Quality Assurance in Practice: Insights from a Cluster Randomized Control Trial Evaluating a novel Spatial Repellent vector control intervention in Kenya

Quality Assurance in Practice: Insights from a Cluster Randomized Control Trial Evaluating a novel Spatial Repellent vector control intervention in Kenya | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Quality Assurance in Practice: Insights from a Cluster Randomized Control Trial Evaluating a novel Spatial Repellent vector control intervention in Kenya Brian Polo, Quentin Awori, Vincent Moshi, Hilda Otanga, Nickline Kuya, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7585900/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The implementation of quality assurance (QA) systems is crucial for generating reliable evidence in large-scale vector control trials. This paper documents the QA framework developed for the Advancing Evidence for the Global Implementation of Spatial Repellents (AEGIS) program in Busia, Kenya. The trial encompassed 60 clusters at baseline dropping to 58 during the intervention phase and followed 5,717 participants from three cohorts spanning two and a half years. Key QA innovations included a slot and sync scheduling system that significantly enhanced participant screening completion rates, scannable health facility registers for adverse event monitoring, electronic systems for managing microscopy samples and investigational products (IP), integration of community engagement in quality processes, and adaptation of procedures during COVID-19. We present the methods, challenges, and solutions in maintaining trial quality throughout the study period, providing valuable insights for future vector control studies in similar settings. Our experience demonstrates that robust QA implementation in resource-limited settings requires adaptable systems, continuous monitoring, a strong community partnership and strong financial support for development and implementation of QA systems. Figures Figure 1 Introduction As the global health community strives to reduce vector-borne diseases, the need for robust evidence supporting new control measures has become increasingly critical 1 . The World Health Organization Global Technical Strategy calls for reductions in case incidence and mortality of 75% by 2025 and 90% by 2030 2 . However, progress towards these targets remains off-track 1 , highlighting the need for innovative strategies that complement existing malaria control measures. Spatial repellents (SRs) represent one such innovation, offering protection against mosquitoes through chemicals that alter insect behaviour, impair host detection, and/or reduce feeding responses 3 , 4 . These products are particularly promising for protecting against daytime, early-morning, and evening-biting vectors in various indoor and peridomestic spaces 5 . However, before SRs can be recommended for integration into public health programs, the intervention product class must undergo rigorous evaluation of protective efficacy through cluster randomized controlled trials (cRCTs) using epidemiological endpoints 6 , 7 . Such evidence is then assessed for determination of public health value and contributes to the decision for WHO recommendation. Quality assurance (QA) forms the foundation of scientific integrity in cRCTs 8 . In large-scale trials, robust QA systems are essential for ensuring reliable data collection, analysis, and reporting while maintaining participant safety and ethical standards 9 , 10 . The implementation of QA systems in vector control trials presents unique challenges compared to traditional pharmaceutical trials 7 . While established QA frameworks exist for drug trials, vector control interventions are often deployed at community rather than individual levels, requiring different approaches to monitoring efficacy and safety. These interventions may range from household-level applications like SRs to village-wide implementations such as larval source management or environmental management. This diversity in intervention scale and deployment methods necessitates adaptable QA systems that can ensure data integrity while accommodating local contexts. In resource-limited settings, particularly rural African communities, these challenges are compounded by several factors: limited research infrastructure and healthcare facilities, variable cellular network coverage affecting digital data collection, diverse local languages and cultural practices, limited previous exposure to clinical research, complex community dynamics affecting intervention acceptance, logistical challenges in product distribution and monitoring, and the need for sustainable and scalable quality control measures 11 . The Advancing Evidence for the Global Implementation of Spatial Repellents (AEGIS) program in Busia, Kenya, was one of the first large-scale trials evaluating SRs in Africa as a malaria control tool 12 . This trial offered unique opportunities to develop and test QA systems suitable for vector control studies in resource-limited settings. The AEGIS-Kenya trial implemented novel approaches to QA, including digital data capture systems, innovative product tracking methods, and comprehensive community engagement strategies. Here we present a detailed description of the QA systems implemented during the AEGIS-Kenya trial, focusing on the methods used to ensure data integrity, maintain SR intervention management and monitor safety, and how these methods were adapted to local contexts and challenges. Our experience provides valuable insights for future community-based cRCTs, particularly in rural, resource-limited settings. We focus on practical solutions to common challenges, offering concrete recommendations for implementing robust QA systems that balance scientific rigor with operational feasibility. By sharing these experiences and lessons learned, we aim to contribute to the broader discussion on QA in vector control trials and support the development of standardized, yet adaptable, QA frameworks for future studies. This knowledge is particularly relevant as the field of vector control continues to evolve, with new tools and technologies requiring robust evaluation before recommending for implementation in national-level public health programs. Methods Clinical Trial Study design and setting The AEGIS-Kenya trial was designed as a double-blinded cRCT conducted in Busia County, western Kenya (Teso South and North sub-Counties) as previously described 12 . In brief, the study comprised 60 clusters at baseline, which was reduced to 58 clusters (2 clusters were dropped due to low incidence at baseline) during the intervention, with 29 receiving active SR product (transfluthrin emanator) and 29 receiving placebo products of matched design. Each cluster included a central village core with a surrounding 300–500-meter buffer zone (separated by at least 300 meters to prevent SR spillover). Participants aged between 6 months and 10 years were enrolled and followed up monthly for malaria cases at 15 study clinics. The rural, research-naïve population presented unique challenges for QA implementation. Quality Assurance (QA) Framework Our QA framework was collaboratively designed by the Kenya cRCT lead organizations (KEMRI, CDC and UND) with the specific objective to ensure the integrity and reliability of trial data, encompassing study protocol compliance, data quality, and safety monitoring. Protocol compliance was based on comprehensive standard operating procedures (SOPs), regular staff training programs, and continuous monitoring. Recognizing the challenge of intermittent connectivity, we implemented a data quality tier using a custom-designed CommCare™ data management system which enabled built-in QA functionality using study design and SOP logic, mobile offline data collection and database synchronization when cellular access was available. Safety monitoring combined passive surveillance through local health facilities with active monitoring during scheduled follow-up visits, allowing for the rapid identification and appropriate response to any adverse events. QA in relation to a strong community partnership Prior to trial initiation, we conducted a Rapid Ethical Assessment (REA) to understand community perspectives and establish appropriate engagement strategies 13 . This process identified key stakeholders and cultural practices, leading to the collaborative development of pictorial guides in three local dialects (English, Kiswahili, and Ateso) (Fig. 1 ) and ( Supplementary Fig. 1 ). We established a Community Advisory Board (CAB), composed of representatives of the local community groups), that met quarterly to provide ongoing feedback and guidance. Regular community forums (barazas) and radio announcements supplemented these efforts, ensuring continuous communication with study participants and the broader community. QA in relation to Subject Eligibility and Randomized Recruitment Process As a component to study protocol compliance, the CommCare™ system used household census data collected during the pre-trial mapping exercise to ensure community interviewers (Cis) engaged only those household members eligible for recruitment based on pre-defined inclusion/exclusion age criteria. The built-in functionality of the CommCare™ system included random selection of the second child at the same household from the household member listing. This functionality was not disclosed to the study team to avoid any bias. QA in relation to Participant Screening and Follow up Visits Scheduling Because of a manual recruitment system, the baseline phase of the cRCT encountered logistical challenges. Long waiting periods for participants screening and follow up and extended working hours for clinic staff were prevalent. During participant enrollment, between five to ten CIs were assigned to each clinic (depending on the number of clusters linked to the clinic). Each CI scheduled approximately two to three participants consented from their homes to report for screening at the clinics within the next day or two. This resulted in unstructured participant flow for clinic screening, causing long patient queues and increasing clinician workload, which contributed to clinician fatigue and empathy. Consequently, screening quality declined, generating data gaps as secondary study documents were often partially completed and deferred. Furthermore, some participants opted out of screening due to the lengthy wait times, causing loss of follow-up. To address these issues during the intervention phase, the team implemented a paper based "Slot and Sync" system. We could not implement a digital system due to patchy internet connectivity. In this innovative approach the CIs, before going out to recruit participants, each had a selection of time slots allocated to them (there were six 1-hour slots between 8:30am and 5:00pm for different days). The time slots for screening and follow up visits were unique to each CI. The CIs had a selection of morning and afternoon clinic slots to offer to the consented participants, allowing flexibility and individual preferences. Importantly, participant contact information was collected by the CIs to facilitate communication and scheduling. At the end of each day, the CIs affiliated with each clinic aggregated the assigned slots into a central table, effectively "syncing" the clinic visit schedule. This ensured that only one participant arrived at the clinic during each designated time slot, minimizing wait times and reducing the workload for clinicians. Every evening the clinician called the guardians of participant scheduled for the following day to confirm their availability and the agreed time slot. Enrolled participants were scheduled for a monthly clinic visit and bi-weekly home visit (conducted by a CI) 12 . We implemented a tablet based electronic medical records system with on device storage and cloud backup (when network was available), allowing real-time access to participant information while maintaining data integrity. Clinic notes were also recorded on the electronic medical records. Data Management to