我国疟疾流行时空分布特征及淮河流域疟疾环境影响因素研究

发布时间：2018-02-02 23:11

本文关键词： 疟疾空间流行病学淮河流域时空聚类环境影响因素　出处：《中国人民解放军军事医学科学院》2013年硕士论文　论文类型：学位论文

【摘要】：目的总结分析近年来我国疟疾时间、空间和人间分布特征，明确我国疟疾高发区域和疟疾季节变化规律；分析淮河流域疟疾时空分布特征和环境因素关系，揭示疟疾时空聚集性和影响疟疾发生及流行的环境因素，建立疟疾风险预测模型。为其他虫媒传染病时空分布研究提供方法、技术借鉴，为疟疾高发区监测预警及科学防控提供参考。方法 1.数据收集和处理方法收集研究区域疟疾疫情网络报告数据、人口信息、气象监测站点数据、水系分布及人均GDP指标，利用Excel2010对数据进行比对、筛选、排序、整理，统一不同数据间相互关联的标准，将环境数据与发病数据进行关联；利用Kriging空间插值法，估计未知区域的气象数据；利用Supermap软件计算淮河流域各区县河网密度。 2.分析方法 2.1建立地理信息数据库将不同研究区域和不同年份的疟疾发病率与1：400万电子地图关联，利用ArcGIS9.0建立全国省（直辖市、自治区）级和淮河流域县（市、区）级地理信息数据库。 2.2描述性分析方法利用描述性流行病学分析方法从时间、空间、人间三个方面分析全国疟疾分布特征，利用ArcGIS制图展示全国及淮河流域疟疾的空间分布特征。 2.3时空聚类分析方法利用SaTScan时空扫描分析方法，从纯空间聚类分析、纯时间聚类分析和时空聚类分析三个角度探索淮河流域疟疾时空聚集性，分析淮河流域疟疾高发区的时空分布特征。 2.4偏相关和Spearman等级相关分析法利用偏相关分析方法分析疟疾发病与气象因子的相关关系；利用Spearman等级相关分析方法分析疟疾发病与河网密度、人均GDP的相关关系。 2.5二项Logistic回归和负二项回归分析法利用二项Logistic回归方法分析影响疟疾是否发生的自然因素；利用负二项回归分析方法分析影响疟疾流行的环境因素，建立负二项回归模型并对模型进行评价。结果 1.全国疟疾分布特征 1.1全国疟疾疫情概况 2006-2010年我国共报告疟疾发病病例154,711例，死亡病例94例，疟疾年均发病率为2.34/10万，疟疾发病逐年递减。发病病例中，间日疟占79.42%，恶性疟占5.02%，未分型占15.56%；死亡病例中，恶性疟占80.85%，间日疟占11.70%，未分型占7.45%。病例报告中，，实验室诊断病例占63.06%，临床诊断病例占36.94%。 1.2全国疟疾疫情空间分布安徽、海南、云南三省疟疾五年总发病数占全国疟疾五年总发病数的77.48%，三省年均发病率分别为27.29/10万、23.28/10万、11.73/10万位居全国前三位，河南、贵州、湖北、西藏次之，发病率在1/10万-5/10万之间，东北和西北的大部分地区年均发病率低于0.1/10万。间日疟病例中，安徽省间日疟最多，占总间日疟的58.59%，其次为云南省和河南省，分别占15.58%和11.52%，其余各省间日疟构成比均低于4%，其中内蒙古近五年无间日疟病例报告；恶性疟病例中，云南省恶性疟最多，占总恶性疟的69.07%，其余各省恶性疟构成比均低于6%，其中西藏和宁夏近五年无恶性疟病例报告。 1.3全国疟疾疫情时间分布全国疟疾疫情主要集中在夏秋季节（7-10月），8月发病率最高，冬季发病率较低（11-2月），2月发病率最低。不同地区疟疾发病呈不同的季节性，且疟疾发病季节性逐年减弱。 1.4全国疟疾疫情人群分布从性别分布来看，疟疾患者以男性为主，占64.8%；从职业分布来看，农民占63.67%，其次是学生和城镇居民，分别占14.52%和10.12%；从年龄分布来看，年龄超过60岁的老年人疟疾发病率较高为3.41/10万，0-5岁的婴幼儿发病率较低为1.32/10万。 2.淮河流域疟疾时空聚类分析 2.1淮河流域疟疾流行时空分布特征 2006-2010年淮河流域疟疾五年总发病数占全国疟疾五年总发病数的62.42%，该流域年均发病率为11.53/10万，约为全国年均发病率的5倍，疟疾发病呈逐年递减趋势。安徽省的涡阳县、蒙城县、利辛县、濉溪县、烈山区和河南省的永城市年均发病率较高均超过100/10万。该区域疟疾发病主要集中在7-10月，其中8月发病率最高，2月发病率最低，且疟疾发病季节性逐年减弱。 