【作者】 杨坤；

【导师】 周晓农；

【作者基本信息】 中国疾病预防控制中心 ， 流行病与卫生统计学， 2008， 博士

【摘要】 随着气候变暖、退田还湖、人类迁移活动等生态环境因素的改变,钉螺所处地理环境、生物群落、种群密度及其分布区域等都在发生变化。因此,血吸虫病的发生、发展及流行趋势也将随之变化,以科学的模型来预测血吸虫病的发展趋势是疾病预防控制中的重要内容之一。本研究在地理信息系统(GIS)和遥感(RS)技术的支持下,以主要的自然环境因素(水、植被、温度等)、景观因素(土地利用、土地类型等)及社会因素等作为研究指标,在血吸虫病流行区分别构建基于景观格局和贝叶斯模型的钉螺和血吸虫病分布复合模型,阐明和预测同一环境不同尺度、同一尺度不同环境类型的钉螺和血吸虫病分布的时空规律,为血吸虫病的监测和防治策略提供参考依据。首先在云南洱源县利用GPS仪记录沟渠的形状、空间位置与村庄边界,并收集2000至2006年查螺数据,利用遥感图像提取植被指数(NDVI)、湿度(Wetness)、地表温度(LST)与土地利用/类型等信息,进一步提取景观指数。在村级尺度上,构建钉螺分布非时空与时空贝叶斯复合模型,利用2006年钉螺分布数据与SPOT5遥感图像构建更小尺度(钉螺孳生环境)点数据贝叶斯复合模型,预测钉螺分布。显示山丘型钉螺分布在村级水平上无显著性时间和空间相关性;小尺度下,钉螺分布存在一定空间相关性。在村级水平上钉螺密度与NDVI、湿度、沟渠坡度等呈一定的相关性。小尺度下钉螺密度与上述因素无显著性相关,而与景观指数MSI(Mean shape index,平均形状指标)与SEI(Shannon’sevenness index,香农均匀度指标)呈显著性正相关,感染性钉螺密度与居民区面积比例成正相关。提示改变血吸虫病流行区的景观异质性可以起到降低钉螺密度的作用;山丘型钉螺与血吸虫病分布研究,宜采用高分辨率遥感图像进行小尺度研究。其次,我们在云南洱源县血吸虫病流行村开展了入户调查,对年龄≥5岁的居民开展血吸虫病检查(单纯血清学检查、单纯病原学检查和血检阳性者进行病原学检查),分别对血检阳性率和感染率构建贝叶斯多水平模型(个体、户级与村级)。结果显示人群血检阳性率和血吸虫感染率的空间相关性主要发生在村级内部,不同年龄组与性别的人群血检阳性率与血吸虫感染率无显著性差异。村级水平上,人群血检阳性率与景观指数SEI和LPI(Largest patches index,最大斑块指数)呈正相关;人群血吸虫感染的危险因素为村周围钉螺平均密度。户级水平上,人群血检阳性的危险因素为较多水阳面积、无沼气池;人群血吸虫感染的危险因素主要为家庭无卫生畜圈。提示本区域控制人群血吸虫感染的主要措施为改造畜圈与降低流行村周围钉螺密度。第三,我们利用湖南汉寿县1995-2006年查螺数据及相应年份遥感图像提取NDVI、Wetness、LST与景观指数,构建钉螺分布贝叶斯复合模型用于钉螺分布预测。结果显示钉螺与感染性钉螺分布呈显著的时间负相关;垸内钉螺分布的空间相关性随距离增加而减少的速度明显快于垸外钉螺;垸内钉螺密度与NDVI呈负相关,垸内钉螺密度与景观指数SEI和LPl分别呈正相关和负相关。每年垸外钉螺与感染性钉螺分布的空间结构基本相似,变异较大;垸外钉螺与NDVI呈正相关,垸外钉螺密度分别与LST和Wetness呈负相关和正相关;垸外感染性钉螺密度与景观指数MSI、SDI(Shannon’s diversity index,香农多样性指数)呈正相关,与SEI、LPI和LSI呈负相关。结合退田还湖政策实施情况,钉螺分布预测图显示退田还湖实施后,垸内的钉螺密度仍处于一个较高的水平,其空间分布较垸外钉螺分布集中,垸外钉螺主要分布在汉寿县西北部垸外洲滩。最后,我们利用湖南汉寿县10年间3次以上(含3次)的血吸虫病查病数据,在考虑检查方法灵敏度和特异度的不确定性基础上构建贝叶斯复合模型。显示全县血吸虫感染率无明显时间相关性,每年人群血吸虫感染率的空间相关性结构差异较大,与NDVI呈显著负相关。预测图显示2002年感染率处于较低水平,感染率大于1%的区域主要沿水系目平湖和沅水分布;2005年全县平均感染率为2.22%,高感染率区域主要沿主要大水系分布;利用单纯血清学或病原学检查的感染率预测值及其预测误差的空间格局分布相似;感染率预测变化图显示汉寿县沅水以南大部分地区人群血吸虫感染率没有明显变化,沅水以北地区人群血吸虫感染率的增加明显,提示单退型退田还湖对人群血吸虫感染率的影响程度强于双退型。比较分析山丘型与湖沼型钉螺与血吸虫病分布的贝叶斯复合模型,可以看出山丘型与湖沼型钉螺和血吸虫病分布的影响因素、时空分布格局、模型构建方法等方面都存有差异,决定了两类血吸虫病流行区的控制措施应该有所不同。湖沼型血吸虫病流行区可以采取相对一致或相似的控制措施,主要应采取人畜同步化疗、家畜圈养和易感地区灭螺为主的综合防治措施,最大限度地控制病情,长期监测平垸行洪区动态变化,及时采取有效控制措施,严防疫情扩散。而在山丘型血吸虫病流行区,防治措施应因地制宜,在不同范围内实施针对性强和可操作性的技术措施,如坚持以环境改造为主的血吸虫病综合治理,实施重点工程灭螺,同时,应采取人畜同步化疗、改水改厕、健康教育、家畜圈养等综合防治措施,最大限度地降低钉螺面积,控制血吸虫病传播。适宜尺度下基于景观格局与贝叶斯模型的钉螺和血吸虫病分布的复合模型,在分析和预测山丘型和湖沼型钉螺及血吸虫病分布中将发挥重要作用,成为确定防治措施、提高防治效果的重要工具。更多还原

【Abstract】 With the changes of ecological environment, including global warming, "breaking dikes or open sluice for water storing", and human migration, the ecology of Oncomelania hupensis, the intermediate host of Schistosoma japonicum, will be also changed in terms of habitats, biocenosis, and density, which finally resuls in changes of transmission of schistosomiasis japonica. Therefore, the way of using the scientific prediction models to predict the epidemic status of schistosomiasis japonica become more and more important approach in the disease control and prevention, and it is also one of the increasingly priorities in the process of disease control.In this study, we used geographic information systems (GIS) and remote sensing (RS) technology to develop Bayesian models for prediction of the distribution of Oncomelania snail and schistosomiasis integrated with landscape pattern analysis by employing environmental factors, e.g. water, vegetation, and land surface temperature, landscape factors, e.g. land-use/type, and socio-economic factors as covariables, in order to understand and predict the spatio-temporal patterns of schistosomiasis in different environmental settings under the same scale or the same environmental setting with different scales. The following four investigation aspects were performed with