钉螺密度时空模型构建及血吸虫病感染率贝叶斯估计
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摘要
第一部分湖北省钉螺分布的现状及空间聚集性分析
目的：了解湖北省钉螺分布的现状,探索钉螺空间分布的地理规律及其空间属性,为制定灭螺、控螺规划等血防工作提供科学依据。方法：以1980-2009年每间隔5年的湖北省各县市血防办提供的村级别的查灭螺资料为基础属性数据,与GIS地理空间数据库进行关联,建立湖北省钉螺地理信息基础数据库。采用描述性分析方法观察查灭螺指标、钉螺面积及钉螺密度等指标的动态变化情况,采用全局空间自相关的Moran's I系数及局部空间自相关的Getis-Ord Gi*指标进行钉螺密度的空间聚集性分析。结果：湖北省1980-2009年每间隔5年的钉螺密度总体呈下降趋势,80年代到90年代间钉螺密度较高,1990年以后出现一个下降阶段,2009年的钉螺密度呈最低状况。湖北省研究区域80年代钉螺面积变化显著,增减幅度较大,90年代及以后以钉螺面积基本保持不变或小幅减少为主,2009年江陵、沙市及公安和天门部分地区为钉螺密度较高地区,全局空间自相关分析的Moran's Index表明湖北省钉螺密度具有高度空间自相关,2009年钉螺密度热点分析表明江陵及阳新、公安的部分地区为钉螺密度的异常高值区。结论：湖北省钉螺控制效果显著,30年来钉螺密度呈现下降趋势,钉螺分布主要沿长江中游、汉江两岸以及与两江相通的大小湖泊洪泛区分布,总体沿两江呈明显的粗带状分布。湖北省钉螺密度各年均具有较强的空间聚集性,但是并没有随时间变化而发生更为聚集的趋势。
第二部分钉螺密度的广义线性混合效应模型及Bayesian时空模型的构建与分析
目的：探索影响湖北省钉螺密度分布的影响因素并构建钉螺密度的时空模型,并研究钉螺密度随时间、空间变化的规律,从而为湖北省钉螺的预测预警及控制提供一定的理论基础。方法：以湖北省钉螺地理信息基础数据库作为基础,将环境因素资料数据如气候因子、遥感因子并接到钉螺空间数据库中,对数据进行预处理,分别利用2009年和1990-2009年钉螺数据构建了无时间效应的广义线性混合模型和时间效应的广义线性混合模型,通过AIC、AICC及BIC比较不同误差分布和连接函数组合的广义线性混合模型拟合效果,通过多水平的分析筛选出了湖北省影响钉螺分布的主要因素,对各影响因素的参数值进行估计,进一步构建钉螺密度的贝叶斯时空模型。结果：单因素分析空气湿度(r=0.320, p<0.05) NDVI(r=0.384, p<0.05)、LST (r=0.318, p<0.05)及村子距水体距离(r=-0.383,p<0.05)与钉螺密度具有相关性,且具有统计学意义。广义线性混合模型的最佳模型以泊松分布为误差分布、log为连接函数和均数为方差函数的模型结果,GLMM结果显示钉螺密度受LST (p=0.020)、样本县离长江距离(p=0.020)是否灭螺(p=0.001)、NDVI (p=0.003)、空气湿度(p=0.001)及时间(p=0.001)的影响。贝叶斯模型结果显示钉螺密度具有逐渐下降的趋势,空间随机变异有统计学意义,且各观察时间点的空间变异不一样。根据DIC越小模型拟合效果越好的原则,贝叶斯时空模型拟合数据效果最佳,贝叶斯时空模型的参数估计值与广义线性混合效应模型所得自变量估计值较为接近。结论：湖北省钉螺密度的广义线性混合模型的最佳结构是以泊松分布为误差分布、log为连接函数和均数为方差函数的模型结构。贝叶斯时空模型表明钉螺密度具有逐渐下降的趋势,空间随机变异有统计学意义,且各观察时间点的空间变异不一样,构建模型时需要考虑时间、空间极其交互作用的影响。湿度、NDVI、LST、距离长江的距离及是否灭螺对于钉螺的分布具有统计学意义,可以据此作为钉螺密度监测及防控的依据。
第三部分湖北省湖泊地区血吸虫病感染率的Bayesian估计
目的：应用贝叶斯方法矫正血吸虫病诊断误差,从而对湖北省湖区血吸虫病感染率进行准确估计,以了解湖北省血吸虫病的真实感染情况。方法：采用多阶段随机整群抽样的方法于2011年在湖北省血吸虫病流行区随机抽取各样本村,分别利用IHA和Kato-Katz法对样本人群进行血吸虫感染的检测,通过两轮问卷调查的方法采用专家意见法获取关于IHA及Kato-Katz法准确度的先验信息,在两种情形下分别用贝叶斯分层模型进行血吸虫病感染率的估计,情形1中,运用IHA检测及Kato-Katz法联合诊断的数据进行感染率的估计,情形2中,仅运用IHA检测数据进行感染率的贝叶斯估计。结果：最终共随机抽取了14个县46个样本村合计50980个受试者进入研究。专家意见法获取的IHA法检验的灵敏度和特异度范围分别是80%～90%、70%～80%,Kato-Katz法检验灵敏度和特异度范围分别是20%to70%,90%to100%,两种情形估计获得的血吸虫病感染率相似,两种情形中所有样本村的估计感染率均低于13%,当仅用IHA数据进行估计时(即情形2)血吸虫病感染率变异范围为0.95%到12.26%。结论：在今后大规模的血吸虫病调查中可以考虑仅用血清学的方法(如IHA法)结合贝叶斯估计方法进行血吸虫病低度流行区血吸虫病流行率的估计。
Part1Study of distribution and spatial clustering of oncomelania in Hubei Province
Objective:To investigate the distribution, geographic regularity of spatial distribution and spatial attributes of the oncomelania in Hubei Province, so as to provide a scientific basis for the development of the oncomelania-controlling plan for schistosomiasis control. Methods:This study was based on the database of eliminating oncomelania that was kept by Hubei provincial schistosomiasis control administration and the GIS geospatial database. We retrieved the two databases from the years1980to2009. The two databases were associated to establish the Hubei geographic information database of oncomelania. Dynamic changes about the oncomelania eleminating, area and density indicators were observed using descriptive analysis method. Spatial clustering analysis was conducted to analyze the oncomelania density. The global and local Moran's Index were calculated. Results: The oncomelania density at intervals of five years from the years1980to2009in Hubei Province showed a downward trend. The oncomelania density was higher from1980s to1990s. After the year of1990, the oncomelania density decreased and was at the lowest level in2009. Oncomelania area changed (increased or decreased) significantly in Hubei Province in1980s. And in1990s, the oncomelania area remained unchanging or slightly reduced. In2009, the oncomelania density was higher in Jiangling, Shashi, Gongan