support Quality Systems The AEGIS-Kenya cRCT implemented a comprehensive data management system on the CommCare™ mobile data collection platform. CommCare™ was selected for its robust offline capabilities, capacity to support longitudinal data collection and linkage, and its built-in validation features, crucial for maintaining data quality in areas with limited connectivity. The platform incorporated on-device data validation rules, skip logic patterns, and automated scheduling alerts, significantly reducing data entry errors and ensuring protocol compliance 14 . Recognizing the data collection challenges inherent in rural settings, particularly unreliable network connectivity, we implemented a multi-layered backup system. Our primary approach utilized CommCare™ with offline data recording capability for efficient digital data capture. To mitigate potential data loss in case of server downtime, we also held printed paper- forms as back-up for recording study information (though we never had to use them) and ensured regular synchronization to the cloud server during periods of connectivity. Furthermore, we performed a weekly physical backup of the entire cloud data image, stored securely on two hard drives managed by the principal investigator and the data manager. This redundancy proved vital in safeguarding our data integrity while still benefiting from the advantages of digital data collection. Our quality control system operated on multiple levels. At the point of data entry, automated checks validated study IDs, enforced range limits, and flagged logical inconsistencies. A second level of review conducted daily completeness checks and cross-reference verifications. We tracked key quality indicators including form completion rates (targeting above 99%), real-time digital data entry, and query resolution time (within 48 hours). This systematic approach helped avoid missing data and protocol deviations throughout the study. Data cleaning followed a structured daily and weekly routine. Each day began with reviews of automated error reports, consistency checks, and query generation. Weekly comprehensive audits were performed to maintain the quality of participant follow-up, microscopy reading and product replacement. These audits specifically measured adherence to protocol in participant contact procedures, verified all attempts to engage participants who missed visits, flagged overdue microscopy results, assessed the adherence to investigational product (IP) replacement schedule, checked for consistency across data forms, and ensured overall protocol compliance. Trend analyses from these audits then guided our quality improvement initiatives. The query management system assigned clear responsibility for resolution to the staff responsible, with standard timeframes and documentation requirements ensuring timely problem solving. Security and privacy protections formed a crucial component of our data management strategy. We implemented role-based access restrictions with unique user credentials and comprehensive activity logging review. All data storage and transmission used encryption protocols, with physical security measures protecting local hardware. For example, all tablets and personal computers (PCs) were kept in facilities with access control and required username and password to access them. Paper forms were filed in lockable cabinets accessed only by authorized individuals. Our laboratory information system (LIS) managed sample tracking and result validation 15 , while the electronic medical records system handled clinical enrollment and follow-up data and visit scheduling. These integrations enabled real-time access to participant information while maintaining data security and quality controls. The only data that was primarily recorded on paper forms included mosquito bionomics transcribed using double entry of paper-based forms into CommCare™ and kept in lockable cabinets. Our continuous improvement process relied on regular system audits done by the data manager, performance metric tracking for data collectors, and user feedback collection. The system remained dynamic, with regular platform updates and form modifications responding to identified needs. This adaptability, combined with consistent monitoring and staff training, helped maintain high data quality standards throughout the trial. This integrated approach to data management proved highly effective, achieving query resolution within 48 hours, and minimal missing data. The system's success demonstrated the value of combining robust digital tools with clear procedures, regular monitoring, and staff training in maintaining data quality for complex clinical trials in resource-limited settings. Laboratory Quality Control Systems Our laboratory quality control centered on malaria microscopy, implementing a blinded dual-read system with third readings for discordant results. Microscopists underwent quarterly external QA testing and were limited to examining 35 slides per day to mitigate the effects of eye strain and fatigue that would have compromised the quality of the microscopy slide reads. The study employed sufficient numbers of readers to complete two slide reads per participant, improving the turnaround time to 48 hours from the time of collection excluding weekends and holidays allowing for prompt treatment of asymptomatic malaria cases 12 . Asymptomatic malaria cases were identified as participants with positive microscopy results who reported no fever during their clinic visit or in the preceding 48 hours. All sample results were tracked and transmitted in real-time using a Laboratory Information System (LIS) 15 . Confirmed asymptomatic malaria cases subsequently received home drug delivery. Quality Assurance to support Investigational Product (IP) Management The management of approximately 100,000 SR products or placebo distributed and retrieved every 28 days, per protocol 12 , required a holistic, comprehensive system of storage, tracking, and distribution. Our infrastructure consisted of three specially modified storage containers: two located at KEMRI-CGHR campus in Kisumu and one at KEMRI-CIPDCR campus in Busia. Each container was equipped with insulation and air conditioning systems to maintain environmental conditions (18–23°C and humidity at 70% ±10%) guided by industry specifications. We implemented both automated (using data loggers) and manual temperature and humidity monitoring systems, with daily checks and immediate corrective actions for any deviations from these specifications. To ensure accurate product transfer and inventory tracking, we developed a customised electronic stock card database using Microsoft Access, replacing traditional paper records. This digital system allowed real-time monitoring of product movements from storage to community locations, batch numbers, expiration dates, and stock levels. Products were arranged in storage facilities using a ‘First-In-First-Out' system, with clear labelling and organization based on assigned cluster codes. The SR distribution network operated through a hierarchical three-tier system. At the top level, a product manager supervised three intervention managers, with each manager responsible for approximately 20 clusters. The second tier consisted of ten intervention specialists, each managing four clusters and reporting to an intervention manager. These specialists coordinated product distribution with the third tier: community health promoters (CHPs) who conducted the actual house-to-house product installations. Each tier had specific responsibilities: intervention managers developed distribution schedules and oversaw product movement from primary storage to satellite storage; intervention specialists distributed the products from satellite storage to the villages and maintained detailed records and monitored deployment of product by the CHPs; and CHPs performed direct product installation in homes, documenting their work based on custom-designed digital forms using the CommCare™ data management system in tablets. Product accountability followed a rigorous process from requisition through disposal. Due to the rolling basis of product deployment across all 58 clusters, daily product preparations by store logistician included counting and organizing products for their designated clusters, with intervention specialists verifying product quantities before transport to CHPs in the field. Every product movement was documented through a comprehensive system of goods issue notes, electronic tracking forms, and distribution logs. During deployment, product barcodes (printed at time of manufacturing) were scanned into the CommCare™ system using tablets in order to verify that the product was being installed in the intended cluster. Products were photographed, with global positioning system (GPS) coordinates recorded for each installation location. After the pre-specified 28-day deployment period, used products were systematically collected, barcodes scanned, and deviations recorded. The goal was to track potential movement across households and/or clusters during the 28-day application period, as well as verify that they were not tampered with: moved between rooms in a structure or moved between structures within a compound before transport back to the storage facility. Used products underwent batched incineration at KEMRI-CGHR per industry specifications (1000°C). Quality Assurance for Entomology Data The entomological QA system centered on rigorous collection protocols and data verification for both human landing catches (HLC) and Centers for Disease Control and Prevention (CDC) light trap sampling developed on digital platform (CommCare™), with immediate supervisory verification of collection records and specimen handling. Paper forms for mosquito morphological identification were double entered onto Commcare™ using computers. Field collectors underwent standardized training and competency assessment before deployment, with quarterly refresher sessions and supervision during collections. For HLCs, we maintained strict safety protocols including weekly malaria prophylaxis (with Mefloquine™) for all collectors during the collection periods and for four weeks after. All Anopheles mosquitoes collected were barcoded upon collection, enabling complete traceability through the laboratory workflow. Regular quality control checks examined collection patterns for anomalies. This comprehensive approach maintained high-quality entomological data throughout the trial. Digital Innovation in Adverse Event Monitoring: Implementation of Scannable Health Facility Registers To enable comprehensive and consistent monitoring of adverse events across the study area, we implemented a system of scannable registers across all 15 study health facilities. This system replaced the traditional Ministry of Health (MoH) paper registers with specially designed scannable versions while maintaining familiar formats and workflows for healthcare staff. Working in collaboration with Quantitative Engineering Design (QED) ( https://about.scanform.qed.ai/programs/ ), AEGIS-Kenya developed registers that preserved the standard MoH format while incorporating machine-readable markers and structured fields to enable digital data capture. The registers were designed to capture all standard patient information including demographics, clinical conditions, and prescribed medications while cutting out personal identifiers. To protect patient privacy, the system automatically excluded identifiable information during the scanning process and implemented robust data encryption for both transmission and storage. Using the QED android application on a smartphone, images of these registers were captured and sent to a cloud server. Machine learning image processing algorithms were used to transcribe the handwritten data into digital data that were aggregated and accessed via a customized online dashboard. We introduced three distinct register types: outpatient registers for patients under 5 years ( Supplementary Fig. 2 ), outpatient registers for patients 5 years and above ( Supplementary