Uncovering physical activity trade-offs in transportation policy: A spatial agent-based model of Bogotá, Colombia.

BACKGROUND: Transportation policies can impact health outcomes while simultaneously promoting social equity and environmental sustainability. We developed an agent-based model (ABM) to simulate the impacts of fare subsidies and congestion taxes on commuter decision-making and travel patterns. We report effects on mode share, travel time and transport-related physical activity (PA), including the variability of effects by socioeconomic strata (SES), and the trade-offs that may need to be considered in the implementation of these policies in a context with high levels of necessity-based physical activity. METHODS: The ABM design was informed by local stakeholder engagement. The demographic and spatial characteristics of the in-silico city, and its residents, were informed by local surveys and empirical studies. We used ridership and travel time data from the 2019 Bogotá Household Travel Survey to calibrate and validate the model by SES. We then explored the impacts of fare subsidy and congestion tax policy scenarios. RESULTS: Our model reproduced commuting patterns observed in Bogotá, including substantial necessity-based walking for transportation. At the city-level, congestion taxes fractionally reduced car use, including among mid-to-high SES groups but not among low SES commuters. Neither travel times nor physical activity levels were impacted at the city level or by SES. Comparatively, fare subsidies promoted city-level public transportation (PT) ridership, particularly under a 'free-fare' scenario, largely through reductions in walking trips. 'Free fare' policies also led to a large reduction in very long walking times and an overall reduction in the commuting-based attainment of physical activity guidelines. Differential effects were observed by SES, with free fares promoting PT ridership primarily among low-and-middle SES groups. These shifts to PT reduced median walking times among all SES groups, particularly low-SES groups. Moreover, the proportion of low-to-mid SES commuters meeting weekly physical activity recommendations decreased under the 'freefare' policy, with no change observed among high-SES groups. CONCLUSIONS: Transport policies can differentially impact SES-level disparities in necessity-based walking and travel times. Understanding these impacts is critical in shaping transportation policies that balance the dual aims of reducing SES-level disparities in travel time (and time poverty) and the promotion of choice-based physical activity.

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.

A complex systems model of breast cancer etiology: The Paradigm II Model.

BACKGROUND: Complex systems models of breast cancer have previously focused on prediction of prognosis and clinical events for individual women. There is a need for understanding breast cancer at the population level for public health decision-making, for identifying gaps in epidemiologic knowledge and for the education of the public as to the complexity of this most common of cancers. METHODS AND FINDINGS: We developed an agent-based model of breast cancer for the women of the state of California using data from the U.S. Census, the California Health Interview Survey, the California Cancer Registry, the National Health and Nutrition Examination Survey and the literature. The model was implemented in the Julia programming language and R computing environment. The Paradigm II model development followed a transdisciplinary process with expertise from multiple relevant disciplinary experts from genetics to epidemiology and sociology with the goal of exploring both upstream determinants at the population level and pathophysiologic etiologic factors at the biologic level. The resulting model reproduces in a reasonable manner the overall age-specific incidence curve for the years 2008-2012 and incidence and relative risks due to specific risk factors such as BRCA1, polygenic risk, alcohol consumption, hormone therapy, breastfeeding, oral contraceptive use and scenarios for environmental toxin exposures. CONCLUSIONS: The Paradigm II model illustrates the role of multiple etiologic factors in breast cancer from domains of biology, behavior and the environment. The value of the model is in providing a virtual laboratory to evaluate a wide range of potential interventions into the social, environmental and behavioral determinants of breast cancer at the population level.

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.

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.

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.

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.

Potential impacts of policies to reduce purchasing of ultra-processed foods in Mexico at different stages of the social transition: an agent-based modelling approach.

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.

Validation and refinement of the Stakeholder-driven Community Diffusion Survey for childhood obesity prevention.

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.

Using cause-effect graphs to elicit expert knowledge for cross-impact balance analysis.

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.

From causal loop diagrams to future scenarios: Using the cross-impact balance method to augment understanding of urban health in Latin America.

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.

A review and agenda for integrated disease models including social and behavioural factors.

Social and behavioural factors are critical to the emergence, spread and containment of human disease, and are key determinants of the course, duration and outcomes of disease outbreaks. Recent epidemics of Ebola in West Africa and coronavirus disease 2019 (COVID-19) globally have reinforced the importance of developing infectious disease models that better integrate social and behavioural dynamics and theories. Meanwhile, the growth in capacity, coordination and prioritization of social science research and of risk communication and community engagement (RCCE) practice within the current pandemic response provides an opportunity for collaboration among epidemiological modellers, social scientists and RCCE practitioners towards a mutually beneficial research and practice agenda. Here, we provide a review of the current modelling methodologies and describe the challenges and opportunities for integrating them with social science research and RCCE practice. Finally, we set out an agenda for advancing transdisciplinary collaboration for integrated disease modelling and for more robust policy and practice for reducing disease transmission.

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.

Evolution of a Coalition Network during a Whole-of-Community Intervention to Prevent Early Childhood Obesity.

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.

Defining and Intervening on Cumulative Environmental Neurodevelopmental Risks: Introducing a Complex Systems Approach.

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.

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.

Associations of mothers' source of feeding information with longitudinal trajectories of sugar-sweetened beverage intake, 100% juice intake and adiposity in early childhood.

BACKGROUND: There remains a need to understand how information sources can promote young children's healthy beverage consumption and prevent obesity. OBJECTIVES: To examine associations of mothers' primary feeding information source with children's sugar-sweetened beverage (SSB) intake, 100% juice intake and adiposity between ages 3 and 7 years. METHODS: We analyzed data from a prospective cohort study (n = 371 children; 13 visits). Mothers reported their primary feeding information source at baseline and completed child 3-day dietary records each visit. Child adiposity indicators were calculated from repeated height/weight measurements and dual-energy X-ray absorptiometry. Longitudinal models examined beverage intakes and adiposity over time by source. RESULTS: Primary feeding information sources included doctors (48.2%), mothers (17.5%), grandmothers (13.5%), other healthcare professionals (11.3%) and other family/friends (9.4%). Children's juice intake with age differed by source (P interaction = 0.03), with steepest and slightest intake decreases in the doctor (-19.7% each year; 95% CI: -23.7%, -15.5%) and grandmother (-5.0%; -14.5%, 5.5%) subgroups, respectively. Children's SSB intake did not differ by source, but increased annually by 7.1% (4.5%, 9.8%) overall. The grandmother subgroup had the greatest child adiposity over time. CONCLUSIONS: Mothers' primary feeding information source may have important, yet heterogeneous, influences on young children's beverage intakes and adiposity over time. Consistent evidence-based messages are likely needed.