Un marco conceptual orientado a la acción para soluciones sistémicas de prevención de la obesidad infantil en Latinoamérica y en las poblaciones latinas de Estados Unidos.

La obesidad infantil en Latinoamérica y en las poblaciones latinas de Estados Unidos es un problema de salud pública complejo y persistente y, como tal, requiere soluciones basadas en la teoría y los métodos de la ciencia de sistemas. En este artículo presentamos un marco conceptual orientado a la acción para diseñar, implementar, evaluar y mantener cambios sistémicos comunitarios con el fin de prevenir la obesidad infantil en Latinoamérica y en las poblaciones latinas de Estados Unidos. Las acciones de nuestro marco conceptual comprenden seis etapas: (1) promover la formación de un equipo multisectorial; (2) mapear el sistema, el contexto y los impulsores; (3) concebir cambios sistémicos; (4) realizar cambios sistémicos; (5) monitorear, aprender y adaptar; (6) escalar y mantener. También proponemos diez principios que colocan los derechos humanos y ambientales y el pensamiento sistémico en el centro de estas soluciones que afectan al conjunto del sistema. A cada etapa de acción le corresponde una lista de actividades, métodos, enfoques y ejemplos concretos que pueden utilizarse como guía y base del trabajo que hay que realizar para alcanzar los resultados esperados. Por último, presentamos cómo ampliar y mantener el uso de la ciencia de sistemas para prevenir la obesidad infantil en Latinoamérica y en las poblaciones latinas de Estados Unidos.

An action-oriented framework for systems-based solutions aimed at childhood obesity prevention in US Latinx and Latin American populations.

Childhood obesity in US Latinx and Latin American populations is a persistent, complex public health issue and, as such, requires solutions grounded on systems science theory and methods. In this paper, we introduce an action-oriented framework to design, implement, evaluate, and sustain whole-of-community systems changes for childhood obesity prevention in US Latinx and Latin American populations. Our framework covers six action steps: (1) foster multisectoral team; (2) map the system, its context, and drivers; (3) envision system-wide changes; (4) effect system-wide changes; (5) monitor, learn, and adapt; and (6) scale and sustain. We also propose 10 principles that put human and environmental rights and systems thinking at the center of these systems-based solutions. For each action step, we provide a list of concrete activities, methods, approaches, and examples that can be used to guide and inform the work needed to achieve the expected outputs. Finally, we discuss how a wider adoption of systems science for childhood obesity prevention among US Latinx and Latin American populations can be encouraged and sustained.

Air pollution, physical activity and health: A mapping review of the evidence.

BACKGROUND: Exposure to air pollution and physical inactivity are both significant risk factors for non-communicable diseases (NCDs). These risk factors are also linked so that the change in exposure in one will impact risks and benefits of the other. These links are well captured in the active transport (walking, cycling) health impact models, in which the increases in active transport leading to increased inhaled dose of air pollution. However, these links are more complex and go beyond the active transport research field. Hence, in this study, we aimed to summarize the empirical evidence on the links between air pollution and physical activity, and their combined effect on individual and population health. OBJECTIVES AND METHODS: We conducted a non-systematic mapping review of empirical and modelling evidence of the possible links between exposure to air pollution and physical activity published until Autumn 2019. We reviewed empirical evidence for the (i) impact of exposure to air pollution on physical activity behaviour, (ii) exposure to air pollution while engaged in physical activity and (iii) the short-term and (iv) long-term health effects of air pollution exposure on people engaged in physical activity. In addition, we reviewed (v) public health modelling studies that have quantified the combined effect of air pollution and physical activity. These broad research areas were identified through expert discussions, including two public events performed in health-related conferences. RESULTS AND DISCUSSION: The current literature suggests that air pollution may decrease physical activity levels during high air pollution episodes or may prevent people from engaging in physical activity overall in highly polluted environments. Several studies have estimated fine particulate matter (PM2.5) exposure in active transport environment in Europe and North-America, but the concentration in other regions, places for physical activity and for other air pollutants are poorly understood. Observational epidemiological studies provide some evidence for a possible interaction between air pollution and physical activity for acute health outcomes, while results for long-term effects are mixed with several studies suggesting small diminishing health gains from physical activity due to exposure to air pollution for long-term outcomes. Public health modelling studies have estimated that in most situations benefits of physical activity outweigh the risks of air pollution, at least in the active transport environment. However, overall evidence on all examined links is weak for low- and middle-income countries, for sensitive subpopulations (children, elderly, pregnant women, people with pre-existing conditions), and for indoor air pollution. CONCLUSIONS: Physical activity and air pollution are linked through multiple mechanisms, and these relations could have important implications for public health, especially in locations with high air pollution concentrations. Overall, this review calls for international collaboration between air pollution and physical activity research fields to strengthen the evidence base on the links between both and on how policy options could potentially reduce risks and maximise health benefits.