Fig. 3 ), and antenatal clinic registers ( Supplementary Fig. 4 ). Implementation began with comprehensive training of health facility staff on proper register completion and handling. Healthcare workers continued their standard practice of recording patient information, with pages being photographed daily or weekly depending on facility patient volume. Quality control was maintained through a rigorous verification process. Two dedicated staff members reviewed all the transcribed data before they were cleared for aggregation in the central database. The system automatically flagged potential errors or inconsistencies for manual verification, and regular cross-checking against the photographed image of the registers ensuring data accuracy. The dashboard integration proved particularly valuable for trial monitoring, providing data aggregation and visualization of verified data. At the beginning of the trial, MoH staff were reluctant to adopt the new system due to their limited prior experience with such platforms, but over time, they grew to appreciate it. This enabled automated adverse event monitoring and trend analysis for the Data Safety and Monitoring Boad (DSMB), significantly improving our ability to detect and respond to potential safety signals. The system successfully processed thousands of records throughout the study period, maintaining high data accuracy while reducing the administrative burden on health facility staff. While the challenge of system adoption was aggreviated by frequent staff turnover at the facilities, (caused by transfers initiated by the county government), the study team mitigated this risk by providing ongoing and frequent retraining of MoH personnel. Despite these challenges, this innovative technical approach represented a significant advancement in adverse event monitoring for community-wide interventions, providing comprehensive surveillance while maintaining efficient health facility operations. The success of this implementation demonstrated the potential for digital innovations to enhance QA in resource-limited settings while respecting existing healthcare workflows. Ethical and Regulatory Monitoring The AEGIS-Kenya trial maintained rigorous ethical and regulatory oversight through multiple review mechanisms. As previously described 12 , primary ethical approval was received from the Scientific and Ethics Review Unit (SERU) in Kenya, with additional oversight from the University of Notre Dame (prime awardee) Institutional Review Board (UND-IRB), Centers for Disease Control and Prevention Institutional Review Board (CDC-IRB) and the World Health Organization Ethical Review Committee (WHO-ERC). All protocol amendments, safety reports, and annual renewals underwent review by SERU before submission to other ethical review committees, ensuring coordinated oversight throughout the trial. A DSMB provided independent safety oversight, reviewing all adverse events and monitoring trial progress on a quarterly basis throughout the study period. Site readiness, site activation, quarterly interim monitoring visits, and a site close out visit was conducted by FHI Clinical. for trial oversight. COVID-19 Adaptations and Mitigation The emergence of COVID-19 necessitated substantial adaptations to trial procedures while maintaining study integrity and participant safety. We implemented comprehensive infection prevention measures including daily symptom screening for staff, mandatory mask-wearing for all study interactions, and restructured clinic waiting areas to ensure social distancing. The study procured personal protective equipment for all staff and participants, including masks and hand sanitizers, and established sanitization protocols for all study equipment and facilities. Staff underwent additional training on COVID-19 prevention measures and modified participant interaction protocols to minimize close contact while maintaining essential study procedures. We implemented a testing and quarantine protocol for symptomatic staff members following MoH guidelines, with successful management of several COVID-19 cases among study personnel including the Clinical Research Associate. Despite these challenges, the adapted procedures allowed continued trial operation without compromising data quality or participant safety, demonstrating the resilience of our QA systems in responding to unexpected public health challenges. Discussion Many of the QA systems developed and implemented during the AEGIS-Kenya trial reflect those outlined for standard Good Clinical Practices 8 (SOPs, training, IP monitoring etc.) and demonstrate that rigorous clinical research standards can be maintained in resource-limited settings through innovative approaches and careful attention to the local context. Our experience highlights the importance of adaptable systems, continuous monitoring, strong community partnerships and financial support for QA system design, development and maintenance for ensuring trial data integrity. Our trial demonstrates that implementing a slot and sync system significantly enhanced participant screening and follow up visit completion rates at our study facilities since participants preferred to maintain the same day of the week and time throughout the follow-up study period. This innovation led to a marked reduction in screening non-completion rates after consent—decreasing from 5.4% during baseline to 2.4% in cohort I and 1.6% in cohort II, representing two-fold and three-fold improvements respectively 12 . This scheduling approach represents a valuable strategy that could be widely adopted to maximize participant enrollment and retention across various clinical trial settings. However, our experience suggests that optimal results are achieved when this system is implemented alongside complementary strategies, including digital mobile data capture system adoption, thorough feasibility assessments to realistically estimate required time and resources, and continuous monitoring and evaluation of enrollment progress to promptly identify and address emerging challenges. The substantial improvement in screening completion rates underscores the importance of structured participant flow management in clinical trial execution. By reducing logistical barriers and improving coordination between study components, the slot and sync system created a more efficient participant experience that likely contributed to higher retention rates throughout the trial phases. While our implementation proved successful in this context, future research should explore adaptations of this approach across diverse clinical trial settings and participant populations. Cost-effective analyses comparing traditional scheduling approaches with our slot and sync system would provide valuable insights for trial planners and could inform best practices in clinical trial management. The development of electronic systems for managing microscopy results and product accountability proved to be efficient. Our LIS enabled rapid result reporting and enhanced quality control, improving the median turnaround time from 15.3 days in the paper-based system to 3.6 days after implementing the LIS 15 . This was more than our intended target of 48hrs due to our staff resting during the two days of weekends and all public holidays. The approach builds on established frameworks for laboratory QA 16 while introducing innovations for real-time data access enabling delivery of treatment to asymptomatic malaria cases. Similarly, our electronic product management system maintained monthly IP stock tracking of about 100,000 IP, demonstrating how digital tools can enhance compliance with International Council for Harmonisation (ICH) guidelines for investigational product accountability 8 . The implementation of mobile data collection using tablets as the primary method of data collection represented another significant advancement in our trial operations. This approach eliminated paper-based data collection errors, enabled real-time data validation, and significantly reduced the time required for data cleaning and processing. Field workers could efficiently capture participant information, including GPS coordinates for household visits, ensuring accurate spatial data for subsequent analyses. The tablet-based system also facilitated immediate synchronization with central databases when connectivity was available, creating a more responsive and transparent data management pipeline. Community engagement emerged as a critical QA component, particularly in our research-naïve setting. The implementation of REA prior to trial initiation, as recommended by Negussie et al. (2016) 13 , helped identify potential barriers to participation and informed our communication strategies. This proactive approach was especially valuable given the complex social dynamics surrounding blood collection and the need to maintain long-term community support for the intervention. The QED scannable health facility registers addressed a fundamental challenge in vector control trials: how to monitor safety for community-wide deployed interventions. By digitizing existing MoH registers, we created an efficient surveillance system that maintained local healthcare workflows while improving data quality. This approach aligns with recent recommendations for leveraging existing health systems in clinical trials 11 and demonstrates the value of digital solutions in resource-limited settings 14 . The COVID-19 pandemic necessitated significant adaptations to our QA procedures. While this was challenging, the modifications demonstrated the resilience of our systems and the importance of flexible protocols in maintaining trial integrity during unexpected events. Our experience adds to the growing literature on conducting clinical trials during public health emergencies 17 . Lessons Learned Several lessons emerged that have broad implications for future vector control trials. First, the digitization of data collection systems, while initially resource-intensive, provided substantial benefits in data quality and operational efficiency. Second, continuous mapping and updating household structures proved essential in maintaining accurate coverage metrics, particularly in dynamic rural settings. Third, the slot-and-sync system for participant visits significantly improved clinic efficiency and participant retention, addressing a common challenge in longitudinal studies. In addition, the implementation of scannable registers initially faced resistance from some health facility staff, highlighting the need for sustained engagement with local healthcare providers. Next, the complexity of our QA systems required ongoing training and supervision, with associated resource implications. Lastly, our QA framework required substantial investment in infrastructure and training, including the recruitment of specialized personnel and installation of backup power systems in all the 15 health facilities with study clinics. While these requirements may seem burdensome, they proved essential for maintaining GCP compliance and data integrity. This aligns with findings from other large-scale trials in similar settings 18 . Limitations Some limitations should be noted. While the digital system effectively managed subject recruitment, enrollment and follow up scheduling, intermittent cellular network coverage posed challenges for real-time data synchronization. To address this, we maintained paper backup systems, implemented local data storage protocols, used the slot and sync system to prevent duplicate enrollments and established clear procedures for data reconciliation when connectivity was restored. Conclusion Insights we have described here contribute to the broader discussion on QA in vector control trials 19 and support the development of standardized yet adaptable frameworks for future studies. Our experience demonstrates that successful QA implementation in resource-limited settings requires a combination of technological innovation, community partnership, and flexible systems capable of responding to emerging challenges. Our experience supports the effectiveness