JMIR Publications

ABSTRACT

Background:

Background:

As most people in developing countries do not have access to reliable laboratory services, early diagnosis of life-threatening diseases like COVID-19 remains challenging. Mobile phone (mHealth)-supported syndrome surveillance might help identify disease conditions in a community earlier and save much life cost-effectively.

Objective:

Objectives: 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:

Methods:

This study was a part of a national mHealth-supported prospective study that provided active syndrome surveillance for COVOD-19. Based on a baseline cross-sectional comparison of syndrome diagnosis against confirmed laboratory tests. This survey was conducted among adults randomly selected from the Ethio-Telecom list of mobile phone numbers Participants underwent a comprehensive phone interview for syndromic assessments of COVID-19 and their data was captured using an electronic data collection platform. For those who self-reported their COVID-19 test result as they had facility-based COVID-19 testing, their test results and other data were collected directly from respective healthcare facilities and cross-checked. Estimates of COVID–19 detection between mHealth-supported syndrome assessments and facility-based test results were compared using Cohen’s Kappa (k), ROC curve, sensitivity and specificity analysis

Results:

Result: A total of 2,741 adults were interviewed through the mHealth platform in the period December 2021 to February 2022. 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 ROC curve was 0.87 (95% CI 0.83-0.91). The level of agreement between the syndrome assessment and the COVID-19 test result was moderate (k = 0.54, 95% CI 0.46-0.60).

Conclusions:

Conclusion: In this study, the level of agreement for COVID-19 results between the mHealth-supported syndrome assessment and the actual laboratory-confirmed result was reasonable at 89%. mHealth-supported syndromic assessment of COVID-19 is a potential alternative method to the standard laboratory-based confirmatory diagnosis to detect COVID-19 cases earlier in hard-to-reach communities and advise patients on self-care and management of the disease cost-effectively.