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Geographical factors affecting the implementation of alternative strategies for lymphatic filariasis elimination in post-conflict countries

Lymphatic filariasis, like malaria, is a mosquito-born disease. Below, Michelle C. Stanton, Moses J. Bockarie and Louise A. Kelly-Hope of the Centre for Neglected Tropical Diseases, Liverpool School of Tropical Medicine, share an abstract of their study on vector control for lymphatic filariasis. Michelle was one of the candidates for the Young Investigators’ Award at the 2012 American Society for Tropical Medicine and Hygiene meeting in Atlanta.

cntd-banner-sm.jpgAbstract

Vector control, including the use of bed nets, is recommended as a possible strategy for eliminating lymphatic filariasis (LF) in post-conflict countries such as the Democratic Republic of Congo (DRC).

This study examined the geographical factors that influence community bed net coverage in DRC in order to identify the hard-to-reach areas that need to be better targeted. In particular, urban/rural differences and the influence of population density, proximity to cities and health facilities, plus access to major transport networks were investigated.

Demographic and Health Survey geo-referenced cluster data were used to map the proportion of households with at least one bed net (unspecified), with at least one insecticide-treated net (ITN) and ITNs per person for 300 communities. Spatial statistical methods and bivariate and multiple logistic regression analyses were used to determine significant relationships.

Overall, bed net (30%) and ITN (9%) coverage were very low with significant differences found between urban and rural communities. In rural communities coverage was significantly positively correlated with population density (p<0.01), and negatively with the distance to the two largest cities, Kinshasa or Lubumbashi (p<0.0001). Further, coverage was significant negatively correlated with the distance to primary national roads and railways (all bed net measures), distance to the main river (unspecified only) and the distance to the nearest health facility (ITNs only).

Logistic and Poisson regression models fitted to the rural community data indicated that, after controlling for the effects of the measured covariates, coverage levels in the Bas-Congo province close to Kinshasa were much larger than expected. This was most noticeable when considering ITNs and ITN density which were 5.1 times higher in the Bas-Congo province compared to all other provinces.

These maps and spatial analyses provide key insights into the barriers of bed net coverage, which will help inform both LF and malaria bed net distribution campaigns as part an integrated vector management (IVM) strategy.

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