of using integrated digital systems - from mobile data collection on tablets to electronic product management to scannable health facility registers - in maintaining high-quality standards while reducing administrative burden. This, combined with robust product accountability, the successful implementation of REA, and ongoing community dialogue proved essential to trial success, particularly in our research-naïve setting. Looking ahead, successful implementation requires significant investment in infrastructure, training, and community relationships for balancing scientific rigor with operational feasibility. Future research should focus on further innovations in digital tools and community engagement strategies that can accommodate the unique challenges of community-level interventions while ensuring sustainable, high-quality data collection and management. Abbreviations AEGIS Advancing Evidence for the Global Implementation of Spatial Repellents CDC Centers for Disease Control and Prevention CHP Community Health Promoters CI Community Interviewer cRCT cluster Randomized Controlled Trials DSMB Data Safety and Monitoring Boad ERC Ethical Review Committee GPS Global Positioning System ICH International Council for Harmonisation IP Investigational Product IRB Institutional Review Board LIS Laboratory Information system MoH Ministry of Health PC Personal Computer QA Quality Assurance QED Quantitative Engineering Design REA Rapid Ethical Assessment SERU Scientific and Ethics Review Unit SOP Standard Operating procedure SR Spatial Repellent UND University of Notre Dame WHO World Health Organization Declarations Ethics approval and consent to participate The study protocol for the clinical trial was reviewed and approved by Kenya Medical Research Institute Scientific and Ethics Review Unit (protocol 3870, reference KEMRI/RES/7/3/1), Institutional Review Boards at Centers for Disease Control and Prevention (protocol number 7252), University of Notre Dame (protocol number 19-08-5506), and the WHO Ethical Review Committee (ERC.0003185). Study staff obtained the written informed consent of parents or legal guardians of study participants for cohort participation, mosquito collectors conducting human landing catch (HLC) collections, and heads of households for product placement. Acknowledgements This study would not have been possible without the generous participation of the communities in Teso South and Teso North sub-counties, Busia County, Kenya. We are deeply grateful for their willingness to collaborate with us. We also extend our sincere thanks to the KEMRI staff for their dedication in developing and implementing the study while upholding rigorous quality assurance guidelines, even amidst the challenges of the COVID-19 pandemic. Special regards to Ashley Scott for her role in Kenya cRCT ND program management related to contract establishment for Burness, weekly coordination of CommCare™ data form development, SOP formatting and archiving, WHO ERC, ND IRB submissions and amendments, DSMB safety reporting, data cleaning contributions in preparation for analyses, and outcome reporting. Our appreciation also goes to Silver Wevill, Valentine Veena, Loice Magaria, Anne Wangwe, and Rose Adeny of the FHI Clinical for their detailed and independent monitoring of the AEGIS-Kenya cRCT. We thank Anne-Marie Schryer-Roy, Ellen Wilson and Saburi Chirimi of Burness for their assistance in developing the graphic design for study brochures used for community engagement. Finally, we thank William Wu and the entire QED team for their contribution in developing the scannable registers that supported passive adverse event monitoring in this trial. Authors contributions Conceptualization: BP, JEG, NLA and EO. Project administration: BP, QA, VM, HO, NK, FA, BA, JH, JEG, NLA, JPG and EO Writing and editing the manuscript: BP, QA, VM, HO, NK, FA, BA, JH, JEG, NLA, JPG and EO Supervision: JEG, NLA, JPG and EO All authors contributed to reviewing the manuscript. Funding statement This research project is made possible through the funding and support of Unitaid, a global health organization that saves lives by making new health products available and affordable for people in low- and middle-income countries. Unitaid works with partners to identify innovative treatments, tests and tools, help tackle the market barriers that are holding them back, and get them to the people who need them most – fast. Since Unitaid was created in 2006, the organization has unlocked access to more than 100 groundbreaking health products to help address the world’s biggest health challenges, including HIV, TB and malaria; women’s and children’s health; and pandemic prevention, preparedness and response. Every year, more than 300 million people benefit from the products Unitaid has helped roll out. Unitaid is hosted by the World Health Organization. Disclaimer The findings and conclusions in this manuscript are those of the authors and do not necessarily represent the official position of the Kenya Medical Research Institute or the US Centers for Disease Control and Prevention. References WHO World Malaria Reoprt. 2024. https://www.who.int/teams/global-malaria-programme/reports/world-malaria-report-2024 (2024). World Health Organization. Global Technical Strategy for Malaria 2016–2030 . https://iris.who.int/handle/10665/176712 (2015). Achee NL, Sardelis MR, Dusfour I, Chauhan KR, Grieco JP. Characterization of Spatial Repellent, Contact Irritant, and Toxicant Chemical Actions of Standard Vector Control Compounds1. J Am Mosq Control Assoc. 2009;25:156–67. Ogoma SB, Moore SJ, Maia MF. 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Multiple-level stakeholder engagement in malaria clinical trials: addressing the challenges of conducting clinical research in resource-limited settings. Trials. 2018;19:1–11. Ochomo EO et al. Effect of a spatial repellent on malaria incidence in an area of western Kenya characterised by high malaria transmission, insecticide resistance, and universal coverage of insecticide treated nets (part of the AEGIS Consortium): a cluster-randomised, controlled trial. Lancet (2024). Negussie H, Addissie T, Addissie A, Davey G. Preparing for and Executing a Randomised Controlled Trial of Podoconiosis Treatment in Northern Ethiopia: The Utility of Rapid Ethical Assessment. PLoS Negl Trop Dis. 2016;10:e0004531. Elson WH, et al. Use of mobile data collection systems within large-scale epidemiological field trials: findings and lessons-learned from a vector control trial in Iquitos, Peru. BMC Public Health. 2022;22:1924. Awori Q et al. A user-centred approach to developing a digital laboratory information system for high-volume clinical data: lessons learned from a malaria study in Kenya. medRxiv 2025–04 (2025). World Health Organization. Malaria Microscopy Quality Assurance Manual-Version 2. World Health Organization; 2016. Van den Broeck J, et al. Maintaining data integrity in a rural clinical trial. Clin Trials. 2007;4:572–82. Ottevanger P, et al. Quality assurance in clinical trials. Crit Rev Oncol Hematol. 2003;47:213–35. Wilson AL, et al. Evidence-based vector control? Improving the quality of vector control trials. Trends Parasitol. 2015;31:380–90. Additional Declarations No competing interests reported. Supplementary Files Supplementaryfigures.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7585900","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":515147552,"identity":"a8fe000a-19f6-45da-bb5d-20846642acbb","order_by":0,"name":"Brian Polo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYFACxgYgcYCBgZn54AMgi4ePoA42mBb2tmQDkBY2wlrAJFALzxkzCYQAHsAv39z4uKDiTr65RI5Z5dccOxk2BuaHj27g0SLZxthsPOPMM8udM9LKbstuSwY6jM3YOAePFoNjjG3SvG2HDQxuJG+7LbmNGaiFh02agJb23xAtCWbFktvqidLSxgzWcuaIGePHbYcJa5FsS2yW5jnzzMDgeFuyNOO24zxszAT8ws98/OFnnoo7BgaHmQ9+/Lmt2p6fvfnhY3xaUAAzD5gkVjkIMP4gRfUoGAWjYBSMGAAAfg1HRqKf8tAAAAAASUVORK5CYII=","orcid":"","institution":"Kenya Medical Research Institute","correspondingAuthor":true,"prefix":"","firstName":"Brian","middleName":"","lastName":"Polo","suffix":""},{"id":515147553,"identity":"6b448676-c747-436d-ba2c-75104051468a","order_by":1,"name":"Quentin Awori","email":"","orcid":"","institution":"Kenya Medical Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Quentin","middleName":"","lastName":"Awori","suffix":""},{"id":515147554,"identity":"43873c28-9fc4-4c47-86c0-ba7628d89ab1","order_by":2,"name":"Vincent Moshi","email":"","orcid":"","institution":"Kenya Medical Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Vincent","middleName":"","lastName":"Moshi","suffix":""},{"id":515147555,"identity":"1eca7f25-e4ca-40a9-ac0a-b2913fad3bd4","order_by":3,"name":"Hilda Otanga","email":"","orcid":"","institution":"Kenya Medical Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Hilda","middleName":"","lastName":"Otanga","suffix":""},{"id":515147556,"identity":"4ec66d0c-3386-4649-8129-d842ec469424","order_by":4,"name":"Nickline Kuya","email":"","orcid":"","institution":"Kenya Medical Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Nickline","middleName":"","lastName":"Kuya","suffix":""},{"id":515147557,"identity":"efd20cd1-1e0c-45c9-8637-b104ed517d2d","order_by":5,"name":"Ferdinard Adungo","email":"","orcid":"","institution":"Kenya Medical Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Ferdinard","middleName":"","lastName":"Adungo","suffix":""},{"id":515147558,"identity":"4c8b85fc-81b9-4b13-8851-25cad572e788","order_by":6,"name":"Bernard Abongo","email":"","orcid":"","institution":"Kenya Medical Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Bernard","middleName":"","lastName":"Abongo","suffix":""},{"id":515147559,"identity":"3a39183c-aabc-49bd-b394-61975a2925c1","order_by":7,"name":"Jared Hendrickson","email":"","orcid":"","institution":"University of Notre Dame","correspondingAuthor":false,"prefix":"","firstName":"Jared","middleName":"","lastName":"Hendrickson","suffix":""},{"id":515147560,"identity":"92ba66e5-59e6-4d42-9a3b-3e4246d7e92b","order_by":8,"name":"John E. 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18:21:20","extension":"xml","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":77361,"visible":true,"origin":"","legend":"","description":"","filename":"295a501f729c4af9adcf0cc91586b0d41structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7585900/v1/7757d71d0862872683ac0637.xml"},{"id":91739147,"identity":"cf622dc5-f50c-45d1-8b31-92ca0a0e5bd9","added_by":"auto","created_at":"2025-09-19 18:13:20","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":91089,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7585900/v1/54da6b51855a288b03d0f7f8.html"},{"id":91739136,"identity":"c6f04f46-617b-4ec3-aedb-c93075d5e179","added_by":"auto","created_at":"2025-09-19 18:13:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1317485,"visible":true,"origin":"","legend":"\u003cp\u003ePictorial brochure of AEGIS-Kenya study illustrating the mode of action of the spatial repellent intervention and core study procedures. The brochure was used at the household level during recruitment and converted to poster size for hanging at community health clinics in the Teso North sub-County, Busia County study area \u003cem\u003e(graphic art\u003c/em\u003e \u003cem\u003edeveloped by Burness \u003c/em\u003e\u003ca href=\"https://burness.com/\"\u003e\u003cem\u003ehttps://burness.com\u003c/em\u003e\u003c/a\u003e).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7585900/v1/2c7349aac6f9f42d0e515516.png"},{"id":91963757,"identity":"847280d4-d7fa-4dd9-aa94-7cb06a1a0ea6","added_by":"auto","created_at":"2025-09-23 08:10:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2105220,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7585900/v1/b099bb1f-9e68-4989-a15c-77e4165e467b.pdf"},{"id":91739138,"identity":"c036d5c6-85d8-4fb2-9c3b-b11afabb3af2","added_by":"auto","created_at":"2025-09-19 18:13:20","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1297808,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-7585900/v1/d48efb98e56e2011ede89d14.