Mapping helminth co-infection and co-intensity: geostatistical prediction in ghana.
Mapping helminth co-infection and co-intensity: geostatistical prediction in ghana.
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DOI：
10.1371/journal.pntd.0001200
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发表时间：
2011-06
影响因子：
3.8
通讯作者：
Clements AC
中科院分区：
文献类型：
--
作者：
Soares Magalhães RJ;Biritwum NK;Gyapong JO;Brooker S;Zhang Y;Blair L;Fenwick A;Clements AC
埃及血吸虫和钩虫感染引起的死亡在这些寄生虫严重合并感染的人中很明显。空间预测决策支持工具的开发对于将综合大规模药物管理（MDA）提供给最需要的人至关重要。我们研究了S.在加纳，两种寄生虫感染强度的空间重叠。其目的是绘制地图，以协助规划和评估国家寄生虫病控制方案。2008年，加纳采用标准化抽样和寄生虫学方法，开展了一项全国性的学校寄生虫学横断面调查。建立了贝叶斯地质统计模型，包括S. hematobium和钩虫的单一和混合感染和零膨胀泊松回归模型的S。血吸虫和钩虫感染强度分别通过尿液和粪便中的虫卵计数来测量。将由此产生的感染强度图重叠，以确定S.钩虫感染强度。在加纳，S.钩虫感染率为3.2%，血吸虫感染率为14.4%，钩虫感染率为3.2%;钩虫和血吸虫混合感染率为0.7%。距水体距离与S.钩虫感染、钩虫单一感染和钩虫混合感染。血吸虫感染强度地表温度与钩虫单一感染和钩虫感染呈正相关。血吸虫感染强度虽然预计沃尔特湖周围地区合并感染的风险很高（患病率>10-20%），但预计该地区内的病灶合并感染强度最高。我们的方法，基于合并感染和共同强度地图相结合，可以识别社区的严重发病率和环境污染的风险增加，并提供了一个平台，以评估控制工作的进展。尿路血吸虫病和钩虫感染在西非学龄儿童中造成相当高的发病率。严重的发病率主要是在感染两种寄生虫类型的个体中观察到的，特别是重度感染。首次研究了S.加纳的埃及血吸虫和钩虫合并感染以及这些寄生虫的合并强度分布。贝叶斯地质统计模型被开发出来，以生成一个国家的共同感染地图和国家强度地图，为每种寄生虫，使用数据的S。血吸虫和钩虫的流行率和虫卵浓度（以每10 mL尿液中的虫卵表示）。2008年在加纳进行的干预前基线调查期间收集的数据显示，2008年在加纳，每克粪便中的钩虫卵数为钩虫卵数（每克粪便中的钩虫卵数）。与以往在东非地区的研究结果相比，我们发现，S。埃及血蜱和钩虫感染是高度集中的，导致小的、局部的合并感染群和高合并强度的区域。叠加在一个单一的地图上的合并感染和多种寄生虫感染的强度，可以识别寄生虫环境污染和发病率最高的地区，同时提供了一个证据基础，评估连续几轮的大规模药物管理（MDA）在综合寄生虫病控制计划的进展。
Morbidity due to Schistosoma haematobium and hookworm infections is marked in those with intense co-infections by these parasites. The development of a spatial predictive decision-support tool is crucial for targeting the delivery of integrated mass drug administration (MDA) to those most in need. We investigated the co-distribution of S. haematobium and hookworm infection, plus the spatial overlap of infection intensity of both parasites, in Ghana. The aim was to produce maps to assist the planning and evaluation of national parasitic disease control programs. A national cross-sectional school-based parasitological survey was conducted in Ghana in 2008, using standardized sampling and parasitological methods. Bayesian geostatistical models were built, including a multinomial regression model for S. haematobium and hookworm mono- and co-infections and zero-inflated Poisson regression models for S. haematobium and hookworm infection intensity as measured by egg counts in urine and stool respectively. The resulting infection intensity maps were overlaid to determine the extent of geographical overlap of S. haematobium and hookworm infection intensity. In Ghana, prevalence of S. haematobium mono-infection was 14.4%, hookworm mono-infection was 3.2%, and S. haematobium and hookworm co-infection was 0.7%. Distance to water bodies was negatively associated with S. haematobium and hookworm co-infections, hookworm mono-infections and S. haematobium infection intensity. Land surface temperature was positively associated with hookworm mono-infections and S. haematobium infection intensity. While high-risk (prevalence >10–20%) of co-infection was predicted in an area around Lake Volta, co-intensity was predicted to be highest in foci within that area. Our approach, based on the combination of co-infection and co-intensity maps allows the identification of communities at increased risk of severe morbidity and environmental contamination and provides a platform to evaluate progress of control efforts. Urinary schistosomiasis and hookworm infections cause considerable morbidity in school age children in West Africa. Severe morbidity is predominantly observed in individuals infected with both parasite types and, in particular, with heavy infections. We investigated for the first time the distribution of S. haematobium and hookworm co-infections and distribution of co-intensity of these parasites in Ghana. Bayesian geostatistical models were developed to generate a national co-infection map and national intensity maps for each parasite, using data on S. haematobium and hookworm prevalence and egg concentration (expressed as eggs per 10 mL of urine for S. haematobium and expressed as eggs per gram of faeces for hookworm), collected during a pre-intervention baseline survey in Ghana, 2008. In contrast with previous findings from the East Africa region, we found that both S. haematobium and hookworm infections are highly focal, resulting in small, localized clusters of co-infection and areas of high co-intensity. Overlaying on a single map the co-infection and the intensity of multiple parasite infections allows identification of areas where parasite environmental contamination and morbidity are at its highest, while providing an evidence base for the assessment of the progress of successive rounds of mass drug administration (MDA) in integrated parasitic disease control programs.
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影响因子：
2.4
作者：
Clements, A. C. A.;Moyeed, R.;Brooker, S.
通讯作者：
Brooker, S.
影响因子：
9.6
作者：
Brooker S;Hay SI;Bundy DA
通讯作者：
Bundy DA
DOI：
10.1179/136485913x13789813917463
发表时间：
2007-09-01
影响因子：
--
作者：
Diggle, P. J.;Thomson, M. C.;Molyneux, D. H.
通讯作者：
Molyneux, D. H.
DOI：
10.1016/s0169-4758(00)01779-8
发表时间：
2000-12-01
期刊：
PARASITOLOGY TODAY
影响因子：
--
作者：
Hall, A;Holland, C
通讯作者：
Holland, C
DOI：
10.1016/s0035-9203(96)90304-1
发表时间：
1996-09-01
影响因子：
2.2
作者：
Engels, D;Nahimana, S;Gryseels, B
通讯作者：
Gryseels, B