Childhood Sugar-Sweetened Beverage Consumption: an Agent-Based Model of Context-Specific Reduction Efforts.

INTRODUCTION: Despite widespread recognition among public health experts that childhood sugar-sweetened beverage consumption should be reduced, doing so has proven to be a challenge. An agent-based model of early childhood sugar-sweetened beverage consumption was applied to data from three high-quality, longitudinal cohort studies to gain insight into potentially effective intervention strategies across contexts. METHODS: From 2021 to 2023, a single agent-based model design was applied to data sets derived from three separate cohorts of children followed from infancy to childhood, with very different populations and environments (participants recruited in 1999-2002; 2003-2010; and 2009-2014). After assessing its ability to reproduce observed consumption patterns across cohorts, it was used to simulate potential impacts of multiple intervention strategies across contexts. RESULTS: Interventions reducing home availability of sugar-sweetened beverages consistently had the largest potential effects. Impact differed between cohort settings: a complete decrease in availability resulted in an estimated 87% decrease in overall early childhood consumption for one of the cohorts, compared with 61% and 54% in the others. Reducing availability in center-based child care resulted in substantially greater reduction in one cohort relative to the other two. CONCLUSIONS: There is untapped potential for strategies targeting children's sugar-sweetened beverage consumption in the home, but in some instances, other approaches might also yield meaningful effects. Tailoring approach to setting may be important, and agent-based models can be informative for doing so. This agent-based model has broad generalizability and potential to serve as a tool for designing effective, context-specific strategies to reduce childhood sugar-sweetened beverage consumption.

Understanding Misimplementation in U.S. State Health Departments: An Agent-Based Model.

INTRODUCTION: The research goal of this study is to explore why misimplementation occurs in public health agencies and how it can be reduced. Misimplementation is ending effective activities prematurely or continuing ineffective ones, which contributes to wasted resources and suboptimal health outcomes. METHODS: The study team created an agent-based model that represents how information flow, filtered through organizational structure, capacity, culture, and leadership priorities, shapes continuation decisions. This agent-based model used survey data and interviews with state health department personnel across the U.S. between 2014 and 2020; model design and analyses were conducted with substantial input from stakeholders between 2019 and 2021. The model was used experimentally to identify potential approaches for reducing misimplementation. RESULTS: Simulations showed that increasing either organizational evidence-based decision-making capacity or information sharing could reduce misimplementation. Shifting leadership priorities to emphasize effectiveness resulted in the largest reduction, whereas organizational restructuring did not reduce misimplementation. CONCLUSIONS: The model identifies for the first time a specific set of factors and dynamic pathways most likely driving misimplementation and suggests a number of actionable strategies for reducing it. Priorities for training the public health workforce include evidence-based decision making and effective communication. Organizations will also benefit from an intentional shift in leadership decision-making processes. On the basis of this initial, successful application of agent-based model to misimplementation, this work provides a framework for further analyses.

Using Agent-Based Modeling to Extrapolate Community-Wide Impact from a Stakeholder-Driven Childhood Obesity Prevention Intervention: Shape Up Under 5.

Objective: Whole-of-community interventions are a promising systems-based approach to childhood obesity prevention. A theorized driver of success is "Stakeholder-Driven Community Diffusion" (SDCD): the spread of knowledge about and engagement with obesity prevention efforts from a committee of stakeholder representatives. We focus on the potential of SDCD to affect the broader community. Methods: We use an agent-based model of SDCD to dynamically represent the interpersonal interactions that drive community diffusion of knowledge and engagement. We test its explanatory power using longitudinal data from a sample of community members and then use simulations to extrapolate from this limited sample to the unobserved community at large. We also consider counterfactual scenarios that show how changes in implementation strategy might have led to different patterns of community change. Results: Our model can reproduce real-world patterns of diffusion. Simulations show a substantial increase in knowledge (an approximate doubling) and a slight increase in engagement throughout the broader community. A relatively small amount of this change in knowledge (∼10%), and all the change in engagement is attributable to direct intervention effects on committee members. Conclusions: SDCD is premised on creating preconditions for sustainable change. Previous work has estimated impact on small samples closely linked to the stakeholder committee, but the degree to which this translates into the much broader diffusion envisioned by SDCD theory is unknown. This analysis demonstrates the potential of interventions to do just that. Additionally, the counterfactual scenarios suggest that simulation can help tailor implementation of SDCD interventions to increase impact.

TRACE-Omicron: Policy Counterfactuals to Inform Mitigation of COVID-19 Spread in the United States.

