Factors affecting integration of an early warning system for antimalarial drug resistance within a routine surveillance system in a pre-elimination setting in Sub-Saharan Africa
| dc.contributor.author | Kagoro F.M. | |
| dc.contributor.author | Allen E. | |
| dc.contributor.author | Raman J. | |
| dc.contributor.author | Mabuza A. | |
| dc.contributor.author | Magagula R. | |
| dc.contributor.author | Kok G. | |
| dc.contributor.author | Malatje G. | |
| dc.contributor.author | Guerin P.J. | |
| dc.contributor.author | Dhorda M. | |
| dc.contributor.author | Maude R.J. | |
| dc.contributor.author | Barnes K.I. | |
| dc.contributor.correspondence | Kagoro F.M. | |
| dc.contributor.other | Mahidol University | |
| dc.date.accessioned | 2025-06-13T18:26:28Z | |
| dc.date.available | 2025-06-13T18:26:28Z | |
| dc.date.issued | 2025-06-01 | |
| dc.description.abstract | To address the current threat of antimalarial resistance, countries need innovative solutions for timely and informed decision-making. Integrating molecular surveillance for drug-resistant malaria into routine malaria surveillance in pre-elimination contexts offers a potential early warning mechanism for further investigation and response. However, there is limited evidence on what influences the performance of such a system in resource-limited settings. From March 2018 to February 2020, a sequential mixed-methods study was conducted in primary healthcare facilities in a South African pre-elimination setting to explore factors influencing the flow, quality and linkage of malaria case notification and molecular resistance marker data. Using a process-oriented framework, we undertook monthly and quarterly data linkage and consistency analyses at different levels of the health system, as well as a survey, focus group discussions and interviews to identify potential barriers to, and enhancers of, the roll-out and uptake of this integrated information system. Over two years, 4,787 confirmed malaria cases were notified from 42 primary healthcare facilities in the Nkomazi sub-district, Mpumalanga, South Africa. Of the notified cases, 78.5% (n = 3,758) were investigated, and 55.1% (n = 2,636) were successfully linked to their Plasmodium falciparum molecular resistance marker profiles. Five tangible processes—malaria case detection and notification, sample collection, case investigation, analysis and reporting—were identified within the process-oriented logic model. Workload, training, ease of use, supervision, leadership, and resources were recognized as cross-cutting influencers affecting the program’s performance. Approaching malaria elimination, linking molecular markers of antimalarial resistance to routine malaria surveillance is feasible. However, cross-cutting barriers inherent in the healthcare system can influence its success in a resource-limited setting. | |
| dc.identifier.citation | Plos One Vol.20 No.6 June (2025) | |
| dc.identifier.doi | 10.1371/journal.pone.0305885 | |
| dc.identifier.eissn | 19326203 | |
| dc.identifier.scopus | 2-s2.0-105007453451 | |
| dc.identifier.uri | https://repository.li.mahidol.ac.th/handle/123456789/110694 | |
| dc.rights.holder | SCOPUS | |
| dc.subject | Multidisciplinary | |
| dc.title | Factors affecting integration of an early warning system for antimalarial drug resistance within a routine surveillance system in a pre-elimination setting in Sub-Saharan Africa | |
| dc.type | Article | |
| mu.datasource.scopus | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=105007453451&origin=inward | |
| oaire.citation.issue | 6 June | |
| oaire.citation.title | Plos One | |
| oaire.citation.volume | 20 | |
| oairecerif.author.affiliation | Mpumalanga Provincial Malaria Elimination Programme | |
| oairecerif.author.affiliation | National Institute for Communicable Disease | |
| oairecerif.author.affiliation | The Open University | |
| oairecerif.author.affiliation | WorldWide Antimalarial Resistance Network | |
| oairecerif.author.affiliation | University of Cape Town | |
| oairecerif.author.affiliation | Infectious Diseases Data Observatory | |
| oairecerif.author.affiliation | Faculty of Health Sciences | |
| oairecerif.author.affiliation | The University of Hong Kong Li Ka Shing Faculty of Medicine | |
| oairecerif.author.affiliation | Nuffield Department of Medicine | |
| oairecerif.author.affiliation | University of the Witwatersrand Faculty of Health Sciences | |
| oairecerif.author.affiliation | Mahidol Oxford Tropical Medicine Research Unit |
