The importance of community health workers as frontline responders during the COVID-19 pandemic, Somalia, 2020-2021.

Introduction: We examined the contribution of community health workers as frontline responders for the community-based surveillance in Somalia during the first year of the COVID-19 pandemic for detection of COVID-19 cases and identification of contacts. Methods: We retrieved COVID-19 surveillance data from 16 March 2020 to 31 March 2021 from the health ministry's central database. These data were collected through community health workers, health facilities or at the points of entry. We compared the number of suspected COVID-19 cases detected by the three surveillance systems and the proportion that tested positive using the chi-squared test. We used logistic regression analysis to assess association between COVID-19 infection and selected variables. Results: During the study period, 154,004 suspected cases of COVID-19 were detected and tested, of which 10,182 (6.6%) were positive. Of the notified cases, 32.7% were identified through the community-based surveillance system, 54.0% through the facility-based surveillance system, and 13.2% at points of entry. The positivity rate of cases detected by the community health workers was higher than that among those detected at health facilities (8.6% versus 6.4%; p < 0.001). The community health workers also identified more contacts than those identified through the facility-based surveillance (13,279 versus 1,937; p < 0.001). The odds of COVID-19 detection generally increased by age. Community-based surveillance and health facility-based surveillance had similar odds of detecting COVID-19 cases compared with the points-of-entry surveillance (aOR: 7.0 (95% CI: 6.4, 7.8) and aOR: 7.5 (95% CI: 6.8, 8.3), respectively). Conclusion: The community health workers proved their value as first responders to COVID-19. They can be effective in countries with weak health systems for targeted community surveillance in rural and remote areas which are not covered by the facility-based surveillance system.

Evaluation of the electronic Early Warning and Response Network (EWARN) system in Somalia, 2017-2020.

BACKGROUND: In 2008, Somalia introduced an electronic based Early Warning Alert and Response Network (EWARN) for real time detection and response to alerts of epidemic prone diseases in a country experiencing a complex humanitarian situation. EWARN was deactivated between 2008 to 2016 due to civil conflict and reactivated in 2017 during severe drought during a cholera outbreak. We present an assessment of the performance of the EWARN in Somalia from January 2017 to December 2020, reflections on the successes and failures, and provide future perspectives for enhancement of the EWARN to effectively support an Integrated Disease Surveillance and Response strategy. METHODS: We described geographical coverage of the EWARN, system attributes, which included; sensitivity, flexibility, timeliness, data quality (measured by completeness), and positive predictive value (PPV). We tested for trends of timeliness of submission of epidemiological reports across the years using the Cochran-Mantel-Haenszel stratified test of association. RESULTS: By December 2020, all 6 states and the Banadir Administrative Region were implementing EWARN. In 2017, only 24.6% of the records were submitted on time, but by 2020, 96.8% of the reports were timely (p < 0.001). Completeness averaged < 60% in all the 4 years, with the worst-performing year being 2017. Overall, PPV was 14.1%. Over time, PPV improved from 7.1% in 2017 to 15.4% in 2019 but declined to 9.7% in 2020. Alert verification improved from 2.0% in 2017 to 52.6% by 2020, (p < 0.001). In 2020, EWARN was enhanced to facilitate COVID-19 reporting demonstrating its flexibility to accommodate the integration of reportable diseases. CONCLUSIONS: During the past 4 years of implementing EWARN in Somalia, the system has improved significantly in timeliness, disease alerts verification, and flexibility in responding to emerging disease outbreaks, and enhanced coverage. However, the system is not yet optimal due to incompleteness and lack of integration with other systems suggesting the need to build additional capacity for improved disease surveillance coverage, buttressed by system improvements to enhance data quality and integration.