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A strong body of evidence links young children's intake of sugar-sweetened beverages (SSBs) with myriad negative outcomes.Our research provides insight into whether and to what extent potential intervention strategies can reduce young children's consumption of SSBs.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.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%.These results suggest that interventions reducing SSB availability in the home would have the strongest effects on SSB consumption.

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.

Background: Community coalitions often lead and coordinate “whole-of-community” childhood obesity prevention interventions. A growing body of work suggests that coalition network ties, which facilitate transmission of information and advocacy, may be a key part of how such leadership operates. This study provides an understanding of the structure of coalition networks and how this structure changes over time. Methods: We administered semiannual network surveys during a pilot whole-of-community intervention in Somerville, MA (2015–2017). Participants included 16 multisector coalition members and their nominated discussion partners (“first-degree alters”) related to childhood obesity prevention. Coalition and first-degree alter respondents named up to 20 discussion partners and reported ties' interaction frequency and perceived influence. Networks were assessed with visualization, descriptive analysis, and exponential random graph models. Results: Total network included 558 stakeholders representing community-based organizations, parents, health care, childcare, universities, among others. Size and membership varied over time. We observed the largest network (n = 256) during intervention planning, and the largest proportion of stakeholders communicating frequently (daily/weekly) about childhood obesity prevention during the peak intervention period. Networks were sparsely interconnected (1%–3% of possible ties observed) and most and least centralized at baseline and follow-up, respectively. Over time, ties were increasingly perceived as influential and siloed within community groups. Conclusions: The network's extensive evolving membership may indicate access to a wide range of resources, ideas, and an ability to broadly disseminate intervention messages. The attenuating network hierarchy over time may have supported more equal participation and control over intervention efforts. Future research should assess generalizability of network patterns, network influences on implementation processes, and possible network interventions.

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.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.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.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.

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.

Objectives:To develop a simulation framework for assessing how combinations of taxes, nutrition warning labels and advertising levels could affect purchasing of ultra-processed foods (UPF) in Latin American countries and to understand whether policies reinforce or reduce pre-existing social disparities in UPF consumption.Design:We developed an agent-based simulation model using international evidence regarding the effect of price, nutrition warning labels and advertising on UPF purchasing.Setting:We estimated policy effects in scenarios representing two stages of the ‘social transition’ in UPF purchasing: (1) a pre-transition scenario, where UPF purchasing is higher among high-income households, similar to patterns in Mexico; and (2) a post-transition scenario where UPF purchasing is highest among low-income households, similar to patterns in Chile.Participants:A population of 1000 individual agents with levels of age, income, educational attainment and UPF purchasing similar to adult women in Mexico.Results:A 20 % tax would decrease purchasing by 24 % relative to baseline in both the pre- and post-transition scenarios, an effect that is similar in magnitude to that of a nutrition warning label policy. A 50 % advertising increase or decrease had a comparatively small effect. Nutrition warning labels were most effective among those with higher levels of educational attainment. Labelling reduced inequities in the pre-transition scenario (i.e. highest UPF purchasing among the highest socio-economic group) but widened inequities in the post-transition scenario.Conclusions:Effective policy levers are available to reduce UPF purchasing, but policymakers should anticipate that equity impacts will differ depending on existing social patterns in UPF purchasing.

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.

Background: The combined effects of multiple environmental toxicants and social stressor exposures are widely recognized as important public health problems contributing to health inequities. However cumulative environmental health risks and impacts have received little attention from U.S. policy makers at state and federal levels to develop comprehensive strategies to reduce these exposures, mitigate cumulative risks, and prevent harm. An area for which the inherent limitations of current approaches to cumulative environmental health risks are well illustrated is children’s neurodevelopment, which exhibits dynamic complexity of multiple interdependent and causally linked factors and intergenerational effects. Objectives: We delineate how a complex systems approach, specifically system dynamics, can address shortcomings in environmental health risk assessment regarding exposures to multiple chemical and nonchemical stressors and reshape associated public policies. Discussion: Systems modeling assists in the goal of solving problems by improving the “mental models” we use to make decisions, including regulatory and policy decisions. In the context of disparities in children’s cumulative exposure to neurodevelopmental stressors, we describe potential policy insights about the structure and behavior of the system and the types of system dynamics modeling that would be appropriate, from visual depiction (i.e., informal maps) to formal quantitative simulation models. A systems dynamics framework provides not only a language but also a set of methodological tools that can more easily operationalize existing multidisciplinary scientific evidence and conceptual frameworks on cumulative risks. Thus, we can arrive at more accurate diagnostic tools for children’s’ environmental health inequities that take into consideration the broader social and economic environment in which children live, grow, play, and learn. https://doi.org/10.1289/EHP7333