2.2空间聚类分析 2006年聚集区中心点坐标为（33.92N，116.77E），半径为71.64km，包括濉溪县及周边共15个县的区域；2007-2009年聚集区中心点坐标为（33.27N，116.56E），半径为92.78km，包括蒙城县及周边共30个县的区域；2010年聚集区中心点坐标为（33.27N，116.56E），半径为109.80km，包括蒙城县及周边共36个县的区域。 2.3时间聚类分析 2006年疟疾发病相对高发期为7-11月，2007-2009年疟疾发病相对高发期均为6-10月，2010年疟疾发病相对高发期为5-10月。 2.4时空聚类分析 2006-2010年淮河流域疟疾发病的聚集区中心点坐标为(33.27N，116.56E)，半径为92.78km，包括蒙城县及周边共30个县在内的区域，疟疾发病相对高发期自2006年6月至2008年11月。 2.5淮河流域疟疾高发区疟疾时空分布特征淮河流域疟疾高发区中25个县全年均有疟疾发生，且疟疾发病呈明显季节性，发病大多集中在7-10月，10月发病率最高，2月发病率最低。发病率较高的地区为涡阳县、濉溪县、蒙城县和烈山区。 3.淮河流域疟疾发病与环境因素的关系 3.1疟疾发病与环境因素的相关性在淮河流域疟疾高发期，疟疾发病与当月最高温、前一月降雨量、河网密度呈显著正相关，与当月降雨量、人均GDP呈显著负相关；与前一月最高温、当月及前一月最低温、当月及前一月平均温、当月及前一月相对湿度无显著相关性。 3.2影响疟疾是否发生的环境因素在疟疾高发期，当月最高温超过27.25℃的地区，有发生疟疾的可能。 3.3影响疟疾流行的环境因素在有疟疾发生的地区，当月降雨量(R0)过大，疟疾发病率(Y)较低，预测疟疾流行的负二项回归模型为Y=exp(17.1897-0.2387R_0)。结论 1.2006-2010年我国疟疾发病大幅度下降，但抗疟形势仍为严峻。我国疟疾流行呈明显的空间异质性特点，疟疾发病多集中在淮河流域、西南边境和海南岛。我国疟疾发病多集中在夏秋季，且疟疾发病季节性逐年减弱。疟疾患者多为务农者，以男性为主，老年人发病率较高。 2.我国淮河流域疟疾疫情分布在空间上呈明显聚集性，在时间上呈明显季节性。不同年份疟疾发病的空间聚集区不同，多以蒙城县及周边共30个县的区域为主。不同年份疟疾发病的时间聚集期不同，一般发病多集中在6-10月。淮河流域疟疾高发区大部分县（市、区）全年均有疟疾发生，春季疫情开始扩散，秋季发病率较高。 3.淮河流域疟疾高发期的疟疾发病与当月最高温、前一月降雨量、河网密度、当月降雨量、人均GDP存在相关性，与相对湿度等其他气象因素无显著相关性。疟疾是否发生受当月最高气温的影响，疟疾流行强弱受当月降雨量影响，当月降雨量过大会抑制疟疾流行。环境因素能够影响疟疾的发生和流行，可以通过对环境因素的监测来预测淮河流域疟疾的流行潜势。
[Abstract]:objective
Analysis of malaria in China in recent years, the time, space and human distribution, clear rules of our regional and seasonal variation of malaria malaria malaria; analysis of the relationship between the Huaihe basin and spatial distribution characteristics and environmental factors, revealing the environmental factors of malaria temporal aggregation and influence of malaria incidence and prevalence of malaria risk prediction model is established for other insect borne infectious. Study on temporal and spatial distribution of disease methods provide technical reference, to provide reference for malaria area monitoring and early warning of scientific prevention and control.
