Ranking malaria risk factors to guide malaria control efforts in African highlands

• Published in: PloS one Vol. 4; no. 11; pp. e8022 - 8022
• Main Authors: Protopopoff, Natacha, Van Bortel, Wim, Speybroeck, Niko, Van Geertruyden, Jean-Pierre, Baza, Dismas, D'Alessandro, Umberto, Coosemans, Marc
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 25.11.2009; Public Library of Science (PLoS)
• Subjects: Adolescent; Adult; Animals; Anopheles; Bedding and Linens; Breeding sites; Burundi; Child; Classification; Consent; Control methods; Culicidae; Decision analysis; Decision Support Techniques; Diptera; Disease transmission; Epidemics; Female; Health risks; Highlands; Housing; Humans; Infectious Diseases; Infectious Diseases/Epidemiology and Control of Infectious Diseases; Infectious Diseases/Neglected Tropical Diseases; Insect Vectors; Insecticides - pharmacology; Literature reviews; Malaria; Malaria - diagnosis; Malaria - genetics; Male; Mathematical models; Medicine; Minimum temperatures; Mosquito Control - methods; Mosquito Nets; Mosquitoes; Multivariate Analysis; Parasitology; Plasmodium falciparum; Population (statistical); Rainfall; Regression Analysis; Residential areas; Risk analysis; Risk Factors; Statistical analysis; Statistical methods; Temperature; Trends; Vector-borne diseases; Burundi; Belgium; Africa
• DOI: 10.1371/journal.pone.0008022
• ISSN: 1932-6203

Malaria is re-emerging in most of the African highlands exposing the non immune population to deadly epidemics. A better understanding of the factors impacting transmission in the highlands is crucial to improve well targeted malaria control strategies. A conceptual model of potential malaria risk factors in the highlands was built based on the available literature. Furthermore, the relative importance of these factors on malaria can be estimated through "classification and regression trees", an unexploited statistical method in the malaria field. This CART method was used to analyse the malaria risk factors in the Burundi highlands. The results showed that Anopheles density was the best predictor for high malaria prevalence. Then lower rainfall, no vector control, higher minimum temperature and houses near breeding sites were associated by order of importance to higher Anopheles density. In Burundi highlands monitoring Anopheles densities when rainfall is low may be able to predict epidemics. The conceptual model combined with the CART analysis is a decision support tool that could provide an important contribution toward the prevention and control of malaria by identifying major risk factors.