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Published on in Vol 12 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/43492, first published .
Mobile Health–Supported Active Syndrome Surveillance for COVID-19 Early Case Finding in Addis Ababa, Ethiopia: Comparative Study

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

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

Authors of this article:

Haileleul Bisrat1 Author Orcid Image ;   Tsegahun Manyazewal1 Author Orcid Image ;   Abebaw Fekadu1 Author Orcid Image
Article Authors Cited by (1) Tweetations (2) Metrics

    Original Paper

    Center for Innovative Drug Development and Therapeutic Trials for Africa (CDT-Africa), College of Health Sciences, Addis Ababa University, Addis Abeba, Ethiopia

    *all authors contributed equally

    Corresponding Author:

    Haileleul Bisrat, MSc

    Center for Innovative Drug Development and Therapeutic Trials for Africa (CDT-Africa)

    College of Health Sciences

    Addis Ababa University

    BL_03_420 St.

    Addis Abeba, 9086

    Ethiopia

    Phone: 251 911940459

    Email: haylishb2@gmail.com


    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.

    Interact J Med Res 2023;12:e43492

    doi:10.2196/43492

    Keywords

    mobile health; mHealth; digital health; COVID-19; syndrome assessment; surveillance; Ethiopia; public health; syndrome surveillance; self-care; telemedicine; telecom, SARS-CoV-2 


    The global health community learned a lesson from the COVID-19 pandemic: each country needs to strengthen its epidemic preparedness and response capacity to mitigate emerging and reemerging infectious diseases at an earlier stage. It was noted that trajectories of the COVID-19 pandemic would have been curtailed through active and adequate disease surveillance, laboratory infrastructure, health workforce, pandemic planning, and management systems [1,2]. With limited disease preparedness and response capacity and a largely susceptible economy, Africa was endangered by the pandemic and its interrelated economic consequences [3-5]. Although the pandemic progressed more slowly in Africa compared to the rest of the globe, almost all African countries have been affected by the pandemic in one way or another [5-7]. More than 9.5 million COVID-19 confirmed cases were recorded across the African continent as of May 08, 2023 [8].

    Ethiopia, a country in sub-Saharan Africa, confirmed its first case of COVID-19 on March 13, 2020. Two days later, the World Health Organization declared a pandemic of the disease [9]. As of May 10, 2023, there have been 500,853 confirmed cases of COVID-19, with 7574 deaths, reported to the World Health Organization [8]. COVID-19 placed a significant burden on patients with chronic diseases in Ethiopia, affecting their ability to access their routine clinical care and treatment [10,11]. Health care workers [12,13] and the community at large [14,15] had uncertainties in implementing preventive measures against the disease. Working with the global health community, the Ethiopian government introduced vaccines against COVID-19, and as of May 6, 2023, the country administered 54,041,862 vaccine doses [9]. Ethiopia is one of the resource-constrained countries, overwhelmed by a double burden of infectious and noninfectious diseases and with limited capacity to find and treat cases at an early stage [15-18]. The country has limited health care infrastructure and workforce [19-21].

    The systematic digitalization of the health care industry may improve access to quality care and lower health care costs. Through the use of smartphones, health information technology, wearable devices, telemedicine, and personalized medicine, digital health technologies have the potential to facilitate health care and attain intended health outcomes [22-24]. Mobile health (mHealth) apps are emerging as a strategy to improve health care delivery and outcomes [25-27]. As mobile phones are more accessible to many people in low- and middle-income nations like Ethiopia, the technology is likely a means to offer impactful and affordable solutions to address diseases of significant public health importance.

    In the context of COVID-19, the potential use of mHealth technology to prevent and control the pandemic in Africa has been studied [28-32]; however, evidence is limited, especially on the potential impact of mHealth technology on active syndrome surveillance. As the number of mobile phone owners continues to rise in many African countries, including Ethiopia, mHealth technologies may aid in the early identification of COVID-19 cases within the community and link them to diagnostic testing centers.

    Africa showed limited capacity and flexibility to scale up COVID-19 testing, effectively tracing contacts of confirmed cases, and promptly training and deploying community health workers to help in the early identification of cases and their connection to appropriate care [33]. Existing conventional surveillance systems were not sufficient enough to respond to the COVID-19 pandemic. Moreover, it was difficult for the majority of the African population to fully comply with the preventive measures due to socioeconomic consequences. Hence, the problem of mitigating COVID-19 spanned across infrastructure, human resources for health, diagnosis, logistics, population literacy, and economy. There was a strong need to develop a simple alternative method to help detect COVID-19 cases in the community quickly and cost-effectively. Digital health interventions offered such potential.

