Kagoro, Frank M.Allen, ElizabethRaman, JaishreeMabuza, AaronMagagula, RayKok, GerdalizeMalatje, GillianGuerin, Philippe J.Dhorda, MehulMaude, Richard J.Barnes, Karen I.2025-10-242025-10-242025-06Kagoro, F.M., Allen, E., Raman, J., Mabuza, A., Magagula, R., Kok, G., et al. (2025) 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. PLoS One 20(6): e0305885. https://doi.org/10.1371/journal.pone.0305885.1932-6203 (online)10.1371/journal.pone.0305885http://hdl.handle.net/2263/104988DATA AVAILABILITY : Data Availability: All qualitative relevant data are within the paper and its Supporting Information files. The quantitative datasets generated and/or analysed during the study are publicly available from the WWARN Tracking Resistance website (https://www.wwarn.org/tracking-resistance/artemisinin-molecular-surveyor). SUPPORTING INFORMATION FIGURE S1. Malaria Notification Systems: An illustration showing the different malaria notification systems involved during the study on integrating molecular markers of resistance into routine malaria notification system. https://doi.org/10.1371/journal.pone.0305885.s001. FIGURE S2. Malaria Cases Notified and Investigated. Comparison of malaria case notifications (S2a) and investigations (S2b) from source (Health Care Facilities – HCF), DCC and DHIS2 using three HCFs as the primary source. Malaria case data were aggregated and compared in the different levels for every first month of the five quarters in Nkomazi, Mpumalanga South Africa. https://doi.org/10.1371/journal.pone.0305885.s002. FIGURE S3. Linkage of individual patient data on malaria cases notified and their laboratory data: Data flow from notifiable medical condition (NMC) forms captured into the District Health Information System II (DHIS2), linked with their individual blood samples (malaria rapid diagnostic tests (mRDTs) and filter paper-dried blood spots) analysed using PCR for species confirmation and detection of molecular markers of antimalarial drug resistance. https://doi.org/10.1371/journal.pone.0305885.s003. TOOL S1. SS4ME Survey: A survey tool to collect primary healthcare staff’s experience on additional activities introduced during SS4ME on integrating molecular markers of resistance into routine malaria notification system. https://doi.org/10.1371/journal.pone.0305885.s004. TOOL S2. Information sheet and consent forms for SS4ME Survey (S2a) and Semi-Structured Interviews for Focus Group Discussions (S2b) and In-depth Interviews (S2c). https://doi.org/10.1371/journal.pone.0305885.s005. TOOL S3. Semi-structured guide for Focus Group Discussions (S3a) and In-depth Interviews (S3b) on integrating molecular surveillance for markers of resistance into the routine malaria notification system. https://doi.org/10.1371/journal.pone.0305885.s006. TABLE S1. A Process-oriented logic model for assessing the integration of early warning interventions in existing surveillance system. https://doi.org/10.1371/journal.pone.0305885.s007.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.en© 2025 Kagoro et al. This is an open access article distributed under the terms of the Creative Commons Attribution License.Sub-Saharan Africa (SSA)Sub-Saharan Africa (SSA)Molecular surveillancePlasmodium falciparumArticle