A systematic review of system dynamics and agent‐based obesity models: Evaluating obesity as part of the global syndemic

Abstract

Summary The problem of obesity has recently been reframed as part of the global syndemic—the co‐occurring, interacting pandemics of obesity, undernutrition, and climate change that are driven by common underlying societal drivers. System science modeling approaches may help clarify how these shared drivers operate and the best ways to address them. The objective of this paper was to determine to what extent existing agent‐based and system dynamics computational models of obesity provide insights into the shared drivers of the global syndemic. Peer‐reviewed studies published until July 2018 were identified from Scopus, Web of Science, and PubMed databases. Thirty‐eight studies representing 30 computational models were included. They show a growing use of system dynamics and agent‐based modeling in the past decade. They most often examined mechanisms and interventions in the areas of social network‐based influences on obesity, physiology and disease state mechanics, and the role of food and physical activity environments. Usefulness for identifying common drivers of the global syndemic was mixed; most models represented Western settings and focused on obesity determinants close to the person (eg, social circles, school settings, and neighborhood environments), with a relative paucity in models at mesolevel and macrolevel and in developing country contexts.

A systems map of the determinants of child health inequalities in England at the local level

“…The majority of published studies using system approaches to address child health have focused on healthy eating and obesity, with a paucity of studies using systems approaches to other child health outcomes [19,21,[26][27][28][29]. The current study seeks to extend the scope of a systems mapping approach beyond single child health outcomes to address inequalities in child health more broadly.…”

Agent-based modeling for migration and modern slavery research: a systematic review

“…As referenced earlier, to our knowledge, there are three reviews of migration ABMs relevant to the aim of this review, including Klabunde and Willeken's review focusing on decision-model choice including integration of social networks (n = 22 included publications) [2], Thober and colleagues' review of ABMs of environmental-migration linkages (n = 21) [4], and Bell and colleagues' brief background review on ABMs exploring migration push-pull factors to present their migration ABM framework (nonsystematic, n = ~ 8) [3]. Other examples of ABM reviews include previous reviews on the use of ABM to study other social or health science topics, such as urban crime (n = 45) [32], non-communicable diseases (NCDs) (n = 22) [33], obesity (n = 38) [34], public health (non-systematic, n = ~ 45) [21], and health systems (n = 11) [35]. These reviews reported notable increased interest in and application of ABM methods in their respective field of study, despite still small yields compared to other reviews including different methods.…”

“…Therefore, the data extraction process captured the authors' affiliations and study aim, model inputs, model development, model process and the analysis and validation methods completed by the authors. This method is similar to systematic reviews of ABMs on other social or health science research topics that focused on the study design, model specifications (agents, environment, decision rules), and model analysis (sensitivity analysis, model validation) for data extraction and synthesis [33,34,[38][39][40][41]. The data extraction tool was informed by the guiding questions outlined in the Overview, Design Concepts and Details + Decision-Making (ODD + D) protocol for ABM development, which is an adaptation of the original Overview, Design Concepts and Details (ODD) protocol developed to standardize descriptions of individual-based models (IBMs) and ABMs [5,42,43].…”

“…The model descriptions in the articles included in this review were overwhelmingly opaque with various critical details omitted. This review is not the first to critique the lack of a 'best practice' in reporting model development [1,4] and as one reviewer noted there are severe limitations to systematic reviews that aim to synthesise findings across a body of studies whose methods cannot be easily compared, not to mention that there is still work to be done to develop standard methods for systematically reviewing ABMs [34].…”

The role of systematic reviews in the system dynamics modelling process

“…It should be noted that systematic reviews are increasingly being conducted and published for Systems Science‐related and simulation methodologies (Carey et al, 2015; Luke & Stamatakis, 2012). A number of systematic reviews addressing non‐communicable diseases which included system dynamic (SD) studies have been conducted, but these have been mostly combined with other System Science's approaches such as Agent‐Based Modelling (ABM) and Discrete‐Event Simulation (Levy et al, 2011; Morshed, Kasman, Heuberger, Hammond, & Hovmand, 2019; Nianogo & Arah, 2015; Skinner & Foster, 2013; Xue, Slivka, Igusa, Huang, & Wang, 2018). Although there are some systematic reviews including SD studies in the literature, the application of systematic reviews to inform the SD modelling process is very scarce and barely appreciated (Darabi & Hosseinichimeh, 2020).…”