    Therefore, we aimed to evaluate the potential use of mHealth-supported active syndrome surveillance for the early identification of COVID-19 cases in Addis Ababa, Ethiopia.


    Study Design and Participants

    This study is part of an ongoing national, population-based cohort mHealth-supported study—“mHealth-supported continuous national surveillance of COVID-19 for early case finding and population-level impact and control in Ethiopia (EPIC).” This specific study is a population-based cross-sectional comparative study, using mobile phone call surveys and a cross-sectional comparison of mHealth-supported COVID-19 syndrome diagnosis versus confirmatory laboratory tests. In this study, a mobile call survey was deemed an appropriate data collection method, given the nature of the pandemic, the wide geographic area of the country, and economic feasibility.

    Eligible participants were adults aged 18 years and older living in Ethiopia, speaking one or more of the 3 Ethiopian working languages (Amharic, Afan Oromo, and Tigrigna), and having no hearing or cognitive impairment or serious mental illness that impedes participation in the study. Potential participants were selected from the population of individuals with mobile phones registered centrally with either the federal or Addis Ababa authorities. Hence, participants were randomly selected from the list of mobile phone numbers available in the country using computer-generated random numbers. Initially, 11 million numbers were generated, from which 30,000 phone numbers were randomly selected. Our study uses the first 4180 phone numbers from the 30,000 randomly generated numbers.

    Data Collection

    Participants underwent a comprehensive phone interview for COVID-19 syndromic assessments, and their symptoms were scored and interpreted based on national guidelines. COVID-19–like symptoms were measured using a syndromic assessment for acute respiratory illnesses (fever and at least one sign or symptom of respiratory diseases, such as cough, sore throat, runny nose, shortness of breath, loss of smell, and loss of taste). Participants who had COVID-19 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 crosschecked against the actual facility-based data.

    Questionnaires were implemented on an electronic data capture platform, Open Data Kit, the free app for Android devices that is used to collect and compile data. Whenever a phone number was not responsive or unanswered during the first attempt, repeated calls were made up to 3 times before excluding it from the study.

    Data on COVID-19 laboratory test results were collected by reviewing log charts in hospitals and health centers where the participants had undergone laboratory testing. During the phone interview, those who had been tested and had received the COVID-19 test results, along with other indicators, were cross-checked. From the data gathered from government referrals and specialized hospitals, government health centers, private hospitals, and private clinics, it was possible to determine whether the information provided to the participants who indicated they had undergone a COVID-19 test, along with other indicators, matched the registration logs at health care facilities.

    Different quality assurance strategies were followed to ensure data integrity, quality, and reliability. In the data collection process, 13 data collectors with a minimum of BSc degrees in the health profession, along with 1 supervisor, undertook the data collection responsibilities after all contractual and training aspects were finalized. The survey procedures and tools were pretested for utility, feasibility, and acceptability. There was a face-to-face meeting with the supervisor every morning for reviewing and planning purposes. The data were stored daily on a central server at Center for Innovative Drug Development and Therapeutic Trials (CDT)-Africa in Addis Ababa University, Ethiopia. Figure 1 presents the overall procedures followed for data collection.

    ‎
    Figure 1. Data collection procedure.

    Ethics Approval

    This study has been reviewed and approved by the Institutional Review Board of the College of Health Sciences at Addis Ababa University (086/20/CDT). The study participants were briefed about the study over the phone and asked about their willingness to participate in the study. They were enrolled only after giving verbal consent. There was no compensation for participation in the study. The data were kept confidential and used for study purposes only.

    Statistical Analysis

    Interpretation of syndrome assessment followed the guidelines of the Ethiopian Ministry of Health. COVID-19 and non–COVID-19 signs and symptoms were categorized by assigning a score of 1 to COVID-19 and a score of 0 to Non–COVID-19 suspects. The formula used to determine the distribution of participants based on syndrome assessment and test results is as follows (Table 1):

    κ = Po – Pe / 1 – Pe = 1 – 1 – Po / 1 – Pe

    Where Po = relative observed agreement among raters, Pe = hypothetical probability of chance agreement, and κ = kappa status.

    Po = (a + d) / N

    Where a = the total number of instances that both raters said were correct (ie, the raters are in agreement), b = the total number of instances where what rater 2 said was incorrect, but what rater 1 said was correct (ie, rater disagreement), c = the total number of instances where what rater 1 said was incorrect, but what rater 2 said was correct (ie, rater disagreement), and d = the total number of instances where what both raters said was incorrect (ie, raters are in agreement).

