How did it get this way? Disentangling the sources of teacher quality gaps through agent-based modeling.

We develop a novel simulation methodology to study the extent to which three interrelated processes-teacher attrition from the state teaching workforce, teacher mobility between teaching positions, and teacher hiring for open positions-contribute to "teacher quality gaps" (TQGs) between students of color and other students in K-12 public schools. We apply this methodology to data from Washington State to provide estimates that eliminating inequities in teacher mobility and hiring across different schools would close TQGs within 5 years, while just eliminating inequities in teacher hiring would close gaps within 10 years. On the other hand, eliminating inequities in teacher attrition without addressing mobility and hiring does little to close gaps.

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.

Community coalition efforts to prevent childhood obesity: two-year results of the Shape Up Under 5 study.

BACKGROUND: Cross-sector collaborations and coalitions are promising approaches for childhood obesity prevention, yet there is little empirical evidence about how they affect change. We hypothesized that changes in knowledge of, and engagement with, childhood obesity prevention among coalition members can diffuse through social networks to influence policies, systems, and environments. METHODS: We studied a community coalition (N = 16, Shape Up Under 5 "SUU5 Committee") focused on early childhood obesity prevention in Somerville, MA from 2015-17. Knowledge, engagement, and social network data were collected from Committee members and their network contacts (n = 193) at five timepoints over two years. Policy, systems, and environment data were collected from the SUU5 Committee. Data were collected via the validated COMPACT Stakeholder-driven Community Diffusion survey and analyzed using regression models and social network analysis. RESULTS: Over 2 years, knowledge of (p = 0.0002), and engagement with (p = 0.03), childhood obesity prevention increased significantly among the SUU5 Committee. Knowledge increased among the Committee's social network (p = 0.001). Significant changes in policies, systems, and environments that support childhood obesity prevention were seen from baseline to 24 months (p = 0.003). CONCLUSION: SUU5 had positive effects on "upstream" drivers of early childhood obesity by increasing knowledge and engagement. These changes partially diffused through networks and may have changed "midstream" community policies, systems, and environments.

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.

An application of agent-based modeling to explore the impact of decreasing incarceration rates and increasing drug treatment access on sero-discordant partnerships among people who inject drugs.

BACKGROUND: People who inject drugs (PWID) lag behind other key populations in HIV care continuum outcomes. The impacts of criminal justice reform and increasing drug treatment access on HIV have been underexplored. METHODS: We developed agent-based models (ABM) of sexual partnerships among PWID and non-PWID, and injection equipment-sharing partnerships among PWID in five US cities (Baltimore, Boston, Miami, New York City, San Francisco) over 3 years. The first set of ABM projected changes in partnership discordance among PWID as a function of decreasing ZIP code-level incarceration rates. The second set projected discordance as a function of increasing ZIP code-level drug treatment access. ABM were parameterized and validated overall, and by city and PWID race/ethnicity (Black, Latino, White) using National HIV Behavioral Surveillance data, administrative ZIP code-level data, surveillance reports and prior literature. Informed by research on prisoner release and community-level HIV prevalence, reductions in incarceration rates were fixed at 5% and 30% and respectively projected to increase ZIP code-level HIV prevalence by 2% and 12%. Increases in drug treatment access were fixed at 30% and 58%. RESULTS: In each city, a 30% reduction in ZIP code-level incarceration rates and 12% increase in ZIP code-level HIV prevalence significantly increased sero-discordance among at least one racial/ethnic group of PWID by 1-3 percentage points. A 5% reduction in incarceration rates, and 30% and 58% increases in drug treatment access, led to isolated significant changes in sero-discordance among Black and White PWID that were less than 1 percentage point. CONCLUSION: Reductions in incarceration rates may lead to short-term increases in sero-discordant partnerships among some PWID by increasing community-level HIV prevalence. Efforts to increase HIV testing, engagement in care and community reintegration post release, should be strengthened in the wake of incarceration reform. Additional research should confirm these findings and explore the lack of widespread impacts of drug treatment in this study.

