PLoS ONE (Jan 2012)

Dry season determinants of malaria disease and net use in Benin, West Africa.

  • Nicolas Moiroux,
  • Olayidé Boussari,
  • Armel Djènontin,
  • Georgia Damien,
  • Gilles Cottrell,
  • Marie-Claire Henry,
  • Hélène Guis,
  • Vincent Corbel

DOI
https://doi.org/10.1371/journal.pone.0030558
Journal volume & issue
Vol. 7, no. 1
p. e30558

Abstract

Read online

BackgroundTo achieve malaria eradication, control efforts have to be sustained even when the incidence of malaria cases becomes low during the dry season. In this work, malaria incidence and its determinants including bed net use were investigated in children of under 5 years of age in 28 villages in southern Benin during the dry season.Methods and findingsMean malaria clinical incidence was measured in children aged 0-5 years by active case detection in 28 villages of the Ouidah-Kpomasse-Tori Bossito sanitary district between November 2007 and March 2008. Using Poisson mixed-effect models, malaria incidence was assessed according to the level of transmission by different vector species, and Long-Lasting Insecticide-treated mosquito Nets (LLIN) use and ownership. Then, a Binomial mixed-effect model was developed to assess whether nighttime temperature (derived from MODIS remote sensing data), biting nuisance and LLIN ownership are good predictors of LLIN use >60%. Results suggested that Anopheles funestus (Incidence Rates Ratio (IRR) = 3.38 [IC95 1.91-6]) rather than An. gambiae s.s. is responsible for malaria transmission. A rate of LLIN use >60% was associated with a lower risk of malaria (IRR = 0.6 [IC95 0.37-0.99]). Low nocturnal temperature and high biting nuisance were good predictors of LLIN use >60%.ConclusionsAs recommended by the Malaria Eradication (MalERA) Consultative Group on Modelling, there is a need to understand better the effects of seasonality on malaria morbidity. This study highlights the need to take into account the specificity of malaria epidemiology during the dry-hot season and get a better understanding of the factors that influence malaria incidence and net use. These findings should help National Malaria Control Programmes to implement more effective and sustainable malaria control strategies in Africa.