and Tianmen. Form the global Moran's I index, the results showed that the oncomelania density in Hubei had significant spatial autocorrelation. Hotspot analysis indicated that Jiangling, Yangxin and Gongan were the significantly higher oncomelania density areas in2009. Conclusion:The oncomelania density showed a downward trend in the past30years, which indicated that the control for oncomelania in Hubei was effective. Oncomelania in Hubei was distributed mainly along the middle reaches of the Yangtze River, the Han River, and the lake floodplains between the two rivers. The oncomelania was zonary-distributed along the two rivers mentioned above. The oncomelania density in Hubei showed a strong spatial clustering over time, but a trend of more gathering was not shown.
Part2Analysis of the oncomelania density using mixed-effects model and Bayesian space-time model
Objective:We explored the factors that may affect the oncomelania density distribution in Hubei Province, and studied the changes of oncomelania density over time and space. Our findings can provide some theoretical basis for early prediction and control of oncomelania in Hubei Province. Methods:The study was based on the Hubei geographic information database. Environmental factors including climatic factors, and remote sensing factor were collected, associated with the oncomelania spatial database. Data from2009and1990-2009, respectively, were used to construct the no-time-effect mixed model and time mixed-effects model. AIC, AICC and BIC rules were employed to compare different error distributions and the connection function combination of generalized linear mixed models. The main factors that affecting oncomelania distribution were analyzed by a multi-level analysis, and the parameter were estimated. Bayesian space-time model was adopted to analyze the oncomelania density. Results:Results from the univariate analysis showed that air humidity (r=0.320, p<0.05), NDVI (r=0.384, p<0.05), LST (r=0.318, p<0.05), and the distance between the water and the village (r=-0.383, p<0.05correlation) were significantly associated with oncomelania density. The best generalized linear mixed model (GLMM) was Poisson-distributed error, log link function and variance function mean. GLMM results showed that oncomelania density was affected by LST (p=0.020), the distance between the sampled counties and the Yangtze (p=0.020), and whether oncomelania control or not (p=0.001), NDVI (p=0.003), air humidity (p=0.001) and time (p=0.001). Results from the Bayesian model showed a gradually downward trend. Spatial random variability was statistically significant, and each observation point of the spatial variability was different. Bayesian space-time model was the best according to the principles of the smaller the DIC, the better the model. Parameter estimates both using the Bayesian space-time model and generalized linear models were close. Conclusion:The best generalized linear mixed model (GLMM) was Poisson-distributed error, log link function and variance function mean using the data of oncomelania density in Hubei. Bayesian space-time model indicated that the oncomelania density had a gradually downward trend, and the spatial random variability had statistically significant. When building models, it is necessary to consider the effects of time, space and their interaction. Humidity, NDVI, LST, from the distance between the Yangtze River and village and whether oncomelania control or not were significantly associated with the distribution of the oncomelania. Findings from this study can provide the basis for the density monitoring and prevention and control of the oncomelania.
