Microsimulation Modeling in Food Policy: A Scoping Review of Methodological Aspects

Abstract

Food policies for the prevention and management of diet-related non-communicable diseases (NCDs) have been increasingly relying on microsimulation models (MSMs) to assess effectiveness. Given the increased uptake of MSMs, this review aims to provide an overview of the characteristics of MSMs that link diets with NCDs. A comprehensive review was conducted in PubMed and Web of Knowledge. Inclusion criteria were: (i) findings from a MSM, (ii) diets, foods or nutrients as main exposure of interest, (iii) NCDs, such as overweight/obesity, type 2 diabetes, coronary heart disease, stroke or cancer as disease outcome for impact assessment. This review included information from 33 studies using MSM in analyzing diet and diverse food policies on NCDs. Hereby, most models employed stochastic, discrete-time, dynamic microsimulation techniques to calculate anticipated (cost-)effectiveness of strategies based on food pricing, food reformulation or dietary (lifestyle) interventions. Currently available models differ in the methodology used for quantifying the effect of the dietary changes on disease, and in the method for modelling disease incidence and mortality. However, all studies provided evidence that the models were sufficiently capturing the close-to-reality situation by justifying their choice of model parameters and validating externally their modelled disease incidence and mortality with observed or predicted event data. With the increasing use of various MSMs, between-model comparisons, facilitated by open access models and good reporting practices, would be important for judging model's accuracy, leading to continued improvement in the methodologies for developing and applying MSMs, and subsequently a better understanding of the results by policymakers. A statement of significance Given the advancement in the application of microsimulation modelling in evaluating food policies and measuring diet-related disease burdens, the present scoping review serves as an exercise to inform future modelling, hereby highlighting the need for transparency in model development, application and dissemination to advance and safeguard accuracy and relevance in modelling efforts.

Simulation models of sugary drink policies: A scoping review

“…Most models we reviewed used either cohort or microsimulation modeling methods, simulated a population defined by age and gender or sex, and projected changes in weight, diabetes, or cardiovascular disease. These results are in line with findings from other reviews of food policies [ 8 , 9 ]. Our results point to norms in the literature and highlight areas for future work to build on this strong foundation.…”

“…Use of simulation models to evaluate SSB policies has grown over time; all studies were published after 2011, and over half were published within the past four years (2017–2021). Consistent with prior literature, we find that the most commonly evaluated SSB policy is a tax, with the tax literature dominated by ad valorem and volumetric tax policies [ 8 , 9 ]. We also document an emerging literature that includes other policy options such as purchasing bans, warning labels, and portion size restrictions.…”

“…For example, equity-based weighting involves increasing (or decreasing) the contribution of outcomes for different groups (e.g., increasing the weight of quality-adjusted life years gained among low-income cohorts or individuals) [ 112 , 114 ]. As authors seek to further consider equity implications, techniques like microsimulation models that allow for using distributions from the relevant population(s) in question will become increasingly important [ 9 ].…”

“…Very few articles we reviewed provided code or data to replicate their work, and supplementary material often focused more on supplementary results rather than providing additional methodological details that would support replication. We advocate for increased transparency and code sharing of simulation models, as other reviews have called for [ 8 , 9 ]. For example, researchers should consider the framework set out by Alarid-Escudero and colleagues [ 123 ] and utilize platforms such as GitHub or the Open Science Framework.…”

“…Standardized reporting guidelines for simulation modeling could also help push the field towards more consistent and transparent modeling [ 8 , 9 ]. In our study, data extraction was at times challenging due the many disciplines (e.g., health economics, epidemiology, behavioral science) represented in this research.…”