Comparison of infant malaria incidence in districts of Maputo province, Mozambique

Malaria Journal

Research

Peter Majlender¹  Orlando P Zacarias² 

[1] Department of Computer and System Sciences (DSV), Stockholm University, Sweden;Department of Mathematics and Informatics (DMI), Eduardo Mondlane University, Mozambique;Department of Computer and System Sciences (DSV), Stockholm University, Sweden;

关键词: Malaria;    Markov Chain Monte Carlo;    Malaria Transmission;    Malaria Case;    Malaria Incidence;   

DOI  :  10.1186/1475-2875-10-93

 received in 2010-11-19, accepted in 2011-04-17,  发布年份 2011

来源: Springer

【 摘 要 】

BackgroundMalaria is one of the principal health problems in Mozambique, representing 48% of total external consultations and 63% of paediatric hospital admissions in rural and general hospitals with 26.7% of total mortality. Plasmodium falciparum is responsible for 90% of all infections being also the species associated with most severe cases. The aim of this study was to identify zones of high malaria risk, showing their spatially and temporal pattern.MethodsSpace and time Poison model for the analysis of malaria data is proposed. This model allows for the inclusion of environmental factors: rainfall, temperature and humidity as predictor variables. Modelling and inference use the fully Bayesian approach via Markov Chain Monte Carlo (MCMC) simulation techniques. The methodology is applied to analyse paediatric data arising from districts of Maputo province, Mozambique, between 2007 and 2008.ResultsMalaria incidence risk is greater for children in districts of Manhiça, Matola and Magude. Rainfall and humidity are significant predictors of malaria incidence. The risk increased with rainfall (relative risk - RR: .006761, 95% interval: .001874, .01304), and humidity (RR: .049, 95% interval: .03048, .06531). Malaria incidence was found to be independent of temperature.ConclusionsThe model revealed a spatial and temporal pattern of malaria incidence. These patterns were found to exhibit a stable malaria transmission in most non-coastal districts. The findings may be useful for malaria control, planning and management.

【 授权许可】

CC BY   
© Zacarias and Majlender; licensee BioMed Central Ltd. 2011