Method
1. data collection and processing methods
Collected from the study area of malaria epidemic reporting network data, population information data, meteorological monitoring stations, water distribution and the per capita GDP, using Excel2010 to compare the data, screening, sorting, sorting, the relationship between different data standards, data association and incidence data environment; using Kriging spatial interpolation method, estimation of Meteorological data the unknown area of the Huaihe basin are calculated by using Supermap software; the county drainage density.
2. analysis method
2.1 the establishment of geographic information database
The incidence of malaria in different research areas and different years is associated with 1:400 million electronic map, and ArcGIS9.0 is used to establish the National Geographic Information Database of provinces (municipalities and autonomous regions) and Huaihe basin counties (cities, districts).
2.2 descriptive analysis method
Descriptive epidemiological analysis was used to analyze the distribution characteristics of malaria in China from three aspects: time, space and human. The spatial distribution characteristics of malaria in the whole country and the Huaihe River Basin were revealed by ArcGIS cartography.
2.3 method of spatio-temporal clustering analysis
Using SaTScan spatial and temporal scanning analysis method, from the three aspects of pure spatial cluster analysis, pure time clustering analysis and spatio-temporal cluster analysis, we explored the spatial and temporal clustering characteristics of malaria in Huaihe River Basin, and analyzed the temporal and spatial distribution characteristics of malaria in Huaihe basin.
2.4 partial correlation and Spearman grade correlation analysis
The correlation between malaria incidence and meteorological factors was analyzed by partial correlation analysis. Correlation between malaria incidence and river network density and GDP per capita was analyzed by Spearman rank correlation analysis.
2.5 two term Logistic regression and negative two regression analysis
Two Logistic regression methods were used to analyze the natural factors that affected malaria occurrence. The negative two regression analysis method was used to analyze the environmental factors that affected malaria epidemic. A negative two regression model was established and the model was evaluated.
Result
1. national distribution characteristics of malaria
1.1 malaria epidemic situation in China
2006-2010 years of China reported malaria cases in 154711 cases, 94 cases of malaria deaths, the average annual incidence rate of 2.34/10 million, the incidence of malaria incidence decreased year by year. In case of vivax malaria falciparum malaria accounted for 79.42%, accounting for 5.02%, undifferentiated type accounted for 15.56%; death cases of falciparum malaria, Plasmodium vivax malaria accounted for 80.85%, accounting for 11.70%, not type accounted for 7.45%. case reports, laboratory diagnosed cases of clinically diagnosed cases accounted for 63.06%, accounted for 36.94%.
1.2 spatial distribution of malaria in China
Anhui, Hainan, Yunnan provinces five years the total incidence of malaria malaria accounted for five years the total incidence of 77.48% provinces, the average annual incidence was 27.29/10 million, 23.28/10 million, 11.73/10 million, ranked the top three, Henan, Guizhou, Hubei, Tibet, the incidence rate of between 1/10 million -5 million /10, the average annual in most parts of the northeast and northwest of the rate of less than 0.1/10 million. Vivax malaria cases in Anhui province accounted for most of vivax malaria, Plasmodium vivax 58.59%, followed by Yunnan province and Henan Province, accounted for 15.58% and 11.52%, the rest of the vivax constituent ratio was lower than 4%, which departed Inner Mongolia for nearly five years, malaria case report; malignant malaria, Plasmodium falciparum malaria in Yunnan Province, the total 69.07% falciparum malaria, Plasmodium falciparum constitute more than the rest of the provinces were less than 6%, of which Tibet and Ningxia nearly five years without a malignant malaria case report.
1.3 time distribution of malaria in China
Malaria prevalence in China is mainly concentrated in the summer and autumn season (7-10 months). The incidence is the highest in August, the incidence is low in winter (11-2 months), and the incidence is lowest in February. The incidence of malaria varies seasonally in different regions, and the seasonal incidence of malaria decreases year by year.
1.4 population distribution of malaria in China
From the gender distribution of malaria patients, male dominated, accounting for 64.8%; from the occupation distribution, farmers accounted for 63.67%, followed by students and urban residents, which accounted for 14.52% and 10.12%; from the age distribution, people over the age of 60 malaria high incidence rate of 3.41/10 million, 0-5 year old infant morbidity rate is low 1.32/10 million.
