Systems Thinking and Complexity Science Methods and the Policy Process in Non-Communicable Disease Prevention: A Systematic Scoping Review

Document Type : Review Article

Authors

1 School of Global Health, York University, Toronto, ON, Canada

2 Department of Health Services Research and Policy, London School of Hygiene & Tropical Medicine, London, UK

3 Department of Public Health, Environments and Society, London School of Hygiene & Tropical Medicine, London, UK

4 World Health Organization European Office for the Prevention and Control of Noncommunicable Diseases, Moscow, Russian Federation

5 Department of Nutrition, Exercise and Sports, University of Copenhagen, Copenhagen, Denmark

Abstract

Background 
Given the complex determinants of non-communicable diseases (NCDs), and the dynamic policy landscape, researchers and policymakers are exploring the use of systems thinking and complexity science (STCS) in developing effective policies. The aim of this review is to systematically identify and analyse existing applications of STCS-informed methods in NCD prevention policy.

Methods 
Systematic scoping review: We searched academic databases (Medline, Scopus, Web of Science, EMBASE) for all publications indexed by 13 October 2020, screening titles, abstracts and full texts and extracting data according to published guidelines. We summarised key data from each study, mapping applications of methods informed by STCS to policy process domains. We conducted a thematic analysis to identify advantages, limitations, barriers and facilitators to using STCS.

Results 
4681 papers were screened and 112 papers were included in this review. The most common policy areas were tobacco control, obesity prevention and physical activity promotion. Methods applied included system dynamics modelling, agent-based modelling and concept mapping. Advantages included supporting evidence-informed decision-making; modelling complex systems and addressing multi-sectoral problems. Limitations included the abstraction of reality by STCS methods, despite aims of encompassing greater complexity. Challenges included resource-intensiveness; lack of stakeholder trust in models; and results that were too complex to be comprehensible to stakeholders. Ensuring stakeholder ownership and presenting findings in a user-friendly way facilitated STCS use.

Conclusion 
This review maps the proliferating applications of STCS methods in NCD prevention policy. STCS methods have the potential to generate tailored and dynamic evidence, adding robustness to evidence-informed policymaking, but must be accessible to policy stakeholders and have strong stakeholder ownership to build consensus and change stakeholder perspectives. Evaluations of whether, and under what circumstances, STCS methods lead to more effective policies compared to conventional methods are lacking, and would enable more targeted and constructive use of these methods.

