Mobile Health-Supported Active Syndrome Surveillance for COVID-19 Early Case Finding in Addis Ababa, Ethiopia: Comparative Study.

IF 1.9 Q3 MEDICINE, RESEARCH & EXPERIMENTAL
Haileleul Bisrat, Tsegahun Manyazewal, Abebaw Fekadu
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引用次数: 0

Abstract

Background: 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.

Objective: This study aimed to evaluate the potential use of mHealth-supported active syndrome surveillance for COVID-19 early case finding in Addis Ababa, Ethiopia.

Methods: 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.

Results: 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).

Conclusions: 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.

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在埃塞俄比亚亚的斯亚贝巴开展移动医疗支持的主动综合征监测以早期发现 COVID-19 病例:比较研究。
背景:由于低收入国家的大多数人无法获得可靠的实验室服务,因此像 COVID-19 这样危及生命的疾病的早期诊断仍具有挑战性。移动医疗(mHealth)支持的综合征监测可对特定人群的健康状况进行实时评估,有助于更早地发现疾病状况,以经济有效的方式挽救生命:本研究旨在评估移动医疗支持的主动综合征监测在埃塞俄比亚亚的斯亚贝巴用于 COVID-19 早期病例发现的潜力:方法:从埃塞俄比亚电信公司的手机号码列表中随机抽取成年人进行横断面比较研究。参与者接受了一次全面的电话访谈,以进行 COVID-19 综合征评估,并根据国家指导方针对其症状进行评分和解释。建议出现 COVID-19 综合征的参与者到附近的医疗机构进行 COVID-19 诊断测试,并寻求相应的治疗。我们会询问参与者的检测结果,并将这些结果与医疗机构的实际数据进行核对。通过科恩卡帕(κ)、接收器操作特征曲线、灵敏度和特异性分析,比较了移动医疗支持的综合征评估和基于设施的检测对 COVID-19 检测的估计值:在 2021 年 12 月至 2022 年 2 月期间,共有 2741 名成年人(n=1476,53.8% 为男性;n=1265,46.2% 为女性)通过移动保健平台接受了访谈。其中,1371 人(50%)至少出现过一次 COVID-19 症状,并根据自我报告接受了基于设施的 COVID-19 诊断测试,884 人(64.5%)的确诊病例记录在基于设施的登记簿中。综合征评估模型的最佳似然截断点灵敏度为 46%(95% CI 38.4-54.6),特异性为 98%(95% CI 96.7-98.9)。接收者操作特征曲线下的面积为 0.87 (95% CI 0.83-0.91)。移动医疗支持的综合征评估与 COVID-19 测试结果之间的一致程度为中等(κ=0.54,95% CI 0.46-0.60):本研究发现,移动医疗支持的综合征评估与 COVID-19 的实际实验室确证结果之间的吻合度为 89%,属于合理水平。移动医疗支持的 COVID-19 症状评估是标准实验室确诊的一种潜在替代方法,它能在难以到达的社区及早发现 COVID-19 病例,并以经济有效的方式告知患者有关自我保健和疾病管理的信息。这些发现可以指导未来的研究工作,开发数字健康技术并将其整合到对新发传染病的持续主动监测中。
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来源期刊
Interactive Journal of Medical Research
Interactive Journal of Medical Research MEDICINE, RESEARCH & EXPERIMENTAL-
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发文量
45
审稿时长
12 weeks
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