Systemic Risk in the Financial System: Insights From Network Science

Analyzes systemic risk from the perspective of network structure and the connectivity links between actors. Explores how the markets' lack of robustness, the pattern of network links, and the lack of diversity in networks contributed to the crisis

The economic impact of obesity in the United States

Over the past several decades, obesity has grown into a major global epidemic. In the United States (US), more than two-thirds of adults are now overweight and one-third is obese. In this article, we provide an overview of the state of research on the likely economic impact of the US obesity epidemic at the national level. Research to date has identified at least four major categories of economic impact linked with the obesity epidemic: direct medical costs, productivity costs, transportation costs, and human capital costs. We review current evidence on each set of costs in turn, and identify important gaps for future research and potential trends in future economic impacts of obesity. Although more comprehensive analysis of costs is needed, substantial economic impacts of obesity are identified in all four categories by existing research. The magnitude of potential economic impact underscores the importance of the obesity epidemic as a focus for policy and a topic for future research

Rugged landscapes: Complexity and implementation science

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\u27s results emphasize the importance of adapting one\u27s 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

Tobacco Town: Computational Modeling of Policy Options to Reduce Tobacco Retailer Density

To identify the behavioral mechanisms and effects of tobacco control policies designed to reduce tobacco retailer density

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

National Commission on Social, Emotional, and Academic Development: A Policy Agenda in Support of How Learning Happens

Policy can play an essential role in moving efforts to support the whole learner from the periphery to the mainstream of American education, and from the realm of ideas to implementation. This document is rooted in the belief that policy should create enabling conditions for communities to implement locally crafted practices that drive more equitable outcomes by supporting each and every student's social, emotional, and academic development

Assessing Within-Field Variation in Alfalfa Leaf Area Index Using UAV Visible Vegetation Indices

This study examines the use of leaf area index (LAI) to inform variable-rate irrigation (VRI) for irrigated alfalfa (Medicago sativa). LAI is useful for predicting zone-specific evapotranspiration (ETc). One approach toward estimating LAI is to utilize the relationship between LAI and visible vegetation indices (VVIs) using unmanned aerial vehicle (UAV) imagery. This research has three objectives: (1) to measure and describe the within-field variation in LAI and canopy height for an irrigated alfalfa field, (2) to evaluate the relationships between the alfalfa LAI and various VVIs with and without field average canopy height, and (3) to use UAV images and field average canopy height to describe the within-field variation in LAI and the potential application to VRI. The study was conducted in 2021–2022 in Rexburg, Idaho. Over the course of the study, the measured LAI varied from 0.23 m2 m−2 to 11.28 m2 m−2 and canopy height varied from 6 cm to 65 cm. There was strong spatial clustering in the measured LAI but the spatial patterns were dynamic between dates. Among eleven VVIs evaluated, the four that combined green and red wavelengths but excluded blue wavelengths showed the most promise. For all VVIs, adding average canopy height to multiple linear regression improved LAI prediction. The regression model using the modified green–red vegetation index (MGRVI) and canopy height (R2 = 0.93) was applied to describe the spatial variation in the LAI among VRI zones. There were significant (p \u3c 0.05) but not practical differences

Design and methods of Shape Up Under 5: Integration of systems science and community-engaged research to prevent early childhood obesity

Shape Up Under 5 (SUU5) was a two-year early childhood obesity prevention pilot study in Somerville, Massachusetts (2015–2017) designed to test a novel conceptual framework called Stakeholder-driven Community Diffusion. For whole-of-community interventions, this framework posits that diffusion of stakeholders’ knowledge about and engagement with childhood obesity prevention efforts through their social networks will improve the implementation of health-promoting policy and practice changes intended to reduce obesity risk. SUU5 used systems science methods (agent-based modeling, group model building, social network analysis) to design, facilitate, and evaluate the work of 16 multisector stakeholders (‘the Committee’). In this paper, we describe the design and methods of SUU5 using the conceptual framework: the approach to data collection, and methods and rationale for study inputs, activities and evaluation, which together may further our understanding of the hypothesized processes within Stakeholder-driven Community Diffusion. We also present a generalizable conceptual framework for addressing childhood obesity and similar complex public health issues through whole-of-community interventions

Mobilisation of Public Support for Policy Actions to Prevent Obesity

Public mobilisation is needed to enact obesity prevention policies and to mitigate backlash against their implementation. However, current approaches in public health focus primarily on dialogue between public health professionals and political leaders. Strategies to increase popular demand for obesity prevention policies include refining and streamlining public information, identifying effective frames for each population, enhancing media advocacy, building citizen protest and engagement, and developing a receptive political environment with change agents embedded across organisations and sectors. Long-term support and investment in collaboration among diverse stakeholders to create shared value is also important. Each actor in an expanded coalition for obesity prevention can make specific contributions to engaging, mobilising and coalescing the public. Shifting from a top-down to an integrated bottom-up and top-down approach would require an overhaul of current strategies and re-prioritisation of resources