Keywords


  1. Lich, K. H., Ginexi, E. M., Osgood, N. D. & Mabry, P. L. A Call to Address Complexity in Prevention Science Research. Prev Sci 14, 279–289 (2013).
  2. Rusoja, E. et al. Thinking about complexity in health: A systematic review of the key systems thinking and complexity ideas in health. Journal of Evaluation in Clinical Practice 24, 600–606 (2018).
  3. Gates, E. F. Making sense of the emerging conversation in evaluation about systems thinking and complexity science. Evaluation and Program Planning 59, 62–73 (2016).
  4. Meadows, D. H. Thinking in Systems: A Primer. (Chelsea Green Publishing, 2008).
  5. Sterman, J. D. Learning from Evidence in a Complex World. Am J Public Health 96, 505–514 (2006).
  6. Systems thinking for health systems strengthening. (Alliance for Health Policy and Systems Research ; World Health Organization, 2009).
  7. Rutter, H. et al. The need for a complex systems model of evidence for public health. The Lancet 390, 2602–2604 (2017).
  8. Vennix, J. A. M., Akkermans, H. A. & Rouwette, E. A. J. A. Group model-building to facilitate organizational change: an exploratory study. System Dynamics Review 12, 39–58 (1996).
  9. Rouwette, E. A. J. A., Vennix, J. A. M. & Mullekom, T. van. Group model building effectiveness: a review of assessment studies. System Dynamics Review 18, 5–45 (2002).
  10. Swinburn, B. A. et al. The Global Syndemic of Obesity, Undernutrition, and Climate Change: The Lancet Commission report. The Lancet 393, 791–846 (2019).
  11. Bryony Butland et al. Tackling obesities: future choices - project report (2nd edition). 164 https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/287937/07-1184x-tackling-obesities-future-choices-report.pdf (2007).
  12. More active people for a healthier world: Global action plan on physical activity 2018-2030. 104 https://apps.who.int/iris/bitstream/handle/10665/272722/9789241514187-eng.pdf (2018).
  13. Office of Behavioral and Social Sciences Research. Office of Behavioral and Social Sciences Research https://obssr.od.nih.gov/.
  14. Wutzke, S. et al. Knowledge mobilisation for chronic disease prevention: the case of the Australian Prevention Partnership Centre. Health Research Policy and Systems 16, 109 (2018).
  15. Atkinson, J.-A., Page, A., Prodan, A., McDonnell, G. & Osgood, N. Systems modelling tools to support policy and planning. The Lancet 391, 1158–1159 (2018).
  16. Atkinson, J.-A. M. et al. Applications of system dynamics modelling to support health policy. Public Health Res Pract 25, e2531531 (2015).
  17. Langellier, B. A. et al. Complex Systems Approaches to Understand Drivers of Mental Health and Inform Mental Health Policy: A Systematic Review. Adm Policy Ment Health 46, 128–144 (2019).
  18. Carey, G. et al. Systems science and systems thinking for public health: a systematic review of the field. BMJ Open 5, e009002 (2015).
  19. WHO | World Health Assembly resolution WHA51.12 - Health promotion. WHO https://www.who.int/healthpromotion/wha51-12/en/.
  20. Bambra, C. The primacy of politics: the rise and fall of evidence-based public health policy? J Public Health (Oxf) 35, 486–487 (2013).
  21. Petticrew, M., Whitehead, M., Macintyre, S. J., Graham, H. & Egan, M. Evidence for public health policy on inequalities: 1: The reality according to policymakers. Journal of Epidemiology & Community Health 58, 811–816 (2004).
  22. McGill, E. et al. Trading quality for relevance: non-health decision-makers’ use of evidence on the social determinants of health. BMJ Open 5, e007053 (2015).
  23. Barbrook-Johnson, P., Proctor, A., Giorgi, S. & Phillipson, J. How do policy evaluators understand complexity? Evaluation 26, 315–332 (2020).
  24. Haynes, A., Garvey, K., Davidson, S. & Milat, A. What Can Policy-Makers Get Out of Systems Thinking? Policy Partners’ Experiences of a Systems-Focused Research Collaboration in Preventive Health. Int J Health Policy Manag 9, 65–76 (2019).
  25. Knai, C. et al. Systems Thinking as a Framework for Analyzing Commercial Determinants of Health. The Milbank Quarterly 96, 472–498 (2018).
