安徽疟疾疫情时空分析及影响因素研究

发布时间：2018-04-21 01:15

本文选题：疟疾 + 发病率　；参考：《中国疾病预防控制中心》2008年博士论文

【摘要】： 背景:疟疾是一种由疟原虫引起的、经按蚊叮咬而传播的重要的寄生虫病。全球仍有107个国家和地区,约32亿人口受到疟疾的威胁,每年有3亿多人受感染,病死者超过100万。上世纪50、60年代,我国疟疾流行严重,经过半个多世纪的努力,我国疟疾防治工作取得巨大成效,20世纪末全国疟疾疫情下降到最低,但近年来疟疾发病数呈明显回升。2000年以来以安徽省为代表的中部地区在北纬32°以北的单一中华按蚊区陆续出现疟疾疫情回升、小暴发点或局部暴发流行。影响疟疾流行的因素复杂,寄生虫、蚊媒、人类宿主和环境等因素及其交互作用决定了疟疾传播和感染的风险。近年来基于地理信息系统的3S已成为疟疾等自然疫源性传染病研究新的技术手段与方法,而将统计学方法与空间技术相结合也成为一个新的研究方向。目的:通过对20世纪90年代以来安徽省疟疾疫情时空分析及影响因素研究,为该省及类似地区的疟疾防控提供科学依据。通过尝试3S空间分析技术与统计分析方法在疟疾研究领域的综合应用,为类似的研究提供方法学参考。方法:收集整理1990～2006年安徽省疟疾疫情监测数据,安徽省1:1 000 000县区级数字区,并加工处理安徽省1:5万的乡镇边界图。采用空间分析技术提取安徽省各乡镇温度、降雨量等气象监测数据,海拔、NDVI、湿度指数、水体等环境数据以及人口、GDP等数据,建立乡镇疟疾流行的地理信息系统数据库。对淮北地区高发自然村庄采用抽样调查及GPS定位的方法,收集居民生产生活及行为因素、疟疾病例诊治及水体定位等相关信息,建立村庄尺度的研究数据库。综合应用时空扫描聚类分析方法、时间序列分析方法、主成分分析及logistic回归模型、Poisson回归模型等统计分析方法对资料进行分析处理。所采用的软件包括Arc GIS 9.0软件、SaTScan7.0空间聚类分析软件、SAS9.1及SPSS13.0统计分析软件。结果:①“2004～2006年淮北地区”是安徽省20世纪90年代以来疟疾流行新“热点”,淮北地区疟疾传播季节延长。②时间纵向上,温度、降雨量与疟疾发病率序列呈现显著的互相关关系,即“月平均气温”升高则“月疟疾发病率”上升,呈现正相关关系,在滞后0～3个月的相关性均显著;“月降雨量”增加则“月疟疾发病率”上升,在滞后1～3个月的相关性显著,不同的是,其相关性出现在至少滞后1个月,表明降雨量与疟疾发病间的关系相对缓慢一些。③地区横向上,安徽省各乡镇的疟疾发病率与温度、降雨量、NDVI和海拔因素有关,即“冬季/年最低气温”、“年降雨总量”、“海拔”和“NDVI”4个因素与安徽乡镇是否发生疟疾有关。在控制模型中其它自变量不变的情况下,前3个因素数值增加,则乡镇疟疾发生的可能性越小,而NDVI增加,则乡镇发生疟疾的可能性越大。④淮北地区居民不良生活生产行为使该地区疟疾发病风险增加,分析结果显示,村民露宿习惯比例每增加1%,村发生疟疾病例的风险增加18%,而耕种豆类农作物也增加了村民发生疟疾的危险。⑤采用2000～2007年5月安徽省疟疾月发病率建立的ARIMA时间序列模型对2007年6月份的发病率的预测值为5.437/10万,95%可信区间为[2.308/10万,12.808/0万],实际监测发病率为5.334/10万,预测的相对误差为1.9%,预测效果良好。结论:本课题研究重点回答了2000年以来安徽省疟疾疫情回升的新的时空热点地区,并重点回答了安徽省南北地区疟疾疫情存在差异的主要影响因素。淮北地区除当地地形地貌、自然环境等特点影响其疟疾流行外,当地居民不良的生产生活行为因素加大了蚊媒接触机率,增加了疟疾发生的机率,研究结果具有很现实的指导意义。研究中虽然没有直接得出病例诊治及时性对该地区疟疾疫情的实际影响,但从控制传染源的理论出发,疟疾病人的及时发现和治疗无疑也是控制疟疾疫情流行的重要方面。课题研究结果将为安徽等我国中部疟疾流行地区建立疟疾的早期预警预测系统奠定基础。此外,时间序列模型可以很好地拟合疟疾发病率在时间序列上的变动趋势,在人群免疫状态、人口流动、防制措施等人群疟疾易感性指标没有发生大幅度变化的情况下,可以用来对未来的疟疾发病率进行预测,为疟疾防治工作提供服务。
[Abstract]:Background: malaria is an important parasitic disease caused by the malaria parasite, which is transmitted by the Anopheles bite. There are still 107 countries and regions around the world, about 3 billion 200 million people are threatened by malaria, more than 300 million people are infected each year and the deceased more than 1 million. In the 50,60 years of last century, China's malaria epidemic was serious, after half a century of efforts, our country The malaria control work has made great achievements, and the malaria epidemic in the whole country declined to the lowest level at the end of twentieth Century, but in recent years, the number of malaria incidence showed an obvious recovery in.2000 years since.2000, the single Anopheles sinensis area in the central region of the north latitude 32 degrees north of the north latitude was gradually rising, the small outbreak point or the local outbreak. The factors such as parasites, mosquito vectors, human hosts and environment and their interaction determine the risk of malaria transmission and infection. In recent years, 3S based on GIS has become a new technical means and method for the research of natural epidemic infectious diseases such as malaria, and the combination of the method of integration and the space technology has also become a one. New research direction.
Objective: to provide scientific basis for the prevention and control of malaria in the province and the similar areas, through the analysis of the spatio-temporal analysis of the epidemic situation of malaria in Anhui province since 1990s, and the comprehensive application of the 3S spatial analysis technique and statistical analysis method in the field of malaria research, and provide a methodological reference for the similar research.
