摘要
目的分析海南省淋病流行特征,对未来发病趋势进行定量预测,为进一步有针对性的提出预防控制策略与措施提供依据。方法采用描述流行病学方法对海南省2010-2016年淋病流行特征进行分析,运用ARIMA模型法对其未来3年发病趋势进行预测。结果 2010-2016年海南省淋病发病率及其在甲乙类传染病疾病谱中构成比均呈现上升趋势。发病地区分布不平衡,三亚市为海南省高发地区,超出全省平均发病水平207.94%。人群分布特征为,性别上男性发病数显著多于女性,性别比达5.19:1。年龄上20~34岁组居多,占62.44%。职业上不详和其它人群最多,占27.41%。根据月发病数创建了模型ARIMA(0,1,1)(0,1,1),预测结果表明,2017-2019年海南省淋病发病水平仍然呈现逐年增长趋势。结论海南省防控形势不容忽视,及时调整防控策略与措施,加大防控力度是减少发病的最佳手段。
Objective To analyze the epidemiological features of gonorrhea in Hainan Province and to quantitatively forecast its incidence tendency so as to provide a basis for further targetedly putting forward prevention and control strategies. Methods Descriptive epidemiological method was used to analyze the epidemiological characteristics of gonorrhea in Hainan Province during 2010-2016, and the ARIMA model was applied to forecasting the incidence trend of gonorrhea during 2017-2019. Results The incidence rate of gonorrhea and its proportion in the class A and B infectious diseases in Hainan Province in 2010-2016 presented an upward tendency. The distribution of the incidence areas in the province was unbalanced, and Sanya City was the high incidence area, exceeding the mean level of the province 207.94%. The male patients were significantly more than the female ones, with the sex ratio being 5.19:1. Most of the cases were patients aged 20-34 years, accounting for 62.44%. The patients with unknown occupation and other occupation occupied the majority (27.41%). The ARIMA model (0,1,1) was established according to the number of monthly incidence, and the prediction results indicated that the incidence of gonorrhea in 2017-2019 still showed an increasing tendency year by year. Conclusions The prevention and control situation of gonorrhea in Hainan Province can not be ignored. Timely adjusting prevention and control strategies and measures and strengthening efforts are the best means of reducing its incidence.
出处
《实用预防医学》
CAS
2018年第1期27-29,共3页
Practical Preventive Medicine
基金
海南省自然科学基金项目(812160)
关键词
淋病
流行特征
ARIMA模型
预测
gonorrhea
epidemiological characteristics
ARIMA model
forecast