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Luke DA, Powell BJ, Paniagua-Avila A. Bridges and Mechanisms: Integrating Systems Science Thinking into Implementation Research. Annu Rev Public Health 2024; 45:7-25. [PMID: 38100647 DOI: 10.1146/annurev-publhealth-060922-040205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2023]
Abstract
We present a detailed argument for how to integrate, or bridge, systems science thinking and methods with implementation science. We start by showing how fundamental systems science principles of structure, dynamics, information, and utility are relevant for implementation science. Then we examine the need for implementation science to develop and apply richer theories of complex systems. This can be accomplished by emphasizing a causal mechanisms approach. Identifying causal mechanisms focuses on the "cogs and gears" of public health, clinical, and organizational interventions. A mechanisms approach focuses on how a specific strategy will produce the implementation outcome. We show how connecting systems science to implementation science opens new opportunities for examining and addressing social determinants of health and conducting equitable and ethical implementation research. Finally, we present case studies illustrating successful applications of systems science within implementation science in community health policy, tobacco control, health care access, and breast cancer screening.
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Affiliation(s)
- Douglas A Luke
- Center for Public Health Systems Science, Brown School, Washington University in St. Louis, St. Louis, Missouri, USA;
| | - Byron J Powell
- Center for Mental Health Services Research, Brown School; Center for Dissemination & Implementation, Institute for Public Health; and Division of Infectious Diseases, John T. Milliken Department of Medicine, School of Medicine, Washington University in St. Louis, St. Louis, Missouri, USA
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Mudd AL, Bal M, Verra SE, Poelman MP, de Wit J, Kamphuis CBM. The current state of complex systems research on socioeconomic inequalities in health and health behavior-a systematic scoping review. Int J Behav Nutr Phys Act 2024; 21:13. [PMID: 38317165 PMCID: PMC10845451 DOI: 10.1186/s12966-024-01562-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 01/14/2024] [Indexed: 02/07/2024] Open
Abstract
BACKGROUND Interest in applying a complex systems approach to understanding socioeconomic inequalities in health is growing, but an overview of existing research on this topic is lacking. In this systematic scoping review, we summarize the current state of the literature, identify shared drivers of multiple health and health behavior outcomes, and highlight areas ripe for future research. METHODS SCOPUS, Web of Science, and PubMed databases were searched in April 2023 for peer-reviewed, English-language studies in high-income OECD countries containing a conceptual systems model or simulation model of socioeconomic inequalities in health or health behavior in the adult general population. Two independent reviewers screened abstracts and full texts. Data on study aim, type of model, all model elements, and all relationships were extracted. Model elements were categorized based on the Commission on Social Determinants of Health framework, and relationships between grouped elements were visualized in a summary conceptual systems map. RESULTS A total of 42 publications were included; 18 only contained a simulation model, 20 only contained a conceptual model, and 4 contained both types of models. General health outcomes (e.g., health status, well-being) were modeled more often than specific outcomes like obesity. Dietary behavior and physical activity were by far the most commonly modeled health behaviors. Intermediary determinants of health (e.g., material circumstances, social cohesion) were included in nearly all models, whereas structural determinants (e.g., policies, societal values) were included in about a third of models. Using the summary conceptual systems map, we identified 15 shared drivers of socioeconomic inequalities in multiple health and health behavior outcomes. CONCLUSIONS The interconnectedness of socioeconomic position, multiple health and health behavior outcomes, and determinants of socioeconomic inequalities in health is clear from this review. Factors central to the complex system as it is currently understood in the literature (e.g., financial strain) may be both efficient and effective policy levers, and factors less well represented in the literature (e.g., sleep, structural determinants) may warrant more research. Our systematic, comprehensive synthesis of the literature may serve as a basis for, among other things, a complex systems framework for socioeconomic inequalities in health.
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Affiliation(s)
- Andrea L Mudd
- Department of Interdisciplinary Social Science- Public Health, Utrecht University, PO Box 80140, 3508 TC, Utrecht, The Netherlands.
| | - Michèlle Bal
- Department of Interdisciplinary Social Science- Public Health, Utrecht University, PO Box 80140, 3508 TC, Utrecht, The Netherlands
| | - Sanne E Verra
- Department of Interdisciplinary Social Science- Public Health, Utrecht University, PO Box 80140, 3508 TC, Utrecht, The Netherlands
| | - Maartje P Poelman
- Chair Group Consumption and Healthy Lifestyles, Wageningen University & Research, Hollandseweg 1, 6706 KN, Wageningen, the Netherlands
| | - John de Wit
- Department of Interdisciplinary Social Science- Public Health, Utrecht University, PO Box 80140, 3508 TC, Utrecht, The Netherlands
| | - Carlijn B M Kamphuis
- Department of Interdisciplinary Social Science- Public Health, Utrecht University, PO Box 80140, 3508 TC, Utrecht, The Netherlands
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Squires H, Kelly MP, Gilbert N, Sniehotta F, Purshouse RC. The long-term effectiveness and cost-effectiveness of public health interventions; how can we model behavior? A review. HEALTH ECONOMICS 2023; 32:2836-2854. [PMID: 37681282 PMCID: PMC10843043 DOI: 10.1002/hec.4754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 05/15/2023] [Accepted: 08/14/2023] [Indexed: 09/09/2023]
Abstract
The effectiveness and cost of a public health intervention is dependent on complex human behaviors, yet health economic models typically make simplified assumptions about behavior, based on little theory or evidence. This paper reviews existing methods across disciplines for incorporating behavior within simulation models, to explore what methods could be used within health economic models and to highlight areas for further research. This may lead to better-informed model predictions. The most promising methods identified which could be used to improve modeling of the causal pathways of behavior-change interventions include econometric analyses, structural equation models, data mining and agent-based modeling; the latter of which has the advantage of being able to incorporate the non-linear, dynamic influences on behavior, including social and spatial networks. Twenty-two studies were identified which quantify behavioral theories within simulation models. These studies highlight the importance of combining individual decision making and interactions with the environment and demonstrate the importance of social norms in determining behavior. However, there are many theoretical and practical limitations of quantifying behavioral theory. Further research is needed about the use of agent-based models for health economic modeling, and the potential use of behavior maintenance theories and data mining.
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Affiliation(s)
- Hazel Squires
- Sheffield Centre for Health and Related Research, University of Sheffield, Sheffield, UK
| | - Michael P Kelly
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Nigel Gilbert
- Centre for Research in Social Simulation, University of Surrey, Guildford, UK
| | - Falko Sniehotta
- Faculty of Medicine Mannheim and Clinic Mannheim, Universität Heidelberg, Heidelberg, Germany
| | - Robin C Purshouse
- Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, UK
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Aguiar A, Gebremariam MK, Romanenko E, Önal F, Kopainsky B, Savona N, Brown A, Allender S, Lien N. System dynamics simulation models on overweight and obesity in children and adolescents: A systematic review. Obes Rev 2023; 24 Suppl 2:e13632. [PMID: 37753602 DOI: 10.1111/obr.13632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 07/19/2023] [Accepted: 07/22/2023] [Indexed: 09/28/2023]
Abstract
It has increasingly been recognized that developing successful obesity prevention policies and interventions requires understanding of the complex mechanisms driving the obesity pandemic and that models could be useful tools for simulating policies. This paper reviews system dynamics simulation models of mechanisms driving childhood overweight and obesity and/or testing of preventive interventions. A systematic literature search was conducted in six databases from inception to January 2023 using terms related to overweight/obesity, children, and system dynamics. Study descriptives, mechanisms, and where to intervene (the leverage points), as well as quality assessments of the simulation models were extracted by two researchers into a predetermined template and narratively synthesized. Seventeen papers describing 15 models were included. Models describing the mechanisms ranged from only intrapersonal factors to models cutting across multiple levels of the ecological model, but mechanisms across levels were lacking. The majority of interventions tested in the simulation models were changes to existing model parameters with less emphasis on models that alter system structure. In conclusion, existing models included mechanisms driving youth obesity at multiple levels of the ecological model. This is useful for developing an integrated simulation model combining mechanisms at multiple levels and allowing for testing fundamental system changes.