  26. Canty-Waldron, J. Using Systems Thinking to Create more Impactful Social Policy. Journal of Future Studies 19, 61–86 (2014).
  27. Forrester, J. W. System dynamics—the next fifty years. System Dynamics Review 23, 359–370 (2007).
  28. Ghaffarzadegan, N., Lyneis, J. & Richardson, G. P. How small system dynamics models can help the public policy process. System Dynamics Review 27, 22–44 (2011).
  29. Clifford Astbury, C., McGill, E., Egan, M. & Penney, T. L. Systems thinking and complexity science methods and the policy process in non-communicable disease prevention: a systematic scoping review protocol. BMJ Open (2021).
  30. Page, M. J. et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372, n71 (2021).
  31. Arksey, H. & O’Malley, L. Scoping studies: towards a methodological framework. International Journal of Social Research Methodology 8, 19–32 (2005).
  32. Levac, D., Colquhoun, H. & O’Brien, K. K. Scoping studies: advancing the methodology. Implementation Sci 5, 69 (2010).
  33. Colquhoun, H. L. et al. Scoping reviews: time for clarity in definition, methods, and reporting. Journal of Clinical Epidemiology 67, 1291–1294 (2014).
  34. Jenkins, W. I. Policy Analysis: A Political and Organisational Perspective. (M. Robertson, 1978).
  35. Howlett, M. & Cashore, B. Conceptualizing Public Policy. in Comparative Policy Studies: Conceptual and Methodological Challenges (eds. Engeli, I. & Allison, C. R.) 17–33 (Palgrave Macmillan UK, 2014). doi:10.1057/9781137314154_2.
  36. Centers for Disease Control and Prevention. Overview of CDC’s policy process. https://www.cdc.gov/policy/analysis/process/docs/CDCPolicyProcess.pdf (2012).
  37. JBI Manual for Evidence Synthesis. (Joanna Briggs Institute, 2020). doi:10.46658/JBIMES-20-01.
  38. Peters, M. D. J. et al. Guidance for conducting systematic scoping reviews. JBI Evidence Implementation 13, 141–146 (2015).
  39. Covidence - Better systematic review management. https://www.covidence.org/home.
  40. Dixon-Woods, M. et al. Conducting a critical interpretive synthesis of the literature on access to healthcare by vulnerable groups. BMC Medical Research Methodology 6, 35 (2006).
  41. Braun, V. & Clarke, V. Using thematic analysis in psychology. Qualitative Research in Psychology 3, 77–101 (2006).
  42. Dedoose Version 8.3.47, web application for managing, analyzing, and presenting qualitative and mixed method research data. (SocioCultural Research Consultants, LLC, 2021).
  43. Willis, C. Outcomes of Interorganizational Networks in Canada for Chronic Disease Prevention: Insights From a Concept Mapping Study, 2015. Prev. Chronic Dis. 12, (2015).
  44. Stankov, I., Howard, N. J., Daniel, M. & Cargo, M. Policy, Research and Residents’ Perspectives on Built Environments Implicated in Heart Disease: A Concept Mapping Approach. International Journal of Environmental Research and Public Health 14, 170 (2017).
  45. Crespo, R., Alvarez, C., Hernandez, I. & García, C. A spatially explicit analysis of chronic diseases in small areas: A case study of diabetes in Santiago, Chile. International Journal of Health Geographics 19, (2020).
  46. Giles, B. G. et al. Integrating conventional science and aboriginal perspectives on diabetes using fuzzy cognitive maps. Social Science & Medicine 64, 562–576 (2007).
  47. Auchincloss, A. H., Riolo, R. L., Brown, D. G., Cook, J. & Roux, A. V. D. An Agent-Based Model of Income Inequalities in Diet in the Context of Residential Segregation. American Journal of Preventive Medicine 40, 303–311 (2011).
  48. Gerritsen, S. et al. Systemic Barriers and Equitable Interventions to Improve Vegetable and Fruit Intake in Children: Interviews with National Food System Actors. International Journal of Environmental Research and Public Health 16, 1387 (2019).
  49. Guariguata, L. et al. Using group model building to describe the system driving unhealthy eating and identify intervention points: A participatory, stakeholder engagement approach in the Caribbean. Nutrients 12, (2020).
  50. Mazzocchi, G. & Marino, D. Rome, a policy without politics: The participatory process for a metropolitan scale food policy. International Journal of Environmental Research and Public Health 17, (2020).