the purpose of providing scientific results contributed to formulation of a more effective strategy for control and prevention of S. japonicum transmission in both mountainous and lake regions of China.Firstly, an area-wide ditch map with the boundary of villages was generated in the study area, namely Eryuan county of Yunnan province, by tracing the ditch network on foot by use of a global position system (GPS) unit, and the data on the distribution and density of Oncomelania snails in the study area were extracted from the annual schistosomiasis records of Eryuan county recoreded from 2000 to 2006. The varaiables, e.g. normalized difference vegetation index (NDVI) , wetness, land surface temperature (LST), and land-use/type, were extracted from remote sensing images, and then landscape metrics were calculated. The spatio-temporal Bayesian models with area data were established at village scale, and then spatial Bayesian model with point data was established using the data of snail survey and SPOT5 satellite image at local scale (or snail habitat). The results indicated there was no significant spatial and temporal correlation of live and infected snail densities at village scale, but there was spatial correlation at local scale. Hence, spatial Bayesian model was used to predict the distribution of snails at local scale. The correlation between the snail density and NDVI, wetness and the slope of ditch was significantly presented at village scales, however this correlation was not significant at local scales. The correlation between snail density and mean shape index (MSI) and Shannon’s evenness index (SEI) was significantly presented at local scale. A prediction map was generated by the Bayesian model employing with environmental surrogates and landscape metrics at local scale, and findings of the study suggested that decreasing the heterogeneity of the landscape can reduce snail density and the established model by using higher resolution satellite data at local scale was suitable to be applied in the mountainous region.Secondly, residents aged over 5 years old were screened for S. japonicum infection using indirect haemagglutination test (IHA) and micracidium hatching method. Bayesian multilevel models including spatial correlation were built for serological status and the underlying infection status of S. japonicum, respectively, at the three levels, e.g. individual, family and village. The variability of the distribution pattern of the serological status and underlying infection of S. japonicum occured within village boundary. At individual level, all resident were susceptible to be infected with S. japomicum, and health education should be strengthened on all individuals. At family level, reducing the area of paddy farmland, and building methane gas pit can decrease the seroprevalence, and building sanitary breeding stall for livestock can decrease the underlying infection rate, respectively. At village level, changing the landscape heterogeneity and snail density around villages can decrease the seroprevalence and the prevalence of S. japonicum infection, respectively.Thirdly, the data about the distribution and density of snail from 1995 to 2006 in Hanshou county,Hunan province were collected, and NDVI, wetness and LST were also extracted from remote sensing images, different Bayesian models were established to predict the distribution of snail. Results showed the negative temporal correlations in distribution of live and infected snail were occurred. The rate of decline in spatial correlation of snail distribution between points inside embankment of lake was faster than that outside