The use of m-Health active participant centred (MAPC) systems to improve surveillance of adverse events following Immunization (AEFIs) in Zimbabwe - Nyambayo, Priscilla Patricia Munyaradzi (Department of Public Health and Family Medicine, 2025)
Introduction A robust national AEFI surveillance system ensures timely AEFI detection, good quality AEFI reports, prompt case investigation and robust causality assessment for corrective AEFI case management, signal detection and appropriate feedback ultimately to improve public safety and trust in vaccines and the immunization programme. Each AEFI surveillance method has advantages and disadvantages. This thesis aimed to develop an evidence-based and empirical foundation to guide recommendations for the use of mHealth for active vaccine safety surveillance (AVSS) in Zimbabwe to strengthen its passive (spontaneous) AEFI surveillance system. The primary hypothesis of the thesis is that an mHealth application system that supports AEFI detection and reporting is a feasible approach to supporting active AEFI surveillance in Zimbabwe. Method I used mixed methods comprising a scoping and narrative literature review, a descriptive evaluation of Zimbabwe's AEFI system, a randomised control trial (RCT) to assess the impact of the Zimbabwe stimulated telephone assisted rapid safety surveillance (Zm-STARSS) approach, and a consumer and healthcare professional (HCP) survey to assess their experience and the acceptability of Zm-STARSS. Results The scoping and narrative review revealed that most MAPC AEFI surveillance studies (92%, 24/26) were conducted in High Income Countries(HICs) and only two in Low Middle-Income Countries (LMICs). The mean response rate to (Short Message Services)SMS prompts was 71% among 23 studies. Out of 1440 assessed Zimbabwean AEFI reports 54.2% were non-serious, 29.7% non-serious but deemed medically important, 6.6% causing prolonged hospitalizations and 8.1% fatal. In the Zm-STARSS RCT, despite a relatively low (31%, n = 704) response rate, we demonstrated that the SMS group had a 2% AEFI detection rate compared to 0% in the passive control arm. Of the 31 HCPs and 96 consumers who responded, 96% and 71%, respectively, supported the use of Zm-STARSS for improving AEFI reporting. Respondents identified lack of feedback after reporting, fear of negative consequences, and mobile phone costs as major barriers to SMS reporting. Conclusion and recommendations The paucity of MAPC surveillance in LMICs highlights the need for more active surveillance of AEFIs in these regions. Zm-STARSS AEFI surveillance improved AEFI detection and reporting in an LMIC setting. Although the response rate was lower than what was seen in HICs, potential barriers to responding can be mitigated with simple reprogramming. Therefore, we recommend its use in LMIC settings. To support this improved reporting and ensure appropriate responses to these reports, it is imperative to strengthen the remaining elements of AEFI surveillance, including case investigation, causality assessment, case management and feedback. In addition, prioritising training and awareness initiatives aimed at mitigating factors contributing to underreporting, including addressing HCPs and consumers' fear of victimisation, is essential. The cost of MAPC for both consumers and HCPs should be minimised to improve AEFI reporting in Zimbabwe and similar LMICs. This may require engagement with mobile phone operators to lower rates (toll-free) for mHealth surveillance systems. Further studies should investigate the feasibility and effectiveness of the mHealth approach in other LMIC settings, particularly consumer response rates, impact on AEFI reporting rates and the regulatory and Immunization programmes' responses to these reports.