertaken to reduce the incidence of syphilis.
作者
龚志勇
赵侃仪
陈金燕
郑少敏
罗文勇
GONG Zhiyong;ZHAO Kanyi;CHEN Jinyan;ZHENG Shaomin;LUO Wenyong(Houjie Town Health Service Center,Dongguan 523960,Gongdong)
出处
《中国艾滋病性病》
CAS
CSCD
北大核心
2020年第11期1240-1243,共4页
Chinese Journal of Aids & STD
关键词
梅毒
流行特征
预测
ARIMA模型
syphilis
epidemic characteristics
prediction
ARIMA model