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Affiliation(s)
- Anaely Aguiar
- System Dynamics Group, University of Bergen, Bergen, Norway
| | | | | | - Furkan Önal
- System Dynamics Group, University of Bergen, Bergen, Norway
| | | | - Natalie Savona
- Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, UK
| | - Andrew Brown
- Global Centre for Preventive Health and Nutrition, Deakin University, Geelong, Victoria, Australia
| | - Steven Allender
- Global Centre for Preventive Health and Nutrition, Deakin University, Geelong, Victoria, Australia
| | - Nanna Lien
- Department of Nutrition, University of Oslo, Oslo, Norway
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Aguiar A, Önal F, Hendricks G, Blanchard L, Romanenko E, Fismen AS, Nwosu E, Herstad S, Savona N, Harbron J, Knai C, Samdal O, Rutter H, Lien N, Jalali MS, Kopainsky B. Understanding the dynamics emerging from the interplay among poor mental wellbeing, energy balance-related behaviors, and obesity prevalence in adolescents: A simulation-based study. Obes Rev 2023; 24 Suppl 2:e13628. [PMID: 37753604 DOI: 10.1111/obr.13628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 06/16/2023] [Accepted: 07/18/2023] [Indexed: 09/28/2023]
Abstract
Both obesity and poor mental wellbeing have a high prevalence in European youth. Adolescents in six countries identified mental wellbeing factors as main drivers of youth obesity through systems mapping. This study sought to (1) explore the dynamics of the interplay between poor mental wellbeing, energy balance-related behaviors, and adolescent overweight and obesity prevalence and (2) test the effect of intervention point scenarios to reduce adolescent obesity. Drawing on the youth-generated systems maps and a literature synthesis, we built a simulation model that represents the links from major feedback pathways for poor mental wellbeing to changes in dietary, physical activity, and sleep behaviors. The model was calibrated using survey data from Norway, expert input, and literature and shows a good fit between simulated behavior and available statistical data. The simulations indicate that adolescent mental wellbeing is harmed by socio-cultural pressures and stressors, which trigger reinforcing feedback mechanisms related to emotional/binge eating, lack of motivation to engage in physical activity, and sleep difficulty. Targeting a combination of intervention points that support a 25% reduction of pressure on body image and psychosocial stress showed potentially favorable effects on mental wellbeing-doubling on average for boys and girls and decreasing obesity prevalence by over 4%.
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Affiliation(s)
- Anaely Aguiar
- System Dynamics Group, Department of Geography, University of Bergen, Bergen, Norway
| | - Furkan Önal
- System Dynamics Group, Department of Geography, University of Bergen, Bergen, Norway
| | | | - Laurence Blanchard
- Faculty of Public Health Policy, London School of Hygiene and Tropical Medicine, London, UK
| | - Eduard Romanenko
- Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Anne-Siri Fismen
- Department of Health and Caring Services, Western Norway University of Applied Science, Bergen, Norway
| | - Emmanuel Nwosu
- Department of Human Biology, University of Cape Town, Cape Town, South Africa
| | - Sondre Herstad
- Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Natalie Savona
- Faculty of Public Health Policy, London School of Hygiene and Tropical Medicine, London, UK
| | - Janetta Harbron
- Department of Human Biology, University of Cape Town, Cape Town, South Africa
| | - Cécile Knai
- Faculty of Public Health Policy, London School of Hygiene and Tropical Medicine, London, UK
| | - Oddrun Samdal
- Department of Health Promotion and Development, Faculty of Psychology, University of Bergen, Bergen, Norway
| | - Harry Rutter
- Department of Social & Policy Sciences, University of Bath, Bath, UK
| | - Nanna Lien
- Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Mohammad S Jalali
- MGH Institute for Technology Assessment, Harvard Medical School, Harvard University, Boston, Massachusetts, USA
| | - Birgit Kopainsky
- System Dynamics Group, Department of Geography, University of Bergen, Bergen, Norway
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Whelan J, Fraser P, Bolton KA, Love P, Strugnell C, Boelsen-Robinson T, Blake MR, Martin E, Allender S, Bell C. Combining systems thinking approaches and implementation science constructs within community-based prevention: a systematic review. Health Res Policy Syst 2023; 21:85. [PMID: 37641151 PMCID: PMC10463953 DOI: 10.1186/s12961-023-01023-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 06/22/2023] [Indexed: 08/31/2023] Open
Abstract
BACKGROUND Systems science offers methods for designing population health interventions while implementation science provides specific guidance for successful implementation. Integrating systems and implementation science may strengthen implementation and enhance and sustain systemic change to achieve system-level outcomes. Little is known about the extent to which these two approaches have been integrated to date. This review aimed to identify and synthesise the peer-reviewed literature that has reported the combined use of systems thinking approaches and implementation science constructs (within the same study), to deliver population health interventions. METHODS A systematic literature search of peer-reviewed original research was conducted across six databases from 2009 to 2021. Journal manuscripts were included if they: (1) reported on a population health study conducted in a community, (2) reported the use of a systems method in the design of the intervention, and (3) used an implementation science theory, framework or model in the delivery of the intervention. Data extracted related to the specific systems methods and definitions and implementation science constructs used. The Mixed Methods Appraisal Tool (MMAT) was used to assess study quality. RESULTS Of the 9086 manuscripts returned, 320 manuscripts were included for full-text review. Of these, 17 manuscripts that reported on 14 studies were included in the final extraction. The most frequently reported systems methods were a 'whole of community systems approach' (n = 4/14) and 'community-based system dynamics' (n = 2/14). Nineteen different implementation science theories, frameworks and models were used for intervention delivery, with RE-AIM being the only framework used in more than one study. CONCLUSION There are few published peer-reviewed studies using systems thinking and implementation science for designing and delivering population health interventions. An exploration of synergies is worthwhile to operationalise alignment and improve implementation of systems thinking approaches. Review protocol registration PROSPERO CRD42021250419.
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Affiliation(s)
- Jillian Whelan
- School of Medicine, Deakin University, Geelong, Australia.
- Institute for Health Transformation, Geelong, Australia.
- Global Centre for Preventive Health and Nutrition, Geelong, Australia.
| | - Penny Fraser
- School of Health and Social Development, Deakin University, Geelong, Australia
- Institute for Health Transformation, Geelong, Australia
- Global Centre for Preventive Health and Nutrition, Geelong, Australia
| | - Kristy A Bolton
- School of Exercise and Nutrition Sciences, Deakin University, Geelong, Australia
- Institute for Health Transformation, Geelong, Australia
- Institute for Physical Activity and Nutrition, Geelong, Australia
| | - Penelope Love
- School of Exercise and Nutrition Sciences, Deakin University, Geelong, Australia
- Institute for Physical Activity and Nutrition, Geelong, Australia
| | - Claudia Strugnell
- School of Health and Social Development, Deakin University, Geelong, Australia
- Global Centre for Preventive Health and Nutrition, Geelong, Australia
- Institute for Physical Activity and Nutrition, Geelong, Australia
| | - Tara Boelsen-Robinson
- School of Health and Social Development, Deakin University, Geelong, Australia
- Institute for Health Transformation, Geelong, Australia
- Global Centre for Preventive Health and Nutrition, Geelong, Australia
| | - Miranda R Blake
- School of Health and Social Development, Deakin University, Geelong, Australia
- Institute for Health Transformation, Geelong, Australia
- Global Centre for Preventive Health and Nutrition, Geelong, Australia
| | - Erik Martin
- School of Medicine, Deakin University, Geelong, Australia
| | - Steven Allender
- School of Health and Social Development, Deakin University, Geelong, Australia
- Institute for Health Transformation, Geelong, Australia
- Global Centre for Preventive Health and Nutrition, Geelong, Australia
| | - Colin Bell
- School of Medicine, Deakin University, Geelong, Australia
- Institute for Health Transformation, Geelong, Australia
- Global Centre for Preventive Health and Nutrition, Geelong, Australia
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Breeze PR, Squires H, Ennis K, Meier P, Hayes K, Lomax N, Shiell A, Kee F, de Vocht F, O’Flaherty M, Gilbert N, Purshouse R, Robinson S, Dodd PJ, Strong M, Paisley S, Smith R, Briggs A, Shahab L, Occhipinti J, Lawson K, Bayley T, Smith R, Boyd J, Kadirkamanathan V, Cookson R, Hernandez‐Alava M, Jackson CH, Karapici A, Sassi F, Scarborough P, Siebert U, Silverman E, Vale L, Walsh C, Brennan A. Guidance on the use of complex systems models for economic evaluations of public health interventions. HEALTH ECONOMICS 2023; 32:1603-1625. [PMID: 37081811 PMCID: PMC10947434 DOI: 10.1002/hec.4681] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 03/13/2023] [Accepted: 03/14/2023] [Indexed: 05/03/2023]
Abstract
To help health economic modelers respond to demands for greater use of complex systems models in public health. To propose identifiable features of such models and support researchers to plan public health modeling projects using these models. A working group of experts in complex systems modeling and economic evaluation was brought together to develop and jointly write guidance for the use of complex systems models for health economic analysis. The content of workshops was informed by a scoping review. A public health complex systems model for economic evaluation is defined as a quantitative, dynamic, non-linear model that incorporates feedback and interactions among model elements, in order to capture emergent outcomes and estimate health, economic and potentially other consequences to inform public policies. The guidance covers: when complex systems modeling is needed; principles for designing a complex systems model; and how to choose an appropriate modeling technique. This paper provides a definition to identify and characterize complex systems models for economic evaluations and proposes guidance on key aspects of the process for health economics analysis. This document will support the development of complex systems models, with impact on public health systems policy and decision making.
