Abstract
Long lasting pyrethroid treated bednets are the most important tool for preventing malaria. Pyrethroid resistant Anopheline mosquitoes are now ubiquitous in Africa, though the public health impact remains unclear, impeding the deployment of more expensive nets. Meta-analyses of bioassay studies and experimental hut trials are used to characterise how pyrethroid resistance changes the efficacy of standard bednets, and those containing the synergist piperonyl butoxide (PBO), and assess its impact on malaria control. New bednets provide substantial personal protection until high levels of resistance, though protection may wane faster against more resistant mosquito populations as nets age. Transmission dynamics models indicate that even low levels of resistance would increase the incidence of malaria due to reduced mosquito mortality and lower overall community protection over the life-time of the net. Switching to PBO bednets could avert up to 0.5 clinical cases per person per year in some resistance scenarios.
Highlights
It is estimated that 68% of the 663 million cases of malaria that have been prevented since the year 2000 have been through the use of long-lasting insecticide treated bednets (LLINs) (Bhatt et al, 2015)
Mathematical models can be used to translate entomological endpoint trial data into predictions of public health impact. This has only been done for a small number of sites (Briet et al, 2013) making it difficult for malaria control programmes to understand the problems caused by insecticide resistance in their epidemiological setting
Pyrethroid resistance is widespread across Africa though its public health impact is unknown
Summary
It is estimated that 68% of the 663 million cases of malaria that have been prevented since the year 2000 have been through the use of long-lasting insecticide treated bednets (LLINs) (Bhatt et al, 2015). The efficacy of LLINs against mosquitoes is typically measured in experimental hut trials (WHO, 2013a) These experiments are time consuming, relatively expensive, and geographically limited and by themselves they do not fully account for all effects of the LLIN as they do not show the community impact (herd effects) caused by the insecticide killing mosquitoes (Killeen et al, 2007; Magesa et al, 1991). Mathematical models can be used to translate entomological endpoint trial data into predictions of public health impact. This has only been done for a small number of sites (Briet et al, 2013) making it difficult for malaria control programmes to understand the problems caused by insecticide resistance in their epidemiological setting
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