Making data map-worthy-enhancing routine malaria data to support surveillance and mapping of Plasmodium falciparum anti-malarial resistance in a pre-elimination sub-Saharan African setting: a molecular and spatiotemporal epidemiology study

Frank M Kagoro; Elizabeth Allen; Aaron Mabuza; Lesley Workman; Ray Magagula; Gerdalize Kok; Craig Davies; Gillian Malatje; Philippe J Guérin; Mehul Dhorda; Richard J Maude; Jaishree Raman; Karen I Barnes

doi:10.1186/s12936-022-04224-4

Making data map-worthy-enhancing routine malaria data to support surveillance and mapping of Plasmodium falciparum anti-malarial resistance in a pre-elimination sub-Saharan African setting: a molecular and spatiotemporal epidemiology study

Malar J. 2022 Jun 29;21(1):207. doi: 10.1186/s12936-022-04224-4.

Authors

Frank M Kagoro^{1

2

3

4

5}, Elizabeth Allen^{1

3

4}, Aaron Mabuza^{1

3}, Lesley Workman^{1

3

4}, Ray Magagula⁶, Gerdalize Kok⁶, Craig Davies⁷, Gillian Malatje⁶, Philippe J Guérin^{3

4

5}, Mehul Dhorda^{2

4

5}, Richard J Maude^{2

5

8

9}, Jaishree Raman^#^{10

11

12}, Karen I Barnes^#^{13

14

15}

Affiliations

¹ Collaborating Centre for Optimising Antimalarial Therapy (CCOAT), Division of Clinical Pharmacology, Department of Medicine, University of Cape Town (UCT), Cape Town, South Africa.
² Mahidol Oxford Tropical Medicine Research Unit (MORU), Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand.
³ WorldWide Antimalarial Resistance Network (WWARN), Southern African Regional Hub, Division of Clinical Pharmacology, Department of Medicine, UCT, Mbombela, South Africa.
⁴ Infectious Diseases Data Observatory (IDDO), Nuffield Department of Medicine, University of Oxford, Oxford, UK.
⁵ Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
⁶ Mpumalanga Provincial Malaria Elimination Programme, Mbombela, Mpumalanga, South Africa.
⁷ Malaria Programme, Clinton Health Access Initiative, Pretoria, South Africa.
⁸ Harvard TH Chan School of Public Health, Harvard University, Boston, MA, USA.
⁹ The Open University, Milton Keynes, UK.
¹⁰ Centre for Emerging Zoonotic and Parasitic Diseases, National Institute for Communicable Disease, Johannesburg, Gauteng, South Africa.
¹¹ Wits Research Institute for Malaria, Faculty of Health Sciences, University of Witwatersrand, Johannesburg, South Africa.
¹² UP Institute for Sustainable Malaria Control, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa.
¹³ Collaborating Centre for Optimising Antimalarial Therapy (CCOAT), Division of Clinical Pharmacology, Department of Medicine, University of Cape Town (UCT), Cape Town, South Africa. karen.barnes@uct.ac.za.
¹⁴ WorldWide Antimalarial Resistance Network (WWARN), Southern African Regional Hub, Division of Clinical Pharmacology, Department of Medicine, UCT, Mbombela, South Africa. karen.barnes@uct.ac.za.
¹⁵ Infectious Diseases Data Observatory (IDDO), Nuffield Department of Medicine, University of Oxford, Oxford, UK. karen.barnes@uct.ac.za.

^# Contributed equally.

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.

Keywords: Africa; Artemisinin resistance; Kelch 13, k13; Lumefantrine; Malaria; Plasmodium falciparum; Spatiotemporal model.

MeSH terms

Antimalarials* / pharmacology
Antimalarials* / therapeutic use
Drug Resistance / genetics
Humans
Lumefantrine / pharmacology
Malaria, Falciparum* / drug therapy
Malaria, Falciparum* / epidemiology
Plasmodium falciparum / genetics
Protozoan Proteins / genetics
South Africa

Substances

Antimalarials
Protozoan Proteins
Lumefantrine

Grants and funding

WT_/Wellcome Trust/United Kingdom