A systematic review of empirical and simulation studies evaluating the health impact of transportation interventions.

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.

Exploring the emergence and evolution of population patterns of leisure-time physical activity through agent-based modelling.

INTRODUCTION: Most interventions aiming to promote leisure-time physical activity (LTPA) at population level showed small or null effects. Approaching the problem from a systems science perspective may shed light on the reasons for these results. We developed an agent-based model to explore how the interactions between psychological attributes and built and social environments may lead to the emergence and evolution of LTPA patterns among adults. METHODS: The modeling process consisted of four stages: (1) conceptual model development, (2) formulation of the agent-based model, (3) parametrization and calibration, and (4) consistency and sensitivity analyses. The model represents a stylized community containing two types of agents: persons and LTPA sites. Persons interact with each other (proximal network and perceived community) and with the built environment (LTPA sites) over time. Decision-making is based on the person's intention to practice LTPA, conditioned to the perceived environment. Each iteration is equivalent to one week and we assessed a period of 10 years. RESULTS: The model was able to reproduce population temporal trends of intention and LTPA reported in the literature. Sensitivity analyses indicated that population patterns and trends of intention and LTPA were highly influenced by the relationship between a person's behavior in the preceding week and his current intention, the person's access to built and social environment, and the density of LTPA sites. CONCLUSIONS: The proposed agent-based model is suitable to explore the emergence and evolution of LTPA patterns among adults, considering the dynamic interaction between individuals' psychological attributes and the built and social environments in which they live. The model is available at https://doi.org/10.17605/OSF.IO/J2KAS .

Estimating city-level travel patterns using street imagery: A case study of using Google Street View in Britain.

BACKGROUND: Street imagery is a promising and growing big data source providing current and historical images in more than 100 countries. Studies have reported using this data to audit road infrastructure and other built environment features. Here we explore a novel application, using Google Street View (GSV) to predict travel patterns at the city level. METHODS: We sampled 34 cities in Great Britain. In each city, we accessed 2000 GSV images from 1000 random locations. We selected archived images from time periods overlapping with the 2011 Census and the 2011-2013 Active People Survey (APS). We manually annotated the images into seven categories of road users. We developed regression models with the counts of images of road users as predictors. The outcomes included Census-reported commute shares of four modes (combined walking plus public transport, cycling, motorcycle, and car), as well as APS-reported past-month participation in walking and cycling. RESULTS: We found high correlations between GSV counts of cyclists ('GSV-cyclists') and cycle commute mode share (r = 0.92)/past-month cycling (r = 0.90). Likewise, GSV-pedestrians was moderately correlated with past-month walking for transport (r = 0.46), GSV-motorcycles was moderately correlated with commute share of motorcycles (r = 0.44), and GSV-buses was highly correlated with commute share of walking plus public transport (r = 0.81). GSV-car was not correlated with car commute mode share (r = -0.12). However, in multivariable regression models, all outcomes were predicted well, except past-month walking. The prediction performance was measured using cross-validation analyses. GSV-buses and GSV-cyclists are the strongest predictors for most outcomes. CONCLUSIONS: GSV images are a promising new big data source to predict urban mobility patterns. Predictive power was the greatest for those modes that varied the most (cycle and bus). With its ability to identify mode of travel and capture street activity often excluded in routinely carried out surveys, GSV has the potential to be complementary to new and traditional data. With half the world's population covered by street imagery, and with up to 10 years historical data available in GSV, further testing across multiple settings is warranted both for cross-sectional and longitudinal assessments.

Development of a dynamic framework to explain population patterns of leisure-time physical activity through agent-based modeling.

Despite the increasing body of evidences on the factors influencing leisure-time physical activity, our understanding of the mechanisms and interactions that lead to the formation and evolution of population patterns is still limited. Moreover, most frameworks in this field fail to capture dynamic processes. Our aim was to create a dynamic conceptual model depicting the interaction between key psychological attributes of individuals and main aspects of the built and social environments in which they live. This conceptual model will inform and support the development of an agent-based model aimed to explore how population patterns of LTPA in adults may emerge from the dynamic interplay between psychological traits and built and social environments. We integrated existing theories and models as well as available empirical data (both from literature reviews), and expert opinions (based on a systematic expert assessment of an intermediary version of the model). The model explicitly presents intention as the proximal determinant of leisure-time physical activity, a relationship dynamically moderated by the built environment (access, quality, and available activities) - with the strength of the moderation varying as a function of the person's intention- and influenced both by the social environment (proximal network's and community's behavior) and the person's behavior. Our conceptual model is well supported by evidence and experts' opinions and will inform the design of our agent-based model, as well as data collection and analysis of future investigations on population patterns of leisure-time physical activity among adults.