Explaining variation in adult Anopheles indoor resting abundance: the relative effects of larval habitat proximity and insecticide-treated bed net use

Abstract

Background: Spatial determinants of malaria risk within communities are associated with heterogeneity of exposure to vector mosquitoes. The abundance of adult malaria vectors inside people's houses, where most transmission takes place, should be associated with several factors: proximity of houses to larval habitats, structural characteristics of houses, indoor use of vector control tools containing insecticides, and human behavioural and environmental factors in and near houses. While most previous studies have assessed the association of larval habitat proximity in landscapes with relatively low densities of larval habitats, in this study these relationships were analysed in a region of rural, lowland western Kenya with high larval habitat density.

Methods: 525 houses were sampled for indoor-resting mosquitoes across an 8 by 8 km study area using the pyrethrum spray catch method. A predictive model of larval habitat location in this landscape, previously verified, provided derivations of indices of larval habitat proximity to houses. Using geostatistical regression models, the association of larval habitat proximity, long-lasting insecticidal nets (LLIN) use, house structural characteristics (wall type, roof type), and peridomestic variables (cooking in the house, cattle near the house, number of people sleeping in the house) with mosquito abundance in houses was quantified.

Results: Vector abundance was low (mean, 1.1 adult Anopheles per house). Proximity of larval habitats was a strong predictor of Anopheles abundance. Houses without an LLIN had more female Anopheles gambiae s.s., Anopheles arabiensis and Anopheles funestus than houses where some people used an LLIN (rate ratios, 95% CI 0.87, 0.85-0.89; 0.84, 0.82-0.86; 0.38, 0.37-0.40) and houses where everyone used an LLIN (RR, 95% CI 0.49, 0.48-0.50; 0.39, 0.39-0.40; 0.60, 0.58-0.61). Cooking in the house also reduced Anopheles abundance across all species. The number of people sleeping in the house, presence of cattle near the house, and house structure modulated Anopheles abundance, but the effect varied with Anopheles species and sex.

Conclusions: Variation in the abundance of indoor-resting Anopheles in rural houses of western Kenya varies with clearly identifiable factors. Results suggest that LLIN use continues to function in reducing vector abundance, and that larval source management in this region could lead to further reductions in malaria risk by reducing the amount of an obligatory resource for mosquitoes near people's homes.

Keywords: Anopheles arabiensis; Anopheles funestus; Anopheles gambiae; Generalized linear models; Geostatistical models; Larval habitats; Malaria vectors; Spatial heterogeneity.

Fig. 1

Study site in western Kenya. Inset shows Kenya with small red square indicating location of the study region in western Kenya. The larger map shows the boundaries of 76 villages in Asembo and the seasonal streams within the community, with black dots showing the geolocations of all households in the community. The 8 by 8 km border shows the extent of pyrethrum spray catch sampling in this study, with red dots showing the 525 houses sampled for adult Anopheles. The 10 by 10 km border shows the extent of the larval habitat model described in “Methods”

Fig. 2

Predictive model of Anopheles larval habitat presence. The top two panels show the output of the predictive model as the probability (P) of a larval habitat being present in each 20 by 20 m pixel. The bottom two panels show the probabilities converted to either “present” (i.e. at least one larval habitat is expected to be present in the 20 by 20 m pixel) or “absent”, using a threshold of P = 0.020. Maps on the right show close-up views of the maps on the left

Fig. 3

Weather data prior to and during study. The daily total precipitation (blue bars) and daily mean temperature (black line) from 1 January to 31 July 2011 at the Kisumu Airport weather station

Fig. 4

Number of mosquitoes collected per house by species and sex. Each dot within a species by sex represents a sample by PSC at one house. Black horizontal lines show the mean of each species by sex. Only those specimens identified morphologically as An. gambiae species complex and not identified further by PCR are counted for An. gambiae s.l. The sum total for each species by sex is shown at the top

Fig. 5

Scatterplots comparing two different larval habitat indices (a number of habitats within 500 m; b distance to nearest habitat in metres) with the mean probability of a habitat within 500 m. Each dot represents one of the 525 houses sampled in this study. P (habitat), probability of a larval habitat

Fig. 6

Area required for full larval habitat surveys compared to modelling. A circle (or buffer) with 500 m radius is drawn around the geolocation of each house sampled during pyrethrum spray catch sampling. Overlapping buffers are combined (or dissolved) with one another before calculating the total area required for ground surveys of larval Anopheles habitats. The 10 by 10 km study site is shown as a 500 by 500 m grid, from which 31 quadrats were actually surveyed to build the model used in this study