    For Cohen kappa, the chance agreement is defined as the sum of the products of marginal distributions, which is as follows:

    Pe (κ) = (P.1 P1.) + (P.2 P2.)
    Table 1. Determination of the distribution of participants based on syndrome assessment and test results.
    Test resultSyndrome assessment

    		Category 1 (yes)Category 2 (no)Total
    Category 1 (yes)aba + bP1. = (a + b) / N
    Category 2 (no)cdc + dP2. = (c + d) / N
    Totala + cb + dN

    		P.1 = (a + c) / NP.2 = (b + d) / N

    For both the syndrome assessment and test results, the area under the receiver operating characteristic (ROC) curve was used to assess the overall diagnostic performance, indicating the possibility of accurately diagnosing all symptoms. The area under the curve of a procedure should be close to 1 for it to be highly sensitive and specific. The approach is considered more accurate if the curve closely aligns with the left-hand border and the top border of the ROC space. If the area under the ROC curve exceeded 0.75, we deemed our methods to be appropriate.

    For validity measures, sensitivity and specificity along with their corresponding 95% CIs were compared between syndrome assessment and laboratory test results. Sensitivity refers to the ability of the COVID-19 syndrome assessment to correctly identify COVID-19 test results as designated by the test outcome, while specificity refers to the proportion of cases caused by other factors, correctly identified as non–COVID-19. These 2 measures are closely related to type 1 and type 2 errors. Hence, both sensitivity and specificity were calculated. The formula for the calculation was defined as follows: sensitivity = TP / (TP + FN) and specificity = TN / (FP + TN). In these formulas, TP is true positive, FP is false positive, TN is true negative, and FN is false negative.

    Data analysis was conducted using STATA 17 (StataCorp) software. Individual participants had a unique ID, and all data sets were merged using this ID before analysis. Two separate variables, COVID-19 syndrome assessment and COVID-19 laboratory test results, were generated for comparison purposes and for documenting the trend of COVID-19 results. Coding, recording, labeling, and analysis were also done.

    Estimates of COVID-19 detection through syndrome assessment and test results were compared using Cohen kappa (κ) and ROC curve analysis. A κ value less than 0 indicates no agreement, and values between 0 and 0.20 indicate slight agreement; values from 0.21 to 0.40 denote fair agreement; values from 0.41 to 0.60 suggest moderate agreement; those from 0.61 to 0.80 imply substantial agreement; and finally, values from 0.81 to 1 indicate an almost perfect agreement [34].


    Background Characteristics

    In this study, a total of 35,646 national calls were made, of which 2741 participants were from Addis Ababa. Of the 6818 calls made, 4077 were characterized as unavailable, unanswered, switched off, disconnected, or hung up, resulting in a response rate of 67.2% among the participants from Addis Ababa (Figure 2).

    A total of 2741 data points were collected through telephone (mobile phone) interviews for the period from December 2021 to February 2022. Of these, 1476 (53.85%) were male and 1265 (46.15%) were female individuals; 1213 (44.25%) were from the age group of 18-29 years, 1151 (41.99%) had a diploma or higher academic qualification, and 1443 (52.65%) were married (Table 2).

    ‎
    Figure 2. Background characteristics.
    Table 2. Background characteristics of the study participants (N=2741).
    Background characteristicsValues, n (%)
    Sex


    		Male1476 (53.85)

    		Female1265 (46.15)
    Age category (years)


    		18-291213 (44.25)

    		30-39879 (32.07)

    		40-49379 (13.83)

    		50-59159 (5.80)

    		60-6978 (2.85)

    		70-7931 (1.13)

    		≥802 (0.07)
    Level of education


    		Cannot read or write75 (2.74)

    		Primary school450 (16.42)

    		Secondary school993 (36.23)

    		Certificate72 (2.63)

    		Diploma or above1151 (41.99)
    Marital status


    		Single1162 (42.39)

    		Married1443 (52.65)

    		Divorced86 (3.14)

    		Widowed50 (1.82)

    Comparison of mHealth-Supported Syndrome Assessment Versus Laboratory Test Results

    Of the total 2741 data points collected through mobile phones, 1371 participants had undergone a COVID-19 test. Among the tested participants, 884 (64.5%) received confirmation of their results from the respective hospitals and health centers; however, the remaining 487 participants did not have their results recorded in the health care facilities and were consequently excluded from this particular analysis.

