文章基本信息

标题：Impact of Different Policies on Unhealthy Dietary Behaviors in an Urban Adult Population: An Agent-Based Simulation Model
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作者：Donglan Zhang ; Philippe J. Giabbanelli ; Onyebuchi A. Arah 等
期刊名称：American journal of public health
印刷版ISSN：0090-0036
出版年度：2014
卷号：104
期号：7
页码：1217-1222
DOI：10.2105/AJPH.2014.301934
语种：English
出版社：American Public Health Association
摘要：Objectives. Unhealthy eating is a complex-system problem. We used agent-based modeling to examine the effects of different policies on unhealthy eating behaviors. Methods. We developed an agent-based simulation model to represent a synthetic population of adults in Pasadena, CA, and how they make dietary decisions. Data from the 2007 Food Attitudes and Behaviors Survey and other empirical studies were used to calibrate the parameters of the model. Simulations were performed to contrast the potential effects of various policies on the evolution of dietary decisions. Results. Our model showed that a 20% increase in taxes on fast foods would lower the probability of fast-food consumption by 3 percentage points, whereas improving the visibility of positive social norms by 10%, either through community-based or mass-media campaigns, could improve the consumption of fruits and vegetables by 7 percentage points and lower fast-food consumption by 6 percentage points. Zoning policies had no significant impact. Conclusions. Interventions emphasizing healthy eating norms may be more effective than directly targeting food prices or regulating local food outlets. Agent-based modeling may be a useful tool for testing the population-level effects of various policies within complex systems. 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. 8 The 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). 11,12 The 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 particularly in addressing food system issues. 17,18 Such techniques can provide virtual laboratories for testing policies 19 and can be useful in studying complex systems, such as dietary behaviors, wherein decisions are influenced by the interplay of socioeconomic, psychological, and physiological forces. 20–22 We used ABM, a powerful technique when there is heterogeneity among members of the population and when behavior changes as a result of interactions between different individuals or between individuals and their environment (i.e., adaptation). 23,24 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 incentives—as 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.