Geographical patterns and predictors of malaria risk in Zambia: Bayesian geostatistical modelling of the 2006 Zambia national malaria indicator survey (ZMIS)

Malaria Journal

Research

Elizabeth Chizema-Kawesha¹  Victor Mukonka¹  Laura Gosoniu²  Nadine Riedel²  Penelope Vounatsou²  John M Miller³  Rick W Steketee³ 

[1] Departement of Public Health and Research, Ministry of Health, Zambia;Department of Public Health and Epidemiology, Swiss Tropical Institute, PO Box, 4002, Basel, Switzerland;Malaria Control and Evaluation Partnership in Africa, PATH, Ferney, France;

关键词: Malaria;    Normalize Difference Vegetation Index;    Land Surface Temperature;    Malaria Risk;    Posterior Predictive Distribution;   

DOI  :  10.1186/1475-2875-9-37

 received in 2009-11-10, accepted in 2010-02-01,  发布年份 2010

来源: Springer

【 摘 要 】

BackgroundThe 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.MethodsThe 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.ResultsModel 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.ConclusionsThe 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.

【 授权许可】

Unknown   
© Riedel et al; licensee BioMed Central Ltd. 2010. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.