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Affiliation(s)
- Penny R. Breeze
- School of Health and Related ResearchUniversity of SheffieldSheffieldUK
| | - Hazel Squires
- School of Health and Related ResearchUniversity of SheffieldSheffieldUK
| | - Kate Ennis
- British Medical Journal Technology Appraisal GroupLondonUK
| | - Petra Meier
- MRC/CSO Social and Public Health Sciences UnitUniversity of GlasgowScotlandUK
| | - Kate Hayes
- School of Health and Related ResearchUniversity of SheffieldSheffieldUK
| | - Nik Lomax
- School of GeographyUniversity of LeedsLeedsUK
| | - Alan Shiell
- Department of Public HealthLaTrobe UniversityMelbourneAustralia
| | - Frank Kee
- Centre for Public HealthQueen's University BelfastBelfastUK
| | - Frank de Vocht
- Population Health SciencesBristol Medical SchoolUniversity of BristolBristolUK
- NIHR Applied Research Collaboration West (ARC West)BristolUK
| | - Martin O’Flaherty
- Department of Public Health, Policy and SystemsUniversity of LiverpoolLiverpoolUK
| | | | - Robin Purshouse
- Department of Automatic Control and Systems EngineeringUniversity of SheffieldSheffieldUK
| | | | - Peter J Dodd
- School of Health and Related ResearchUniversity of SheffieldSheffieldUK
| | - Mark Strong
- School of Health and Related ResearchUniversity of SheffieldSheffieldUK
| | | | - Richard Smith
- College of Medicine and HealthUniversity of ExeterExeterUK
| | - Andrew Briggs
- London School of Hygiene & Tropical MedicineLondonUK
| | - Lion Shahab
- Department of Behavioural Science and HealthUCLLondonUK
| | - Jo‐An Occhipinti
- Brain and Mind CentreUniversity of SydneyNew South WalesCamperdownAustralia
| | - Kenny Lawson
- Brain and Mind CentreUniversity of SydneyNew South WalesCamperdownAustralia
| | | | - Robert Smith
- School of Health and Related ResearchUniversity of SheffieldSheffieldUK
| | - Jennifer Boyd
- School of Health and Related ResearchUniversity of SheffieldSheffieldUK
- MRC/CSO Social and Public Health Sciences UnitUniversity of GlasgowGlasgowUK
| | | | | | | | | | - Amanda Karapici
- NIHR SPHRLondon School of Hygiene and Tropical MedicineLondonUK
| | - Franco Sassi
- Centre for Health Economics & Policy InnovationImperial College Business SchoolLondonUK
| | - Peter Scarborough
- Nuffield Department of Population HealthUniversity of OxfordOxfordshireOxfordUK
| | - Uwe Siebert
- Department of Public Health, Health Services Research and Health Technology AssessmentUMIT TIROL ‐ University for Health Sciences and TechnologyHall in TirolTyrolAustria
- Division of Health Technology Assessment and BioinformaticsONCOTYROL ‐ Center for Personalized Cancer MedicineInnsbruckAustria
- Center for Health Decision ScienceDepartments of Epidemiology and Health Policy & ManagementHarvard T.H. Chan School of Public HealthMassachusettsBostonUSA
- Program on Cardiovascular Research, Institute for Technology Assessment and Department of RadiologyMassachusetts General HospitalHarvard Medical SchoolMassachusettsBostonUSA
| | - Eric Silverman
- MRC/CSO Social and Public Health Sciences UnitUniversity of GlasgowGlasgowUK
| | - Luke Vale
- Health Economics GroupPopulation Health Sciences InstituteNewcastle UniversityNewcastleUK
| | - Cathal Walsh
- Health Research Institute and MACSIUniversity of LimerickLimerickIreland
| | - Alan Brennan
- School of Health and Related ResearchUniversity of SheffieldSheffieldUK
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Kasman M, Hammond RA, Purcell R, Saliba LF, Mazzucca-Ragan S, Padek M, Allen P, Luke DA, Moreland-Russell S, Erwin PC, Brownson RC. Understanding Misimplementation in U.S. State Health Departments: An Agent-Based Model. Am J Prev Med 2023; 64:525-534. [PMID: 36509634 PMCID: PMC10033358 DOI: 10.1016/j.amepre.2022.10.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Revised: 10/14/2022] [Accepted: 10/20/2022] [Indexed: 12/14/2022]
Abstract
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.
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Affiliation(s)
- Matt Kasman
- Center on Social Dynamics and Policy, The Brookings Institution, Washington, District of Columbia.
| | - Ross A Hammond
- Center on Social Dynamics and Policy, The Brookings Institution, Washington, District of Columbia; Brown School, Washington University in St. Louis, St. Louis, Missouri; Santa Fe Institute, Santa Fe, New Mexico
| | - Rob Purcell
- Center on Social Dynamics and Policy, The Brookings Institution, Washington, District of Columbia
| | - Louise Farah Saliba
- Prevention Research Center, Brown School at Washington University in St. Louis, St. Louis, Missouri
| | - Stephanie Mazzucca-Ragan
- Prevention Research Center, Brown School at Washington University in St. Louis, St. Louis, Missouri
| | - Margaret Padek
- Prevention Research Center, Brown School at Washington University in St. Louis, St. Louis, Missouri
| | - Peg Allen
- Prevention Research Center, Brown School at Washington University in St. Louis, St. Louis, Missouri
| | - Douglas A Luke
- Center for Public Health Systems Science, Brown School, Washington University in St. Louis, St. Louis, Missouri
| | - Sarah Moreland-Russell
- Prevention Research Center, Brown School at Washington University in St. Louis, St. Louis, Missouri
| | - Paul C Erwin
- School of Public Health, The University of Alabama at Birmingham, Birmingham, Alabama
| | - Ross C Brownson
- Prevention Research Center, Brown School at Washington University in St. Louis, St. Louis, Missouri; Public Health Sciences Division, Department of Surgery, Washington University School of Medicine in St. Louis, St. Louis, Missouri; Alvin J. Siteman Cancer Center, Washington University School of Medicine in St. Louis, St. Louis, Missouri
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9
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Stankov I, Henson RM, Headen I, Purtle J, Langellier BA. Use of qualitative systems mapping and causal loop diagrams to understand food environments, diet and obesity: a scoping review protocol. BMJ Open 2023; 13:e066875. [PMID: 36931683 PMCID: PMC10030560 DOI: 10.1136/bmjopen-2022-066875] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 02/20/2023] [Indexed: 03/19/2023] Open
Abstract
INTRODUCTION Food systems can shape dietary behaviour and obesity outcomes in complex ways. Qualitative systems mapping using causal loop diagrams (CLDs) can depict how people understand the complex dynamics, inter-relationships and feedback characteristic of food systems in ways that can support policy planning and action. To date, there has been no attempt to review this literature. The objectives of this review are to scope the extent and nature of studies using qualitative systems mapping to facilitate the development of CLDs by stakeholders to understand food environments, including settings and populations represented, key findings and the methodological processes employed. It also seeks to identify gaps in knowledge and implications for policy and practice. METHODS AND ANALYSIS This protocol describes a scoping review guided by the Joanna Briggs Institute manual, the framework by Khalil and colleagues and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews checklist reporting guidelines. A search strategy was iteratively developed with two academic librarians and the research team. This strategy will be used to search six databases, including Ovid MEDLINE, Embase, EmCare, Web of Science, Scopus and ProQuest Central. Identified citations will be screened by two independent reviewers; first, by title and abstract, and then full-text articles to identify papers eligible for inclusion. The reference lists of included studies and relevant systematic reviews will be searched to identify other papers eligible for inclusion. Two reviewers will extract information from all included studies and summarise the findings descriptively and numerically. ETHICS AND DISSEMINATION The scoping review will provide an overview of how CLDs developed by stakeholders have been elicited to understand food environments, diet and obesity, the insights gained and how the CLDs have been used. It will also highlight gaps in knowledge and implications for policy and practice. The review will be disseminated through publication in an academic journal and conference presentations.