    There were some variations in laboratory results collected in different months. In December 2021, confirmed COVID-19 cases accounted for 37% (32/86), while results from syndrome assessment constituted only 29% (45/155) of the participants with a positive COVID-19 outcome. In January, confirmed results accounted for 63% (54/86), while syndrome assessment indicated that 67% (489/729) were non–COVID-19 cases. Table 3 summarizes the COVID-19 mHealth-supported syndrome assessment and laboratory test results categorized by months and COVID-19 results.

    Table 3. COVID-19 mHealth-supported syndrome assessment and laboratory test results by months and test results (N=884).
    Month and yearCOVID-19 laboratory test resultCOVID-19 mHealth-supported syndrome assessment

    		Non–COVID-19 (n=798), n (%)COVID-19 (n=86), n (%)Non–COVID-19 (n=729), n (%)COVID-19 (n=155), n (%)
    December 2021252 (32)32 (37)239 (33)45 (29)
    January 2022544 (68)54 (63)489 (67)109 (70)
    February 20222 (0.25)0 (0)1 (0.14)1 (1)

    Agreement Between mHealth-Supported Syndrome Assessment and Laboratory Test Results

    The observed agreement between the mHealth-supported syndrome assessment and laboratory results was 0.89 and Cohen kappa for the hypothetical probability of chance agreement was 0.54. 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; Table 4).

    Table 4. Distribution of participants by mHealth-supported syndrome assessment and test results for COVID-19 using mHealth categories.
    Laboratory test resultsmHealth-supported syndrome assessment
    COVID-19Non–COVID-19Total
    COVID-19721486P1. = 0.097
    Non–COVID-1983715798P2. = 0.902
    Total155729884

    		P.1 = 0.175P.2 = 0.824

    ROC analysis

    Figure 3 presents the ROC analysis in a one-to-one square. The area under the curve captures the relationship between the sensitivity and specificity of the COVID-19 test results and reflects the performance of the method used. The curve follows the left-hand border and the top border of the ROC space, indicating an acceptable level of accuracy. The ROC curve shows that the predicted area under the ROC curve for COVID-9 is 0.87 for the mHealth-supported syndrome assessment results compared to the laboratory test results. This indicates the good diagnostics performance of the syndrome assessment method used.

    ‎
    Figure 3. Receiver operating characteristic (ROC) curve.

    Principal Findings

    In this study, we evaluated the potential use of mHealth-supported active syndrome surveillance for COVID-19 early case finding. Interviewing a total of 2741 adults through the mHealth platform, we found 50% (1371/2741) had COVID-19 symptoms at least once, and 64.5% (884/2741) had laboratory test results recorded in facility-based registries. The syndrome assessment model had an optimal likelihood cut-off point sensitivity and specificity of 46% and 98%, respectively, with an area under the ROC curve of 0.87 and a moderate level of agreement between the mHealth-supported syndrome assessment and the laboratory-confirmed COVID-19 test results (κ=0.54).

    One of the efficient methods for addressing epidemic or pandemic outbreaks is the use of health information technologies, such as mHealth, which facilitate remote communication. To manage and regulate the recently emerged COVID-19 pandemic, studies are underway to assess the functionalities of different digital health technologies. This study described the use of mHealth-supported active surveillance for COVID-19 early case findings by evaluating the agreement between the mHealth-supported syndrome assessment and laboratory-confirmed COVID-19 test results. The outcomes revealed a moderate level of agreement between the results of the COVID-19 syndrome assessment supported by mHealth and the laboratory test results, at 89%, with a kappa value of 0.54. This suggests that mHealth could be a potential alternative to the standard laboratory-based confirmatory diagnosis to find COVID-19 cases. Previous studies have shown that mHealth technologies could have significant contributions to self-care for patients with COVID-19 [35] and the dissemination of COVID-19–related information [36,37]. As mHealth and other digital health technologies are yet at an early stage of development in Africa, the capacity and readiness of each country to effectively adopt, implement, and scale up digital health interventions require due diligence [38-42].