Background Whole-of-community interventions hold promise in addressing childhood obesity. The COMPACT Stakeholder-driven Community Diffusion theory posits that stakeholders’ knowledge of childhood obesity prevention efforts and engagement with the issue contribute to successful intervention implementation. Building on completed formative research and pilot testing, we describe the validation and refinement of knowledge and engagement measures. Methods We assessed content validity using a modified Delphi process with science (n=18) and practice-based (n=16) experts. Next, we refined the survey based on input from science- and practice-based experts, cognitive response testing, and item analysis of extant survey data. Field testing of the refined survey involved community stakeholders in Greenville County, South Carolina (n=50), East Boston, Massachusetts (n=30), and Tucson, Arizona (n=84) between 2019 and 2020. Construct validity was assessed with confirmatory factor analysis (CFA). Two-week test-retest reliability was assessed among a subsample of 14 paired respondents in South Carolina. Results Experts rated existing knowledge domains (intervention factors, roles, sustainability, problem, resources) and engagement domains (dialogue/mutual learning, flexibility, influence/power, leadership/stewardship, trust) highly for their importance in addressing childhood obesity. Expert input resulted in 11 new knowledge items and 7 new engagement items that mapped onto existing domains. Correspondingly, two domain names were modified: implementation/sustainability and trust/trustworthiness. We also eliminated 8 extant items (4 knowledge and 4 engagement) and adapted item language for comprehension and consistency. Further modifications based on CFA results and item analyses resulted in 23 knowledge items across four domains (roles and resources merged) and 23 engagement items across five domains. Modified knowledge and engagement scales had adequate fit and strong item factor loadings (most >0.7 and all >0.5). Knowledge (α=0.86–0.87) and engagement (α=0.75–0.90) subscales had high internal scale consistency. Knowledge intraclass correlation coefficients (ICCs) for test-retest agreement of subscale scores ranged from 0.50 for intervention factors to 0.86 for roles/resources. For engagement subscale scores, ICCs ranged from 0.70 for trust/trustworthiness to 0.96 for leadership/stewardship. Conclusions Findings from this multi-method survey development process increase our confidence of the knowledge and engagement measures’ content validity, construct validity, and reliability.

Cross-impact balance (CIB) analysis leverages expert knowledge pertaining to the nature and strength of relationships between components of a system to identify the most plausible future ‘scenarios’ of the system. These scenarios, also referred to as ‘storylines’, provide qualitative insights into how the state of one factor can either promote or restrict the future state of one or multiple other factors in the system. This paper presents a novel, visually oriented questionnaire developed to elicit expert knowledge about the relationships between key factors in a system, for the purpose of CIB analysis. The questionnaire requires experts to make selections from a series of standardized cause-effect graphical profiles that depict a range of linear and non-linear relationships between factor pairs. The questionnaire and the process of translating the graphical selections into data that can be used for CIB analysis is described using an applied example which focuses on urban health in Latin American cities. • A questionnaire featuring a set of standardized cause-effect profiles was developed. • Cause-effect profiles were used to elicit information about the strength of linear and non-linear bivariate relationships. • The questionnaire represents an intuitive visual means for collecting data required for the conduct of CIB analysis.

