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. 2013 Jan 18:12:24.
doi: 10.1186/1475-2875-12-24.

Geographic coincidence of increased malaria transmission hazard and vulnerability occurring at the periphery of two Tanzanian villages

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Geographic coincidence of increased malaria transmission hazard and vulnerability occurring at the periphery of two Tanzanian villages

Tanya L Russell et al. Malar J. .

Abstract

Background: The goal of malaria elimination necessitates an improved understanding of any fine-scale geographic variations in transmission risk so that complementary vector control tools can be integrated into current vector control programmes as supplementary measures that are spatially targeted to maximize impact upon residual transmission. This study examines the distribution of host-seeking malaria vectors at households within two villages in rural Tanzania.

Methods: Host-seeking mosquitoes were sampled from 72 randomly selected households in two villages on a monthly basis throughout 2008 using CDC light-traps placed beside occupied nets. Spatial autocorrelation in the dataset was examined using the Moran's I statistic and the location of any clusters was identified using the Getis-Ord Gi* statistic. Statistical associations between the household characteristics and clusters of mosquitoes were assessed using a generalized linear model for each species.

Results: For both Anopheles gambiae sensu lato and Anopheles funestus, the density of host-seeking females was spatially autocorrelated, or clustered. For both species, houses with low densities were clustered in the semi-urban village centre while houses with high densities were clustered in the periphery of the villages. Clusters of houses with low or high densities of An. gambiae s.l. were influenced by the number of residents in nearby houses. The occurrence of high-density clusters of An. gambiae s.l. was associated with lower elevations while An. funestus was also associated with higher elevations. Distance from the village centre was also positively correlated with the number of household occupants and having houses constructed with open eaves.

Conclusion: The results of the current study highlight that complementary vector control tools could be most effectively targeted to the periphery of villages where the households potentially have a higher hazard (mosquito densities) and vulnerability (open eaves and larger households) to malaria infection.

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Figures

Figure 1
Figure 1
Kilombero and Ulanga districts (8.1°S and 36.6°E) in Tanzania showing Namawala and Idete villages (left) and spatial distribution of sentinel houses used for sampling the local anopheline population (right).
Figure 2
Figure 2
Spatial and temporal distribution of the sibling species Anopheles gambiae s.s. and Anopheles arabiensis.
Figure 3
Figure 3
Spatial distribution of Anopheles gambiae s.l. and Anopheles funestus densities over time in Namawala and Idete villages. Note: Scale represents the total number of mosquitoes caught in all light trap sampling efforts for each household.
Figure 4
Figure 4
Spatial clustering of Anopheles gambiae s.l. and Anopheles funestus densities over time in Namawala and Idete villages. Note: The blue end of the scale represents clustering of households with low mosquito densities, where the red end of the scale represents high mosquito densities.
Figure 5
Figure 5
The spatial clustering of Anopheles gambiae s.l. and Anopheles funestus densities computed over each month in 2008. As well as the elevation profile, distribution of houses with closed/open eaves, and the spatial clustering of the number of household occupants and the number of LLINs per person. Note: The blue end of the scale represents clustering of households with low mosquito densities, where the red end of the scale represents high mosquito densities.
Figure 6
Figure 6
Scatter plots comparing the spatial clustering of Anopheles gambiae s.l. and Anopheles funestus densities with distance from the village centre. Spatial clustering is represented by the species specific Getis-Ord Gi* Z Score calculated for each household; clusters of households with high densities were identified with Z scores >1.96 (shaded area at top), or vice versa, clustered households with low densities of anophelines were identified with Z scores < −1.96 (shaded area at bottom).

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