BackgroundTo implement an Insecticide Resistance Management (IRM) strategy through a randomized controlled trial (phase III), 28 villages were selected in southern Benin. No recent entomological data being available in these villages, entomological surveys were performed between October 2007 and May 2008, before vector control strategies implementation, to establish baseline data.MethodsMosquitoes were sampled by human landing collection (16 person-nights per village per survey per village) during 5 surveys. Mosquitoes were identified morphologically and by molecular methods. The Plasmodium falciparum circumsporozoïte indexes were measured by ELISA, and the entomological inoculation rates (EIRs) were calculated. Molecular detection of pyrethroid knock down resistance (Kdr) and of insensitive acetylcholinesterase were performed.Results44,693 mosquitoes belonging to 28 different species were caught from October 2007 to May 2008. Among mosquitoes caught, 318 were An. gambiae s.s., 2 were An. nili, 568 were An. funestus s.s., and one individual was An. leesoni. EIR was 2.05 ± 1.28 infective bites per human per 100 nights on average, of which 0.67 ± 0.60 were from An. funestus and 1.38 ± 0.94 infective bites were from An. gambiae. Important variations were noted between villages considering mosquito density and malaria transmission indicating a spatial heterogeneity in the study area. The kdr allelic frequency was 28.86% in An. gambiae s.s. on average and significantly increases from October 2007 (10.26%) to May 2008 (33.87%) in M molecular form of An. gambiae s.s. Ace 1 mutation was found in S molecular of An. gambiae s.s at a low frequency (< 1%).ConclusionThis study updates information on mosquito diversity and malaria risk in rural villages from south Benin. It showed a high spatial heterogeneity in mosquito distribution and malaria transmission and underlines the need of further investigations of biological, ecological, and behavioral traits of malaria vectors species and forms. This study is a necessary prerequisite to cartography malaria risk and to improve vector control operations in southern Benin.

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A study on the use of pesticides in market-gardening production was carried out on 108 market-gardeners in the rural city of Tori-Bossito in Southern Benin. The objective of the study was to characterize the potential risks of pesticides usage by farmers and the impacts on their health and on the environment. Two risk indexes were calculated for each pesticide: an environmental risk index (ERI) and a health risk index (HRI). First stage larva of the mosquito Aedes aegypti were used as bio-indicator for detecting insecticide residue in vegetable before their harvesting on the farms. The highest ERI were obtained for carbofuran, chlorpyriphos ethyl and endosulfan. Pesticide residues were found in 42% of the samples of leaves of eggplant, cucumber, amaranth and solanum. Vegetables growers used pesticides that may be highly hazardous and which were not registered in most cases. These situations could have unexpected consequences including the exposure of consumers to health hazards

Vector control is a major step in the process of malaria control and elimination. This requires vector counts and appropriate statistical analyses of these counts. However, vector counts are often overdispersed. A non-parametric mixture of Poisson model (NPMP) is proposed to allow for overdispersion and better describe vector distribution. Mosquito collections using the Human Landing Catches as well as collection of environmental and climatic data were carried out from January to December 2009 in 28 villages in Southern Benin. A NPMP regression model with “village” as random effect is used to test statistical correlations between malaria vectors density and environmental and climatic factors. Furthermore, the villages were ranked using the latent classes derived from the NPMP model. Based on this classification of the villages, the impacts of four vector control strategies implemented in the villages were compared. Vector counts were highly variable and overdispersed with important proportion of zeros (75%). The NPMP model had a good aptitude to predict the observed values and showed that: i) proximity to freshwater body, market gardening, and high levels of rain were associated with high vector density; ii) water conveyance, cattle breeding, vegetation index were associated with low vector density. The 28 villages could then be ranked according to the mean vector number as estimated by the random part of the model after adjustment on all covariates. The NPMP model made it possible to describe the distribution of the vector across the study area. The villages were ranked according to the mean vector density after taking into account the most important covariates. This study demonstrates the necessity and possibility of adapting methods of vector counting and sampling to each setting.