Urban health is shaped by a system of factors spanning multiple levels and scales, and through a complex set of interactions. Building on causal loop diagrams developed via several group model building workshops, we apply the cross-impact balance (CIB) method to understand the strength and nature of the relationships between factors in the food and transportation system, and to identify possible future urban health scenarios (i.e., permutations of factor states that impact health in cities). We recruited 16 food and transportation system experts spanning private, academic, non-government, and policy sectors from six Latin American countries to complete an interviewer-assisted questionnaire. The questionnaire, which was pilot tested on six researchers, used a combination of questions and visual prompts to elicit participants’ perceptions about the bivariate relationships between 11 factors in the food and transportation system. Each participant answered questions related to a unique set of relationships within their domain of expertise. Using CIB analysis, we identified 21 plausible future scenarios for the system. In the baseline model, ‘healthy’ scenarios (with low chronic disease, high physical activity, and low consumption of highly processed foods) were characterized by high public transportation subsidies, low car use, high street safety, and high free time, illustrating the links between transportation, free time and dietary behaviors. In analyses of interventions, low car use, high public transport subsidies and high free time were associated with the highest proportion of factors in a healthful state and with high proportions of ‘healthy’ scenarios. High political will for social change also emerged as critically important in promoting healthy systems and urban health outcomes. The CIB method can play a novel role in augmenting understandings of complex urban systems by enabling insights into future scenarios that can be used alongside other approaches to guide urban health policy planning and action., Highlights • We used the CIB method to augment findings from group model building workshops. • The CIB method highlights possible future scenarios to guide health policy planning. • We identified 21 plausible future scenarios for cities in Latin America. • Healthy scenarios featured low car use, high public transport subsidies and safety. • Political will appears critically important in promoting healthy urban scenarios.

Background Mis-implementation—defined as failure to successfully implement and continue evidence-based programs—is widespread in public health practice. Yet the causes of this phenomenon are poorly understood. Methods We develop an agent-based computational model to explore how complexity hinders effective implementation. The model is adapted from the evolutionary biology literature and incorporates three distinct complexities faced in public health practice: dimensionality, ruggedness, and context-specificity. Agents in the model attempt to solve problems using one of three approaches—Plan-Do-Study-Act (PDSA), evidence-based interventions (EBIs), and evidence-based decision-making (EBDM). Results The model demonstrates that the most effective approach to implementation and quality improvement depends on the underlying nature of the problem. Rugged problems are best approached with a combination of PDSA and EBI. Context-specific problems are best approached with EBDM. Conclusions The model’s results emphasize the importance of adapting one’s approach to the characteristics of the problem at hand. Evidence-based decision-making (EBDM), which combines evidence from multiple independent sources with on-the-ground local knowledge, is a particularly potent strategy for implementation and quality improvement.

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 ten 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 a 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.

Urban transportation is an important determinant of health and environmental outcomes, and therefore essential to achieving the United Nation's Sustainable Development Goals. To better understand the health impacts of transportation initiatives, we conducted a systematic review of longitudinal health evaluations involving: a) bus rapid transit (BRT); b) bicycle lanes; c) Open Streets programs; and d) aerial trams/cable cars. We also synthesized systems-based simulation studies of the health-related consequences of walking, bicycling, aerial tram, bus and BRT use. Two reviewers screened 3302 unique titles and abstracts identified through a systematic search of MEDLINE (Ovid), Scopus, TRID and LILACS databases. We included 39 studies: 29 longitudinal evaluations and 10 simulation studies. Five studies focused on low- and middle-income contexts. Of the 29 evaluation studies, 19 focused on single component bicycle lane interventions; the rest evaluated multi-component interventions involving: bicycle lanes (n = 5), aerial trams (n = 1), and combined bicycle lane/BRT systems (n = 4). Bicycle lanes and BRT systems appeared effective at increasing bicycle and BRT mode share, active transport duration, and number of trips using these modes. Of the 10 simulation studies, there were 9 agent-based models and one system dynamics model. Five studies focused on bus/BRT expansions and incentives, three on interventions for active travel, and the rest investigated combinations of public transport and active travel policies. Synergistic effects were observed when multiple policies were implemented, with several studies showing that sizable interventions are required to significantly shift travel mode choices. Our review indicates that bicycle lanes and BRT systems represent promising initiatives for promoting population health. There is also evidence to suggest that synergistic effects might be achieved through the combined implementation of multiple transportation policies. However, more rigorous evaluation and simulation studies focusing on low- and middle-income countries, aerial trams and Open Streets programs, and a more diverse set of health and health equity outcomes is required., Graphical abstract This figure represents a word network created by extracting keywords from the paper abstracts included in our systematic review. Each keyword represents a node in the network; its size is proportional to the number of abstracts in which it appears. Keywords are connected if they are found in the same abstract. The colours represent different communities of words as identified using the Louvain method.Image 1, Highlights • Bike lanes and bus rapid transit systems promote physical activity and active travel. • Multi-component interventions can act synergistically to increase active travel. • Few longitudinal health evaluations of aerial trams and open streets programs exist. • Low-and-middle income countries are understudied in the literature.