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Quality Assurance in Practice: Insights from a Cluster Randomized Control Trial Evaluating a novel Spatial Repellent vector control intervention in Kenya","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAs the global health community strives to reduce vector-borne diseases, the need for robust evidence supporting new control measures has become increasingly critical\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. The World Health Organization Global Technical Strategy calls for reductions in case incidence and mortality of 75% by 2025 and 90% by 2030\u003csup\u003e2\u003c/sup\u003e. However, progress towards these targets remains off-track\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, highlighting the need for innovative strategies that complement existing malaria control measures. Spatial repellents (SRs) represent one such innovation, offering protection against mosquitoes through chemicals that alter insect behaviour, impair host detection, and/or reduce feeding responses\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. These products are particularly promising for protecting against daytime, early-morning, and evening-biting vectors in various indoor and peridomestic spaces\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. However, before SRs can be recommended for integration into public health programs, the intervention product class must undergo rigorous evaluation of protective efficacy through cluster randomized controlled trials (cRCTs) using epidemiological endpoints\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Such evidence is then assessed for determination of public health value and contributes to the decision for WHO recommendation.\u003c/p\u003e\u003cp\u003eQuality assurance (QA) forms the foundation of scientific integrity in cRCTs\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. In large-scale trials, robust QA systems are essential for ensuring reliable data collection, analysis, and reporting while maintaining participant safety and ethical standards\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. The implementation of QA systems in vector control trials presents unique challenges compared to traditional pharmaceutical trials\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. While established QA frameworks exist for drug trials, vector control interventions are often deployed at community rather than individual levels, requiring different approaches to monitoring efficacy and safety. These interventions may range from household-level applications like SRs to village-wide implementations such as larval source management or environmental management. This diversity in intervention scale and deployment methods necessitates adaptable QA systems that can ensure data integrity while accommodating local contexts. In resource-limited settings, particularly rural African communities, these challenges are compounded by several factors: limited research infrastructure and healthcare facilities, variable cellular network coverage affecting digital data collection, diverse local languages and cultural practices, limited previous exposure to clinical research, complex community dynamics affecting intervention acceptance, logistical challenges in product distribution and monitoring, and the need for sustainable and scalable quality control measures\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe Advancing Evidence for the Global Implementation of Spatial Repellents (AEGIS) program in Busia, Kenya, was one of the first large-scale trials evaluating SRs in Africa as a malaria control tool\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. This trial offered unique opportunities to develop and test QA systems suitable for vector control studies in resource-limited settings. The AEGIS-Kenya trial implemented novel approaches to QA, including digital data capture systems, innovative product tracking methods, and comprehensive community engagement strategies. Here we present a detailed description of the QA systems implemented during the AEGIS-Kenya trial, focusing on the methods used to ensure data integrity, maintain SR intervention management and monitor safety, and how these methods were adapted to local contexts and challenges. Our experience provides valuable insights for future community-based cRCTs, particularly in rural, resource-limited settings. We focus on practical solutions to common challenges, offering concrete recommendations for implementing robust QA systems that balance scientific rigor with operational feasibility.\u003c/p\u003e\u003cp\u003eBy sharing these experiences and lessons learned, we aim to contribute to the broader discussion on QA in vector control trials and support the development of standardized, yet adaptable, QA frameworks for future studies. This knowledge is particularly relevant as the field of vector control continues to evolve, with new tools and technologies requiring robust evaluation before recommending for implementation in national-level public health programs.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eClinical Trial Study design and setting\u003c/h2\u003e\u003cp\u003eThe AEGIS-Kenya trial was designed as a double-blinded cRCT conducted in Busia County, western Kenya (Teso South and North sub-Counties) as previously described\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. In brief, the study comprised 60 clusters at baseline, which was reduced to 58 clusters (2 clusters were dropped due to low incidence at baseline) during the intervention, with 29 receiving active SR product (transfluthrin emanator) and 29 receiving placebo products of matched design. Each cluster included a central village core with a surrounding 300\u0026ndash;500-meter buffer zone (separated by at least 300 meters to prevent SR spillover). Participants aged between 6 months and 10 years were enrolled and followed up monthly for malaria cases at 15 study clinics. The rural, research-na\u0026iuml;ve population presented unique challenges for QA implementation.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eQuality Assurance (QA) Framework\u003c/h3\u003e\n\u003cp\u003eOur QA framework was collaboratively designed by the Kenya cRCT lead organizations (KEMRI, CDC and UND) with the specific objective to ensure the integrity and reliability of trial data, encompassing study protocol compliance, data quality, and safety monitoring. Protocol compliance was based on comprehensive standard operating procedures (SOPs), regular staff training programs, and continuous monitoring. Recognizing the challenge of intermittent connectivity, we implemented a data quality tier using a custom-designed CommCare\u0026trade; data management system which enabled built-in QA functionality using study design and SOP logic, mobile offline data collection and database synchronization when cellular access was available. Safety monitoring combined passive surveillance through local health facilities with active monitoring during scheduled follow-up visits, allowing for the rapid identification and appropriate response to any adverse events.\u003c/p\u003e\n\u003ch3\u003eQA in relation to a strong community partnership\u003c/h3\u003e\n\u003cp\u003ePrior to trial initiation, we conducted a Rapid Ethical Assessment (REA) to understand community perspectives and establish appropriate engagement strategies\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. This process identified key stakeholders and cultural practices, leading to the collaborative development of pictorial guides in three local dialects (English, Kiswahili, and Ateso) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and (\u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e). We established a Community Advisory Board (CAB), composed of representatives of the local community groups), that met quarterly to provide ongoing feedback and guidance. Regular community forums (barazas) and radio announcements supplemented these efforts, ensuring continuous communication with study participants and the broader community.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eQA in relation to Subject Eligibility and Randomized Recruitment Process\u003c/h3\u003e\n\u003cp\u003eAs a component to study protocol compliance, the CommCare\u0026trade; system used household census data collected during the pre-trial mapping exercise to ensure community interviewers (Cis) engaged only those household members eligible for recruitment based on pre-defined inclusion/exclusion age criteria. The built-in functionality of the CommCare\u0026trade; system included random selection of the second child at the same household from the household member listing. This functionality was not disclosed to the study team to avoid any bias.\u003c/p\u003e\n\u003ch3\u003eQA in relation to Participant Screening and Follow up Visits Scheduling\u003c/h3\u003e\n\u003cp\u003eBecause of a manual recruitment system, the baseline phase of the cRCT encountered logistical challenges. Long waiting periods for participants screening and follow up and extended working hours for clinic staff were prevalent. During participant enrollment, between five to ten CIs were assigned to each clinic (depending on the number of clusters linked to the clinic). Each CI scheduled approximately two to three participants consented from their homes to report for screening at the clinics within the next day or two. This resulted in unstructured participant flow for clinic screening, causing long patient queues and increasing clinician workload, which contributed to clinician fatigue and empathy. Consequently, screening quality declined, generating data gaps as secondary study documents were often partially completed and deferred. Furthermore, some participants opted out of screening due to the lengthy wait times, causing loss of follow-up.\u003c/p\u003e\u003cp\u003eTo address these issues during the intervention phase, the team implemented a paper based \"Slot and Sync\" system. We could not implement a digital system due to patchy internet connectivity. In this innovative approach the CIs, before going out to recruit participants, each had a selection of time slots allocated to them (there were six 1-hour slots between 8:30am and 5:00pm for different days). The time slots for screening and follow up visits were unique to each CI. The CIs had a selection of morning and afternoon clinic slots to offer to the consented participants, allowing flexibility and individual preferences. Importantly, participant contact information was collected by the CIs to facilitate communication and scheduling.\u003c/p\u003e\u003cp\u003eAt the end of each day, the CIs affiliated with each clinic aggregated the assigned slots into a central table, effectively \"syncing\" the clinic visit schedule. This ensured that only one participant arrived at the clinic during each designated time slot, minimizing wait times and reducing the workload for clinicians. Every evening the clinician called the guardians of participant scheduled for the following day to confirm their availability and the agreed time slot.\u003c/p\u003e\u003cp\u003eEnrolled participants were scheduled for a monthly clinic visit and bi-weekly home visit (conducted by a CI)\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. We implemented a tablet based electronic medical records system with on device storage and cloud backup (when network was available), allowing real-time access to participant information while maintaining data integrity. Clinic notes were also recorded on the electronic medical records.