Msuya Hajirani M, Ali Ali M, Mrisho Mwifadhi, Lweno Omar N, Temu Silas G, Msuya Ibrahim, Kalabamu Frank, Milando Florence A, Rashid Mohammed, Said Ali H, Tumbo Anneth M, Jongo Said A, Kassim Kamaka R, Abdallah Gumi, Mbarak Hussein, Mbarak Hassan A, Berenge Hassan T, Mshana Prosper, Awadh Khamis, Mmbaga Selemani, Ndanzi Tunu, Nyaulingo Gloria D, Iddy Lathma, Bajaria Shraddha, Kao Kekeletso, Mwangoka Grace W, Abdulla Salim, Mkopi Abdallah
Ifakara Health Institute, Dar es Salaam, Tanzania.
Independent Researcher, Dar es Salaam, Tanzania.
Am J Trop Med Hyg. 2025 Feb 25;112(6):1280-1288. doi: 10.4269/ajtmh.24-0161. Print 2025 Jun 4.
A critical impediment to efficient outbreak response is the availability of timely and complete data on cases and their linkage to care. To inform solutions to this issue, this study investigated the utility of self-testers reporting their coronavirus disease 2019 results using a mobile health platform. Our study has demonstrated that the mobile health platform is feasible; it achieved a 74.5% reporting rate, indicating a strong likelihood of data entry into the unstructured supplementary service data platform. Support from community health workers (CHWs) and healthcare professionals, particularly nurses, contributed to this success, especially among users with limited digital literacy. Specifically, 44.9% of self-test results were submitted by study participants themselves, 24.7% were submitted with the assistance of healthcare professionals, and 30.4% were submitted with the assistance of CHWs. The platform broadens the population base by increasing access and equity, allowing participation even among users without smartphones. Additionally, it integrates rapid antigen diagnostic tests with digital reporting, simplifying data processing and enabling standardized screening, real-time data capture, and effective patient follow-up. This technology also lays a foundation for pandemic preparedness in low- and middle-income countries by demonstrating the feasibility of fully integrating response loops for disease management and interventions. Future response loops could leverage artificial intelligence, machine learning, and integration with existing health surveillance systems, directly benefiting users through enhanced support.
高效应对疫情的一个关键障碍是能否及时获得关于病例及其与医疗关联的完整数据。为了为解决这一问题提供信息，本研究调查了使用移动健康平台的自我检测者报告其2019冠状病毒病检测结果的效用。我们的研究表明，移动健康平台是可行的；它实现了74.5%的报告率，这表明数据很有可能录入非结构化补充服务数据平台。社区卫生工作者（CHW）和医疗专业人员，尤其是护士的支持促成了这一成功，在数字素养有限的用户中尤为如此。具体而言，44.9%的自我检测结果由研究参与者自己提交，24.7%在医疗专业人员的协助下提交，30.4%在社区卫生工作者的协助下提交。该平台通过增加可及性和公平性扩大了人群基础，甚至允许没有智能手机的用户参与。此外，它将快速抗原诊断检测与数字报告相结合，简化了数据处理，并实现了标准化筛查、实时数据采集和有效的患者随访。这项技术还通过展示将疾病管理和干预的应对循环完全整合的可行性，为低收入和中等收入国家的大流行防范奠定了基础。未来的应对循环可以利用人工智能、机器学习，并与现有的健康监测系统整合，通过加强支持直接使用户受益。
