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. 2019 Aug;57(2):273-281.
doi: 10.1016/j.amepre.2019.03.017.

Complex Systems Approaches to Diet: A Systematic Review

Brent A Langellier  1 , Usama Bilal  2 , Felipe Montes  3 , Jose D Meisel  4 , Letícia de Oliveira Cardoso  5 , Ross A Hammond  6
Affiliations

Complex Systems Approaches to Diet: A Systematic Review

Brent A Langellier et al. Am J Prev Med. 2019 Aug.

Abstract

Context: Complex systems approaches can help to elucidate mechanisms that shape population-level patterns in diet and inform policy approaches. This study reports results of a structured review of key design elements and methods used by existing complex systems models of diet.

Evidence acquisition: The authors conducted systematic searches of the PubMed, Web of Science, and LILACS databases between May and September 2018 to identify peer-reviewed manuscripts that used agent-based models or system dynamics models to explore diet. Searches occurred between November 2017 and May 2018. The authors extracted relevant data regarding each study's diet and nutrition outcomes; use of data for parameterization, calibration, and validation; results; and generated insights. The literature search adhered to PRISMA guidelines.

Evidence synthesis: Twenty-two agent-based model studies and five system dynamics model studies met the inclusion criteria. Mechanistic studies explored neighborhood- (e.g., residential segregation), interpersonal- (e.g., social influence) and individual-level (e.g., heuristics that guide food purchasing decisions) mechanisms that influence diet. Policy-oriented studies examined policies related to food pricing, the food environment, advertising, nutrition labels, and social norms. Most studies used empirical data to inform values of key parameters; studies varied in their approaches to calibration and validation.

Conclusions: Opportunities remain to advance the state of the science of complex systems approaches to diet and nutrition. These include using models to better understand mechanisms driving population-level diet, increasing use of models for policy decision support, and leveraging the wide availability of epidemiologic and policy evaluation data to improve model validation.

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Figures

Figure 1.
Figure 1.
PRISMA 2009 flow diagram.
See this image and copyright information in PMC

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