The Omicron wave was the largest wave of COVID-19 pandemic to date, more than doubling any other in terms of cases and hospitalizations in the United States. In this paper, we present a large-scale agent-based model of policy interventions that could have been implemented to mitigate the Omicron wave. Our model takes into account the behaviors of individuals and their interactions with one another within a nationally representative population, as well as the efficacy of various interventions such as social distancing, mask wearing, testing, tracing, and vaccination. We use the model to simulate the impact of different policy scenarios and evaluate their potential effectiveness in controlling the spread of the virus. Our results suggest the Omicron wave could have been substantially curtailed via a combination of interventions comparable in effectiveness to extreme and unpopular singular measures such as widespread closure of schools and workplaces, and highlight the importance of early and decisive action.

An agent-based model of child sugar-sweetened beverage consumption: implications for policies and practices.

BACKGROUND: A strong body of evidence links young children's intake of sugar-sweetened beverages (SSBs) with myriad negative outcomes. OBJECTIVES: Our research provides insight into whether and to what extent potential intervention strategies can reduce young children's consumption of SSBs. METHODS: We built an agent-based model (ABM) of SSB consumption representing participants in the Project Viva longitudinal study between ages 2 and 7 y. In addition to extensive data from Project Viva, our model used nationally representative data as well as recent, high-quality literature. We tested the explanatory power of the model through comparison to consumption patterns observed in the Project Viva cohort. Then, we applied the model to simulate the potential impact of interventions that would reduce SSB availability in 1 or more settings or affect how families receive and respond to pediatrician advice. RESULTS: Our model produced age-stratified trends in beverage consumption that closely match those observed in Project Viva cohort data. Among the potential interventions we simulated, reducing availability in the home-where young children spend the greatest amount of time-resulted in the largest consumption decrease. Removing access to all SSBs in the home resulted in them consuming 1.23 (95% CI: 1.21, 1.24) fewer servings of SSBs per week on average between the ages of 2 and 7 y, a reduction of ∼60%. By comparison, removing all SSB availability outside of the home (i.e., in schools and childcare) had a smaller impact (0.77; CI: 0.75, 0.78), a reduction of ∼40%. CONCLUSIONS: These results suggest that interventions reducing SSB availability in the home would have the strongest effects on SSB consumption.

Using a Microsimulation of Energy Balance to Explore the Influence of Prenatal Sugar-Sweetened Beverage Intake on Child BMI.

OBJECTIVE: Experiments with animals suggest that high sugar consumption during pregnancy may predispose offspring to obesity, but few human studies have examined this relationship. This study explored the association between the consumption of sugar-sweetened beverages (SSBs) during pregnancy and caloric intake through childhood. METHODS: Using cohort data on child weight, height, and physical activity levels, a lab-validated microsimulation model of energy balance was employed to infer the caloric intake of children through age 11 years. Random effects models were then employed to explore the relationships between prenatal maternal consumption and inferred caloric intake during childhood. RESULTS: An additional daily serving of SSBs during the second trimester of pregnancy was associated with an increase in child consumption of 13 kcal/d (95% CI: 1.2-26.8). Age-stratified models adjusting for maternal and child covariates suggested that this association was strongest for children aged 2.5 to 5.5 years. The consumption of SSBs during the first trimester was not found to have a consistently positive relationship to caloric intake. CONCLUSIONS: These findings suggest that SSB consumption during the second trimester of pregnancy is associated with child energy intake and may influence anthropometry in early childhood, which is consistent with and suggestive of the presence of biological causal pathways alongside likely simultaneous contributions of social and environmental influences.

Activating a Community: An Agent-Based Model of Romp & Chomp, a Whole-of-Community Childhood Obesity Intervention.

OBJECTIVE: Successful whole-of-community childhood obesity prevention interventions tend to involve community stakeholders in spreading knowledge about and engagement with obesity prevention efforts through the community. This process is referred to by the authors as stakeholder-driven community diffusion (SDCD). This study uses an agent-based model in conjunction with intervention data to increase understanding of how SDCD operates. METHODS: This agent-based model retrospectively simulated SDCD during Romp & Chomp, a 4-year whole-of-community childhood obesity prevention intervention in Victoria, Australia. Stakeholder survey data, intervention records, and expert estimates were used to parameterize the model. Model output was evaluated against criteria derived from empirical data and experts' estimates of the magnitude and timing of community knowledge and engagement change. RESULTS: The model was able to produce outputs that met the evaluation criteria: increases in simulated community knowledge and engagement driven by SDCD closely matched expert estimates of magnitude and timing. CONCLUSIONS: Strong suggestive evidence was found in support of a hypothesis that SDCD was a key driver of the success of the Romp & Chomp intervention. Model exploration also provided additional insights about these processes (including where additional data collection might prove most beneficial), as well as implications for the design and implementation of future interventions.