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.

The role of novelty and fat and sugar concentration in food selection by captive tufted capuchins (Sapajus apella).

Capuchins, like other primates, use feedback from sensory cues and digestion to make decisions about which foods to consume and which to avoid. However, little is known about how capuchins make consumption decisions when simultaneously presented with novel and familiar foods, or how food familiarity and macronutrient concentration together influence food choice, topics with potential implications for developmental and health research. In this study, we evaluated the role of familiarity, as well as fat and sugar concentration, in the food selections of captive tufted capuchins (Sapajus apella). In the first experiment, over 10 sessions, subjects were assigned to either a group that chose between one familiar and one novel food item both high in fat or sugar (high condition), or to a group that chose between one familiar and one novel food item both low in fat or sugar (low condition). In the second experiment, subjects were divided into three groups, familiarized with food over five feeding sessions, and then offered the familiarized food and a novel food that varied in fat or sugar for 10 sessions. When offered foods high in fat, capuchins showed no clear signs of neophobia, forming an initial preference for the novel food, rejecting foods less frequently, and selecting foods faster than when offered foods low in fat. These trends were generally not observed in response to foods with sugar. When presented with options that varied in macronutrient concentration, subjects showed an initial interest in the novel food irrespective of whether it was high in fat or sugar, yet formed a final preference for the higher-concentration item. Findings suggest that the concentration of fat or sugar in novel foods may be an important mediator of exploratory behavior and that capuchins rely on immediate feedback from taste and other sensory cues to make consumption decisions.

Development of a computational modeling laboratory for examining tobacco control policies: Tobacco Town.

A key focus of recent policy efforts to curb tobacco product usage has been the role of place-specifically the density of retail and advertising and the resulting spatial pattern of access and exposure for consumers. Policies can alter the environment by reducing density or shifting distribution of tobacco retail and thus limiting access and exposure. Since little empirical evidence exists for the potential impact of these policies across potentially heterogeneous places, we develop and apply an original spatial computational model to simulate place-based retail tobacco control policies. The model is well-grounded in theory and available empirical evidence. We apply the model in four representative settings to demonstrate the utility of this approach as a policy laboratory, to develop general insights on the relationship between retailer density, retail interventions, and tobacco costs incurred by consumers, and to provide a framework to guide future modeling and empirical studies. Our results suggest that the potential impact on costs of reducing tobacco retailer density are highly dependent on context. Projected impacts are also influenced by assumptions made about agent (smoker) purchasing decision-making processes. In the absence of evidence in this area, we tested and compared three alternative decision rules; these interact with environmental properties to produce different results. Agent properties, namely income and cigarettes per day, also shape purchasing patterns before and after policy interventions. We conclude that agent-based modeling in general, and Tobacco Town specifically, hold much potential as a platform for testing and comparing the impact of various retail-based tobacco policies across different communities. Initial modeling efforts uncover important gaps in both data and theory and can provide guidance for new empirical studies in tobacco control.

Modelling the impact of menthol sales restrictions and retailer density reduction policies: insights from tobacco town Minnesota.

INTRODUCTION: Tobacco control policies focused on the retail environment have the potential to reduce tobacco use and tobacco-related health disparities through increasing direct and indirect costs. Recently, national and subnational governments have begun to restrict the sale of menthol products and reduce tobacco retailer density. METHODS: We developed an agent-based model to project the impact of menthol cigarette sales restrictions and retailer density reduction policies for six types of communities and three priority populations. During each simulated day, agents smoke cigarettes, travel in the community and make purchase decisions-whether, where and which product type to purchase-based on a combination of their own properties and the current retail environment. RESULTS: Of the policies tested, restricting all cigarette sales or menthol cigarette sales to tobacco specialty shops may have the largest effect on the total (direct and indirect) costs of purchasing cigarettes. Coupling one of these policies with one that establishes a minimum distance between tobacco retailers may enhance the impact. Combining these policies could also make the costs of acquiring cigarettes more equal across communities and populations. DISCUSSION: Our simulations revealed the importance of context, for example, lower income communities in urban areas begin with higher retailer density and may need stronger policies to show impact, as well as the need to focus on differential effects for priority populations, for example, combinations of policies may equalise the average distance travelled to purchase. Adapting and combining policies could enhance the sustainability of policy effects and reduce tobacco use.

