Examinando por Materia "Chronic disease"

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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.