Mobile Health–Supported Active Syndrome Surveillance for COVID-19 Early Case Finding in Addis Ababa, Ethiopia: Comparative Study
BackgroundSince 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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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JMIR Publications
2023-08-01
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| Series: | Interactive Journal of Medical Research |
| Online Access: | https://www.i-jmr.org/2023/1/e43492 |
| _version_ | 1797735986775457792 |
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| author | Haileleul Bisrat Tsegahun Manyazewal Abebaw Fekadu |
| author_facet | Haileleul Bisrat Tsegahun Manyazewal Abebaw Fekadu |
| author_sort | Haileleul Bisrat |
| collection | DOAJ |
| description |
BackgroundSince 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.
ObjectiveThis study aimed to evaluate the potential use of mHealth-supported active syndrome surveillance for COVID-19 early case finding in Addis Ababa, Ethiopia.
MethodsA 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.
ResultsA 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).
ConclusionsIn 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. |
| first_indexed | 2024-03-12T13:07:02Z |
| format | Article |
| id | doaj.art-168ea95b85204fd0ae2eba23fcc0300a |
| institution | Directory Open Access Journal |
| issn | 1929-073X |
| language | English |
| last_indexed | 2024-03-12T13:07:02Z |
| publishDate | 2023-08-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | Interactive Journal of Medical Research |
| spelling | doaj.art-168ea95b85204fd0ae2eba23fcc0300a2023-08-28T13:01:21ZengJMIR PublicationsInteractive Journal of Medical Research1929-073X2023-08-0112e4349210.2196/43492Mobile Health–Supported Active Syndrome Surveillance for COVID-19 Early Case Finding in Addis Ababa, Ethiopia: Comparative StudyHaileleul Bisrathttps://orcid.org/0000-0001-7089-5362Tsegahun Manyazewalhttps://orcid.org/0000-0002-8360-7574Abebaw Fekaduhttps://orcid.org/0000-0003-2219-0952 BackgroundSince 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. ObjectiveThis study aimed to evaluate the potential use of mHealth-supported active syndrome surveillance for COVID-19 early case finding in Addis Ababa, Ethiopia. MethodsA 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. ResultsA 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). ConclusionsIn 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.https://www.i-jmr.org/2023/1/e43492 |
| spellingShingle | Haileleul Bisrat Tsegahun Manyazewal Abebaw Fekadu Mobile Health–Supported Active Syndrome Surveillance for COVID-19 Early Case Finding in Addis Ababa, Ethiopia: Comparative Study Interactive Journal of Medical Research |
| title | Mobile Health–Supported Active Syndrome Surveillance for COVID-19 Early Case Finding in Addis Ababa, Ethiopia: Comparative Study |
| title_full | Mobile Health–Supported Active Syndrome Surveillance for COVID-19 Early Case Finding in Addis Ababa, Ethiopia: Comparative Study |
| title_fullStr | Mobile Health–Supported Active Syndrome Surveillance for COVID-19 Early Case Finding in Addis Ababa, Ethiopia: Comparative Study |
| title_full_unstemmed | Mobile Health–Supported Active Syndrome Surveillance for COVID-19 Early Case Finding in Addis Ababa, Ethiopia: Comparative Study |
| title_short | Mobile Health–Supported Active Syndrome Surveillance for COVID-19 Early Case Finding in Addis Ababa, Ethiopia: Comparative Study |
| title_sort | mobile health supported active syndrome surveillance for covid 19 early case finding in addis ababa ethiopia comparative study |
| url | https://www.i-jmr.org/2023/1/e43492 |
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