Design and methods of Shape Up Under 5: Integration of systems science and community-engaged research to prevent early childhood obesity.

Shape Up Under 5 (SUU5) was a two-year early childhood obesity prevention pilot study in Somerville, Massachusetts (2015-2017) designed to test a novel conceptual framework called Stakeholder-driven Community Diffusion. For whole-of-community interventions, this framework posits that diffusion of stakeholders' knowledge about and engagement with childhood obesity prevention efforts through their social networks will improve the implementation of health-promoting policy and practice changes intended to reduce obesity risk. SUU5 used systems science methods (agent-based modeling, group model building, social network analysis) to design, facilitate, and evaluate the work of 16 multisector stakeholders ('the Committee'). In this paper, we describe the design and methods of SUU5 using the conceptual framework: the approach to data collection, and methods and rationale for study inputs, activities and evaluation, which together may further our understanding of the hypothesized processes within Stakeholder-driven Community Diffusion. We also present a generalizable conceptual framework for addressing childhood obesity and similar complex public health issues through whole-of-community interventions.

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.

A systematic review of system dynamics and agent-based obesity models: Evaluating obesity as part of the global syndemic.

The problem of obesity has recently been reframed as part of the global syndemic-the co-occurring, interacting pandemics of obesity, undernutrition, and climate change that are driven by common underlying societal drivers. System science modeling approaches may help clarify how these shared drivers operate and the best ways to address them. The objective of this paper was to determine to what extent existing agent-based and system dynamics computational models of obesity provide insights into the shared drivers of the global syndemic. Peer-reviewed studies published until July 2018 were identified from Scopus, Web of Science, and PubMed databases. Thirty-eight studies representing 30 computational models were included. They show a growing use of system dynamics and agent-based modeling in the past decade. They most often examined mechanisms and interventions in the areas of social network-based influences on obesity, physiology and disease state mechanics, and the role of food and physical activity environments. Usefulness for identifying common drivers of the global syndemic was mixed; most models represented Western settings and focused on obesity determinants close to the person (eg, social circles, school settings, and neighborhood environments), with a relative paucity in models at mesolevel and macrolevel and in developing country contexts.

Development and testing of a novel survey to assess Stakeholder-driven Community Diffusion of childhood obesity prevention efforts.

BACKGROUND: Involving groups of community stakeholders (e.g., steering committees) to lead community-wide health interventions appears to support multiple outcomes ranging from policy and systems change to individual biology. While numerous tools are available to measure stakeholder characteristics, many lack detail on reliability and validity, are not context specific, and may not be sensitive enough to capture change over time. This study describes the development and reliability of a novel survey to measure Stakeholder-driven Community Diffusion via assessment of stakeholders' social networks, knowledge, and engagement about childhood obesity prevention. METHODS: This study was completed in three phases. Phase 1 included conceptualization and online survey development through literature reviews and expert input. Phase 2 included a retrospective study with stakeholders from two completed whole-of-community interventions. Between May-October 2015, 21 stakeholders from the Shape Up Somerville and Romp & Chomp interventions recalled their social networks, knowledge, and engagement pre-post intervention. We also assessed one-week test-retest reliability of knowledge and engagement survey modules among Shape Up Somerville respondents. Phase 3 included survey modifications and a second prospective reliability assessment. Test-retest reliability was assessed in May 2016 among 13 stakeholders involved in ongoing interventions in Victoria, Australia. RESULTS: In Phase 1, we developed a survey with 7, 20 and 50 items for the social networks, knowledge, and engagement survey modules, respectively. In the Phase 2 retrospective study, Shape Up Somerville and Romp & Chomp networks included 99 and 54 individuals. Pre-post Shape Up Somerville and Romp & Chomp mean knowledge scores increased by 3.5 points (95% CI: 0.35-6.72) and (- 0.42-7.42). Engagement scores did not change significantly (Shape Up Somerville: 1.1 points (- 0.55-2.73); Romp & Chomp: 0.7 points (- 0.43-1.73)). Intraclass correlation coefficients (ICCs) for knowledge and engagement were 0.88 (0.67-0.97) and 0.97 (0.89-0.99). In Phase 3, the modified knowledge and engagement survey modules included 18 and 25 items, respectively. Knowledge and engagement ICCs were 0.84 (0.62-0.95) and 0.58 (0.23-0.86). CONCLUSIONS: The survey measures upstream stakeholder properties-social networks, knowledge, and engagement-with good test-retest reliability. Future research related to Stakeholder-driven Community Diffusion should focus on prospective change and survey validation for intervention effectiveness.