Part3A Bayesian Approach to Estimate the Prevalence of Schistosomiasis Japonicum Infection in the lake region, Hubei Province, China
Objective:A Bayesian technology was introduced to estimate community prevalence of schistosomiasis japonicum infection based on the data of a large-scale survey of schistosomiasis japonicum in lake-regions in Hubei Province. Methods:A multistage cluster random sampling approach was applied to the endemic villages in lake-regions of Hubei province in2011. IHA test and Kato-Katz test were applied for the detection of the S. japonicum infection for sampled population. Expert knowledge on sensitivities and specificities of IHA test and Kato-Katz test were collected based on a two-round interview. Prevalence of S. japonicum infection was estimated by Bayesian Hierarchical model in two different situations. Results:In situation1, Bayesian estimation used both IHA test data and Kato-Katz test data to estimate the prevalence of S. japonicum. In situation2, only IHA test data was used for Bayesian estimation. Finally14cities and46villages including50980residents were sampled from lake-regions of Hubei province. Sensitivity and specificity for IHA test ranged from80%to90%and70%to80%, respectively. And for the Kato-Katz test, sensitivity and specificity were from20%to70%and90%to100%, respectively. Similar estimated prevalence was obtained in the two situations. Estimated prevalence among sampled villages was almost below13%in both situations and varied from0.95%to12.26%when using data of IHA test only. Conclusion:The study indicated that it is feasible to apply IHA test only combining with Bayesian method to estimate the prevalence of S.japonicum infection in large-scale surveys.
目的：了解湖北省钉螺分布的现状,探索钉螺空间分布的地理规律及其空间属性,为制定灭螺、控螺规划等血防工作提供科学依据。方法：以1980-2009年每间隔5年的湖北省各县市血防办提供的村级别的查灭螺资料为基础属性数据,与GIS地理空间数据库进行关联,建立湖北省钉螺地理信息基础数据库。采用描述性分析方法观察查灭螺指标、钉螺面积及钉螺密度等指标的动态变化情况,采用全局空间自相关的Moran's I系数及局部空间自相关的Getis-Ord Gi*指标进行钉螺密度的空间聚集性分析。结果：湖北省1980-2009年每间隔5年的钉螺密度总体呈下降趋势,80年代到90年代间钉螺密度较高,1990年以后出现一个下降阶段,2009年的钉螺密度呈最低状况。湖北省研究区域80年代钉螺面积变化显著,增减幅度较大,90年代及以后以钉螺面积基本保持不变或小幅减少为主,2009年江陵、沙市及公安和天门部分地区为钉螺密度较高地区,全局空间自相关分析的Moran's Index表明湖北省钉螺密度具有高度空间自相关,2009年钉螺密度热点分析表明江陵及阳新、公安的部分地区为钉螺密度的异常高值区。结论：湖北省钉螺控制效果显著,30年来钉螺密度呈现下降趋势,钉螺分布主要沿长江中游、汉江两岸以及与两江相通的大小湖泊洪泛区分布,总体沿两江呈明显的粗带状分布。湖北省钉螺密度各年均具有较强的空间聚集性,但是并没有随时间变化而发生更为聚集的趋势。
第二部分钉螺密度的广义线性混合效应模型及Bayesian时空模型的构建与分析
目的：探索影响湖北省钉螺密度分布的影响因素并构建钉螺密度的时空模型,并研究钉螺密度随时间、空间变化的规律,从而为湖北省钉螺的预测预警及控制提供一定的理论基础。方法：以湖北省钉螺地理信息基础数据库作为基础,将环境因素资料数据如气候因子、遥感因子并接到钉螺空间数据库中,对数据进行预处理,分别利用2009年和1990-2009年钉螺数据构建了无时间效应的广义线性混合模型和时间效应的广义线性混合模型,通过AIC、AICC及BIC比较不同误差分布和连接函数组合的广义线性混合模型拟合效果,通过多水平的分析筛选出了湖北省影响钉螺分布的主要因素,对各影响因素的参数值进行估计,进一步构建钉螺密度的贝叶斯时空模型。结果：单因素分析空气湿度(r=0.320, p<0.05) NDVI(r=0.384, p<0.05)、LST (r=0.318, p<0.05)及村子距水体距离(r=-0.383,p<0.05)与钉螺密度具有相关性,且具有统计学意义。广义线性混合模型的最佳模型以泊松分布为误差分布、log为连接函数和均数为方差函数的模型结果,GLMM结果显示钉螺密度受LST (p=0.020)、样本县离长江距离(p=0.020)是否灭螺(p=0.001)、NDVI (p=0.003)、空气湿度(p=0.001)及时间(p=0.001)的影响。贝叶斯模型结果显示钉螺密度具有逐渐下降的趋势,空间随机变异有统计学意义,且各观察时间点的空间变异不一样。根据DIC越小模型拟合效果越好的原则,贝叶斯时空模型拟合数据效果最佳,贝叶斯时空模型的参数估计值与广义线性混合效应模型所得自变量估计值较为接近。结论：湖北省钉螺密度的广义线性混合模型的最佳结构是以泊松分布为误差分布、log为连接函数和均数为方差函数的模型结构。贝叶斯时空模型表明钉螺密度具有逐渐下降的趋势,空间随机变异有统计学意义,且各观察时间点的空间变异不一样,构建模型时需要考虑时间、空间极其交互作用的影响。湿度、NDVI、LST、距离长江的距离及是否灭螺对于钉螺的分布具有统计学意义,可以据此作为钉螺密度监测及防控的依据。
