Malaria in central Vietnam: analysis of risk factors by multivariate analysis and classification tree models
Faculty of Pharmaceutical, Biomedical and Veterinary Sciences. Pharmacy
Publication type
London ,
Human medicine
Source (journal)
Malaria journal. - London
7(2008) , p. 28,1-28,9
Target language
English (eng)
Full text (Publishers DOI)
University of Antwerp
Background In Central Vietnam, forest malaria remains difficult to control due to the complex interactions between human, vector and environmental factors. Methods Prior to a community-based intervention to assess the efficacy of long-lasting insecticidal hammocks, a complete census (18,646 individuals) and a baseline cross-sectional survey for determining malaria prevalence and related risk factors were carried out. Multivariate analysis using survey logistic regression was combined to a classification tree model (CART) to better define the relative importance and inter-relations between the different risk factors. Results The study population was mostly from the Ra-glai ethnic group (88%), with both low education and socio-economic status and engaged mainly in forest activities (58%). The multivariate analysis confirmed forest activity, bed net use, ethnicity, age and education as risk factors for malaria infections, but could not handle multiple interactions. The CART analysis showed that the most important risk factor for malaria was the wealth category, the wealthiest group being much less infected (8.9%) than the lower and medium wealth category (16.6%). In the former, forest activity and bed net use were the most determinant risk factors for malaria, while in the lower and medium wealth category, insecticide treated nets were most important, although the latter were less protective among Ra-glai people. Conclusion The combination of CART and multivariate analysis constitute a novel analytical approach, providing an accurate and dynamic picture of the main risk factors for malaria infection. Results show that the control of forest malaria remains an extremely complex task that has to address poverty-related risk factors such as education, ethnicity and housing conditions.
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