Background: Our earlier work identified discordance between the way in which evidence on children’s health is produced and its intended application including: (i) between the focus of policymakers and researchers on more clinical measures of obesity/overweight (namely a focus on body mass index) in contrast to the need perceived by some practitioners and those with lived experience for a greater focus on broader markers of child health; (ii) between the questions addressed within a typical systematic review, which are narrow and often focus on a single factor, and the complexity of child health; (iii) a tendency for theoretical models, such as systems-based logic models, to be absent of empirical evidence, limiting their application in decision-making; and (iv) a need for evidence on the likely effectiveness of a given intervention in a local setting.
Objective: Examine the utility of a "whole outcome and whole system" approach to creating evidence on child health.
Methods and approach: Firstly, we coproduce an understanding of the "whole outcome whole system" as applied to child health through building on an existing systems-based logic model and identifying key pathways of interest. Secondly, we examine evidence for and against putative pathways within the model through secondary data analyses of existing data sources. Thirdly, we use systematic mapping techniques to identify interventions that can enhance health-promoting pathways and disrupt health-inhibiting pathways. In addition, methodologically novel techniques are applied to systematic review-level evidence to explore when interventions are better suited to some UK contexts than others. Finally, we evaluate the utility of our approaches to create whole system whole outcome evidence.
Results: In this presentation, we describe the steps taken to prioritize which factors to explore using coproduction and the ways in which competing priorities were reflected in the model. We also show how the assumptions within the model were challenged through secondary data analyses, leading to further prioritization. The resulting model, which reflects evidence on risk as well as interventions, is presented, and we reflect on the success of our approaches, particularly on contextualizing evidence.
Conclusions: Decision-makers value evidence that addresses the complexity of decision-making.