    The findings of this study show that the mHealth-supported COVID-19 syndrome assessment model has an optimal likelihood cut-off point sensitivity and specificity when compared with the laboratory-based tests. The ROC curve indicates the good diagnostics performance of the mHealth syndrome assessment model. The study used a holistic syndrome assessment approach, based on the national guidelines, to examine and interpret the COVID-19 status of individuals participating in the study. Previous studies show that syndromic diagnosis of COVID-19 based on a single symptom cannot accurately identify individuals who might have the virus, and hence, investigating cases through combinations of syndromes along with additional information, such as recent contacts, travel history, or vaccination status, is necessary [43-45]. Studies show that although digital health–enabled communication may not be as effective as in-person communication, it represents a safe and efficient alternative to collecting evidence-based medical history, especially during the COVID-19 period, when in-person care cannot be provided [46].

    Investigating the potential use of mHealth-supported active syndrome surveillance for COVID-19 early case finding in Ethiopia, this study provides important insights. Future studies can further explore how digital health technologies can be used for the early identification of emerging infectious diseases, reducing their transmissions, as well as monitoring and mitigating their undue impact. Our findings inform the scientific community about how mHealth can be adapted for health system responses and the implementation challenges and opportunities within a resource-constrained country context. Scientific evidence regarding the potential use of such digital health technologies and their interrelated challenges is important for guiding policy and practice, especially in countries that have not yet fully embraced digital health interventions.

    Study Limitations

    This research has certain limitations. The response rate of the participants was not as anticipated. Because the survey was conducted over the phone, only those who had cell phones at the time of data collection were included in the survey, which limits the generalizability of our findings. Interrupted call connectivity, inconsistency between the participants’ responses and the data in facility registries, and participants undergoing COVID-19 testing for reasons other than syndromes (eg, travel health certificates) were some of the shortcomings in the data collection process. To mitigate these issues, only clear and consistent data were included and used in the analysis and interpretation of the study.

    Conclusions

    In this study, the level of agreement between the mHealth-supported syndrome assessment results and the actual laboratory-confirmed COVID-19 results 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 at an earlier stage in hard-to-reach communities and to advise patients on self-care and disease management in a cost-effective way. The findings show that the mHealth platform is valid for COVID-19 surveillance in Ethiopia, where health infrastructures are limited. Given the growing digital health landscape, the findings of our study offer valuable insight for guiding future research efforts in developing and integrating digital health into continuous active surveillance of emerging infectious diseases.

    Acknowledgments

    This research received funding from Addis Ababa University through the thematic grant scheme. Additional support was obtained from the National Institute for Health Research (NIHR) Global Health Research Unit on Health System Strengthening in Sub-Saharan Africa, King’s College London (GHRU 16/136/54) using UK aid from the UK Government to support global health research, and the World Bank grant (P151847-IDA57940) supporting the Center for Innovative Drug Development and Therapeutic Trials (CDT)-Africa. The authors are thankful to CDT-Africa in Addis Ababa University for the successful coordination of this work. The authors are also grateful to the research team of “the early case finding and population-level impact and control in Ethiopia (EPIC)” study.

    All authors declared that they had insufficient or no funding to support open access publication of this manuscript, including from affiliated organizations or institutions, funding agencies, or other organizations. JMIR Publications provided article processing fee (APF) support for the publication of this article.

    Data Availability

    The data set supporting the conclusions of this study is included within the paper. Any additional material can be obtained upon reasonable request.

    Authors' Contributions

    HB implemented the study, analyzed the data, and developed the first draft of the manuscript. AF and TM implemented and supervised the study, supported the data analysis, and revised the manuscript. All authors approved the final version for publication.

    Conflicts of Interest

    None declared.