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eData Management to support Quality Systems\u003c/h2\u003e\u003cp\u003eThe AEGIS-Kenya cRCT implemented a comprehensive data management system on the CommCare\u0026trade; mobile data collection platform. CommCare\u0026trade; was selected for its robust offline capabilities, capacity to support longitudinal data collection and linkage, and its built-in validation features, crucial for maintaining data quality in areas with limited connectivity. The platform incorporated on-device data validation rules, skip logic patterns, and automated scheduling alerts, significantly reducing data entry errors and ensuring protocol compliance\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Recognizing the data collection challenges inherent in rural settings, particularly unreliable network connectivity, we implemented a multi-layered backup system. Our primary approach utilized CommCare\u0026trade; with offline data recording capability for efficient digital data capture. To mitigate potential data loss in case of server downtime, we also held printed paper- forms as back-up for recording study information (though we never had to use them) and ensured regular synchronization to the cloud server during periods of connectivity. Furthermore, we performed a weekly physical backup of the entire cloud data image, stored securely on two hard drives managed by the principal investigator and the data manager. This redundancy proved vital in safeguarding our data integrity while still benefiting from the advantages of digital data collection.\u003c/p\u003e\u003cp\u003eOur quality control system operated on multiple levels. At the point of data entry, automated checks validated study IDs, enforced range limits, and flagged logical inconsistencies. A second level of review conducted daily completeness checks and cross-reference verifications. We tracked key quality indicators including form completion rates (targeting above 99%), real-time digital data entry, and query resolution time (within 48 hours). This systematic approach helped avoid missing data and protocol deviations throughout the study. Data cleaning followed a structured daily and weekly routine. Each day began with reviews of automated error reports, consistency checks, and query generation. Weekly comprehensive audits were performed to maintain the quality of participant follow-up, microscopy reading and product replacement. These audits specifically measured adherence to protocol in participant contact procedures, verified all attempts to engage participants who missed visits, flagged overdue microscopy results, assessed the adherence to investigational product (IP) replacement schedule, checked for consistency across data forms, and ensured overall protocol compliance. Trend analyses from these audits then guided our quality improvement initiatives. The query management system assigned clear responsibility for resolution to the staff responsible, with standard timeframes and documentation requirements ensuring timely problem solving.\u003c/p\u003e\u003cp\u003eSecurity and privacy protections formed a crucial component of our data management strategy. We implemented role-based access restrictions with unique user credentials and comprehensive activity logging review. All data storage and transmission used encryption protocols, with physical security measures protecting local hardware. For example, all tablets and personal computers (PCs) were kept in facilities with access control and required username and password to access them. Paper forms were filed in lockable cabinets accessed only by authorized individuals. Our laboratory information system (LIS) managed sample tracking and result validation\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, while the electronic medical records system handled clinical enrollment and follow-up data and visit scheduling. These integrations enabled real-time access to participant information while maintaining data security and quality controls. The only data that was primarily recorded on paper forms included mosquito bionomics transcribed using double entry of paper-based forms into CommCare\u0026trade; and kept in lockable cabinets.\u003c/p\u003e\u003cp\u003eOur continuous improvement process relied on regular system audits done by the data manager, performance metric tracking for data collectors, and user feedback collection. The system remained dynamic, with regular platform updates and form modifications responding to identified needs. This adaptability, combined with consistent monitoring and staff training, helped maintain high data quality standards throughout the trial. This integrated approach to data management proved highly effective, achieving query resolution within 48 hours, and minimal missing data. The system's success demonstrated the value of combining robust digital tools with clear procedures, regular monitoring, and staff training in maintaining data quality for complex clinical trials in resource-limited settings.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eLaboratory Quality Control Systems\u003c/h3\u003e\n\u003cp\u003eOur laboratory quality control centered on malaria microscopy, implementing a blinded dual-read system with third readings for discordant results. Microscopists underwent quarterly external QA testing and were limited to examining 35 slides per day to mitigate the effects of eye strain and fatigue that would have compromised the quality of the microscopy slide reads. The study employed sufficient numbers of readers to complete two slide reads per participant, improving the turnaround time to 48 hours from the time of collection excluding weekends and holidays allowing for prompt treatment of asymptomatic malaria cases\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Asymptomatic malaria cases were identified as participants with positive microscopy results who reported no fever during their clinic visit or in the preceding 48 hours. All sample results were tracked and transmitted in real-time using a Laboratory Information System (LIS)\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Confirmed asymptomatic malaria cases subsequently received home drug delivery.\u003c/p\u003e\n\u003ch3\u003eQuality Assurance to support Investigational Product (IP) Management\u003c/h3\u003e\n\u003cp\u003eThe management of approximately 100,000 SR products or placebo distributed and retrieved every 28 days, per protocol\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, required a holistic, comprehensive system of storage, tracking, and distribution. Our infrastructure consisted of three specially modified storage containers: two located at KEMRI-CGHR campus in Kisumu and one at KEMRI-CIPDCR campus in Busia. Each container was equipped with insulation and air conditioning systems to maintain environmental conditions (18\u0026ndash;23\u0026deg;C and humidity at 70% \u0026plusmn;10%) guided by industry specifications. We implemented both automated (using data loggers) and manual temperature and humidity monitoring systems, with daily checks and immediate corrective actions for any deviations from these specifications.\u003c/p\u003e\u003cp\u003eTo ensure accurate product transfer and inventory tracking, we developed a customised electronic stock card database using Microsoft Access, replacing traditional paper records. This digital system allowed real-time monitoring of product movements from storage to community locations, batch numbers, expiration dates, and stock levels. Products were arranged in storage facilities using a \u0026lsquo;First-In-First-Out' system, with clear labelling and organization based on assigned cluster codes. The SR distribution network operated through a hierarchical three-tier system. At the top level, a product manager supervised three intervention managers, with each manager responsible for approximately 20 clusters. The second tier consisted of ten intervention specialists, each managing four clusters and reporting to an intervention manager. These specialists coordinated product distribution with the third tier: community health promoters (CHPs) who conducted the actual house-to-house product installations. Each tier had specific responsibilities: intervention managers developed distribution schedules and oversaw product movement from primary storage to satellite storage; intervention specialists distributed the products from satellite storage to the villages and maintained detailed records and monitored deployment of product by the CHPs; and CHPs performed direct product installation in homes, documenting their work based on custom-designed digital forms using the CommCare\u0026trade; data management system in tablets.\u003c/p\u003e\u003cp\u003eProduct accountability followed a rigorous process from requisition through disposal. Due to the rolling basis of product deployment across all 58 clusters, daily product preparations by store logistician included counting and organizing products for their designated clusters, with intervention specialists verifying product quantities before transport to CHPs in the field. Every product movement was documented through a comprehensive system of goods issue notes, electronic tracking forms, and distribution logs. During deployment, product barcodes (printed at time of manufacturing) were scanned into the CommCare\u0026trade; system using tablets in order to verify that the product was being installed in the intended cluster. Products were photographed, with global positioning system (GPS) coordinates recorded for each installation location. After the pre-specified 28-day deployment period, used products were systematically collected, barcodes scanned, and deviations recorded. The goal was to track potential movement across households and/or clusters during the 28-day application period, as well as verify that they were not tampered with: moved between rooms in a structure or moved between structures within a compound before transport back to the storage facility. Used products underwent batched incineration at KEMRI-CGHR per industry specifications (1000\u0026deg;C).\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eQuality Assurance for Entomology Data\u003c/h2\u003e\u003cp\u003eThe entomological QA system centered on rigorous collection protocols and data verification for both human landing catches (HLC) and Centers for Disease Control and Prevention (CDC) light trap sampling developed on digital platform (CommCare\u0026trade;), with immediate supervisory verification of collection records and specimen handling. Paper forms for mosquito morphological identification were double entered onto Commcare\u0026trade; using computers. Field collectors underwent standardized training and competency assessment before deployment, with quarterly refresher sessions and supervision during collections. For HLCs, we maintained strict safety protocols including weekly malaria prophylaxis (with Mefloquine\u0026trade;) for all collectors during the collection periods and for four weeks after. All \u003cem\u003eAnopheles\u003c/em\u003e mosquitoes collected were barcoded upon collection, enabling complete traceability through the laboratory workflow. Regular quality control checks examined collection patterns for anomalies. This comprehensive approach maintained high-quality entomological data throughout the trial.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eDigital Innovation in Adverse Event Monitoring: Implementation of Scannable Health Facility Registers\u003c/h2\u003e\u003cp\u003eTo enable comprehensive and consistent monitoring of adverse events across the study area, we implemented a system of scannable registers across all 15 study health facilities. This system replaced the traditional Ministry of Health (MoH) paper registers with specially designed scannable versions while maintaining familiar formats and workflows for healthcare staff. Working in collaboration with Quantitative Engineering Design (QED) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://about.scanform.qed.ai/programs/\u003c/span\u003e\u003cspan address=\"https://about.scanform.qed.ai/programs/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), AEGIS-Kenya developed registers that preserved the standard MoH format while incorporating machine-readable markers and structured fields to enable digital data capture. The registers were designed to capture all standard patient information including demographics, clinical conditions, and prescribed medications while cutting out personal identifiers. To protect patient privacy, the system automatically excluded identifiable information during the scanning process and implemented robust data encryption for both transmission and storage. Using the QED android application on a smartphone, images of these registers were captured and sent to a cloud server. Machine learning image processing algorithms were used to transcribe the handwritten data into digital data that were aggregated and accessed via a customized online dashboard. We introduced three distinct register types: outpatient registers for patients under 5 years (\u003cb\u003eSupplementary Fig.