  51. Baugh Littlejohns, L., Baum, F., Lawless, A. & Freeman, T. The value of a causal loop diagram in exploring the complex interplay of factors that influence health promotion in a multisectoral health system in Australia. Health Research Policy and Systems 16, 126 (2018).
  52. Witter, S. et al. Opportunities and challenges for delivering non-communicable disease management and services in fragile and post-conflict settings: Perceptions of policy-makers and health providers in Sierra Leone. Conflict and Health 14, (2020).
  53. Roblin, L., Truscott, R. & Boddy, M. R. The Development of a Provincial Food and Nutrition Strategy through Cross-Sector Collaboration. Canadian Journal of Dietetic Practice and Research 79, 28–34 (2018).
  54. Nelson, D. A. et al. Using Group Model Building to Understand Factors That Influence Childhood Obesity in an Urban Environment. Journal of Public Health Management and Practice 21, S74 (2015).
  55. Nau, T. et al. Toward Whole-of-System Action to Promote Physical Activity: A Cross-Sectoral Analysis of Physical Activity Policy in Australia. Journal of Physical Activity and Health 16, 1029–1038 (2019).
  56. Malhi, L. et al. Places to Intervene to Make Complex Food Systems More Healthy, Green, Fair, and Affordable. Journal of Hunger and Environmental Nutrition 4, 466–476 (2009).
  57. Buck, C. et al. Factors influencing sedentary behaviour: A system based analysis using Bayesian networks within DEDIPAC. Plos One 14, e0211546 (2019).
  58. Chao, D., Hashimoto, H. & Kondo, N. Dynamic impact of social stratification and social influence on smoking prevalence by gender: An agent-based model. Social Science & Medicine 147, 280–287 (2015).
  59. Stillman, F., Hoang, M., Linton, R., Ritthiphakdee, B. & Trochim, W. Mapping tobacco industry strategies in South East Asia for action planning and surveillance. Tobacco Control 17, e1–e1 (2008).
  60. Tan, D. T. et al. Systems approaches for localising the SDGs: Co-production of place-based case studies. Globalization and Health 15, (2019).
  61. Langellier, B. A. et al. Using community-based system dynamics modeling to understand the complex systems that influence health in cities: The SALURBAL study. Health & Place 60, 102215 (2019).
  62. Yang, Y. & Diez-Roux, A. V. Using an agent-based model to simulate children’s active travel to school. International Journal of Behavioral Nutrition and Physical Activity 10, (2013).
  63. Atkinson, J.-A. et al. Harnessing advances in computer simulation to inform policy and planning to reduce alcohol-related harms. Int J Public Health 63, 537–546 (2018).
  64. Castillo-Carniglia, A., Pear, V. A., Tracy, M., Keyes, K. M. & Cerdá, M. Limiting Alcohol Outlet Density to Prevent Alcohol Use and Violence: Estimating Policy Interventions Through Agent-Based Modeling. Am J Epidemiol 188, 694–702 (2019).
  65. Holder, H. D. & Blose, J. O. Reduction of community alcohol problems: Computer simulation experiments in three counties. Journal of Studies on Alcohol 48, 124–135 (1987).
  66. Kang, H., Nembhard, H. B., Ghahramani, N. & Curry, W. A system dynamics approach to planning and evaluating interventions for chronic disease management. Journal of the Operational Research Society 69, 987–1005 (2018).
  67. Hirsch, G., Homer, J., Evans, E. & Zielinski, A. A System Dynamics Model for Planning Cardiovascular Disease Interventions. Am J Public Health 100, 616–622 (2010).
  68. Li, Y., Kong, N., Lawley, M., Weiss, L. & Pagán, J. A. Advancing the Use of Evidence-Based Decision-Making in Local Health Departments With Systems Science Methodologies. Am J Public Health 105, S217–S222 (2015).
  69. Loyo, H. K. et al. From Model to Action: Using a System Dynamics Model of Chronic Disease Risks to Align Community Action. Health Promotion Practice 14, 53–61 (2013).
  70. Yarnoff, B. Estimating the Relative Impact of Clinical and Preventive Community-Based Interventions: An Example Based on the Community Transformation Grant Program. Prev. Chronic Dis. 16, (2019).
  71. Freebairn, L. et al. ‘Turning the tide’ on hyperglycemia in pregnancy: insights from multiscale dynamic simulation modeling. Bmj Open Diabetes Research & Care 8, e000975 (2020).