Toward optimal implementation of cancer prevention and control programs in public health: a study protocol on mis-implementation.

BACKGROUND: Much of the cancer burden in the USA is preventable, through application of existing knowledge. State-level funders and public health practitioners are in ideal positions to affect programs and policies related to cancer control. Mis-implementation refers to ending effective programs and policies prematurely or continuing ineffective ones. Greater attention to mis-implementation should lead to use of effective interventions and more efficient expenditure of resources, which in the long term, will lead to more positive cancer outcomes. METHODS: This is a three-phase study that takes a comprehensive approach, leading to the elucidation of tactics for addressing mis-implementation. Phase 1: We assess the extent to which mis-implementation is occurring among state cancer control programs in public health. This initial phase will involve a survey of 800 practitioners representing all states. The programs represented will span the full continuum of cancer control, from primary prevention to survivorship. Phase 2: Using data from phase 1 to identify organizations in which mis-implementation is particularly high or low, the team will conduct eight comparative case studies to get a richer understanding of mis-implementation and to understand contextual differences. These case studies will highlight lessons learned about mis-implementation and identify hypothesized drivers. Phase 3: Agent-based modeling will be used to identify dynamic interactions between individual capacity, organizational capacity, use of evidence, funding, and external factors driving mis-implementation. The team will then translate and disseminate findings from phases 1 to 3 to practitioners and practice-related stakeholders to support the reduction of mis-implementation. DISCUSSION: This study is innovative and significant because it will (1) be the first to refine and further develop reliable and valid measures of mis-implementation of public health programs; (2) bring together a strong, transdisciplinary team with significant expertise in practice-based research; (3) use agent-based modeling to address cancer control implementation; and (4) use a participatory, evidence-based, stakeholder-driven approach that will identify key leverage points for addressing mis-implementation among state public health programs. This research is expected to provide replicable computational simulation models that can identify leverage points and public health system dynamics to reduce mis-implementation in cancer control and may be of interest to other health areas.

Tobacco Town: Computational Modeling of Policy Options to Reduce Tobacco Retailer Density.

OBJECTIVES: To identify the behavioral mechanisms and effects of tobacco control policies designed to reduce tobacco retailer density. METHODS: We developed the Tobacco Town agent-based simulation model to examine 4 types of retailer reduction policies: (1) random retailer reduction, (2) restriction by type of retailer, (3) limiting proximity of retailers to schools, and (4) limiting proximity of retailers to each other. The model examined the effects of these policies alone and in combination across 4 different types of towns, defined by 2 levels of population density (urban vs suburban) and 2 levels of income (higher vs lower). RESULTS: Model results indicated that reduction of retailer density has the potential to decrease accessibility of tobacco products by driving up search and purchase costs. Policy effects varied by town type: proximity policies worked better in dense, urban towns whereas retailer type and random retailer reduction worked better in less-dense, suburban settings. CONCLUSIONS: Comprehensive retailer density reduction policies have excellent potential to reduce the public health burden of tobacco use in communities.