    1. Global Burden of Disease 2021 Health Financing Collaborator Network. Global investments in pandemic preparedness and COVID-19: development assistance and domestic spending on health between 1990 and 2026. Lancet Glob Health. Mar 2023;11(3):e385-e413. [CrossRef] [Medline]
    2. Fauci A, Folkers G. Pandemic preparedness and response: lessons from COVID-19. J Infect Dis. Apr 10, 2023:jiad095. [CrossRef] [Medline]
    3. Morsy H, Balma L, Mukasa AN. 'Not a good time': assessing the economic impact of COVID-19 in Africa using a macro-micro simulation approach. Afr Dev Rev. Apr 18, 2021;33(Suppl 1):S17-S30. [FREE Full text] [CrossRef] [Medline]
    4. Bitanihirwe BKY, Ssewanyana D. The health and economic burden of the coronavirus in sub-Saharan Africa. Glob Health Promot. Mar 10, 2021;28(1):70-74. [CrossRef] [Medline]
    5. Atim MG, Kajogoo VD, Amare D, Said B, Geleta M, Muchie Y, et al. COVID-19 and health sector development plans in Africa: the impact on maternal and child health outcomes in Uganda. RMHP. Oct 2021;Volume 14:4353-4360. [CrossRef]
    6. Salyer SJ, Maeda J, Sembuche S, Kebede Y, Tshangela A, Moussif M, et al. The first and second waves of the COVID-19 pandemic in Africa: a cross-sectional study. The Lancet. Apr 2021;397(10281):1265-1275. [CrossRef]
    7. Kawuki J, Chan PS, Fang Y, Chen S, Mo PKH, Wang Z. Knowledge and practice of personal protective measures against COVID-19 in Africa: systematic review. JMIR Public Health Surveill. May 16, 2023;9:e44051. [FREE Full text] [CrossRef] [Medline]
    8. WHO coronavirus (COVID-19) dashboard. World Health Organization. URL: https://covid19.who.int/ [accessed 2023-08-09]
    9. Mohammed H, Oljira L, Roba KT, Yimer G, Fekadu A, Manyazewal T. Containment of COVID-19 in Ethiopia and implications for tuberculosis care and research. Infect Dis Poverty. Sep 16, 2020;9(1):131. [FREE Full text] [CrossRef] [Medline]
    10. Chilot D, Woldeamanuel Y, Manyazewal T. COVID-19 burden on HIV patients attending antiretroviral therapy in Addis Ababa, Ethiopia: a multicenter cross-sectional study. Front Med (Lausanne). Mar 2, 2022;9:741862. [FREE Full text] [CrossRef] [Medline]
    11. Chilot D, Woldeamanuel Y, Manyazewal T. Real-time impact of COVID-19 on clinical care and treatment of patients with tuberculosis: a multicenter cross-sectional study in Addis Ababa, Ethiopia. Ann Glob Health. 2021;87(1):109. [FREE Full text] [CrossRef] [Medline]
    12. Hasen AA, Seid AA, Mohammed AA. Anxiety and stress among healthcare professionals during COVID-19 in Ethiopia: systematic review and meta-analysis. BMJ Open. Feb 07, 2023;13(2):e070367. [FREE Full text] [CrossRef] [Medline]
    13. Deressa W, Worku A, Abebe W, Gizaw M, Amogne W. Risk perceptions and preventive practices of COVID-19 among healthcare professionals in public hospitals in Addis Ababa, Ethiopia. PLoS One. Jun 25, 2021;16(6):e0242471. [FREE Full text] [CrossRef] [Medline]
    14. Umer A, Abdella K, Tekle Y, Debebe A, Manyazewal T, Yuya M, et al. Community engagement in the fight against COVID-19: knowledge, attitude, and prevention practices among Dire Dawa residents, Eastern Ethiopia. Front Public Health. Nov 30, 2021;9:9. [CrossRef]
    15. Mose A, Zewdie A, Sahle T. Pregnant women's knowledge, attitude, and practice towards COVID-19 infection prevention in Ethiopia: a systematic review and meta-analysis. PLoS One. Oct 26, 2022;17(10):e0276692. [FREE Full text] [CrossRef] [Medline]
    16. Mengesha D, Manyazewal T, Woldeamanuel Y. Five-year trend analysis of tuberculosis in Bahir Dar, Northwest Ethiopia, 2015-2019. Int J Mycobacteriol. 2021;10(4):437-441. [FREE Full text] [CrossRef] [Medline]
    17. Gebrie D, Manyazewal T, Ejigu DA, Makonnen E. Metformin-insulin versus metformin-sulfonylurea combination therapies in type 2 diabetes: a comparative study of glycemic control and risk of cardiovascular diseases in Addis Ababa, Ethiopia. DMSO. Jul 2021;Volume 14:3345-3359. [CrossRef]
    18. Manyazewal T, Sisay Z, Biadgilign S, Abegaz W. Hepatitis B and hepatitis C virus infections among antiretroviral-naive and -experienced HIV co-infected adults. J Med Microbiol. May 2014;63(Pt 5):742-747. [CrossRef] [Medline]
