Geographical patterns and predictors of malaria risk in Zambia: Bayesian geostatistical modelling of the 2006 Zambia national malaria indicator survey (ZMIS).
Geographical patterns and predictors of malaria risk in Zambia: Bayesian geostatistical modelling of the 2006 Zambia national malaria indicator survey (ZMIS).
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DOI：
10.1186/1475-2875-9-37
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发表时间：
2010-02-01
期刊：
影响因子：
3
通讯作者：
Steketee RW
中科院分区：
文献类型：
--
作者：
Riedel N;Vounatsou P;Miller JM;Gosoniu L;Chizema-Kawesha E;Mukonka V;Steketee RW
2006年赞比亚疟疾指标调查是第一次全国范围的疟疾调查，将寄生虫学数据与蚊帐使用、室内滞留喷洒和家庭相关方面等其他疟疾指标结合起来。这项调查由赞比亚卫生部和合作伙伴进行，目的是估计干预措施的覆盖范围和五岁以下儿童的疟疾相关负担。在这项研究中，ZMIS数据进行了分析，以（i）估计经验的高分辨率寄生虫风险图在该国和（ii）评估疟疾干预措施和寄生虫病风险之间的关系调整后的环境和社会经济的混杂因素。寄生虫风险预测贝叶斯地质统计和空间独立的模型，有关寄生虫病的风险和疟疾的环境/气候预测。一些模型进行了拟合，以捕捉（潜在的）非线性的疟疾与环境的关系，并确定环境影响和寄生虫病风险之间的流逝时间。这些模型包括协变量（a）分类量表和（B）惩罚和基础样条项。使用不同的模型验证方法来确定最佳拟合模型。在未观察到的位置，基于模型的风险预测，通过贝叶斯预测分布的最佳拟合模型。模型验证表明，线性环境预测能够拟合的数据以及或甚至更好地比更复杂的非线性项，数据不支持空间依赖。总体而言，小于5岁儿童的平均人群调整寄生虫病风险为20.0%，预测北方省的风险最高（38.3%）。与没有蚊帐的家庭相比，生活在至少有一个蚊帐的家庭中的儿童患寄生虫病的几率降低了40%（CI：12%，61%）。寄生虫病风险图连同预测误差和风险人口，对赞比亚的疟疾形势提供了重要的概述。这些地图可以帮助实现更好的资源分配、健康管理和有针对性的额外干预措施，以大幅减轻赞比亚的疟疾负担。重复的调查将能够评估正在进行的干预措施的有效性。
The Zambia Malaria Indicator Survey (ZMIS) of 2006 was the first nation-wide malaria survey, which combined parasitological data with other malaria indicators such as net use, indoor residual spraying and household related aspects. The survey was carried out by the Zambian Ministry of Health and partners with the objective of estimating the coverage of interventions and malaria related burden in children less than five years. In this study, the ZMIS data were analysed in order (i) to estimate an empirical high-resolution parasitological risk map in the country and (ii) to assess the relation between malaria interventions and parasitaemia risk after adjusting for environmental and socio-economic confounders. The parasitological risk was predicted from Bayesian geostatistical and spatially independent models relating parasitaemia risk and environmental/climatic predictors of malaria. A number of models were fitted to capture the (potential) non-linearity in the malaria-environment relation and to identify the elapsing time between environmental effects and parasitaemia risk. These models included covariates (a) in categorical scales and (b) in penalized and basis splines terms. Different model validation methods were used to identify the best fitting model. Model-based risk predictions at unobserved locations were obtained via Bayesian predictive distributions for the best fitting model. Model validation indicated that linear environmental predictors were able to fit the data as well as or even better than more complex non-linear terms and that the data do not support spatial dependence. Overall the averaged population-adjusted parasitaemia risk was 20.0% in children less than five years with the highest risk predicted in the northern (38.3%) province. The odds of parasitaemia in children living in a household with at least one bed net decreases by 40% (CI: 12%, 61%) compared to those without bed nets. The map of parasitaemia risk together with the prediction error and the population at risk give an important overview of the malaria situation in Zambia. These maps can assist to achieve better resource allocation, health management and to target additional interventions to reduce the burden of malaria in Zambia significantly. Repeated surveys will enable the evaluation of the effectiveness of on-going interventions.
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DOI：
10.1016/s0035-9203(98)90936-1
发表时间：
1998-01-01
影响因子：
2.2
作者：
Hay, SI;Snow, RW;Rogers, DJ
通讯作者：
Rogers, DJ
影响因子：
15.8
作者：
Guerra, Carlos A.;Gikandi, Priscilla W.;Tatem, Andrew J.;Noor, Abdisalan M.;Smith, Dave L.;Hay, Simon I.;Snow, Robert W.
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Snow, Robert W.
影响因子：
3.3
作者：
Gemperli, Armin;Sogoba, Nafomon;Vounatsou, Penelope
通讯作者：
Vounatsou, Penelope
影响因子：
1.8
作者：
Gosoniu, L.;Vounatsou, P.;Smith, T.
通讯作者：
Smith, T.
DOI：
10.1111/1467-9876.00283
发表时间：
2002-01-01
影响因子：
1.6
作者：
Diggle, P;Moyeed, R;Thomson, M
通讯作者：
Thomson, M