Spatio-temporal clustering analysis of malaria in 2. Huaihe Basin
Spatial and temporal distribution characteristics of malaria epidemic in 2.1 Huaihe River Basin
2006-2010 years of Huaihe river five years the total incidence of malaria malaria accounted for five years the total incidence of 62.42%, the annual incidence rate of 11.53/10 million, about 5 times the average annual incidence, the incidence of malaria was decreasing year by year. Anhui province Guoyang County, Mengcheng County, Lixin County, Suixi County, Lieshan District Henan province and Yongcheng city is higher than the average annual incidence rate of 100/10 million. The incidence of malaria is mainly concentrated in the 7-10 month, which in August the highest incidence rate in February, the lowest incidence of malaria, and the seasonal incidence was decreasing year by year.
2.2 space clustering analysis
In 2006 the gathering area of the center coordinates is (33.92N, 116.77E), radius of 71.64km, including the Suixi county and the surrounding 15 counties in the region; 2007-2009 years gathering area for the center coordinates (33.27N, 116.56E), radius of 92.78km, including the Mengcheng county and the surrounding 30 counties in the region gathered in the centre of 2010; coordinates (33.27N, 116.56E), radius of 109.80km, including the Mengcheng county and the surrounding 36 county area.
2.3 time clustering analysis
The relative high incidence of malaria in 2006 is 7-11 months, and the relative high incidence of malaria in 2007-2009 years is 6-10 months, and the relative high incidence of malaria in 2010 is 5-10 months.
2.4 spatio-temporal clustering analysis
In the 2006-2010 years, the central point coordinates of malaria accumulation in the Huaihe River Basin were (33.27N, 116.56E), and the radius was 92.78km, including 30 counties in Mengcheng county and its surrounding areas. The incidence of malaria was relatively high from June 2006 to November 2008.
Spatial and temporal distribution characteristics of malaria in high incidence area of malaria in 2.5 Huaihe Basin
Malaria incidence occurred in 25 counties in the high incidence area of malaria in Huaihe basin all year round, and the incidence of malaria was significantly seasonal. Most of the cases occurred in 7-10 months. The incidence was the highest in October, and the lowest incidence in February. The areas with high incidence were: Woyang County, Suixi County, Mengcheng county and Lieshan district.
The relationship between the incidence of malaria and environmental factors in 3. Huaihe River Basin
3.1 correlation between the incidence of malaria and environmental factors
The high incidence of malaria in the Huaihe River Basin, the incidence of malaria and the highest temperature, January before rainfall, showed a significant positive correlation with the rainfall, river density, was negatively related to GDP per capita; and the most high temperature before January, and January month before the most low temperature, and the month before January month and average temperature, relative humidity had no significant January before the correlation.
3.2 environmental factors affecting the occurrence of malaria
In the period of high incidence of malaria, there is a possibility of malaria in the region where the highest temperature of the month is over 27.25 degrees centigrade.
3.3 environmental factors affecting the malaria epidemic
In areas with malaria, the monthly rainfall (R0) is too large, the incidence of malaria (Y) is low, and the negative two regression model for predicting malaria epidemic is Y=exp (17.1897-0.2387R_0).
conclusion
1.2006-2010 China's malaria incidence decreased significantly, but the situation is still grim. Anti malaria malaria epidemic in China has obvious characteristics of spatial heterogeneity, the incidence of malaria and more concentrated in the Huaihe basin, southwest border and Hainan Island in China. The incidence of malaria is more concentrated in summer and autumn, and the incidence of malaria was decreasing year by year. The seasonal malaria patients for farmers, male dominated, the elderly high incidence.
2. China's Huaihe basin malaria epidemic distribution were obviously gathered in space, showed obvious seasonal time in different years. The incidence of malaria in the spatial aggregation in different areas, in Mengcheng county and the surrounding 30 county area. The incidence of malaria in different years time aggregation time is different, the general incidence is more concentrated in 6-10 June. Most of the high incidence of malaria in the Huaihe River Basin county (city, district) malaria epidemic occurred all year round, spring began to spread, fall incidence rate is high.
The incidence of malaria in 3. in Huaihe basin of malaria and the most high temperature period, before January rainfall, river density, monthly rainfall, the correlation between per capita GDP, no significant correlation with relative humidity and other meteorological factors is affected by malaria. The highest temperature of the malaria epidemic are influenced by the effect of rainfall, the rainfall will suppress the malaria epidemic. Environmental factors can influence the occurrence and prevalence of malaria, epidemic potential can be predicted by monitoring of malaria in Huaihe watershed environmental factors.

【学位授予单位】：中国人民解放军军事医学科学院
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：R531.3;R181.3

【参考文献】