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Affiliation(s)
- Ivana Stankov
- Urban Health Collaborative, Drexel University, Philadelphia, Pennsylvania, USA
- UniSA Allied Health & Human Performance, University of South Australia, Adelaide, South Australia, Australia
| | - Rosie Mae Henson
- Department of Health Management and Policy, Drexel University, Philadelphia, Pennsylvania, USA
| | - Irene Headen
- Department of Community Health and Prevention, Drexel University, Philadelphia, Pennsylvania, USA
| | - Jonathan Purtle
- Department of Public Health Policy and Management, New York University, New York, New York, USA
| | - Brent A Langellier
- Department of Health Management and Policy, Drexel University, Philadelphia, Pennsylvania, USA
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Romanenko E, Homer J, Fismen AS, Rutter H, Lien N. Assessing policies to reduce adolescent overweight and obesity: Insights from a system dynamics model using data from the Health Behavior in School-Aged Children study. Obes Rev 2023; 24 Suppl 1:e13519. [PMID: 36416189 DOI: 10.1111/obr.13519] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 08/10/2022] [Accepted: 10/12/2022] [Indexed: 11/23/2022]
Abstract
Adolescent overweight and obesity (AdOWOB) in Europe has proven to be a persistent and complex problem, and appropriate systems methods may help in evaluating potential policy options. This paper describes the development of a system dynamics model of AdOWOB as part of the EU-funded CO-CREATE project. The model was developed using literature and data from the Health Behavior in School-Aged Children (HBSC) study across 31 European countries. We identified 10 HBSC variables that were included as direct or indirect drivers of AdOWOB in the dynamic model, seven at the level of the individual, and three related to the social environment. The model was calibrated to 24 separate cases based on four gender and perceived wealth segments for each of the five CO-CREATE countries (The Netherlands, Norway, Poland, Portugal, and the UK) and for Europe overall. Out of 10 possible intervention points tested, exercise, fruit, life dissatisfaction, school pressure, and skipping breakfast were identified as the top five most influential ones across the 24 cases. These model-based priorities can be compared with the policy ideas suggested by the CO-CREATE adolescents.
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Affiliation(s)
- Eduard Romanenko
- Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Jack Homer
- Homer Consulting and MIT Research Affiliate, Barrytown, New York, USA
| | - Anne-Siri Fismen
- Department of Health Promotion, Norwegian Institute of Public Health, Bergen, Norway.,Center for Evaluation of Public Health Measures, Norwegian Institute of Public Health, Bergen, Norway
| | - Harry Rutter
- Department of Social and Policy Sciences, University of Bath, Bath, UK
| | - Nanna Lien
- Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
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11
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Bhatia A, Smetana S, Heinz V, Hertzberg J. Modeling obesity in complex food systems: Systematic review. Front Endocrinol (Lausanne) 2022; 13:1027147. [PMID: 36313777 PMCID: PMC9606209 DOI: 10.3389/fendo.2022.1027147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 09/27/2022] [Indexed: 11/20/2022] Open
Abstract
Obesity-related data derived from multiple complex systems spanning media, social, economic, food activity, health records, and infrastructure (sensors, smartphones, etc.) can assist us in understanding the relationship between obesity drivers for more efficient prevention and treatment. Reviewed literature shows a growing adaptation of the machine-learning model in recent years dealing with mechanisms and interventions in social influence, nutritional diet, eating behavior, physical activity, built environment, obesity prevalence prediction, distribution, and healthcare cost-related outcomes of obesity. Most models are designed to reflect through time and space at the individual level in a population, which indicates the need for a macro-level generalized population model. The model should consider all interconnected multi-system drivers to address obesity prevalence and intervention. This paper reviews existing computational models and datasets used to compute obesity outcomes to design a conceptual framework for establishing a macro-level generalized obesity model.
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Affiliation(s)
- Anita Bhatia
- Food Data Group, German Institute of Food Technologies (DIL e.V.), Quakenbrück, Germany
- Knowledge-Based Systems Research Group, Institute of Computer Science, University of Osnabrück, Osnabrück, Germany
| | - Sergiy Smetana
- Food Data Group, German Institute of Food Technologies (DIL e.V.), Quakenbrück, Germany
| | - Volker Heinz
- Food Data Group, German Institute of Food Technologies (DIL e.V.), Quakenbrück, Germany
| | - Joachim Hertzberg
- Knowledge-Based Systems Research Group, Institute of Computer Science, University of Osnabrück, Osnabrück, Germany
- Plan-Based Robot Control German Research Center for Artificial Intelligence, Osnabrück, Germany
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12
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Moore TR, Pachucki MC, Hennessy E, Economos CD. Tracing coalition changes in knowledge in and engagement with childhood obesity prevention to improve intervention implementation. BMC Public Health 2022; 22:1838. [PMID: 36180949 PMCID: PMC9526280 DOI: 10.1186/s12889-022-14208-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 09/05/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND While most coalition research focuses on studying the effects of peer relationship structure, this study examines the coevolution of coalition structure and behavior across three communities in the U.S. with the goal of identifying coalition dynamics that impact a childhood obesity prevention intervention. METHODS: Over two years (2018-2020), three communities within the U.S. participated in a childhood obesity prevention intervention at different times. This intervention was guided by the Stakeholder-Driven Community Diffusion theory, which describes an empirically testable mechanism for promoting community change. Measures are part of the Stakeholder-driven Community Diffusion (SDCD) survey with demonstrated reliability, which include knowledge of and engagement with childhood obesity prevention and social networks. Data from three coalition-committees and their respective networks were used to build three different stochastic actor-oriented models. These models were used to examine the coevolution of coalition structure with coalition behavior (defined a priori as knowledge of and engagement with obesity prevention) among coalition-committee members and their nominated alters (Network A) and coalition-committee members only (Network B). RESULTS: Overall, coalitions decrease in size and their structure becomes less dense over time. Both Network A and B show a consistent preference to form and sustain ties with those who have more ties. In Network B, there was a trend for those who have higher knowledge scores to increase their number of ties over time. The same trend appeared in Network A but varied based on their peers' knowledge in and engagement with childhood obesity prevention. Across models, engagement with childhood obesity prevention research was not a significant driver of changes in either coalition network structure or knowledge. CONCLUSIONS The trends in coalition Network A and B's coevolution models may point to context-specific features (e.g., ties among stakeholders) that can be leveraged for better intervention implementation. To that end, examining tie density, average path length, network diameter, and the dynamics of each behavior outcome (i.e., knowledge in and engagement with childhood obesity prevention) may help tailor whole-of-community interventions. Future research should attend to additional behavioral variables (e.g., group efficacy) that can capture other aspects of coalition development and that influence implementation, and to testing the efficacy of network interventions after trends have been identified.
