PDF(Pubmed)
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.
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.
摘要：
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，对COVID-19等危及生命的疾病的早期诊断仍然具有挑战性。促进对特定人群健康状况的实时评估，移动健康(mHealth)支持的综合征监测可能有助于更早地识别疾病状况并经济有效地挽救生命。
目的：本研究旨在评估mHealth支持的主动综合征监测对亚的斯亚贝巴COVID-19早期病例发现的潜在用途，埃塞俄比亚。
方法：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比较横断面研究。参与者接受了COVID-19综合征评估的全面电话采访，他们的症状根据国家指南进行评分和解释.建议表现出COVID-19综合征的参与者在附近的医疗机构进行COVID-19诊断测试，并寻求相应的治疗。参与者被问及他们的测试结果，这些数据与实际的基于设施的数据进行了交叉检查。使用CohenKappa（κ）比较了mHealth支持的综合征评估和基于设施的测试对COVID-19检测的估计值，接收器工作特性曲线，灵敏度,和特异性分析。
结果：在2021年12月至2022年2月期间，共有2741名成年人（n=1476，男性占53.8%，n=1265，女性占46.2%）通过mHealth平台接受了采访。其中,1371人（50%）至少有一次COVID-19症状，并接受了自我报告的基于设施的COVID-19诊断测试，884例(64.5%)确诊病例记录在基于机构的登记册中。综合征评估模型的最佳可能性截止点灵敏度为46％（95％CI38.4-54.6），特异性为98％（95％CI96.7-98.9）。受试者工作特征曲线下面积为0.87(95%CI0.83-0.91)。mHealth支持的综合征评估与COVID-19测试结果之间的一致性水平中等(κ=0.54，95%CI0.46-0.60)。
结论：在这项研究中，mHealth支持的综合征评估与COVID-19的实际实验室确认结果之间的一致性水平被认为是合理的，在89%。mHealth支持的COVID-19综合征评估代表了标准实验室确证诊断的潜在替代方法，能够在难以到达的社区早期发现COVID-19病例，并以经济有效的方式告知患者自我护理和疾病管理。这些发现可以指导未来的研究工作，将数字健康发展和整合到新出现的传染病的持续积极监测中。
目的：本研究旨在评估mHealth支持的主动综合征监测对亚的斯亚贝巴COVID-19早期病例发现的潜在用途，埃塞俄比亚。
方法：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比较横断面研究。参与者接受了COVID-19综合征评估的全面电话采访，他们的症状根据国家指南进行评分和解释.建议表现出COVID-19综合征的参与者在附近的医疗机构进行COVID-19诊断测试，并寻求相应的治疗。参与者被问及他们的测试结果，这些数据与实际的基于设施的数据进行了交叉检查。使用CohenKappa（κ）比较了mHealth支持的综合征评估和基于设施的测试对COVID-19检测的估计值，接收器工作特性曲线，灵敏度,和特异性分析。
结果：在2021年12月至2022年2月期间，共有2741名成年人（n=1476，男性占53.8%，n=1265，女性占46.2%）通过mHealth平台接受了采访。其中,1371人（50%）至少有一次COVID-19症状，并接受了自我报告的基于设施的COVID-19诊断测试，884例(64.5%)确诊病例记录在基于机构的登记册中。综合征评估模型的最佳可能性截止点灵敏度为46％（95％CI38.4-54.6），特异性为98％（95％CI96.7-98.9）。受试者工作特征曲线下面积为0.87(95%CI0.83-0.91)。mHealth支持的综合征评估与COVID-19测试结果之间的一致性水平中等(κ=0.54，95%CI0.46-0.60)。
结论：在这项研究中，mHealth支持的综合征评估与COVID-19的实际实验室确认结果之间的一致性水平被认为是合理的，在89%。mHealth支持的COVID-19综合征评估代表了标准实验室确证诊断的潜在替代方法，能够在难以到达的社区早期发现COVID-19病例，并以经济有效的方式告知患者自我护理和疾病管理。这些发现可以指导未来的研究工作，将数字健康发展和整合到新出现的传染病的持续积极监测中。