Additional co-authors: Prof Corinna Hawkes PhD, Mario Herrero PhD, Prof Peter S Hovmand PhD, Prof Mark Howden PhD, Lindsay M Jaacks PhD, Ariadne B Kapetanaki PhD, Matt Kasman PhD, Prof Harriet V Kuhnlein PhD, Prof Shiriki K Kumanyika PhD, Prof Bagher Larijani MD, Tim Lobstein PhD, Michael W Long PhD, Victor K R Matsudo MD, Susanna D H Mills PhD, Gareth Morgan PhD, Alexandra Morshed, Patricia M Nece JD, Prof An Pan PhD, David W Patterson, Gary Sacks PhD, Meera Shekar PhD, Geoff L Simmons, Warren Smit PhD, Ali Tootee PhD, Stefanie Vandevijvere PhD, Wilma E Waterlander PhD, Luke Wolfenden PhD, Prof William H Dietz MD

A hallmark of science is the open exchange of knowledge. At this time of crisis, it is more important than ever for scientists around the world to openly share their knowledge, expertise, tools, and technology. Scientific models are critical tools for anticipating, predicting, and responding to complex biological, social, and environmental crises, including pandemics. They are essential for guiding regional and national governments in designing health, social, and economic policies to manage the spread of disease and lessen its impacts. However, presenting modeling results alone is not enough. Scientists must also openly share their model code so that the results can be replicated and evaluated. Given the necessity for rapid response to the coronavirus pandemic, we need many eyes to review and collectively vet model assumptions, parameterizations, and algorithms to ensure the most accurate modeling possible. Transparency engenders public trust and is the best defense against misunderstanding, misuse, and deliberate misinformation about models and their results. We need to engage as many experts as possible for improving the ability of models to represent epidemiological, social, and economic dynamics so that we can best respond to the crisis and plan effectively to mitigate its wider impacts. We strongly urge all scientists modeling the coronavirus disease 2019 (COVID-19) pandemic and its consequences for health and society to rapidly and openly publish their code (along with specifying the type of data required, model parameterizations, and any available documentation) so that it is accessible to all scientists around the world. We offer sincere thanks to the many teams that are already sharing their models openly. Proprietary black boxes and code withheld for competitive motivations have no place in the global crisis we face today. As soon as possible, please place your code in a trusted digital repository ([ 1 ][1]) so that it is findable, accessible, interoperable, and reusable ([ 2 ][2]). 1. [↵][3]CoMSES Network, Trusted Digital Repositories ([www.comses.net/resources/trusted-digital-repositories/][4]). 2. [↵][5]1. M. D. Wilkinson et al ., Sci. Data 3, 160018 (2016). [OpenUrl][6] Correction (1 May 2020): A second affiliation was added for J.-A.A., and the competing interests declarations were updated. All authors have signed on behalf of the listed organizations only. E.B. advises but does not represent the UK Engineering and Physical Sciences Research Council (EPSRC). B.F. is affiliated with but does not represent the Advanced Systems Analysis Program at the International Institute for Applied Systems Analysis in Austria. P.L.M. is a paid consultant for Robert Wood Johnson Foundation Systems for Action (S4A), chair of the board of directors for Computer Simulation and Advanced Research Technologies, and an advisory board member of Systems Science in Public Health and Economics Research, but she does not represent these organizations. [1]: #ref-1 [2]: #ref-2 [3]: #xref-ref-1-1 "View reference 1 in text" [4]: http://www.comses.net/resources/trusted-digital-repositories/ [5]: #xref-ref-2-1 "View reference 2 in text" [6]: {openurl}?query=rft.jtitle%253DSci.%2BData%26rft.volume%253D3%26rft.spage%253D160018%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx

Eliminating childhood hunger and supporting households in need of food are US policy priorities, because food insecurity in the US remains elevated postrecession. The objective of this study was to bring a systems science lens to the issue of childhood food security. We used a qualitative systems mapping process (a “soft” systems science approach) to develop and describe a systems framework for childhood food security using a visual map. A first step was accomplished through convening a facilitated expert workshop in which we developed the systems map shown here. The systems map contains information on the sectors (e.g., government, private etc.), actors (e.g., children, caregivers, etc.), programs and policy levers (e.g., food and nutrition assistance programs, public goods and services, etc.), and flows (e.g., money, food, social support, etc.) pertinent to childhood food security. Situated at the center of the map is the interrelationship of competing demands and resources for caregivers and ch...