    19. Gile PP, van de Klundert J, Buljac-Samardzic M. Strategic human resource management and performance in public hospitals in Ethiopia. Front Public Health. Oct 20, 2022;10:915317. [FREE Full text] [CrossRef] [Medline]
    20. Manyazewal T, Matlakala M. Implementing health care reform: implications for performance of public hospitals in central Ethiopia. J Glob Health. Jun 2018;8(1):010403. [FREE Full text] [CrossRef] [Medline]
    21. Manyazewal T, Oosthuizen MJ, Matlakala MC. Proposing evidence-based strategies to strengthen implementation of healthcare reform in resource-limited settings: a summative analysis. BMJ Open. Sep 20, 2016;6(9):e012582. [FREE Full text] [CrossRef] [Medline]
    22. Manyazewal T, Woldeamanuel Y, Blumberg HM, Fekadu A, Marconi VC. The potential use of digital health technologies in the African context: a systematic review of evidence from Ethiopia. NPJ Digit Med. Aug 17, 2021;4(1):125. [FREE Full text] [CrossRef] [Medline]
    23. Manyazewal T, Woldeamanuel Y, Holland DP, Fekadu A, Marconi VC. Effectiveness of a digital medication event reminder and monitor device for patients with tuberculosis (SELFTB): a multicenter randomized controlled trial. BMC Med. Sep 28, 2022;20(1):310. [FREE Full text] [CrossRef] [Medline]
    24. Manyazewal T, Woldeamanuel Y, Getinet T, Hoover A, Bobosha K, Fuad O, et al. Patient-reported usability and satisfaction with electronic medication event reminder and monitor device for tuberculosis: a multicentre, randomised controlled trial. EClinicalMedicine. Feb 2023;56:101820. [FREE Full text] [CrossRef] [Medline]
    25. Cucciniello M, Petracca F, Ciani O, Tarricone R. Development features and study characteristics of mobile health apps in the management of chronic conditions: a systematic review of randomised trials. NPJ Digit Med. Oct 05, 2021;4(1):144. [FREE Full text] [CrossRef] [Medline]
    26. Katapally TR, Bhawra J, Leatherdale ST, Ferguson L, Longo J, Rainham D, et al. The SMART study, a mobile health and citizen science methodological platform for active living surveillance, integrated knowledge translation, and policy interventions: longitudinal study. JMIR Public Health Surveill. Mar 27, 2018;4(1):e31. [FREE Full text] [CrossRef] [Medline]
    27. Morjaria P, Bastawrous A, Murthy GVS, Evans J, Sagar MJ, Pallepogula DR, et al. Effectiveness of a novel mobile health (Peek) and education intervention on spectacle wear amongst children in India: Results from a randomized superiority trial in India. EClinicalMedicine. Nov 2020;28:100594. [FREE Full text] [CrossRef] [Medline]
    28. Rinke de Wit TF, Janssens W, Antwi M, Milimo E, Mutegi N, Marwa H, et al. Digital health systems strengthening in Africa for rapid response to COVID-19. Front Health Serv. Nov 28, 2022;2:987828. [FREE Full text] [CrossRef] [Medline]
    29. Getachew E, Adebeta T, Muzazu SGY, Charlie L, Said B, Tesfahunei HA, et al. Digital health in the era of COVID-19: reshaping the next generation of healthcare. Front Public Health. Feb 15, 2023;11:942703. [FREE Full text] [CrossRef] [Medline]
    30. Nachega JB, Leisegang R, Kallay O, Mills EJ, Zumla A, Lester RT. Mobile health technology for enhancing the COVID-19 response in Africa: a potential game changer? Am J Trop Med Hyg. Jul 2020;103(1):3-5. [FREE Full text] [CrossRef] [Medline]
    31. Kamulegeya LH, Bwanika JM, Musinguzi D, Bakibinga P. Continuity of health service delivery during the COVID-19 pandemic: the role of digital health technologies in Uganda. Pan Afr Med J. May 20, 2020;35(Suppl 2):43. [FREE Full text] [CrossRef] [Medline]
    32. Chereka AA, Demsash AW, Ngusie HS, Kassie SY. Digital health literacy to share COVID-19 related information and associated factors among healthcare providers worked at COVID-19 treatment centers in Amhara region, Ethiopia: A cross-sectional survey. Inform Med Unlocked. 2022;30:100934. [FREE Full text] [CrossRef] [Medline]
    33. Nkengasong JN, Mankoula W. Looming threat of COVID-19 infection in Africa: act collectively, and fast. The Lancet. Mar 2020;395(10227):841-842. [CrossRef]
    34. Kovács A, Palásti P, Veréb D, Bozsik B, Palkó A, Kincses ZT. The sensitivity and specificity of chest CT in the diagnosis of COVID-19. Eur Radiol. May 2021;31(5):2819-2824. [FREE Full text] [CrossRef] [Medline]
