New Mixed Methods Approach for Monitoring Community Perceptions of Ebola and Response Efforts in the Democratic Republic of the Congo

Autor: Gnakub N. Soke, Christine E. Prue, Lucia Robles Dios, Christina Craig, Vivienne Walz, Ialijaona Voahary, Eva Erlach, Molly R. Kurnit, Daiva Yee, Giulia Earle-Richardson, Ombretta Baggio, Cheick Abdoulaye Camara
Rok vydání: 2021
Předmět:
Zdroj: Global Health: Science and Practice
ISSN: 2169-575X
Popis: The Red Cross community feedback system enabled rapid collection and analysis of extensive verbal feedback during an Ebola outbreak in eastern DRC. Using this information, Ebola response leaders adapted strategies to address community concerns. In an epidemic, community feedback is critical to ensure that response strategies are accepted and appropriate.
Key Findings Ebola-affected communities in the eastern Democratic Republic of the Congo had questions about the outbreak, doubts about the reality of Ebola, and concerns about health care and the Ebola vaccination program.In peak outbreak areas, beliefs that Ebola response teams were stealing organs and bodies declined after burial teams introduced transparent body bags. Similarly, calls for making vaccination “more fair” declined after Ebola vaccination eligibility was expanded. Key Implications The model for recording community feedback provided rapid, ongoing comments from neighborhoods and villages where Ebola response activities were underway, allowing people to share perceptions, questions, and concerns in their own words. Local volunteers were central to the model's success because they were involved in the rapid collection, coding, and interpretation of feedback and applying it to response activities.Collecting continuous community feedback throughout an emergency response enables response teams to answer questions, consider suggestions, and adapt interventions to better meet community needs and preferences. Local health departments everywhere could employ this approach to enhance community engagement.
Background: Efforts to contain the spread of Ebola in the eastern Democratic Republic of the Congo (DRC) during the 2018–2020 epidemic faced challenges in gaining community trust and participation. This affected implementation of community alerts, early isolation, contact tracing, vaccination, and safe and dignified burials. To quickly understand community perspectives and improve community engagement, collaborators from the DRC Red Cross, the International Federation of the Red Cross, and the U.S. Centers for Disease Control and Prevention explored a new method of collecting, coding, and quickly analyzing community feedback. Methods: Over 800 DRC Red Cross local volunteers recorded unstructured, free-text questions and comments from community members during community Ebola awareness activities. Comments were coded and analyzed using a text-coding system developed by the collaborators. Coded comments were then aggregated and qualitatively grouped into major themes, and time trends were examined. Results: Communities reported a lack of information about the outbreak and the response, as well as concerns about the Ebola vaccination program and health care quality. Some doubted that Ebola was real. The response used the feedback to revise some community engagement approaches. For example, 2 procedural changes that were followed by drops in negative community responses were: using transparent body bags, which allayed fears that bodies or organs were being stolen, and widening the eligibility criteria for Ebola vaccination, which addressed concerns that selectively vaccinating individuals within Ebola-affected communities was unfair. Discussion: This system is unique in that unstructured feedback collected by local volunteers in the course of their work was rapidly coded, analyzed, and given to health authorities for use in making course corrections throughout the response. It provides a platform for local voices to be heard throughout an emergency response and provides a mechanism for assessing the effects of program adjustments on community sentiments.
Databáze: OpenAIRE