Background. Whole-of-community interventions have been recommended and show promise for preventing obesity; however, research to understand the mechanisms underlying their success or failure is lacking. Complex systems approaches may be useful to address this gap. Purpose. To describe the evolution and utilization of qualitative and quantitative complex systems methods to understand and model whole-of-community obesity prevention interventions. Approach. We illustrate the retrospective qualitative development of a systems map representing community change dynamic within the Shape Up Somerville (SUS) intervention. We then describe how this systems map, and complementary work of other successful obesity prevention interventions (Romp & Chomp intervention), informed the COMPACT (childhood obesity modeling for prevention and community transformation) study. COMPACT’s design aligns complex systems science principles and community-engaged research to better understand stakeholders’ leadership roles in whole-of-community interventions. We provide an overview of the complex systems tools used in COMPACT: agent-based modeling, group model building, and social network analysis and describe how whole-of-community intervention stakeholders (“agents”) use their social networks to diffuse knowledge about and engagement with childhood obesity prevention efforts, laying the groundwork for community readiness for sustainable change. Conclusion. Complex systems approaches appear feasible and useful to study whole-of-community obesity prevention interventions and provide novel insights that expand on those gained from traditional approaches. Use of multiple methods, both qualitative and quantitative, from the complex systems toolkit working together can be important to success.

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.

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.

IntroductionTobacco 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.MethodsWe 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.ResultsOf 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.DiscussionOur 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.

Despite 2 decades of effort by the public health community to combat obesity, obesity rates in the United States continue to rise. This lack of progress raises fundamental questions about the adequacy of our current approaches. Although the causes of population-wide obesity are multifactorial, attention to food systems as potential drivers of obesity has been prominent. However, the relationships between broader food systems and obesity are not always well understood. Our efforts to address obesity can be advanced and improved by the use of systems approaches that consider outcomes of the interconnected global food system, including undernutrition, climate change, the environmental sustainability of agriculture, and other social and economic concerns. By implementing innovative local and state programs, taking new approaches to overcome political obstacles to effect policy, and reconceptualizing research needs, we can improve obesity prevention efforts that target the food systems, maximize positive outcomes, and minimize adverse consequences. We recommend strengthening innovative local policies and programs, particularly those that involve community members in identifying problems and potential solutions and that embrace a broad set of goals beyond making eating patterns healthier. We also recommend undertaking interdisciplinary research projects that go beyond testing targeted interventions in specific populations and aim to build an understanding of the broader social, political, and economic context.

Background: Childhood obesity prevention interventions have engaged coalitions in study design, implementation, and/or evaluation to improve research outcomes; yet, no systematic reviews have been conducted on this topic. This mixed methods review aims to characterize the processes and dynamics of coalition engagement in community-based childhood obesity prevention interventions. Methods: Data Sources: Studies extracted from Ovid MEDLINE, PubMed, and Web of Science; complementary original survey and interview data among researchers of included studies. Eligible Studies: Multisetting community-based obesity prevention interventions in high-income countries targeting children 0–12 years with anthropometric, behavioral, or environmental/policy outcomes. The Community-Based Participatory Research (CBPR) Conceptual Model was used as an overarching framework. Results: Thirteen studies met inclusion criteria. Elements of CBPR were evident across all studies with community engagement in problem identification (n = 7), design/planning (n = 11), implementation (n = 12), evaluation (n = 4), dissemination (n = 2), and sustainability (n = 10) phases. Five studies reported favorable intervention effects on anthropometric (n = 4), behavioral (n = 1), and/or policy (n = 1) outcomes; descriptive associations suggested that these studies tended to engage community members in a greater number of research phases. Researchers involved in 7 of 13 included studies completed a survey and interview. Respondents recalled the importance of group facilitation, leadership, and shared understanding to multisector coalition work. Perceived coalition impacts included community capacity building and intervention sustainability. Conclusions: This review contributes to a deeper understanding of intervention processes and dynamics within communities engaged in childhood obesity prevention. Future research should more rigorously assess and report on coalition involvement to assess the influence of coalitions on multiple outcomes, including child weight status.