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Affiliation(s)
- Travis R Moore
- Friedman School of Nutrition Science and Policy, ChildObesity180, Tufts University, 150 Harrison Ave, Boston, MA, 02111, USA.
| | - Mark C Pachucki
- Department of Sociology, Computational Social Science Institute, University of Massachusetts Amherst, Amherst, MA, 01003, USA
| | - Erin Hennessy
- Friedman School of Nutrition Science and Policy, ChildObesity180, Tufts University, 150 Harrison Ave, Boston, MA, 02111, USA
| | - Christina D Economos
- Friedman School of Nutrition Science and Policy, ChildObesity180, Tufts University, 150 Harrison Ave, Boston, MA, 02111, USA
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13
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Nau T, Bauman A, Smith BJ, Bellew W. A scoping review of systems approaches for increasing physical activity in populations. Health Res Policy Syst 2022; 20:104. [PMID: 36175916 PMCID: PMC9524093 DOI: 10.1186/s12961-022-00906-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 09/02/2022] [Indexed: 11/27/2022] Open
Abstract
Introduction The past decade has increasingly seen systems approaches as a featured theme in public health studies and policy documents. This trend is evident in the area of physical activity, which is a significant global health risk factor that is addressed in WHO’s Global Action Plan on Physical Activity. We undertook a comprehensive scoping review to characterize the application of systems approaches to physical activity, to develop a typology of the objectives, themes and methods of research papers that purported to apply systems thinking to this issue. Methods We searched electronic databases (PubMed, Web of Science, Scopus and PsycINFO) for studies published during the period 2010–2021 that explicitly applied systems approaches or methods to investigate and/or address population physical activity. A framework using systems-based methodological approaches was adapted to classify physical activity studies according to their predominant approach, covering basic descriptive, complex analytical and advanced forms of practice. We selected case studies from retained studies to depict the current “state of the art”. Results We included 155 articles in our narrative account. Literature reporting the application of systems approaches to physical activity is skewed towards basic methods and frameworks, with most attention devoted to conceptual framing and predictive modelling. There are few well-described examples of physical activity interventions which have been planned, implemented and evaluated using a systems perspective. There is some evidence of “retrofitted” complex system framing to describe programmes and interventions which were not designed as such. Discussion We propose a classification of systems-based approaches to physical activity promotion together with an explanation of the strategies encompassed. The classification is designed to stimulate debate amongst policy-makers, practitioners and researchers to inform the further implementation and evaluation of systems approaches to physical activity. Conclusion The use of systems approaches within the field of physical activity is at an early stage of development, with a preponderance of descriptive approaches and a dearth of more complex analyses. We need to see movement towards a more sophisticated research agenda spanning the development, implementation and evaluation of systems-level interventions. Supplementary Information The online version contains supplementary material available at 10.1186/s12961-022-00906-2.
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Affiliation(s)
- Tracy Nau
- Prevention Research Collaboration, Charles Perkins Centre, School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia. .,The Australian Prevention Partnership Centre, Sydney, NSW, Australia.
| | - Adrian Bauman
- Prevention Research Collaboration, Charles Perkins Centre, School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia.,The Australian Prevention Partnership Centre, Sydney, NSW, Australia
| | - Ben J Smith
- Prevention Research Collaboration, Charles Perkins Centre, School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia.,The Australian Prevention Partnership Centre, Sydney, NSW, Australia
| | - William Bellew
- Prevention Research Collaboration, Charles Perkins Centre, School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia.,The Australian Prevention Partnership Centre, Sydney, NSW, Australia
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14
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Knowledge Management as a Domain, System Dynamics as a Methodology. SYSTEMS 2022. [DOI: 10.3390/systems10030082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
For decades, system dynamics has been utilised as a framework for evaluating and interpreting various types of systems with varying degrees of complexity and knowledge demands. Knowledge management is strongly related to system dynamics on a thematic level. We did a thorough review to identify potential applications and analysed system dynamics and knowledge management domains. The systematic review followed the PRISMA method. We identified two major groups and one subgroup of the combination of system dynamics and knowledge management after examining and categorising 45 papers. Articles were searched for on Web of Science, Scopus, and LENS. We then concentrated on the categorisation of articles by theme. We discovered that system dynamics models were used as a component of a decision support tool or a knowledge management system in some instances, or the integration of knowledge management processes into specific systems. This study contributes to the growth of system dynamics as a methodology capable of generating novel ideas, highlighting limitations, and providing analogies for future research in a variety of academic areas.
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15
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Lemke MK, Wolf DA, Drake SA. A Call for Complex Systems and Syndemic Theory in Firearm Violence Research. Am J Prev Med 2022; 62:459-465. [PMID: 34879969 DOI: 10.1016/j.amepre.2021.08.026] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 07/27/2021] [Accepted: 08/18/2021] [Indexed: 11/01/2022]
Affiliation(s)
- Michael K Lemke
- Department of Social Sciences, College of Humanities and Social Sciences, University of Houston-Downtown, Houston, Texas.
| | - Dwayne A Wolf
- Medical Examiner's Office, Harris County Institute of Forensic Sciences, Houston, Texas
| | - Stacy A Drake
- College of Nursing, Texas A&M University, Houston, Texas
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16
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Agent-Based Modeling of Autosomal Recessive Deafness 1A (DFNB1A) Prevalence with Regard to Intensity of Selection Pressure in Isolated Human Population. BIOLOGY 2022; 11:biology11020257. [PMID: 35205123 PMCID: PMC8869167 DOI: 10.3390/biology11020257] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 01/28/2022] [Accepted: 02/03/2022] [Indexed: 01/09/2023]
Abstract
An increase in the prevalence of autosomal recessive deafness 1A (DFNB1A) in populations of European descent was shown to be promoted by assortative marriages among deaf people. Assortative marriages became possible with the widespread introduction of sign language, resulting in increased genetic fitness of deaf individuals and, thereby, relaxing selection against deafness. However, the effect of this phenomenon was not previously studied in populations with different genetic structures. We developed an agent-based computer model for the analysis of the spread of DFNB1A. Using this model, we tested the impact of different intensities of selection pressure against deafness in an isolated human population over 400 years. Modeling of the "purifying" selection pressure on deafness ("No deaf mating" scenario) resulted in a decrease in the proportion of deaf individuals and the pathogenic allele frequency. Modeling of the "relaxed" selection ("Assortative mating" scenario) resulted in an increase in the proportion of deaf individuals in the first four generations, which then quickly plateaued with a subsequent decline and a decrease in the pathogenic allele frequency. The results of neutral selection pressure modeling ("Random mating" scenario) showed no significant changes in the proportion of deaf individuals or the pathogenic allele frequency after 400 years.
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17
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Winkler MR, Mui Y, Hunt SL, Laska MN, Gittelsohn J, Tracy M. Applications of Complex Systems Models to Improve Retail Food Environments for Population Health: A Scoping Review. Adv Nutr 2021; 13:1028-1043. [PMID: 34999752 PMCID: PMC9340968 DOI: 10.1093/advances/nmab138] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 09/10/2021] [Accepted: 11/17/2021] [Indexed: 12/11/2022] Open
Abstract
Retail food environments (RFEs) are complex systems with important implications for population health. Studying the complexity within RFEs comes with challenges. Complex systems models are computational tools that can help. We performed a systematic scoping review of studies that used complex systems models to study RFEs for population health. We examined the purpose for using the model, RFE features represented, extent to which the complex systems approach was maximized, and quality and transparency of methods employed. The PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) guidelines were followed. Studies using agent-based modeling, system dynamics, discrete event simulations, networks, hybrid, or microsimulation models were identified from 7 multidisciplinary databases. Fifty-six studies met the inclusion criteria, including 23 microsimulation, 13 agent-based, 10 hybrid, 4 system dynamics, 4 network, and 2 discrete event simulation models. Most studies (n = 45) used models for experimental purposes and evaluated effects of simulated RFE policies and interventions. RFE characteristics simulated in models were diverse, and included the features (e.g., prices) customers encounter when shopping (n = 55), the settings (e.g., restaurants, supermarkets) where customers purchase food and beverages (n = 30), and the actors (e.g., store managers, suppliers) who make decisions that influence RFEs (n = 25). All models incorporated characteristics of complexity (e.g., feedbacks, conceptual representation of multiple levels), but these were captured to varying degrees across model types. The quality of methods was adequate overall; however, few studies engaged stakeholders (n = 10) or provided sufficient transparency to verify the model (n = 12). Complex systems models are increasingly utilized to study RFEs and their contributions to public health. Opportunities to advance the use of these approaches remain, and areas to improve future research are discussed. This comprehensive review provides the first marker of the utility of leveraging these approaches to address RFEs for population health.