第三部分湖北省湖泊地区血吸虫病感染率的Bayesian估计
目的：应用贝叶斯方法矫正血吸虫病诊断误差,从而对湖北省湖区血吸虫病感染率进行准确估计,以了解湖北省血吸虫病的真实感染情况。方法：采用多阶段随机整群抽样的方法于2011年在湖北省血吸虫病流行区随机抽取各样本村,分别利用IHA和Kato-Katz法对样本人群进行血吸虫感染的检测,通过两轮问卷调查的方法采用专家意见法获取关于IHA及Kato-Katz法准确度的先验信息,在两种情形下分别用贝叶斯分层模型进行血吸虫病感染率的估计,情形1中,运用IHA检测及Kato-Katz法联合诊断的数据进行感染率的估计,情形2中,仅运用IHA检测数据进行感染率的贝叶斯估计。结果：最终共随机抽取了14个县46个样本村合计50980个受试者进入研究。专家意见法获取的IHA法检验的灵敏度和特异度范围分别是80%～90%、70%～80%,Kato-Katz法检验灵敏度和特异度范围分别是20%to70%,90%to100%,两种情形估计获得的血吸虫病感染率相似,两种情形中所有样本村的估计感染率均低于13%,当仅用IHA数据进行估计时(即情形2)血吸虫病感染率变异范围为0.95%到12.26%。结论：在今后大规模的血吸虫病调查中可以考虑仅用血清学的方法(如IHA法)结合贝叶斯估计方法进行血吸虫病低度流行区血吸虫病流行率的估计。
Part1Study of distribution and spatial clustering of oncomelania in Hubei Province
Objective:To investigate the distribution, geographic regularity of spatial distribution and spatial attributes of the oncomelania in Hubei Province, so as to provide a scientific basis for the development of the oncomelania-controlling plan for schistosomiasis control. Methods:This study was based on the database of eliminating oncomelania that was kept by Hubei provincial schistosomiasis control administration and the GIS geospatial database. We retrieved the two databases from the years1980to2009. The two databases were associated to establish the Hubei geographic information database of oncomelania. Dynamic changes about the oncomelania eleminating, area and density indicators were observed using descriptive analysis method. Spatial clustering analysis was conducted to analyze the oncomelania density. The global and local Moran's Index were calculated. Results: The oncomelania density at intervals of five years from the years1980to2009in Hubei Province showed a downward trend. The oncomelania density was higher from1980s to1990s. After the year of1990, the oncomelania density decreased and was at the lowest level in2009. Oncomelania area changed (increased or decreased) significantly in Hubei Province in1980s. And in1990s, the oncomelania area remained unchanging or slightly reduced. In2009, the oncomelania density was higher in Jiangling, Shashi, Gongan and Tianmen. Form the global Moran's I index, the results showed that the oncomelania density in Hubei had significant spatial autocorrelation. Hotspot analysis indicated that Jiangling, Yangxin and Gongan were the significantly higher oncomelania density areas in2009. Conclusion:The oncomelania density showed a downward trend in the past30years, which indicated that the control for oncomelania in Hubei was effective. Oncomelania in Hubei was distributed mainly along the middle reaches of the Yangtze River, the Han River, and the lake floodplains between the two rivers. The oncomelania was zonary-distributed along the two rivers mentioned above. The oncomelania density in Hubei showed a strong spatial clustering over time, but a trend of more gathering was not shown.
