Childhood Obesity Evidence Base Project: Building Translational Capacity through Meta-Analytic Methods

Abstract

Purpose: The purpose of this article is to demonstrate the need for and utility of using a taxonomic approach for evidence aggregation and meta-analyses, with focus on prevention and reduction of childhood obesity in very young children. As evidence has been generated through heterogeneous efforts, it is important that the field makes use of all available evidence to learn what works, for who, and in what circumstances. Methods: The Childhood Obesity Evidence Base (COEB) project conducted a taxonomic meta-analysis, using Grounded Theory to code elements present in reports of existing studies and initiatives, of diverse design and evaluation approaches, which were then mapped onto the levels of the socio-ecologic model. This article is the fourth in a series that describes the COEB project overall. It discusses both generally and specifically how taxonomies contribute to traditional meta-analytic methods, what questions can and cannot be answered, the method's contribution to translational (implementation) capacity, and ability to inform future efforts. Results: The COEB project illustrates how the taxonomic meta-analytic approach broadens the evidence base, increases translational capacity for effective intervention components, and evaluates the influence of contextual elements to inform future initiatives. How the method is used to establish associations between varying intervention components, contextual elements, and outcomes is discussed. Conclusions: Taxonomies generated through this process can be used for meta-analysis, serving to generate topic-specific questions associated with intervention approaches and outcomes in context, which is adjunctive to traditional meta-analytic methods and can inform public health approaches.

The evolution of systematic evidence reviews: Past and future developments and their implications for policy analysis

“…This approach is, in its coding procedure, inspired by the DMM approach and uses meta‐regression for the final analyses. The approach is presented in a series of articles detailing the rationale (Hedges et al, 2020) and utility for using a taxonomic approach (Young‐Hyman & Khan, 2020) as well as the process (King et al, 2020) and results of a case application (Scott‐Sheldon et al, 2020).…”