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. Electronic supplementary material The online version of this article (10.1186/s12889-018-5588-1) contains supplementary material, which is available to authorized users.

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.

A large body of literature in quantitative criminology finds that the spatio-temporal clustering of burglary is greater than one would expect from chance alone. This suggests that such crimes may exhibit a “boost” effect, wherein each burglary increases the risk to nearby locations for a short period. In this study, we demonstrate that standard tests for spatio-temporal dependence have difficulty distinguishing between clustering caused by contagion and that caused by changing relative risks. Therefore, any estimates of the boost effect drawn from these tests alone will be upwardly biased. We construct an agent-based model to generate simulated burglary data, and explore whether the Knox test can reliably distinguish between contagion (one burglary increases the likelihood of another burglary nearby) and changes in risk (one area gets safer while another gets more dangerous). Incorporating insights from this exercise, we analyze a decade of data on burglary events from Washington, DC. We find that (1) absent contagion, exogenous changes in relative risk can be sufficient to produce statistically significant Knox ratios, (2) if risk is changing over time, estimated Knox ratios are sensitive to one’s choice of time window, and (3) Knox ratios estimated from Washington, DC burglary data are sensitive to one’s choice of time window, suggesting that long-run changes in relative risk are, in part, driving empirical estimates of burglary’s boost effect. Researchers testing for contagion in empirical time series should take precautions to distinguish true contagion from exogenous changes in relative risks. Adjusting the time window of analysis is a useful robustness check, and future studies should be supplemented with new approaches like agent-based modeling or spatial econometric methods.

Summary Prevention of obesity requires policies that work. In this Series paper, we propose a new way to understand how food policies could be made to work more effectively for obesity prevention. Our approach draws on evidence from a range of disciplines (psychology, economics, and public health nutrition) to develop a theory of change to understand how food policies work. We focus on one of the key determinants of obesity: diet. The evidence we review suggests that the interaction between human food preferences and the environment in which those preferences are learned, expressed, and reassessed has a central role. We identify four mechanisms through which food policies can affect diet: providing an enabling environment for learning of healthy preferences, overcoming barriers to the expression of healthy preferences, encouraging people to reassess existing unhealthy preferences at the point-of-purchase, and stimulating a food-systems response. We explore how actions in three specific policy areas (school settings, economic instruments, and nutrition labelling) work through these mechanisms, and draw implications for more effective policy design. We find that effective food-policy actions are those that lead to positive changes to food, social, and information environments and the systems that underpin them. Effective food-policy actions are tailored to the preference, behavioural, socioeconomic, and demographic characteristics of the people they seek to support, are designed to work through the mechanisms through which they have greatest effect, and are implemented as part of a combination of mutually reinforcing actions. Moving forward, priorities should include comprehensive policy actions that create an enabling environment for infants and children to learn healthy food preferences and targeted actions that enable disadvantaged populations to overcome barriers to meeting healthy preferences. Policy assessments should be carefully designed on the basis of a theory of change, using indicators of progress along the various pathways towards the long-term goal of reducing obesity rates.

Social scientists have a longstanding theoretical interest in the relationship between individual behavior and social dynamics. A growing body of work demonstrates that, when human behavior is interdependent—that is, what one person does depends on the past, present, or anticipated future actions of others—there is not a simple or linear relationship between the choices of individuals and their collective consequences. Outside of the academy, policy makers are increasingly aware that well-intentioned interventions can backfire if they fail to account for how people change their behavior in response to the intervention. This type of problem requires a systematic modeling approach. Our entry provides a brief introduction to a growing body of research that brings together two disparate literatures—studies of decision-making and studies of the interplay between individuals' decisions and features of the social environment—through dynamic computational modeling. Cognitive scientists characterize human decision-making under uncertainty using heuristics, rules-of-thumb that produce satisfactory choices quickly and with limited information. The heuristics we use and information samples we gather have profound consequences for the choices we make. At the same time, the social context defined by the choices of others feeds back to affect individual decision-making. In recent years, there has been growing interest in methods such as agent-based modeling and systems dynamics that can capture the dynamic interplay between individuals' choices and features of the environment. However, historically these approaches have not been grounded in cognitively plausible models of human behavior. We identify areas of high potential for future research, and lay out a preliminary framework to help guide understanding of the decision-making process and its consequences in different social domains. Keywords: agent-based modeling; heuristics; social dynamics; decision-making; micro–macro linkages

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.