Part2Analysis of the oncomelania density using mixed-effects model and Bayesian space-time model
Objective:We explored the factors that may affect the oncomelania density distribution in Hubei Province, and studied the changes of oncomelania density over time and space. Our findings can provide some theoretical basis for early prediction and control of oncomelania in Hubei Province. Methods:The study was based on the Hubei geographic information database. Environmental factors including climatic factors, and remote sensing factor were collected, associated with the oncomelania spatial database. Data from2009and1990-2009, respectively, were used to construct the no-time-effect mixed model and time mixed-effects model. AIC, AICC and BIC rules were employed to compare different error distributions and the connection function combination of generalized linear mixed models. The main factors that affecting oncomelania distribution were analyzed by a multi-level analysis, and the parameter were estimated. Bayesian space-time model was adopted to analyze the oncomelania density. Results:Results from the univariate analysis showed that air humidity (r=0.320, p<0.05), NDVI (r=0.384, p<0.05), LST (r=0.318, p<0.05), and the distance between the water and the village (r=-0.383, p<0.05correlation) were significantly associated with oncomelania density. The best generalized linear mixed model (GLMM) was Poisson-distributed error, log link function and variance function mean. GLMM results showed that oncomelania density was affected by LST (p=0.020), the distance between the sampled counties and the Yangtze (p=0.020), and whether oncomelania control or not (p=0.001), NDVI (p=0.003), air humidity (p=0.001) and time (p=0.001). Results from the Bayesian model showed a gradually downward trend. Spatial random variability was statistically significant, and each observation point of the spatial variability was different. Bayesian space-time model was the best according to the principles of the smaller the DIC, the better the model. Parameter estimates both using the Bayesian space-time model and generalized linear models were close. Conclusion:The best generalized linear mixed model (GLMM) was Poisson-distributed error, log link function and variance function mean using the data of oncomelania density in Hubei. Bayesian space-time model indicated that the oncomelania density had a gradually downward trend, and the spatial random variability had statistically significant. When building models, it is necessary to consider the effects of time, space and their interaction. Humidity, NDVI, LST, from the distance between the Yangtze River and village and whether oncomelania control or not were significantly associated with the distribution of the oncomelania. Findings from this study can provide the basis for the density monitoring and prevention and control of the oncomelania.
Part3A Bayesian Approach to Estimate the Prevalence of Schistosomiasis Japonicum Infection in the lake region, Hubei Province, China
Objective:A Bayesian technology was introduced to estimate community prevalence of schistosomiasis japonicum infection based on the data of a large-scale survey of schistosomiasis japonicum in lake-regions in Hubei Province. Methods:A multistage cluster random sampling approach was applied to the endemic villages in lake-regions of Hubei province in2011. IHA test and Kato-Katz test were applied for the detection of the S. japonicum infection for sampled population. Expert knowledge on sensitivities and specificities of IHA test and Kato-Katz test were collected based on a two-round interview. Prevalence of S. japonicum infection was estimated by Bayesian Hierarchical model in two different situations. Results:In situation1, Bayesian estimation used both IHA test data and Kato-Katz test data to estimate the prevalence of S. japonicum. In situation2, only IHA test data was used for Bayesian estimation. Finally14cities and46villages including50980residents were sampled from lake-regions of Hubei province. Sensitivity and specificity for IHA test ranged from80%to90%and70%to80%, respectively. And for the Kato-Katz test, sensitivity and specificity were from20%to70%and90%to100%, respectively. Similar estimated prevalence was obtained in the two situations. Estimated prevalence among sampled villages was almost below13%in both situations and varied from0.95%to12.26%when using data of IHA test only. Conclusion:The study indicated that it is feasible to apply IHA test only combining with Bayesian method to estimate the prevalence of S.japonicum infection in large-scale surveys.
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