Impact of Different Policies on Unhealthy Dietary Behaviors in an Urban Adult Population: An Agent-Based Simulation Model

Abstract

The literature on policy interventions to address obesogenic dietary behaviors can be divided into 3 distinct categories. The first is the use of economic measures to alter food consumption, such as taxing unhealthy ingredients and subsidizing healthy foods, 1---4 in light of studies that have shown the price of a calorie obtained from unhealthful foods is lower than the price of a calorie from more healthful foods. 5,6 The second is targeting the food environment through zoning polices, including increasing the number of healthy food vendors in "food desert" communities 7 and restricting the opening of new fast-food restaurants. 8The third and final category is related to combating unhealthy eating norms, given research showing the power of food marketing to change dietary behaviors and proposing restrictions on the time, place, and manner in which obesogenic foods are marketed. 9,10 Conversely, pro-nutritional marketing focuses on education as a means of increasing consumers' awareness of dietary health (e.g., nutrition disclosure on menus and the issuing of dietary guidelines). 11, 12The research to date focusing on the effects of these various approaches has incorporated theoretical and empirical techniques that rely on the stable unit treatment value assumption, according to which there are no interactions among people who experience an intervention that would alter the effectiveness of the intervention. However, this assumption is known to be violated in the case of obesity-related behaviors.13, 14 What is therefore not clear from existing regression-based and experimental empirical work is the potential magnitude of the population-level impact of these policies if they were implemented in the real world. We performed simulations to contrast the potential of different approaches aiming at tackling unhealthy dietary behaviors in a population of urban US adults. Simulations involving systems dynamics or agent-based modeling (ABM) are increasingly being used in public health, 15,16 We developed a computer model explicitly representing how individuals make decisions to examine the impact of policies on their food consumption. Our model was based on a multilevel theory of population health that emphasizes the role of cognitive habits in human behaviors. 25 According to this theory, individual beliefs are influenced by incentivesas in rational-choice theory-but also shaped by cognitive habits that are reinforced within a population by social norms and culture. Thus, our model examined how individual beliefs are influenced by interventions either in the social network or in the food environment. METHODSOur agent-based model contained 2 kinds of agents: individuals and food outlets. Individuals make dietary choices, and food outlets adapt to those choices. In our model, a single time period was defined as 1 day, and each simulation was run for more than 3 years (1100 days).In our simulations, individuals were assigned demographic characteristics (age, gender, Objectives. Unhealthy eating is a complex-system prob...

Social determinants and health behaviors: conceptual frames and empirical advances

“…Ip and colleagues, for example, inform interventions related to childhood obesity by developing dynamic models that incorporate feedback between health behaviors (food intake, activity levels) and physiology (mood, genetic factors), and include inputs such as poverty and local food environment, by blending agent-based modeling approaches and frequentist statistical approaches [71]. Focusing on policy, Zhang and colleagues develop agent-based simulations to model processes of dietary decision-making to find polices that emphasize healthy eating norms may be more effective than those regulating food prices or local food outlets [72]. …”

Designing and Undertaking a Health Economics Study of Digital Health Interventions

“…44 These models and the techniques to develop them should be more widely embraced in economic analysis of DHIs. 45 As highlighted earlier, 15,16 there appears a role for agent-based modeling. 46,47 Within this approach, individuals make decisions autonomously, as well as interacting with others and with their environment using individually tailored "behavioral rules."…”

“…To illustrate what a complex economic evaluation might look like, consider Zhang and colleagues, 15 who used an agent-based model of social network interactions to examine the effect of different policy instruments in changing dietary behaviors (Figure 1). Based on a multilevel theory of population health that encompasses habitual behaviors, 16 behaviors are influenced by standard economic incentives, such as price, but also affected by cognitive habits that are subject to social norms.…”

Using Agent-Based Models to Develop Public Policy about Food Behaviours: Future Directions and Recommendations

“…The assumption was often made that only the food environment around the home or work environment would impact diet [14–16]; that is, models tend to represent only what surrounds the home or work location (e.g., using a buffer area). We will refer to this assumption as the “proximity hypothesis.” While this hypothesis has been prevalent in studies on individual food exposure [17], it has led to inconsistent findings [18] suggesting that people are not primarily using what is geographically proximate [19].…”

“…We will refer to this assumption as the “proximity hypothesis.” While this hypothesis has been prevalent in studies on individual food exposure [17], it has led to inconsistent findings [18] suggesting that people are not primarily using what is geographically proximate [19]. This is because individuals navigate a multiplicity of environments [20] (e.g., purchase food while commuting) and may even shop far away from home/work in the case of low-income groups, which are disproportionally facing obesity [14, 21]. …”