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Affiliation(s)
| | - Yeeli Mui
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Shanda L Hunt
- Health Sciences Library, University of Minnesota, Minneapolis, MN, USA
| | - Melissa N Laska
- Division of Epidemiology and Community Health, University of Minnesota School of Public Health, Minneapolis, MN, USA
| | - Joel Gittelsohn
- Center for Human Nutrition, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Melissa Tracy
- Department of Epidemiology and Biostatistics, University at Albany School of Public Health, Rensselaer, NY, USA
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18
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Duran AC, Mialon M, Crosbie E, Jensen ML, Harris JL, Batis C, Corvalán C, Taillie LS. [Soluciones relacionadas con el entorno alimentario para prevenir la obesidad infantil en América Latina y en la población latina que vive en Estados Unidos]. Obes Rev 2021; 22 Suppl 5:e13344. [PMID: 34708531 DOI: 10.1111/obr.13344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 02/26/2021] [Indexed: 11/28/2022]
Affiliation(s)
- Ana Clara Duran
- Núcleo de Estudos e Pesquisas em Alimentação (NEPA), Universidade Estadual de Campinas, Campinas, Brasil.,Núcleo de Pesquisas Epidemiológicas em Nutrição e Saúde, Universidade de São Paulo, São Paulo, Brasil
| | - Melissa Mialon
- Trinity Business School, Trinity College Dublin, Dublín, Irlanda
| | - Eric Crosbie
- School of Community and Health Sciences, University of Nevada, Reno, Nevada, EE. UU
| | - Melissa Lorena Jensen
- Rudd Center for Food Policy and Obesity, University of Connecticut, Hartford, Connecticut, EE. UU.,Escuela de Nutrición, Universidad de Costa Rica, San José, Costa Rica
| | - Jennifer L Harris
- Rudd Center for Food Policy and Obesity, University of Connecticut, Hartford, Connecticut, EE. UU
| | - Carolina Batis
- CONACYT, Centro de Investigación en Nutrición y Salud, Instituto Nacional de Salud Pública, Cuernavaca, Mexico
| | - Camila Corvalán
- Instituto de Nutricion y Tecnologia de los Alimentos, Universidad de Chile, Santiago, Chile
| | - Lindsey Smith Taillie
- Department of Nutrition, Gillings School of Global Public Health, and Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, Carolina del Norte, EE. UU
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19
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McGill E, Petticrew M, Marks D, McGrath M, Rinaldi C, Egan M. Applying a complex systems perspective to alcohol consumption and the prevention of alcohol-related harms in the 21st century: a scoping review. Addiction 2021; 116:2260-2288. [PMID: 33220118 DOI: 10.1111/add.15341] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 10/09/2020] [Accepted: 11/17/2020] [Indexed: 12/31/2022]
Abstract
BACKGROUND AND AIMS A complex systems perspective has been advocated to explore multi-faceted factors influencing public health issues, including alcohol consumption and associated harms. This scoping review aimed to identify studies that applied a complex systems perspective to alcohol consumption and the prevention of alcohol-related harms in order to summarize their characteristics and identify evidence gaps. METHODS Studies published between January 2000 and September 2020 in English were located by searching for terms synonymous with 'complex systems' and 'alcohol' in the Scopus, MEDLINE, Web of Science and Embase databases, and through handsearching and reference screening of included studies. Data were extracted on each study's aim, country, population, alcohol topic, system levels, funding, theory, methods, data sources, time-frames, system modifications and type of findings produced. RESULTS Eighty-seven individual studies and three systematic reviews were identified, the majority of which were conducted in the United States or Australia in the general population, university students or adolescents. Studies explored types and patterns of consumption behaviour and the local environments in which alcohol is consumed. Most studies focused on individual and local interactions and influences, with fewer examples exploring the relationships between these and regional, national and international subsystems. The body of literature is methodologically diverse and includes theory-led approaches, dynamic simulation models and social network analyses. The systematic reviews focused on primary network studies. CONCLUSIONS The use of a complex systems perspective has provided a variety of ways of conceptualizing and analyzing alcohol use and harm prevention efforts, but its focus ultimately has remained on predominantly individual- and/or local-level systems. A complex systems perspective represents an opportunity to address this gap by also considering the vertical dimensions that constrain, shape and influence alcohol consumption and related harms, but the literature to date has not fully captured this potential.
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Affiliation(s)
- Elizabeth McGill
- Department of Health Services Research and Policy, London School of Hygiene & Tropical Medicine, London, UK
| | - Mark Petticrew
- Department of Public Health, Environments and Society, London School of Hygiene & Tropical Medicine, London, UK
| | - Dalya Marks
- Department of Public Health, Environments and Society, London School of Hygiene & Tropical Medicine, London, UK
| | - Michael McGrath
- Department of Health Services Research and Policy, London School of Hygiene & Tropical Medicine, London, UK
| | - Chiara Rinaldi
- Department of Health Services Research and Policy, London School of Hygiene & Tropical Medicine, London, UK
| | - Matt Egan
- Department of Public Health, Environments and Society, London School of Hygiene & Tropical Medicine, London, UK
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20
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Stankov I, Useche AF, Meisel JD, Montes F, Morais LM, Friche AA, Langellier BA, Hovmand P, Sarmiento OL, Hammond RA, Diez Roux AV. From causal loop diagrams to future scenarios: Using the cross-impact balance method to augment understanding of urban health in Latin America. Soc Sci Med 2021; 282:114157. [PMID: 34182357 PMCID: PMC8287591 DOI: 10.1016/j.socscimed.2021.114157] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 03/17/2021] [Accepted: 06/17/2021] [Indexed: 11/23/2022]
Abstract
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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Affiliation(s)
- Ivana Stankov
- Urban Health Collaborative, Dornsife School of Public Health, Drexel University, 3600 Market St, Philadelphia, PA, 19104, USA; South Australian Health and Medical Research Institute, North Terrace, Adelaide, SA, 5000, Australia.
| | - Andres Felipe Useche
- Department of Industrial Engineering, Universidad de Los Andes, Bogotá, Colombia; Social and Health Complexity Center, Universidad de Los Andes, Bogotá, Colombia
| | - Jose D Meisel
- Facultad de Ingeniería, Universidad de Ibagué, Carrera 22 Calle 67, Ibagué, 730001, Colombia
| | - Felipe Montes
- Department of Industrial Engineering, Universidad de Los Andes, Bogotá, Colombia; Social and Health Complexity Center, Universidad de Los Andes, Bogotá, Colombia
| | - Lidia Mo Morais
- Observatory for Urban Health in Belo Horizonte, Belo Horizonte, Brazil; School of Medicine, Federal University of Minas Gerais, Belo Horizonte, Brazil
| | - Amelia Al Friche
- Observatory for Urban Health in Belo Horizonte, Belo Horizonte, Brazil; School of Medicine, Federal University of Minas Gerais, Belo Horizonte, Brazil
| | - Brent A Langellier
- Department of Health Management and Policy, Dornsife School of Public Health, Drexel University, 3215 Market St, Philadelphia, PA, 19104, USA
| | - Peter Hovmand
- Center for Community Health Integration, Case Western Reserve University, Cleveland, OH, USA
| | - Olga Lucia Sarmiento
- Department of Public Health, School of Medicine, Universidad de Los Andes, Bogotá, Colombia
| | - Ross A Hammond
- Brown School at Washington University in St. Louis, One Brookings Drive, St Louis, MO, 36130, USA; Center on Social Dynamics and Policy, The Brookings Institution, 1775 Massachusetts Ave NW, Washington, DC, 20036, USA; Santa Fe Institute, 1399 Hyde Park Rd, Santa Fe, NM, 87501, USA
| | - Ana V Diez Roux
- Urban Health Collaborative, Dornsife School of Public Health, Drexel University, 3600 Market St, Philadelphia, PA, 19104, USA
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21
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Duran AC, Mialon M, Crosbie E, Jensen ML, Harris JL, Batis C, Corvalán C, Taillie LS. Food environment solutions for childhood obesity in Latin America and among Latinos living in the United States. Obes Rev 2021; 22 Suppl 3:e13237. [PMID: 34152071 PMCID: PMC8365715 DOI: 10.1111/obr.13237] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 02/26/2021] [Indexed: 12/23/2022]
Abstract
The food environment is a major contributor to unhealthy diets in children and, therefore, to the increasing rates of obesity. Acclaimed by scholars across the world, Latin American countries have been leaders in implementing policies that target different aspects of the food environment. Evidence on the nature and to what extent children are exposed and respond to unhealthy food environments in the region and among Latinos in the United States is, however, deficient. The objective of this review is to use the integrated International Network for Food and Obesity/noncommunicable diseases (NCDs) Research, Monitoring and Action Support (INFORMAS) framework to create healthy food environment to (i) compare the key elements of childhood obesity-related food environments in Latin America and for Latinos living in the United States; (ii) describe the evidence on solutions to improve childhood obesity-related food environments; and (iii) identify research priorities to inform solutions to fight childhood obesity in these populations. We found that an integrated body of evidence is needed to inform an optimal package of policies to improve food environments to which children in Latin America and Latino children in the United States are exposed and more efficiently translate policy solutions to help curb growing childhood obesity levels across borders.
