Geostatistical analysis of active human cysticercosis: Results of a large-scale study in 60 villages in Burkina Faso.
Geostatistical analysis of active human cysticercosis: Results of a large-scale study in 60 villages in Burkina Faso.
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DOI：
10.1371/journal.pntd.0011437
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发表时间：
2023-07
影响因子：
3.8
通讯作者：
中科院分区：
文献类型：
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囊虫病是一种被忽视的热带病，由人畜共患绦虫（猪带绦虫）的幼虫期引起。虽然寄生虫的发生有明显的空间因素，但尚未对活动性人囊虫病进行地质统计分析，也没有对撒哈拉以南非洲进行这种分析，尽管这与指导预防和控制战略有关。本研究的目的是利用布基纳法索60个村庄大规模研究的基线横截面数据，对活动性人囊虫病进行地质统计学分析。结果是使用B158/B60 Ag-ELISA测定活动性人囊虫病（hCC）的患病率，同时探索与疾病传播和传播相关的各种环境变量作为猪尾绦虫空间分布的潜在解释变量。运用广义线性地质统计模型（GLGM），生成预测图。使用在两个层面产生的数据进行分析：个人参与者数据和分组村庄数据。采用逆向变量选择程序选择最佳模型，并使用似然比检验对模型进行比较。最佳的个人水平GLGM包括降水（增加的值与阳性检测结果的几率增加相关）、到最近河流的距离（降低的几率）和夜间陆地温度（降低的几率）作为活动性hCC的预测因子，而村庄水平GLGM仅保留降水和到最近河流的距离。空间相关范围估计为45.0 [95%CI: 34.3；57.8米，28.2米[95%CI: 14.0；在个人和村级数据集上分别为56.2]km。个体和村庄水平的GLGM显示，在研究区域的东南部、极南和西北部，大面积的活动性hCC预测患病率估计至少为4%，而在北部和西部，患病率估计低于2%。需要更多旨在分析hCC空间特征的研究，并采用采样策略，确保适当地描述空间变异性，并在地统计分析中纳入与结果测量和环境变量相关的不确定性。试验注册：ClinicalTrials.gov；NCT0309339。囊虫病是一种严重但被忽视的疾病，由人畜共患绦虫的幼虫期引起，在包括布基纳法索在内的许多发展中国家流行。能够预测疾病发生的地点对于开展有针对性的预防和控制活动至关重要。在我们的研究中，我们旨在描述布基纳法索三个省的人囊虫病病例是否聚集，并调查这种聚集与一些土地和天气变量之间是否存在联系。最后，我们的目标是生成感染发生的高分辨率预测图。我们发现，个体数据集在45米处存在聚类，村级数据集在28.2公里处存在聚类。降雨增加和靠近河流与这种群集有关。生成的预测图表明，在研究区域存在重要的囊虫病热点，特别是在最南部和西北部，该疾病被认为更为重要。进一步的研究应扩大使用空间技术来预测囊虫病的发生，其结果可有助于设计干预方案。
Cysticercosis is a neglected tropical disease caused by the larval stage of the zoonotic tapeworm (Taenia solium). While there is a clear spatial component in the occurrence of the parasite, no geostatistical analysis of active human cysticercosis has been conducted yet, nor has such an analysis been conducted for Sub-Saharan Africa, albeit relevant for guiding prevention and control strategies. The goal of this study was to conduct a geostatistical analysis of active human cysticercosis, using data from the baseline cross-sectional component of a large-scale study in 60 villages in Burkina Faso. The outcome was the prevalence of active human cysticercosis (hCC), determined using the B158/B60 Ag-ELISA, while various environmental variables linked with the transmission and spread of the disease were explored as potential explanatory variables for the spatial distribution of T. solium. A generalized linear geostatistical model (GLGM) was run, and prediction maps were generated. Analyses were conducted using data generated at two levels: individual participant data and grouped village data. The best model was selected using a backward variable selection procedure and models were compared using likelihood ratio testing. The best individual-level GLGM included precipitation (increasing values were associated with an increased odds of positive test result), distance to the nearest river (decreased odds) and night land temperature (decreased odds) as predictors for active hCC, whereas the village-level GLGM only retained precipitation and distance to the nearest river. The range of spatial correlation was estimated at 45.0 [95%CI: 34.3; 57.8] meters and 28.2 [95%CI: 14.0; 56.2] km for the individual- and village-level datasets, respectively. Individual- and village-level GLGM unravelled large areas with active hCC predicted prevalence estimates of at least 4% in the south-east, the extreme south, and north-west of the study area, while patches of prevalence estimates below 2% were seen in the north and west. More research designed to analyse the spatial characteristics of hCC is needed with sampling strategies ensuring appropriate characterisation of spatial variability, and incorporating the uncertainty linked to the measurement of outcome and environmental variables in the geostatistical analysis. Trial registration: ClinicalTrials.gov; NCT0309339. Cysticercosis is a serious, yet neglected disease caused by the larval stage of a zoonotic tapeworm, prevalent in many developing countries, including Burkina Faso. Being able to predict where the disease occurs is essential for running targeted prevention and control activities. In our study, we aimed to describe whether human cysticercosis cases in three provinces in Burkina Faso were clustered, and investigated whether there was a link between this clustering and some land and weather variables. Finally, we aimed to generate high-resolution prediction maps for the occurrence of the infection. We found that there was clustering at 45 meters for the individual- and 28.2 km for the village-level datasets, respectively. Increasing rainfall and proximity to a river were linked with this clustering. The generated prediction maps indicated there were important cysticercosis hotspots in the study area, especially in the extreme south and north-west, where the disease is thought to be more important. Further research should expand the use of spatial techniques to predict the occurrence of cysticercosis, the results of which can aid in the design of intervention programmes.
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DOI：
10.1098/rsif.2021.0104
发表时间：
2021-06
期刊：
Journal of the Royal Society, Interface
影响因子：
--
作者：
Giorgi E;Fronterrè C;Macharia PM;Alegana VA;Snow RW;Diggle PJ
通讯作者：
Diggle PJ
影响因子：
1.7
作者：
Li, Huanzhang;Zang, Xinzhong;Li, Shizhu
通讯作者：
Li, Shizhu
影响因子：
3.2
作者：
Assoum, Mohamad;Ortu, Giuseppina;Magalhaes, Ricardo J. Soares
通讯作者：
Magalhaes, Ricardo J. Soares
影响因子：
3.8
作者：
Carabin, Helene;Millogo, Athanase;Ganaba, Rasmane
通讯作者：
Ganaba, Rasmane
影响因子：
34.3
作者：
Cromwell, Elizabeth A.
通讯作者：
Cromwell, Elizabeth A.