embankment. The spatial structure of live and infected snails outside embankment was similar, but the difference of the spatial structure of those snails in each year was large. The correlation between snail density and NDVI was negatively distributed inside embankment but positively outside embankment. The correlation between snail density and LST outside embankment was negatively presented, but positively occurred with wetness. The correlation between snail density inside embankment was positively related to SEI, but negatively related to LPI. The correlation between infected snails and MSI, SDI (Shannon’s diversity index) outside embankment was positively presented. Predication maps showed the snail density still remained at a high level after implementation of the project of breaking dikes or open sluice for water storing implemented, the spatial distribution of snail inside embankment was much more clustered than that outside embankment, and the distribution of most snails outside embankment was located in the northwest marshland outside embankment in Hanshou county.Fourthly, the Bayesian models were established by employing the data collected from the periodical surveillance on schistosomiasis where survey performed more than 3 times during last 10 years, with taking into account of the uncertainty in sensitivity and specificity of diagnostic test(s). Results showed that no significantly temporal correlation was occurred in human infection rate with S. japonicum, and the difference of spatial structure of human infection between each year was significant. The correlation between the prevalence of S. japonicum infection and NDVI was negatively presented significantly. The prediction map of S. japonicum infection in 2002 showed the whole prevalence of S. japonicum infection was at a low level, and the areas where prevalence more than 1% were mostly located along water courses of the Muping lake and the Yuanshui river. While the average prediction prevalence was 2.22% in 2005, and the higher risk areas distributed along water courses as well. The spatial patterns of prediction and predicted error were similar between results of serological test and that of stool test. The project map of prevalence of S. japonicum infection showed the changes of infection in the south areas was not significant, while the prevalence increased significantly in north areas to the Yuanshui river, and it was indicated the impact of the implemention of project on partial abandon areas for water storing on prevalence of S. japonicum was stronger than that of the project on completed abandon areas for water storing.Based on the results from the Bayisian models prediction on distribution of snail and schistosomiasis both in the mountainous region and in the lake region, it is found that the differences were significantly existed in the risk factors, spatio-temporal patterns, and model building ways, ect., these differences lead to different control measures in these different environmental settings. For examples, in lake regions, the same or similar measures can be implemented in a large scale, while specific measures should be applied to adapt the unique characteristics at a small scale in mountainous region, in order to improve the efficacy of different control efforts.In conclusion, we have developed an integrated model based on both landscape analysis and Bayesian modeling to predict the distribution of snail and schistosomiasis, and this integrated Bayesian model approach with landscape analysis will become a powerful and statistically robust tool for estimating and evaluating the control strategy at an appropriate scale.更多还原