Evaluating insecticide resistance across African districts to aid malaria control decisions

Abstract

Malaria vector control may be compromised by resistance to insecticides in vector populations. Actions to mitigate against resistance rely on surveillance using standard susceptibility tests, but there are large gaps in the monitoring data across Africa. Using a published geostatistical ensemble model, we have generated maps that bridge these gaps and consider the likelihood that resistance exceeds recommended thresholds. Our results show that this model provides more accurate next-year predictions than two simpler approaches. We have used the model to generate district-level maps for the probability that pyrethroid resistance in Anopheles gambiae s.l. exceeds the World Health Organization thresholds for susceptibility and confirmed resistance. In addition, we have mapped the three criteria for the deployment of piperonyl butoxide-treated nets that mitigate against the effects of metabolic resistance to pyrethroids. This includes a critical review of the evidence for presence of cytochrome P450-mediated metabolic resistance mechanisms across Africa. The maps for pyrethroid resistance are available on the IR Mapper website, where they can be viewed alongside the latest survey data.

Keywords: insecticide resistance; insecticide resistance management; malaria control; metabolic resistance; pyrethroid.

Fig. 1.

Gaps in the available insecticide resistance surveillance data from standard WHO and CDC tests using the An. gambiae complex and An. funestus subgroup. (A) Districts with susceptibility test (bioassay) results for 2015 to 2017 from a published susceptibility test dataset that collated data from routine monitoring by agencies such as the President’s Malaria Initiative (PMI) and from research groups in Africa and was subject to rigorous quality-assurance processes (15). Ninety-four percent (3,136/3,323) of malaria-endemic second-order administrative divisions do not have any data for insecticide resistance in this year and 89% (2,962/3,323) do not have any data in this year or the two preceding years. (B) Data shown on the WHO Malaria Threats website for the years 2015 to 2019 (accessed 9 December 2019) (16). (C) Data shown on the IR Mapper website for the years 2015 to 2019 (accessed 9 December 2019) obtained from published articles and PMI surveillance (17).

Fig. 2.

District-level deltamethrin resistance in local An. gambiae s.l. populations. (A) Predicted mean susceptibility test mortality for each district in 2017. (B) Predicted minimum susceptibility test mortality for each district in 2017. (C) Probability that mean susceptibility test mortality for the district is >98%, the definition of a “susceptible” population. (D) Probability that mean susceptibility test mortality for the district is <90%, the definition of a “resistant” population (22).

Fig. 3.

The respective distributions of An.gambiae s.l. and An. funestus. (A) The predicted distribution of An. gambiae s.l. taken from Wiebe et al. (22). (B) The predicted distribution of An. funestus taken from Wiebe et al. (22). (C) The overlap in predicted presence (binary values) of An.gambiae s.l. and An. funestus.

Fig. 4.

The probability of meeting the criteria for the deployment of PBO-treated nets. (A) The predicted probability that the mean mortality for a district is 10 to 80%. (B) Districts with surveillance data from paired standard susceptibility tests using a pyrethroid with and without the synergist PBO. (C) Plot of surveillance data from paired standard susceptibility tests using a pyrethroid with and without the synergist PBO showing the full variation in the results and key thresholds defined in WHO guidelines.