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Affiliation(s)
- Ana Clara Duran
- Center for Food Studies and Research (NEPA)University of CampinasCampinasBrazil
- Center for Epidemiological Studies in Nutrition and HealthUniversity of São PauloSão PauloBrazil
| | - Melissa Mialon
- Trinity Business SchoolTrinity College DublinDublinIreland
| | - Eric Crosbie
- School of Community and Health SciencesUniversity of NevadaRenoNevadaUSA
| | - Melissa Lorena Jensen
- Rudd Center for Food Policy and ObesityUniversity of ConnecticutHartfordConnecticutUSA
- School of Nutrition, University of Costa RicaSan JoséCosta Rica
| | - Jennifer L. Harris
- Rudd Center for Food Policy and ObesityUniversity of ConnecticutHartfordConnecticutUSA
| | - Carolina Batis
- CONACYT, Health and Nutrition Research CenterNational Institute of Public HealthCuernavacaMexico
| | - Camila Corvalán
- Instituto de Nutricion y Tecnologia de AlimentosUniversity of ChileSantiagoChile
| | - Lindsey Smith Taillie
- Department of Nutrition, Gillings School of Global Public Health, and Carolina Population CenterUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
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22
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Keramat SA, Alam K, Al-Hanawi MK, Gow J, Biddle SJH, Hashmi R. Trends in the prevalence of adult overweight and obesity in Australia, and its association with geographic remoteness. Sci Rep 2021; 11:11320. [PMID: 34059752 PMCID: PMC8166878 DOI: 10.1038/s41598-021-90750-1] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 05/10/2021] [Indexed: 01/09/2023] Open
Abstract
The prevalence of overweight and obesity has been increasing globally and has become a significant public health concern in Australia in the two past decades. This study explores the most recent national prevalence and trends of adult overweight and obesity in Australia. It will also investigate geographic remoteness as a potential risk factor for an individual being overweight or obese in adulthood. A retrospective longitudinal study that utilised 14 successive waves (wave 6 through 19) of a nationally representative linked individual-level survey. Data was obtained from the Household, Income and Labour Dynamics in Australia survey. The data on 199,675 observations from 26,713 individuals aged ≥ 15 years over the period 2006 to 2019 was analysed. Random-effects logit model was employed to estimate the association between geographic remoteness and the risk of excessive weight gain. The results reveal that the prevalence of overweight, obesity and combined overweight and obesity among Australian adults in 2019 were 34%, 26% and 60%, respectively. The analysis shows that the prevalence of overweight and obesity varies by geographic remoteness. Adults from regional city urban (OR 1.53, 95% CI 1.16-2.03) and rural areas (OR 1.32, 95% CI 1.18-1.47) were more likely to be obese compared with their counterparts from major city urban areas. The results also show that adults living in major city urban areas, regional city urban areas, and regional city rural areas in Australia were 1.53 (OR 1.53, 95% CI 1.16-2.03), 1.32 (OR 1.32, 95% CI 1.18-1.47), and 1.18 (OR 1.18, 95% CI 1.08-1.29) times more likely to be overweight compared with their counterparts from major city urban areas in Australia. Substantial geographic variation in the prevalence of overweight and obesity exists among Australian adults and appears to be increasing. Public health measures should focus on contextual obesogenic factors and behavioural characteristics to curb the rising prevalence of adult obesity.
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Affiliation(s)
- Syed Afroz Keramat
- Economics Discipline, Social Science School, Khulna University, Khulna, 9208, Bangladesh.
- School of Business, University of Southern Queensland, Toowoomba, QLD, 4350, Australia.
- Centre for Health Research, University of Southern Queensland, Toowoomba, QLD, 4350, Australia.
| | - Khorshed Alam
- School of Business, University of Southern Queensland, Toowoomba, QLD, 4350, Australia
- Centre for Health Research, University of Southern Queensland, Toowoomba, QLD, 4350, Australia
| | - Mohammed Khaled Al-Hanawi
- Department of Health Services and Hospital Administration, Faculty of Economics and Administration, King Abdulaziz University, Jeddah, Saudi Arabia
- Health Economics Research Group, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Jeff Gow
- School of Business, University of Southern Queensland, Toowoomba, QLD, 4350, Australia
- School of Accounting, Economics, and Finance, University of KwaZulu-Natal, Durban, 4000, South Africa
| | - Stuart J H Biddle
- Centre for Health Research, University of Southern Queensland, Toowoomba, QLD, 4350, Australia
| | - Rubayyat Hashmi
- School of Business, University of Southern Queensland, Toowoomba, QLD, 4350, Australia
- Centre for Health Research, University of Southern Queensland, Toowoomba, QLD, 4350, Australia
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Jessiman PE, Powell K, Williams P, Fairbrother H, Crowder M, Williams JG, Kipping R. A systems map of the determinants of child health inequalities in England at the local level. PLoS One 2021; 16:e0245577. [PMID: 33577596 PMCID: PMC7880458 DOI: 10.1371/journal.pone.0245577] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 01/05/2021] [Indexed: 12/03/2022] Open
Abstract
Children and young people in the UK have worse health outcomes than in many similar western countries and child health inequalities are persistent and increasing. Systems thinking has emerged as a promising approach to addressing complex public health issues. We report on a systems approach to mapping the determinants of child health inequalities at the local level in England for young people aged 0-25, and describe the resulting map. Qualitative group concept mapping workshops were held in two contrasting English local authorities with a range of stakeholders: professionals (N = 35); children and young people (N = 33) and carers (N = 5). Initial area maps were developed, and augmented using data from qualitative interviews with professionals (N = 16). The resulting local maps were reviewed and validated by expert stakeholders in each area (N = 9; N = 35). Commonalities between two area-specific system maps (and removal of locality-specific factors) were used to develop a map that could be applied in any English local area. Two rounds of online survey (N = 21; N = 8) experts in public health, local governance and systems science refined the final system map displaying the determinants of child health inequalities. The process created a map of over 150 factors influencing inequalities in health outcomes for children aged 0-25 years at the local area level. The system map has six domains; physical environment, governance, economic, social, service, and personal. To our knowledge this is the first study taking a systems approach to addressing inequalities across all aspects of child health. The study shows how group concept mapping can support systems thinking at the local level. The resulting system map illustrates the complexity of factors influencing child health inequalities, and it may be a useful tool in demonstrating to stakeholders the importance of policies that tackle the systemic drivers of child health inequalities beyond those traditionally associated with public health.
