Research

Malaria in central Vietnam: analysis of risk factors by multivariate analysis and classification tree models

Ngo Duc Thang¹ , Annette Erhart² , Niko Speybroeck^2,3 , Le Xuan Hung¹ , Le Khanh Thuan¹ , Cong Trinh Hung¹ , Pham Van Ky⁴ , Marc Coosemans² and Umberto D'Alessandro²

¹  National Institute of Malariology, Parasitology and Entomology, Luong The Vinh street, BC 10200 Tu Liem district, Hanoi, Vietnam

²  Prince Leopold Institute of Tropical Medicine, Nationalestraat 155, 2000 Antwerp, Belgium

³  Ecole de santé publique, Université Catholique de Louvain, Clos Chapelle-aux-Champs, 1200 Bruxelles, Belgium

⁴  Center of Malariology, Parasitology and Entomology, Ninh Thuan province, 156 Ngo Gia Tu, Phan Rang, Ninh Thuan, Vietnam

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Malaria Journal 2008, 7:28doi:10.1186/1475-2875-7-28

Published:	30 January 2008

Abstract

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.