Abstract
Over the last four decades, Bangladesh has made considerable improvements in population health, this is in part due to the use of evidence to inform policymaking. This systematic review aims to better understand critical factors that have facilitated the diffusion of scientific evidence into multiple phases of health policymaking in Bangladesh. To do this an existing policy framework designed by Shiffman and Smith in 2007, was used to extract and synthesize data from selected policy analyses. This framework was used to ensure the content, context and actors involved with evidence-informed policymaking were considered in each case where research had helped shape a health policy. The ‘PRISMA Checklist’ was employed to design pre-specified eligibility criteria for the selection of information sources, search strategy, inclusion and exclusion criteria, and process of data extraction and synthesis. Through our systematic search conducted from February to May 2017, we initially identified 1859 articles; after removal of duplicates, followed by the screening of titles, abstracts and full-texts, 24 articles were included in the analysis. Health policy issues included the following topics: maternal and child health, tobacco control, reproductive health, infectious disease control and the impact and sustainability of knowledge translation platforms. Findings suggested that research evidence that could be used to meet key targets associated with the Millennium Development Goals (MDGs) were more likely to be considered as a political (and therefore policy) priority. Furthermore, avenues of engagement between research organizations and the government as well as collective action from civil-society organizations were important for the diffusion of evidence into policies. Through this article, it is apparent that the interface between evidence and policy formulation occurs when evidence is, disseminated by a cohesive policy-network with strong leadership and framed to deliver solutions for problems on both the domestic and global development agenda.
Highlights
Evidence-informed policymaking (EIPM) is the process of using research evidence in health policy to strengthen health systems to benefit the health of the wider population (World Health Organization, 2017)
The PRISMA checklist is an appropriate tool for systematic reviews designed to appraise and synthesize qualitative data related to health policy
Multi-country studies centred on the evaluation of stakeholder dialogues; the impact of Knowledge Translation Platforms based in LMICs; outcomes of donor initiated programmes; and smokeless tobacco control programmes (Behague et al, 2009; Bennett et al, 2012; El-Jardali et al, 2014; JacksonMorris et al, 2015; Shroff et al, 2015; Norton et al, 2016; Teerawattananon et al, 2016)
Summary
Evidence-informed policymaking (EIPM) is the process of using research evidence in health policy to strengthen health systems to benefit the health of the wider population (World Health Organization, 2017). Heightened attention to the issue occurred with the formation of the Alliance for Health Policy and Systems Research in 1999 and later in 2003 the World Health Organization released a report called ‘Knowledge for better health—a conceptual framework and foundation for health research systems’ (Pang et al, 2003; Hanney and Gonzalez-Block, 2017) Following this the World Health Assembly passed a resolution based on the ‘Mexico Statement on Health Research’. This resolution is centred on how low and middle income country (LMICs) can bridge the ‘know-do gap’ to create sustainable improvements for evidence-informed health systems (World Health Organization, 2005, 2017) Each of these movements are centred on the ‘facilitational model’, whereby policymakers are engaged with research at an early stage so that it is co-produced and more likely to be translated into policy (Cairney and Oliver, 2017). Over the past two decades, greater recognition has been given to the important role qualitative research has in understanding how social, political and economic networks interact to determine how scientific evidence is prioritized and translated into health policies (Saini and Shlonsky, 2012; Redman et al, 2015)
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