Understanding evidence ecosystems: What influences the production, translation, and use of modeled evidence in Burkina Faso, Nigeria, India, and Kenya? [version 2; peer review: 1 approved, 2 approved with reservations]

• Published in: Gates open research Vol. 7
• Main Authors: Habibou Fofana, Ali Sié, Moussa Ouédraogo, Moubassira Kagoné, Chinyere O. Mbachu, Obinna E. Onwujekwe, Latha Chilgod, Maulik Chokshi, Arun B. Nair, Tushar Mokashi, Abeba Taddese, Peter Muriuki, Apoorva Handigol, Leah Ewald
• Format: Journal Article
• Language: English
• Published: F1000 Research Ltd 01-10-2024
• Subjects: disease modeling; eng; evidence ecoystem; Evidence use; modeling; public health; public health disease modeling
• ISSN: 2572-4754

Background This study sought to document and understand facilitators and barriers to producing, translating, and using modeled evidence in decision-making in Burkina Faso, Nigeria, India, and Kenya. We explored researcher-decision-maker engagement mechanisms as key facilitators of evidence use, with a focus on knowledge brokers and boundary organizations. Methods The study used sequential mixed methods drawing on data collected from surveys and key informant interviews, complemented by a rapid desk review to map modeling activities and actors. The survey was conducted online while the qualitative research entailed in-depth interviews with modelers, knowledge brokers, and decision-makers working in a representative variety of health fields, organizations, and levels of government. This study was approved by Health Media Lab IRB (Institutional Review Board) in the United States and a local IRB in each study country and conducted between September 2021 and June 2022. Results Informants interviewed for this study described a range of factors that facilitate and inhibit the use of modeled evidence in public health decision-making at the individual, organizational, and environmental levels. Key themes included the capacity to produce, translate, and use modeled evidence; the timing and relevance of modeling outputs; the existence of communications channels between modelers and decision-makers; the strength of underlying data systems; the role of sustained funding; and the impact of global crises. Conclusion This study highlights the importance of taking an ecosystem approach to supporting modeling activities, considering individual, organizational, and environmental factors and how different actors and interact to inform the production, translation, and use of modeled evidence. Structured interaction that promotes dialogue, debate, and joint sense making between the producers and users of evidence is critical to informing and influencing the use of evidence in decision-making.