    35. Montazeri M, Galavi Z, Ahmadian L. The role of mobile health in prevention, diagnosis, treatment and self-care of COVID-19 from the healthcare professionals' perspectives. Digit Health. May 02, 2023;9:20552076231171969. [FREE Full text] [CrossRef] [Medline]
    36. Zamberg I, Manzano S, Posfay-Barbe K, Windisch O, Agoritsas T, Schiffer E. A mobile health platform to disseminate validated institutional measurements during the COVID-19 outbreak: utilization-focused evaluation study. JMIR Public Health Surveill. Apr 14, 2020;6(2):e18668. [FREE Full text] [CrossRef] [Medline]
    37. Hajizadeh A, Monaghesh E. Telehealth services support community during the COVID-19 outbreak in Iran: activities of Ministry of Health and Medical Education. Inform Med Unlocked. 2021;24:100567. [FREE Full text] [CrossRef] [Medline]
    38. Manyazewal T, Ali MK, Kebede T, Magee MJ, Getinet T, Patel SA, et al. Mapping digital health ecosystems in Africa in the context of endemic infectious and non-communicable diseases. NPJ Digit Med. May 26, 2023;6(1):97. [FREE Full text] [CrossRef] [Medline]
    39. Marcolino MS, Oliveira JAQ, D'Agostino M, Ribeiro AL, Alkmim MBM, Novillo-Ortiz D. The impact of mHealth interventions: systematic review of systematic reviews. JMIR Mhealth Uhealth. Jan 17, 2018;6(1):e23. [FREE Full text] [CrossRef] [Medline]
    40. Manyazewal T, Woldeamanuel Y, Fekadu A, Holland DP, Marconi VC. Effect of digital medication event reminder and monitor-observed therapy vs standard directly observed therapy on health-related quality of life and catastrophic costs in patients with tuberculosis: a secondary analysis of a randomized clinical trial. JAMA Netw Open. Sep 15, 2022;5(9):e2230509. [FREE Full text] [CrossRef] [Medline]
    41. Getachew E, Woldeamanuel Y, Manyazewal T. Digital health interventions in the clinical care and treatment of tuberculosis and HIV in central Ethiopia: an initial provider perceptions and acceptability study using the unified theory of acceptance and use of technology model. Int J Mycobacteriol. 2022;11(1):1-9. [FREE Full text] [CrossRef] [Medline]
    42. Manyazewal T, Woldeamanuel Y, Blumberg HM, Fekadu A, Marconi VC. The fight to end tuberculosis must not be forgotten in the COVID-19 outbreak. Nat Med. Jun 12, 2020;26(6):811-812. [FREE Full text] [CrossRef] [Medline]
    43. Struyf T, Deeks J, Dinnes J, Takwoingi Y, Davenport C, Leeflang M, et al. Cochrane COVID-19 Diagnostic Test Accuracy Group. Signs and symptoms to determine if a patient presenting in primary care or hospital outpatient settings has COVID-19. Cochrane Database Syst Rev. May 20, 2022;5(5):CD013665. [FREE Full text] [CrossRef] [Medline]
    44. Wu Z, McGoogan JM. Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: summary of a report of 72 314 cases from the Chinese Center for Disease Control and Prevention. JAMA. Feb 24, 2020:1239-1242. [CrossRef] [Medline]
    45. Gebrie D, Getnet D, Manyazewal T. Efficacy of remdesivir in patients with COVID-19: a protocol for systematic review and meta-analysis of randomised controlled trials. BMJ Open. Jun 04, 2020;10(6):e039159. [CrossRef]
    46. Verma N, Buch B, Taralekar R, Acharya S. Diagnostic concordance of telemedicine as compared with face-to-face care in primary health care clinics in rural India: randomized crossover trial. JMIR Form Res. Jun 23, 2023;7:e42775. [FREE Full text] [CrossRef] [Medline]

    ‎
    CDT: Center for Innovative Drug Development and Therapeutic Trials
    EPIC: early case finding and population-level impact and control in Ethiopia
    mHealth: mobile health
    ROC: receiver operating characteristic

    Edited by T de Azevedo Cardoso, A Mavragani; submitted 13.10.22; peer-reviewed by A Anteneh, Y Jeem, S Pesälä; comments to author 17.04.23; revised version received 17.05.23; accepted 30.06.23; published 28.08.23.

    Copyright

    ©Haileleul Bisrat, Tsegahun Manyazewal, Abebaw Fekadu. Originally published in the Interactive Journal of Medical Research (https://www.i-jmr.org/), 28.08.2023.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Interactive Journal of Medical Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.i-jmr.org/, as well as this copyright and license information must be included.