Abstract:
BACKGROUND : Independent emergence and spread of artemisinin-resistant Plasmodium falciparum malaria have recently been confirmed in Africa, with molecular markers associated with artemisinin resistance increasingly detected. Surveillance to promptly detect and effectively respond to anti-malarial resistance is generally suboptimal in Africa, especially in low transmission settings where therapeutic efficacy studies are often not feasible due to recruitment challenges. However, these communities may be at higher risk of anti-malarial resistance.
METHODS : From March 2018 to February 2020, a sequential mixed-methods study was conducted to evaluate the feasibility of the near-real-time linkage of individual patient anti-malarial resistance profiles with their case notifications and treatment response reports, and map these to fine scales in Nkomazi sub-district, Mpumalanga, a pre-elimination area in South Africa.
RESULTS : Plasmodium falciparum molecular marker resistance profiles were linked to 55.1% (2636/4787) of notified malaria cases, 85% (2240/2636) of which were mapped to healthcare facility, ward and locality levels. Over time, linkage of individual malaria case demographic and molecular data increased to 75.1%. No artemisinin resistant validated/associated Kelch-13 mutations were detected in the 2385 PCR positive samples. Almost all 2812 samples assessed for lumefantrine susceptibility carried the wildtype mdr86ASN and crt76LYS alleles, potentially associated with decreased lumefantrine susceptibility.
CONCLUSION : Routine near-real-time mapping of molecular markers associated with anti-malarial drug resistance on a fine spatial scale provides a rapid and efficient early warning system for emerging resistance. The lessons learnt here could inform scale-up to provincial, national and regional malaria elimination programmes, and may be relevant for other antimicrobial resistance surveillance.
Description:
ADDITIONAL FILE 1:
FIGURE S1. Data flow and linkage for notified malaria cases and case investigation reports on drug adherence and response. FIGURE S2. GPS coordinates’ coverage trend over the two-year study period (2018–2020). FIGURE S3. Accuracy trend of malaria case residential coordinates collected over the two-year study period (2018–2020). FIGURE S4. Percentage of mRDTs barcoded over the two-year study period (2018–2020). FIGURE S5. Barcode accuracy trend over the two-year study period (2018–2020). FIGURE S6. Linkage of the patients’ and antimalarial resistance data over the two-year study period (2018–2020). FIGURE S7. Three different types of shapefiles evaluated for the study area. TOOL S1. GPS tools for training malaria case investigators. TOOL S1A. Training guide for trainers. TOOL S1B. Standard operating procedures for eTrex 10 GPS device (adapted from Health Geolab Collaborative, 2018).
AVAILABILITY OF DATA AND MATERIALS : The datasets generated and/or analysed during the current study are available at the WWARN Tracking Resistance website (https://www.wwarn.org/tracking-resistance/artemisinin-molecular-surveyor).
