Bisrat Haileleul, Manyazewal Tsegahun, Fekadu Abebaw
Center for Innovative Drug Development and Therapeutic Trials for Africa (CDT-Africa), College of Health Sciences, Addis Ababa University, Addis Abeba, Ethiopia.
Interact J Med Res. 2023 Aug 28;12:e43492. doi: 10.2196/43492.
Since most people in low-income countries do not have access to reliable laboratory services, early diagnosis of life-threatening diseases like COVID-19 remains challenging. Facilitating real-time assessment of the health status in a given population, mobile health (mHealth)-supported syndrome surveillance might help identify disease conditions earlier and save lives cost-effectively.
This study aimed to evaluate the potential use of mHealth-supported active syndrome surveillance for COVID-19 early case finding in Addis Ababa, Ethiopia.
A comparative cross-sectional study was conducted among adults randomly selected from the Ethio telecom list of mobile phone numbers. Participants underwent a comprehensive phone interview for COVID-19 syndromic assessments, and their symptoms were scored and interpreted based on national guidelines. Participants who exhibited COVID-19 syndromes were advised to have COVID-19 diagnostic testing at nearby health care facilities and seek treatment accordingly. Participants were asked about their test results, and these were cross-checked against the actual facility-based data. Estimates of COVID-19 detection by mHealth-supported syndromic assessments and facility-based tests were compared using Cohen Kappa (κ), the receiver operating characteristic curve, sensitivity, and specificity analysis.
A total of 2741 adults (n=1476, 53.8% men and n=1265, 46.2% women) were interviewed through the mHealth platform during the period from December 2021 to February 2022. Among them, 1371 (50%) had COVID-19 symptoms at least once and underwent facility-based COVID-19 diagnostic testing as self-reported, with 884 (64.5%) confirmed cases recorded in facility-based registries. The syndrome assessment model had an optimal likelihood cut-off point sensitivity of 46% (95% CI 38.4-54.6) and specificity of 98% (95% CI 96.7-98.9). The area under the receiver operating characteristic curve was 0.87 (95% CI 0.83-0.91). The level of agreement between the mHealth-supported syndrome assessment and the COVID-19 test results was moderate (κ=0.54, 95% CI 0.46-0.60).
In this study, the level of agreement between the mHealth-supported syndromic assessment and the actual laboratory-confirmed results for COVID-19 was found to be reasonable, at 89%. The mHealth-supported syndromic assessment of COVID-19 represents a potential alternative method to the standard laboratory-based confirmatory diagnosis, enabling the early detection of COVID-19 cases in hard-to-reach communities, and informing patients about self-care and disease management in a cost-effective manner. These findings can guide future research efforts in developing and integrating digital health into continuous active surveillance of emerging infectious diseases.
由于低收入国家的大多数人无法获得可靠的实验室服务，对像新冠病毒病（COVID-19）这样危及生命的疾病进行早期诊断仍然具有挑战性。移动健康（mHealth）支持的综合征监测有助于促进对特定人群健康状况的实时评估，可能有助于更早地发现疾病状况并以具有成本效益的方式挽救生命。
本研究旨在评估mHealth支持的主动综合征监测在埃塞俄比亚亚的斯亚贝巴早期发现COVID-19病例中的潜在用途。
在从埃塞俄比亚电信手机号码列表中随机选择的成年人中进行了一项比较横断面研究。参与者接受了关于COVID-19综合征评估的全面电话访谈，并根据国家指南对其症状进行评分和解读。出现COVID-19综合征的参与者被建议在附近的医疗机构进行COVID-19诊断检测并相应地寻求治疗。询问参与者他们的检测结果，并将这些结果与实际的基于机构的数据进行交叉核对。使用科恩kappa（κ）、受试者工作特征曲线、敏感性和特异性分析比较了mHealth支持的综合征评估和基于机构的检测对COVID-19的检测估计值。
在2021年12月至2022年2月期间，通过mHealth平台共访谈了2741名成年人（n = 1476，53.8%为男性；n = 1265，46.2%为女性）。其中，1371人（50%）至少有一次COVID-19症状，并如自我报告的那样接受了基于机构的COVID-19诊断检测，在基于机构的登记中记录了884例（64.5%）确诊病例。综合征评估模型的最佳似然截断点敏感性为46%（95%CI 38.4 - 54.6），特异性为98%（95%CI 96.7 - 98.9）。受试者工作特征曲线下面积为0.87（95%CI 0.83 - 0.91）。mHealth支持的综合征评估与COVID-19检测结果之间的一致性水平为中等（κ = 0.54，95%CI 0.46 - 0.60）。
在本研究中，发现mHealth支持的综合征评估与COVID-19实际实验室确诊结果之间的一致性水平合理，为89%。mHealth支持的COVID-19综合征评估是基于标准实验室确诊诊断的一种潜在替代方法，能够在难以到达的社区早期发现COVID-19病例，并以具有成本效益的方式告知患者自我护理和疾病管理。这些发现可为未来在开发数字健康并将其整合到新兴传染病持续主动监测方面的研究工作提供指导。