\u0026nbsp;2\u003c/b\u003e), outpatient registers for patients 5 years and above (\u003cb\u003eSupplementary Fig.\u0026nbsp;3\u003c/b\u003e), and antenatal clinic registers (\u003cb\u003eSupplementary Fig.\u0026nbsp;4\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eImplementation began with comprehensive training of health facility staff on proper register completion and handling. Healthcare workers continued their standard practice of recording patient information, with pages being photographed daily or weekly depending on facility patient volume. Quality control was maintained through a rigorous verification process. Two dedicated staff members reviewed all the transcribed data before they were cleared for aggregation in the central database. The system automatically flagged potential errors or inconsistencies for manual verification, and regular cross-checking against the photographed image of the registers ensuring data accuracy.\u003c/p\u003e\u003cp\u003eThe dashboard integration proved particularly valuable for trial monitoring, providing data aggregation and visualization of verified data. At the beginning of the trial, MoH staff were reluctant to adopt the new system due to their limited prior experience with such platforms, but over time, they grew to appreciate it. This enabled automated adverse event monitoring and trend analysis for the Data Safety and Monitoring Boad (DSMB), significantly improving our ability to detect and respond to potential safety signals. The system successfully processed thousands of records throughout the study period, maintaining high data accuracy while reducing the administrative burden on health facility staff. While the challenge of system adoption was aggreviated by frequent staff turnover at the facilities, (caused by transfers initiated by the county government), the study team mitigated this risk by providing ongoing and frequent retraining of MoH personnel. Despite these challenges, this innovative technical approach represented a significant advancement in adverse event monitoring for community-wide interventions, providing comprehensive surveillance while maintaining efficient health facility operations. The success of this implementation demonstrated the potential for digital innovations to enhance QA in resource-limited settings while respecting existing healthcare workflows.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eEthical and Regulatory Monitoring\u003c/h2\u003e\u003cp\u003eThe AEGIS-Kenya trial maintained rigorous ethical and regulatory oversight through multiple review mechanisms. As previously described\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, primary ethical approval was received from the Scientific and Ethics Review Unit (SERU) in Kenya, with additional oversight from the University of Notre Dame (prime awardee) Institutional Review Board (UND-IRB), Centers for Disease Control and Prevention Institutional Review Board (CDC-IRB) and the World Health Organization Ethical Review Committee (WHO-ERC). All protocol amendments, safety reports, and annual renewals underwent review by SERU before submission to other ethical review committees, ensuring coordinated oversight throughout the trial.\u003c/p\u003e\u003cp\u003eA DSMB provided independent safety oversight, reviewing all adverse events and monitoring trial progress on a quarterly basis throughout the study period. Site readiness, site activation, quarterly interim monitoring visits, and a site close out visit was conducted by FHI Clinical. for trial oversight.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eCOVID-19 Adaptations and Mitigation\u003c/h2\u003e\u003cp\u003eThe emergence of COVID-19 necessitated substantial adaptations to trial procedures while maintaining study integrity and participant safety. We implemented comprehensive infection prevention measures including daily symptom screening for staff, mandatory mask-wearing for all study interactions, and restructured clinic waiting areas to ensure social distancing. The study procured personal protective equipment for all staff and participants, including masks and hand sanitizers, and established sanitization protocols for all study equipment and facilities. Staff underwent additional training on COVID-19 prevention measures and modified participant interaction protocols to minimize close contact while maintaining essential study procedures. We implemented a testing and quarantine protocol for symptomatic staff members following MoH guidelines, with successful management of several COVID-19 cases among study personnel including the Clinical Research Associate. Despite these challenges, the adapted procedures allowed continued trial operation without compromising data quality or participant safety, demonstrating the resilience of our QA systems in responding to unexpected public health challenges.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eMany of the QA systems developed and implemented during the AEGIS-Kenya trial reflect those outlined for standard Good Clinical Practices\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e (SOPs, training, IP monitoring etc.) and demonstrate that rigorous clinical research standards can be maintained in resource-limited settings through innovative approaches and careful attention to the local context. Our experience highlights the importance of adaptable systems, continuous monitoring, strong community partnerships and financial support for QA system design, development and maintenance for ensuring trial data integrity.\u003c/p\u003e\u003cp\u003eOur trial demonstrates that implementing a slot and sync system significantly enhanced participant screening and follow up visit completion rates at our study facilities since participants preferred to maintain the same day of the week and time throughout the follow-up study period. This innovation led to a marked reduction in screening non-completion rates after consent\u0026mdash;decreasing from 5.4% during baseline to 2.4% in cohort I and 1.6% in cohort II, representing two-fold and three-fold improvements respectively\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. This scheduling approach represents a valuable strategy that could be widely adopted to maximize participant enrollment and retention across various clinical trial settings. However, our experience suggests that optimal results are achieved when this system is implemented alongside complementary strategies, including digital mobile data capture system adoption, thorough feasibility assessments to realistically estimate required time and resources, and continuous monitoring and evaluation of enrollment progress to promptly identify and address emerging challenges.\u003c/p\u003e\u003cp\u003eThe substantial improvement in screening completion rates underscores the importance of structured participant flow management in clinical trial execution. By reducing logistical barriers and improving coordination between study components, the slot and sync system created a more efficient participant experience that likely contributed to higher retention rates throughout the trial phases. While our implementation proved successful in this context, future research should explore adaptations of this approach across diverse clinical trial settings and participant populations. Cost-effective analyses comparing traditional scheduling approaches with our slot and sync system would provide valuable insights for trial planners and could inform best practices in clinical trial management.\u003c/p\u003e\u003cp\u003eThe development of electronic systems for managing microscopy results and product accountability proved to be efficient. Our LIS enabled rapid result reporting and enhanced quality control, improving the median turnaround time from 15.3 days in the paper-based system to 3.6 days after implementing the LIS\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. This was more than our intended target of 48hrs due to our staff resting during the two days of weekends and all public holidays. The approach builds on established frameworks for laboratory QA\u003csup\u003e16\u003c/sup\u003e while introducing innovations for real-time data access enabling delivery of treatment to asymptomatic malaria cases. Similarly, our electronic product management system maintained monthly IP stock tracking of about 100,000 IP, demonstrating how digital tools can enhance compliance with International Council for Harmonisation (ICH) guidelines for investigational product accountability\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe implementation of mobile data collection using tablets as the primary method of data collection represented another significant advancement in our trial operations. This approach eliminated paper-based data collection errors, enabled real-time data validation, and significantly reduced the time required for data cleaning and processing. Field workers could efficiently capture participant information, including GPS coordinates for household visits, ensuring accurate spatial data for subsequent analyses. The tablet-based system also facilitated immediate synchronization with central databases when connectivity was available, creating a more responsive and transparent data management pipeline.\u003c/p\u003e\u003cp\u003eCommunity engagement emerged as a critical QA component, particularly in our research-na\u0026iuml;ve setting. The implementation of REA prior to trial initiation, as recommended by Negussie et al. (2016)\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, helped identify potential barriers to participation and informed our communication strategies. This proactive approach was especially valuable given the complex social dynamics surrounding blood collection and the need to maintain long-term community support for the intervention.\u003c/p\u003e\u003cp\u003eThe QED scannable health facility registers addressed a fundamental challenge in vector control trials: how to monitor safety for community-wide deployed interventions. By digitizing existing MoH registers, we created an efficient surveillance system that maintained local healthcare workflows while improving data quality. This approach aligns with recent recommendations for leveraging existing health systems in clinical trials\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e and demonstrates the value of digital solutions in resource-limited settings\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe COVID-19 pandemic necessitated significant adaptations to our QA procedures. While this was challenging, the modifications demonstrated the resilience of our systems and the importance of flexible protocols in maintaining trial integrity during unexpected events. Our experience adds to the growing literature on conducting clinical trials during public health emergencies\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eLessons Learned\u003c/h2\u003e\u003cp\u003eSeveral lessons emerged that have broad implications for future vector control trials. First, the digitization of data collection systems, while initially resource-intensive, provided substantial benefits in data quality and operational efficiency. Second, continuous mapping and updating household structures proved essential in maintaining accurate coverage metrics, particularly in dynamic rural settings. Third, the slot-and-sync system for participant visits significantly improved clinic efficiency and participant retention, addressing a common challenge in longitudinal studies. In addition, the implementation of scannable registers initially faced resistance from some health facility staff, highlighting the need for sustained engagement with local healthcare providers. Next, the complexity of our QA systems required ongoing training and supervision, with associated resource implications. Lastly, our QA framework required substantial investment in infrastructure and training, including the recruitment of specialized personnel and installation of backup power systems in all the 15 health facilities with study clinics. While these requirements may seem burdensome, they proved essential for maintaining GCP compliance and data integrity. This aligns with findings from other large-scale trials in similar settings\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eSome limitations should be noted. While the digital system effectively managed subject recruitment, enrollment and follow up scheduling, intermittent cellular network coverage posed challenges for real-time data synchronization. To address this, we maintained paper backup systems, implemented local data storage protocols, used the slot and sync system to prevent duplicate enrollments and established clear procedures for data reconciliation when connectivity was restored.