INTRODUCTION
Refugee settings may increase the risk of SARS-CoV-2 infection and death, yet data on the response to the pandemic in these populations is scarce.
METHODS
We describe interventions to mitigate SARS-CoV-2 transmission in Dadaab Refugee Camp Complex, Kenya and performed descriptive analyses using March 2020 to December 2022 data from Kenya's national SARS-CoV-2 repository and line list of positive cases maintained by United Nations High Commissioner for Refugees (UNHCR). We calculated case fatality rates (CFR) and attack rates per 100,000 (AR) using the 2019 national census and population statistics from UNHCR and compared them to national figures.
RESULTS
SARS-CoV-2 infection was first reported in April and May 2020, among host community members and refugees respectively. Of 964 laboratory-confirmed cases, 700 (72.6 %) were refugees. The AR was 82.7 (95 % CI 72.6–92.8) for host community members, 228.3 (95 % CI 211.3–245.4) for refugees and 721.1 (95 % CI 718.7–723.5) nationally. The CFR was 1.5 % (95 % CI 0.15–3.18) for host community members, 1.76 % (95 % CI 1.71–1.80) nationally and 7.4 % (95 % CI 5.4–9.4) for refugees.
Mitigation measures implemented by the Government of Kenya, UNHCR and partners during the pandemic included multisectoral coordination, movement restrictions, mass gathering bans, and health promotion. Social distancing, symptom screening and mandatory mask usage were enforced during mass gatherings. Testing capacity was bolstered, quarantine and isolation facilities established, and vaccination initiated.
CONCLUSIONS
Despite a low AR and UNHCR's swift and comprehensive response, refugees' CFR was high, underscoring their vulnerability and need for targeted interventions during epidemic responses.
This study evaluated an early warning, alert and response system for a crisis-affected population in Doolo zone, Somali Region, Ethiopia, in 2019–2021, with a history of epidemics of outbreak-prone diseases. To adequately cover an area populated by a semi-nomadic pastoralist, or livestock herding, population with sparse access to healthcare facilities, the surveillance system included four components: health facility indicator-based surveillance, community indicator- and event-based surveillance, and alerts from other actors in the area. This evaluation described the usefulness, acceptability, completeness, timeliness, positive predictive value, and representativeness of these components.
METHODS
We carried out a mixed-methods study retrospectively analysing data from the surveillance system February 2019–January 2021 along with key informant interviews with system implementers, and focus group discussions with local communities. Transcripts were analyzed using a mixed deductive and inductive approach. Surveillance quality indicators assessed included completeness, timeliness, and positive predictive value, among others.
RESULTS
1010 signals were analysed; these resulted in 168 verified events, 58 alerts, and 29 responses. Most of the alerts (46/58) and responses (22/29) were initiated through the community event-based branch of the surveillance system. In comparison, one alert and one response was initiated via the community indicator-based branch. Positive predictive value of signals received was about 6%. About 80% of signals were verified within 24 h of reports, and 40% were risk assessed within 48 h. System responses included new mobile clinic sites, measles vaccination catch-ups, and water and sanitation-related interventions. Focus group discussions emphasized that responses generated were an expected return by participant communities for their role in data collection and reporting. Participant communities found the system acceptable when it led to the responses they expected. Some event types, such as those around animal health, led to the community’s response expectations not being met.
CONCLUSIONS
Event-based surveillance can produce useful data for localized public health action for pastoralist populations. Improvements could include greater community involvement in the system design and potentially incorporating One Health approaches.
The Somali Region is one of the least developed regions of Ethiopia, with low coverage of healthcare services and recurrent disease outbreaks, floods, and malnutrition emergencies. MSF has been providing medical assistance in the Doolo Zone, Somali Region, since 2007. Following multiple disease outbreaks in 2017, MSF shifted focus to work primarily on early outbreak detection and provision of a timely response. In 2019, MSF established a “Tea Team surveillance team”; this combines health facility data with that from community indicator- and event-based surveillance systems. Data are collected from 32 locations (17 surveillance only and 15 non-permanent mobile clinic sites), as well as alerts from other actors in Doolo Zone. We aimed to evaluate the usefulness of the data generated by these different components.
METHODS
We used a mixed methods approach. Description of the surveillance system, quantitative analysis of retrospective data between February 2019 and January 2021, and focus group discussions were the main methods used to evaluate usefulness, acceptability, and other surveillance attributes. Quantitative analyses were done using R software (R Core Team, 2014) while qualitative data analysis was performed with NVivo software (QSR International Pty Ltd, Australia).
ETHICS
Permission to conduct the study was obtained from the Somali Regional Health Bureau. This study was approved by the MSF Ethics Review Board.
RESULTS
Over 1200 signals were reported to the Tea Team surveillance system over the evaluation period, with the majority being reported via the community event-based surveillance (CEBS) component. There were a total of 31 responses conducted between February 2019 and January 2021. 22 (84.6%) originated from CEBS system signals, one (3.8%) was from the community indicator-based surveillance (CIBS) system, 2 (7.7%) were from health facility indicator-based surveillance (HFIBS), and 6 (23.1%) came from other event-based surveillance systems. Most responses were triggered by population movements, suspected measles, and suspected acute watery diarrhoea. No responses arose from acute jaundice syndrome signals. MSF staff found the “Tea Team surveillance system” to have higher acceptability in non-emergency situations, but indicated lower acceptability during a crisis, due to data processing times and rigidity of the HFIBS online database. The surveillance system has complex data management procedures leading to potential underreporting of signals and difficulties with routine data quality monitoring. Project staff considered the CEBS and CIBS components to be more flexible than HFIBS. The system was sufficiently flexible to integrate with Covid-19 surveillance.
CONCLUSION
The Tea Team surveillance system is a comprehensive and useful system to detect and respond to public health events in a pastoralist population. Simplification of the surveillance system and greater standardisation of the data management processes will increase the utility of the system.
CONFLICTS OF INTEREST
None declared