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.

In this paper, we develop an agent-based model of social influence on body weight. The model's assumptions are grounded in theory and evidence from physiology, social psychology, and behavioral science, and its outcomes are tested against longitudinal data from American youth. We discuss the implementation of the model, the insights it generates, and its implications for public health policy. By explicating a well-grounded dynamic mechanism, our analysis helps clarify important dependencies for both efforts to leverage social influence for obesity intervention and efforts to interpret clustering of BMI in networks.

A1 Introduction to the 8th Annual Conference on the Science of Dissemination and Implementation: Optimizing Personal and Population Health David Chambers1, Lisa Simpson2 1Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Rockville, MD, 20850, USA; 2AcademyHealth, Washington, DC, 20036, USA For the second year in a row, we are pleased to be able to share the proceedings of the Annual Conference on the Science of Dissemination and Implementation in Health, a large meeting reflecting the expanding and evolving research field that seeks to optimize the use of evidence, interventions, and tools from health research within the myriad of settings where people receive health care, make health-related decisions, and increase knowledge of influences on the health of the population. We once again benefitted from a strong partnership, co-led by AcademyHealth and the National Institutes of Health (NIH), with co-sponsorship from the Agency for Healthcare Research and Quality (AHRQ), the Patient Centered Outcomes Research Institute (PCORI), the Robert Wood Johnson Foundation (RWJF), the US Department of Veterans Affairs (VA), and the WT Grant Foundation. In addition, we benefitted from the collaboration of staff from the Centers for Disease Control and Prevention (CDC) and the World Health Organization (WHO). NIH and AcademyHealth again co-led the program planning committee, which focused on the development of the plenary sessions, and convened a scientific advisory panel to suggest speakers and advise on the overall conference development. The planning committee identified four key areas around which to focus the plenary panels and keynote address. Dr. America Bracho, M.D., M.P.H., Executive Director of Latino Health Access in Orange County, California, spoke about the opportunities for implementation science to inform efforts to improve community health and engage underserved populations. The three plenary panels each focused on a significant future direction for dissemination and implementation (D & I) research: the interface between D&I science and population health, emerging opportunities for global implementation science, and the challenges around implementation of precision medicine. The plenary sessions were complemented by facilitated lunchtime discussions on the same three topics, which offered participants an opportunity to identify key research questions for each and brainstorm next steps. Synopses of the lunchtime discussions are included in this supplement. Given the overwhelming success of the 2014 conference and the large number of abstracts received in 2014 (660), the program planning committee identified eight program tracks for abstract submitters to respond to, and through which the concurrent sessions of the conference would be organized. These tracks—Behavioral Health, Big Data and Technology for Dissemination and Implementation Research, Clinical Care Settings, Global Dissemination and Implementation, Promoting Health Equity and Eliminating Disparities, Health Policy Dissemination and Implementation, Prevention and Public Health, and Models, Measures and Methods— were designed to enable conference participants to follow a consistent theme across the multiple sessions of the conference and form the structure of this supplement. The call for abstracts, including individual paper presentations, individual posters and panel presentations, resulted in 515 submissions, spread across the eight thematic tracks. Over one hundred reviewers devoted their time to ensuring a comprehensive and expert review, and reviews were conducted within each track and coordinated by the track leads. For the final program, 64 oral presentations, 12 panels, and 263 posters were presented over the two-day meeting. Slides for the oral presentations and panels (with the agreement of the authors) were posted on the conference website (http://diconference.academyhealth.org/archives/2015archives) and all abstracts were included on the conference webapp (https://academyhealth.confex.com/academyhealth/2015di/meetingapp.cgi). This supplement has compiled the abstracts for presented papers, panel sessions, and lunchtime discussions from the 8th Annual Meeting on the Science of Dissemination and Implementation in Health: Optimizing Personal and Population Health. We are pleased to have the abstracts from the conference together in one volume once again, and look forward to the 9th Annual meeting, scheduled for December in Washington, D.C.