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eInsights we have described here contribute to the broader discussion on QA in vector control trials\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e and support the development of standardized yet adaptable frameworks for future studies. Our experience demonstrates that successful QA implementation in resource-limited settings requires a combination of technological innovation, community partnership, and flexible systems capable of responding to emerging challenges.\u003c/p\u003e\u003cp\u003eOur experience supports the effectiveness of using integrated digital systems - from mobile data collection on tablets to electronic product management to scannable health facility registers - in maintaining high-quality standards while reducing administrative burden. This, combined with robust product accountability, the successful implementation of REA, and ongoing community dialogue proved essential to trial success, particularly in our research-na\u0026iuml;ve setting. Looking ahead, successful implementation requires significant investment in infrastructure, training, and community relationships for balancing scientific rigor with operational feasibility. Future research should focus on further innovations in digital tools and community engagement strategies that can accommodate the unique challenges of community-level interventions while ensuring sustainable, high-quality data collection and management.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAEGIS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Advancing Evidence for the Global Implementation of Spatial Repellents\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCDC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Centers for Disease Control and Prevention\u003c/p\u003e\n\u003cp\u003eCHP\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Community Health Promoters\u003c/p\u003e\n\u003cp\u003eCI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Community Interviewer\u003c/p\u003e\n\u003cp\u003ecRCT\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;cluster Randomized Controlled Trials\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDSMB\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Data Safety and Monitoring Boad\u003c/p\u003e\n\u003cp\u003eERC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Ethical Review Committee\u003c/p\u003e\n\u003cp\u003eGPS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Global Positioning System\u003c/p\u003e\n\u003cp\u003eICH International Council for Harmonisation\u003c/p\u003e\n\u003cp\u003eIP\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Investigational Product\u003c/p\u003e\n\u003cp\u003eIRB\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Institutional Review Board\u003c/p\u003e\n\u003cp\u003eLIS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Laboratory Information system\u003c/p\u003e\n\u003cp\u003eMoH\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Ministry of Health\u003c/p\u003e\n\u003cp\u003ePC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Personal Computer\u003c/p\u003e\n\u003cp\u003eQA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Quality Assurance\u003c/p\u003e\n\u003cp\u003eQED\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Quantitative Engineering Design\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eREA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Rapid Ethical Assessment\u003c/p\u003e\n\u003cp\u003eSERU\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Scientific and Ethics Review Unit\u003c/p\u003e\n\u003cp\u003eSOP\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Standard Operating procedure\u003c/p\u003e\n\u003cp\u003eSR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Spatial Repellent\u003c/p\u003e\n\u003cp\u003eUND\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;University of Notre Dame\u003c/p\u003e\n\u003cp\u003eWHO \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;World Health Organization\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe study protocol for the clinical trial was reviewed and approved by Kenya Medical Research Institute Scientific and Ethics Review Unit (protocol 3870, reference KEMRI/RES/7/3/1), Institutional Review Boards at Centers for Disease Control and Prevention (protocol number 7252), University of Notre Dame (protocol number 19-08-5506), and the WHO Ethical Review Committee (ERC.0003185). Study staff obtained the written informed consent of parents or legal guardians of study participants for cohort participation, mosquito collectors conducting human landing catch (HLC) collections, and heads of households for product placement.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThis study would not have been possible without the generous participation of the communities in Teso South and Teso North sub-counties, Busia County, Kenya. We are deeply grateful for their willingness to collaborate with us. We also extend our sincere thanks to the KEMRI staff for their dedication in developing and implementing the study while upholding rigorous quality assurance guidelines, even amidst the challenges of the COVID-19 pandemic. Special regards to Ashley Scott for her role in Kenya cRCT ND program management related to contract establishment for Burness, weekly coordination of CommCare™ data form development, SOP formatting and archiving, WHO ERC, ND IRB submissions and amendments, DSMB safety reporting, data cleaning contributions in preparation for analyses, and outcome reporting. Our appreciation also goes to Silver Wevill, Valentine Veena, Loice Magaria, Anne Wangwe, and Rose Adeny of the FHI Clinical for their detailed and independent monitoring of the AEGIS-Kenya cRCT. We thank Anne-Marie Schryer-Roy, Ellen Wilson and Saburi Chirimi of Burness for their assistance in developing the graphic design for study brochures used for community engagement. Finally, we thank William Wu and the entire QED team for their contribution in developing the scannable registers that supported passive adverse event monitoring in this trial.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eAuthors contributions\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eConceptualization: BP, JEG, NLA and EO.\u003c/p\u003e\n\u003cp\u003eProject administration: BP, QA, VM, HO, NK, FA, BA, JH, JEG, NLA, JPG and EO\u003c/p\u003e\n\u003cp\u003eWriting and editing the manuscript: BP, QA, VM, HO, NK, FA, BA, JH, JEG, NLA, JPG and EO\u003c/p\u003e\n\u003cp\u003eSupervision: JEG, NLA, JPG and EO\u003c/p\u003e\n\u003cp\u003eAll authors contributed to reviewing the manuscript.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eFunding statement\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThis research project is made possible through the funding and support of Unitaid, a global health organization that saves lives by making new health products available and affordable for people in low- and middle-income countries. Unitaid works with partners to identify innovative treatments, tests and tools, help tackle the market barriers that are holding them back, and get them to the people who need them most – fast. Since Unitaid was created in 2006, the organization has unlocked access to more than 100 groundbreaking health products to help address the world’s biggest health challenges, including HIV, TB and malaria; women’s and children’s health; and pandemic prevention, preparedness and response. Every year, more than 300 million people benefit from the products Unitaid has helped roll out. Unitaid is hosted by the World Health Organization.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eDisclaimer\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe findings and conclusions in this manuscript are those of the authors and do not necessarily represent the official position of the Kenya Medical Research Institute or the US Centers for Disease Control and Prevention.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWHO World Malaria Reoprt. 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/teams/global-malaria-programme/reports/world-malaria-report-2024\u003c/span\u003e\u003cspan address=\"https://www.who.int/teams/global-malaria-programme/reports/world-malaria-report-2024\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. \u003cem\u003eGlobal Technical Strategy for Malaria 2016\u0026ndash;2030\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://iris.who.int/handle/10665/176712\u003c/span\u003e\u003cspan address=\"https://iris.who.int/handle/10665/176712\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAchee NL, Sardelis MR, Dusfour I, Chauhan KR, Grieco JP. 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Improving the quality of vector control trials. Trends Parasitol. 2015;31:380\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7585900/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7585900/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe implementation of quality assurance (QA) systems is crucial for generating reliable evidence in large-scale vector control trials. This paper documents the QA framework developed for the Advancing Evidence for the Global Implementation of Spatial Repellents (AEGIS) program in Busia, Kenya. The trial encompassed 60 clusters at baseline dropping to 58 during the intervention phase and followed 5,717 participants from three cohorts spanning two and a half years. Key QA innovations included a slot and sync scheduling system that significantly enhanced participant screening completion rates, scannable health facility registers for adverse event monitoring, electronic systems for managing microscopy samples and investigational products (IP), integration of community engagement in quality processes, and adaptation of procedures during COVID-19. We present the methods, challenges, and solutions in maintaining trial quality throughout the study period, providing valuable insights for future vector control studies in similar settings. Our experience demonstrates that robust QA implementation in resource-limited settings requires adaptable systems, continuous monitoring, a strong community partnership and strong financial support for development and implementation of QA systems.\u003c/p\u003e","manuscriptTitle":"Quality Assurance in Practice: Insights from a Cluster Randomized Control Trial Evaluating a novel Spatial Repellent vector control intervention in Kenya","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-19 18:13:15","doi":"10.21203/rs.3.rs-7585900/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ddb3947f-b4ab-4b25-837a-af9def5f5d76","owner":[],"postedDate":"September 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-23T05:23:58+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-19 18:13:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7585900","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7585900","identity":"rs-7585900","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}