Examinando por Autor "Langellier, Brent A."

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    Complex Systems Approaches to Diet: A Systematic Review
    (American Journal of Preventive Medicine, 2019-07-17) Langellier, Brent A.; Bilal, Usama; Montes, Felipe; Meisel, Jose D.; de Oliveira Cardoso, Letícia; Hammond, Ross A.
    Context Complex systems approaches can help to elucidate mechanisms that shape population-level patterns in diet and inform policy approaches. This study reports results of a structured review of key design elements and methods used by existing complex systems models of diet. Evidence acquisition The authors conducted systematic searches of the PubMed, Web of Science, and LILACS databases between May and September 2018 to identify peer-reviewed manuscripts that used agent-based models or system dynamics models to explore diet. Searches occurred between November 2017 and May 2018. The authors extracted relevant data regarding each study's diet and nutrition outcomes; use of data for parameterization, calibration, and validation; results; and generated insights. The literature search adhered to PRISMA guidelines. Evidence synthesis Twenty-two agent-based model studies and five system dynamics model studies met the inclusion criteria. Mechanistic studies explored neighborhood- (e.g., residential segregation), interpersonal- (e.g., social influence) and individual-level (e.g., heuristics that guide food purchasing decisions) mechanisms that influence diet. Policy-oriented studies examined policies related to food pricing, the food environment, advertising, nutrition labels, and social norms. Most studies used empirical data to inform values of key parameters; studies varied in their approaches to calibration and validation. Conclusions Opportunities remain to advance the state of the science of complex systems approaches to diet and nutrition. These include using models to better understand mechanisms driving population-level diet, increasing use of models for policy decision support, and leveraging the wide availability of epidemiologic and policy evaluation data to improve model validation.
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    From causal loop diagrams to future scenarios: Using the cross-impact balance method to augment understanding of urban health in Latin America
    (Social Science and Medicine, 2021-06-21) Stankov, Ivana; Useche, Andrés Felipe; Meisel, Jose D.; Montes, Felipe; Morais, Lidia MO.; Friche, Amelia AL.; Langellier, Brent A.; Hovmand, Peter; Sarmiento, Olga Lucia; Hammond, Ross A.; Diez Roux, Ana V.
    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.
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    From causal loop diagrams to future scenarios: Using the cross-impact balance method to augment understanding of urban health in Latin America
    (Social Science and Medicine, 2021-06-21) Stankov, Ivana; Useche, Andrés F.; Meisel, Jose D.; Montes, Felipe; Morais, Lidia MO.; Friche, Amelia AL.; Langellier, Brent A.; Hovmand, Peter; Sarmiento, Olga L.; Hammond, Ross A.; Diez Roux, Ana V.
    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.
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    Using cause-effect graphs to elicit expert knowledge for cross-impact balance analysis
    (MethodsX, 2021-08-17) Stankova, Ivana; Useche, Andres F.; Meisel, Jose D.; Montes, Felipe; Morais, Lidia MO.; Friche, Amelia AL.; Langellier, Brent A.; Hovman, Peter; Sarmiento, Olga L.; Hammond, Ross A.; Diez Roux, Ana V.
    Ross-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.
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    Using community-based system dynamics modeling to understand the complex systems that influence health in cities: The SALURBAL study
    (Health & Place, 2019-10-03) Langellier, Brent A.; Kuhlberg, Jill A.; Ballard, Ellis A.; Slesinski, S. Claire; Stankov, Ivana; Gouveia, Nelson; Meisel, Jose D.; Kroker, Fernanda; Sarmiento, Olga L.; Teixeira Caiaffa, Waleska; Diez Roux, Ana
    We discuss the design, implementation, and results of a collaborative process designed to elucidate the complex systems that drive food behaviors, transport, and health in Latin American cities and to build capacity for systems thinking and community-based system dynamics (CBSD) methods among diverse research team members and stakeholders. During three CBSD workshops, 62 stakeholders from 10 Latin American countries identified 98 variables and a series of feedback loops that shape food behaviors, transportation and health, along with 52 policy levers. Our findings suggest that CBSD can engage local stakeholders, help them view problems through the lens of complex systems and use their insights to prioritize research efforts and identify novel solutions that consider mechanisms of complexity.