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Affiliation(s)
- Patricia E. Jessiman
- Department of Population Health Sciences, University of Bristol, Bristol, United Kingdom
| | - Katie Powell
- School of Health and Related Research, University of Sheffield, Sheffield, United Kingdom
| | - Philippa Williams
- Department of Population Health Sciences, University of Bristol, Bristol, United Kingdom
| | - Hannah Fairbrother
- Health Sciences School, University of Sheffield, Sheffield, United Kingdom
| | - Mary Crowder
- School of Health and Related Research, University of Sheffield, Sheffield, United Kingdom
| | - Joanna G. Williams
- Department of Population Health Sciences, University of Bristol, Bristol, United Kingdom
| | - Ruth Kipping
- Department of Population Health Sciences, University of Bristol, Bristol, United Kingdom
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Samnamul (Shoots of Aruncus dioicus) Inhibit Adipogenesis by Downregulating Adipocyte-Specific Transcription Factors in 3T3-L1 Adipocytes. Processes (Basel) 2020. [DOI: 10.3390/pr8121576] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Adipocyte-specific transcription factors and antioxidants are considered the best target of obesity. Aruncus dioicus var. kamtschaticus (A. dioicus, Samnamul) is easily available owing to edible and inexpensive. However, the anti-adipogenic effects of the underlying mechanism of A. dioicus extract (ADE) have not yet been reported. In the present study, we evaluate anti-adipogenic pathway in 3T3-L1 adipocytes, antioxidant activities and quantified phenolics using high-performance liquid chromatography of ADE. The results revealed ADE had reduced adipocyte differentiation (0.72-fold vs. MDI (media of differentiation) control), triglyceride (TG; 0.50-fold vs. MDI control, p < 0.001), and total cholesterol contents (0.77-fold vs. MDI control) by regulating adipocyte-specific transcription factors (C/EBPα, PPARγ, and SREBP1) and their downstream mRNA (AdipoQ, Ap2, SREBP1-c, and FAS) levels. Furthermore, ADE has higher total phenol and flavonoid contents and scavenging assay in the DPPH and ABTS+. In particularly, ADE contains chlorogenic acid (7.04 mg/kg), caffeic acid (20.14 mg/kg), ferulic acid (1.74 mg/kg), veratric acid (29.31 mg/kg), cinnamic acid (4.70 mg/kg), and quercetin (4.18 mg/kg). In conclusion, since these phenols, especially quercetin, in the ADE appear to reduce differentiation, TG and cholesterol content by regulating adipocyte-specific transcription factors in adipocytes, ADE has the potential to be developed into a new antioxidant and anti-obesity therapeutics.
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Fetal and Placental Weight in Pre-Gestational Maternal Obesity (PGMO) vs. Excessive Gestational Weight Gain (EGWG)-A Preliminary Approach to the Perinatal Outcomes in Diet-Controlled Gestational Diabetes Mellitus. J Clin Med 2020; 9:jcm9113530. [PMID: 33142800 PMCID: PMC7693942 DOI: 10.3390/jcm9113530] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Revised: 10/24/2020] [Accepted: 10/28/2020] [Indexed: 12/15/2022] Open
Abstract
Both pre-gestational maternal obesity (PGMO) and excessive gestational weight gain (EGWG) increase the risk of gestational diabetes mellitus (GDM). Here, we conducted a retrospective study to comparatively examine the relation between fetal birth weight (FW) and placental weight (PW) in PGMO (n = 100) compared to EGWG (n = 100) with respect to perinatal outcomes in diet-controlled GDM. The control group was made up of 100 healthy pregnancies. The mean FW and the mean PW in EGWG were correlated with lowered fetal weight/placental weight ratio (FW/PW ratio). The percentage of births completed by cesarean section accounted for 47%, 32%, and 18% of all deliveries (EGWG, PGMO, and controls, respectively), with the predominance of FW-related indications for cesarean section. Extended postpartum hospital stays due to neonate were more frequent in EGWG, especially due to neonatal jaundice (p < 0.05). The results indicate the higher perinatal risk in mothers with EGWG compared to PGMO during GDM-complicated pregnancy. Further in-depth comparative studies involving larger patient pools are needed to validate these findings, the intent of which is to formulate guidelines for GDM patients in respect to management of PGMO and EGWG.
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Meisel JD, Ramirez AM, Esguerra V, Montes F, Stankov I, Sarmiento OL, Valdivia JA. Using a system dynamics model to study the obesity transition by socioeconomic status in Colombia at the country, regional and department levels. BMJ Open 2020; 10:e036534. [PMID: 32499271 PMCID: PMC7282389 DOI: 10.1136/bmjopen-2019-036534] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
OBJECTIVE We study the obesity transition by socioeconomic status (SES), gender and age within the Colombian urban population at the country, regional and department levels. DESIGN The study is informed by cross-sectional data from the 2005 and 2010 ENSIN survey. We used these data to develop a system dynamics model that simulates the dynamics of obesity by body mass index (BMI) categories, gender and SES at the country, regional and department levels from 2005 to 2030. PARTICIPANTS The sample size of the 2005 ENSIN comprised 8515 children younger than 5 years, 32 009 children and adolescents aged 5-17 years and 48 056 adults aged 18-64 years. In 2010, the corresponding numbers were 11 368, 32 524 and 64 425, respectively. PRIMARY AND SECONDARY OUTCOME MEASURE The obesity prevalence ratio and prevalence rates for each BMI category. RESULTS The results show, at the country level, transitions from overweight to obesity were projected to increase sharply among lower SES adults, particularly among women, suggesting that these groups will undergo an obesity transition by 2030. The model projections also indicate that the regions of Colombia are in different stages of the obesity transition. In the case of women, five out of the six regions were expected to undergo an obesity transition by SES over time. For men, only one region was expected to undergo an obesity transition. However, at the department level, trends in the burden of obesity varied. CONCLUSIONS We evidence that the Colombian population could be experiencing an obesity transition where the increase in the GDP could be related to shifts in the burden of obesity from higher to lower SES, especially in women. These patterns support the need for policy planning that considers SES and gender, at the national and subnational levels, as important determinants of overweight and obesity among adults in Colombia.
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Affiliation(s)
- Jose D Meisel
- Facultad de Ingeniería, Universidad de Ibague, Ibagué, Tolima, Colombia
| | - Angie M Ramirez
- Facultad de Ingeniería, Universidad de Ibague, Ibagué, Tolima, Colombia
| | - Valentina Esguerra
- Department of Industrial Engineering, Social and Health Complexity Center, Universidad de los Andes, Bogota, Colombia
| | - Felipe Montes
- Department of Industrial Engineering, Social and Health Complexity Center, Universidad de los Andes, Bogota, Colombia
| | - Ivana Stankov
- Urban Health Collaborative, Dornsife School of Public Health, Drexel University, Philadelphia, Pennsylvania, USA
| | - Olga L Sarmiento
- Department of Public Health, School of Medicine, Universidad de los Andes, Bogota, Colombia
| | - Juan A Valdivia
- Departamento de Física, Facultad de Ciencias, Universidad de Chile, Santiago de Chile, Chile
- Centro para el Desarrollo de la Nanociencia y la Nanotecnología, CEDENNA, Santiago de Chile, Chile
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Swierad E, Huang TTK, Ballard E, Flórez K, Li S. Developing a Socioculturally Nuanced Systems Model of Childhood Obesity in Manhattan's Chinese American Community via Group Model Building. J Obes 2020; 2020:4819143. [PMID: 33628493 PMCID: PMC7895604 DOI: 10.1155/2020/4819143] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Accepted: 05/27/2020] [Indexed: 01/01/2023] Open
Abstract
The purpose of this study was to develop a qualitative and socioculturally tailored systems model of childhood obesity in the Chinese American community in Manhattan's Chinatown. We utilized group model building (GMB) methodology as a form of participatory systems modeling. The study was conducted in Manhattan's Chinatown community. We recruited 16 Chinese American adults from the community. GMB workshops engendered a causal loop diagram (CLD), the visualization of a complex systems model illustrating the structures, feedbacks, and interdependencies among socioculturally specific pathways underlying childhood obesity, in Manhattan's Chinatown community. The analysis of CLD revealed that participants considered the following factors to influence childhood obesity: (1) traditional social norms affecting body image, how children are raised, parental pressure to study, and trust in health of traditional foods; (2) grandparents' responsibility for children; (3) limited time availability of parents at home; and (4) a significant amount of children's time spent indoors. GMB represents a novel method to understand the complexity of childhood obesity in culturally specific populations and contexts. The study identified sociocultural subsystems that may underlie the development and perpetuation of childhood obesity among Chinese American children. Insights from the study can be useful in the design of future empirical studies and interventions.
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Affiliation(s)
- Ewelina Swierad
- Columbia University Irving Medical Center, New York, NY, USA
- Center for Systems and Community Design, Graduate School of Public Health and Health Policy, City University of New York, New York, NY, USA
| | - Terry T.-K. Huang
- Center for Systems and Community Design, Graduate School of Public Health and Health Policy, City University of New York, New York, NY, USA
| | - Ellis Ballard
- Social System Design Lab, Brown School at Washington University in St. Louis, St. Louis, WA, USA
| | - Karen Flórez
- Center for Systems and Community Design, Graduate School of Public Health and Health Policy, City University of New York, New York, NY, USA
| | - Sheng Li
- Center for Systems and Community Design, Graduate School of Public Health and Health Policy, City University of New York, New York, NY, USA
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