Lessons learned from COVID-19 modelling efforts for policy decision-making in lower- and middle-income countries.

Autor:	Owek CJ; Department of Public and Global Health, University of Nairobi, Nairobi, Kenya., Guleid FH; KEMRI-Wellcome Trust Research Programme Nairobi, Nairobi, Kenya., Maluni J; KEMRI-Wellcome Trust Research Programme Nairobi, Nairobi, Kenya., Jepkosgei J; KEMRI-Wellcome Trust Research Programme Nairobi, Nairobi, Kenya., Were VO; Data Synergy and Evaluation Unit, African Population and Health Research Center, Nairobi, Kenya., Sim SY; World Health Organization, Geneva, Switzerland., Cw Hutubessy R; World Health Organization, Geneva, Switzerland., Hagedorn BL; Institute for Disease Modelling, Bill & Melinda Gates Foundation, Seattle, Washington, USA., Nzinga J; Health Economics Research Unit, KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya., Oliwa J; Health Services Unit, KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya joliwa@kemri-wellcome.org.
Jazyk:	angličtina
Zdroj:	BMJ global health [BMJ Glob Health] 2024 Nov 08; Vol. 9 (11). Date of Electronic Publication: 2024 Nov 08.
DOI:	10.1136/bmjgh-2024-015247
Abstrakt:	Introduction: The COVID-19 pandemic had devastating health and socioeconomic effects, partly due to policy decisions to mitigate them. Little evidence exists of approaches that guided decisions in settings with limited pre-pandemic modelling capacity. We thus sought to identify knowledge translation mechanisms, enabling factors and structures needed to effectively translate modelled evidence into policy decisions. Methods: We used convergent mixed methods in a participatory action approach, with quantitative data from a survey and qualitative data from a scoping review, in-depth interviews and workshop notes. Participants included researchers and policy actors involved in COVID-19 evidence generation and decision-making. They were mostly from lower- and middle-income countries (LMICs) in Africa, Southeast Asia and Latin America. Quantitative and qualitative data integration occurred during data analysis through triangulation and during reporting in a narrative synthesis. Results: We engaged 147 researchers and 57 policy actors from 28 countries. We found that the strategies required to use modelled evidence effectively include capacity building of modelling expertise and communication, improved data infrastructure, sustained funding and dedicated knowledge translation platforms. The common knowledge translation mechanisms used during the pandemic included policy briefs, face-to-face debriefings and dashboards. Some enabling factors for knowledge translation comprised solid relationships and open communication between researchers and policymakers, credibility of researchers, co-production of policy questions and embedding researchers in policymaking spaces. Barriers included competition among modellers, negative attitude of policymakers towards research, political influences and demand for quick outputs. Conclusion: We provide a contextualised understanding of knowledge translation for LMICs during the COVID-19 pandemic. Furthermore, we share key lessons on how knowledge translation from mathematical modelling complements the broader learning agenda related to pandemic preparedness and long-term investments in evidence-to-policy translation. Our findings led to the co-development of a knowledge translation framework useful in various settings to guide decision-making, especially for public health emergencies. Competing Interests: Competing interests: No conflicts other than the The Bill and Melinda Gates Foundation paid for travel expenses to attend working session during which the framework was developed. (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ.)
Databáze:	MEDLINE
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