Methods: to collect and collate the data of malaria surveillance in Anhui province for 1990~2006 years, Anhui province 1:1 000000 county level digital area, and processing and processing the township boundary map of 1:5 million in Anhui Province, and using spatial analysis technology to extract the meteorological monitoring data, such as the temperature and rainfall of each township in Anhui Province, the sea drawing, the NDVI, humidity index, water body and other environmental data and population. Based on the data of GDP and other data, the geographic information system database of malaria in villages and towns is set up. The method of sampling survey and GPS positioning for the high incidence natural villages in Huaibei area is adopted to collect the related information of the living and behavioral factors, the diagnosis and treatment of malaria cases and the location of the water body, and to establish the research database of the village scale. Analysis method, time series analysis method, principal component analysis and logistic regression model, Poisson regression model and other statistical analysis methods are used to analyze the data. The software used includes Arc GIS 9 software, SaTScan7.0 spatial clustering analysis software, SAS9.1 and SPSS13.0 statistical analysis software.
Results: (1) the "2004~2006 year Huaibei area" was the new "hot spot" of malaria epidemic in Anhui province since 1990s, and the malaria transmission season in Huaibei region was prolonged. The correlation is significant in the 0~3 month lag; "monthly rainfall" increases, the incidence of "monthly malaria" rises, and the correlation of the lag of 1~3 months is significant. The correlation occurs at least for 1 months, indicating that the relationship between rainfall and malaria is relatively slow. (3) the region is horizontal, Anhui The incidence of malaria in each township is related to the temperature, rainfall, NDVI and altitude factors, namely, "winter / annual minimum temperature", "annual rainfall total", "altitude" and "NDVI", 4 factors are related to the occurrence of malaria in Anhui township. In the control model, the other independent variables are unchanged, the first 3 factors increase, then the township malaria The smaller the possibility of the occurrence, and the increase in NDVI, the greater the possibility of malaria in the villages and towns. (4) the risk of malaria in the Huaibei region increased the risk of malaria in the region. The analysis showed that the proportion of villagers' habit of sleeping was increased by 1%, and the risk of malaria cases increased by 18%, while the cultivated legumes also increased the villagers. The ARIMA time series model based on the monthly incidence of malaria in Anhui province in May of 2000~2007 years was predicted to be 5.437/10 million in June 2007, 95% confidence interval of [2.308/10 million, 12.808/0 million), the actual monitoring incidence was 5.334/10 million, the relative error of pre test was 1.9%, and the prediction effect was good.
Conclusion: the research focuses on the new hot spots of the recovery of malaria in Anhui province since 2000, and emphatically answered the main factors affecting the difference in malaria epidemic in the northern and southern regions of Anhui province. In addition to the local topography, the natural environment and other characteristics of Huaibei, the poor production of local residents Life and behavior factors increase the rate of mosquito contact and increase the probability of malaria. The research results have practical guiding significance. Although the study does not directly affect the actual impact of case diagnosis and treatment on the epidemic situation in this area, the timely discovery and treatment of malaria patients are undoubtedly also from the theory of controlling the source of infection. The results of the study will lay the foundation for the establishment of early warning system for early warning of malaria in Anhui and other malaria endemic areas in Central China.
In addition, the time series model can well fit the change trend of the incidence of malaria in time series. In the case of population immune state, population flow, control measures and others, the malaria susceptibility index can be used to predict the incidence of malaria in the future and provide service for malaria control work.

【学位授予单位】：中国疾病预防控制中心
【学位级别】：博士
【学位授予年份】：2008
【分类号】：R181.8;R531.3

【引证文献】