  72. Freebairn, L. et al. Turning conceptual systems maps into dynamic simulation models: An Australian case study for diabetes in pregnancy. PLoS ONE 14, (2019).
  73. Li, Y. et al. Using Systems Science to Inform Population Health Strategies in Local Health Departments: A Case Study in San Antonio, Texas: Public Health Reports (2017) doi:10.1177/0033354917722149.
  74. Li, Y. et al. Assessing the role of access and price on the consumption of fruits and vegetables across New York City using agent-based modeling. Preventive Medicine 106, 73–78 (2018).
  75. Widener, M. J., Metcalf, S. S. & Bar-Yam, Y. Agent-based modeling of policies to improve urban food access for low-income populations. Applied Geography 40, 1–10 (2013).
  76. Abdollahiasl, A. et al. A system dynamics model for national drug policy. Daru-Journal of Pharmaceutical Sciences 22, 34 (2014).
  77. Honeycutt, A. A., Wile, K., Dove, C., Hawkins, J. & Orenstein, D. Strategic Planning for Chronic Disease Prevention in Rural America: Looking Through a PRISM Lens. Journal of Public Health Management and Practice 21, 392–399 (2015).
  78. Signal, L. N. et al. Tackling ‘wicked’ health promotion problems: a New Zealand case study. Health Promot Int 28, 84–94 (2013).
  79. Zhang, D., Giabbanelli, P. J., Arah, O. A. & Zimmerman, F. J. Impact of Different Policies on Unhealthy Dietary Behaviors in an Urban Adult Population: An Agent-Based Simulation Model. Am J Public Health 104, 1217–1222 (2014).
  80. Carrete, L., Arroyo, P. & Villaseñor, R. A socioecological view toward an understanding of how to prevent overweight in children. Journal of Consumer Marketing 34, 156–168 (2017).
  81. El-Sayed, A. M., Seemann, L., Scarborough, P. & Galea, S. Are Network-Based Interventions a Useful Antiobesity Strategy? An Application of Simulation Models for Causal Inference in Epidemiology. American Journal of Epidemiology 178, 287–295 (2013).
  82. Johnston, L. M., Matteson, C. L. & Finegood, D. T. Systems Science and Obesity Policy: A Novel Framework for Analyzing and Rethinking Population-Level Planning. Am J Public Health 104, 1270–1278 (2014).
  83. Liu, S., Osgood, N., Gao, Q., Xue, H. & Wang, Y. Systems simulation model for assessing the sustainability and synergistic impacts of sugar-sweetened beverages tax and revenue recycling on childhood obesity prevention. Journal of the Operational Research Society 67, 708–721 (2016).
  84. Orr, M. G., Kaplan, G. A. & Galea, S. Neighbourhood food, physical activity, and educational environments and black/white disparities in obesity: a complex systems simulation analysis. J Epidemiol Community Health 70, 862–867 (2016).
  85. Powell, K. E. et al. Systems Thinking and Simulation Modeling to Inform Childhood Obesity Policy and Practice. Public health reports (Washington, D.C. : 1974) 132, 33S-38S (2017).
  86. Roberts, N. et al. Can the Target Set for Reducing Childhood Overweight and Obesity Be Met? A System Dynamics Modelling Study in New South Wales, Australia. Systems Research and Behavioral Science 36, 36–52 (2019).
  87. Bellew, W. et al. Whole of Systems Approaches to Physical Activity Policy and Practice in Australia: The ASAPa Project Overview and Initial Systems Map. Journal of Physical Activity and Health 17, 68–73 (2020).
  88. Browne, J. et al. A network approach to policy framing: A case study of the National Aboriginal and Torres Strait Islander Health Plan. Social Science and Medicine 172, 10–18 (2017).
  89. Brennan, L. K., Brownson, R. C., Kelly, C., Ivey, M. K. & Leviton, L. C. Concept Mapping: Priority Community Strategies to Create Changes to Support Active Living. American Journal of Preventive Medicine 43, S337–S350 (2012).
  90. Yang, Y. et al. Modeling spatial segregation and travel cost influences on utilitarian walking: Towards policy intervention. Computers, Environment and Urban Systems 51, 59–69 (2015).
  91. Ahmad, S. & Franz, G. A. Raising taxes to reduce smoking prevalence in the US: A simulation of the anticipated health and economic impacts. Public Health 122, 3–10 (2008).
  92. Cavana, R. Y. & Tobias, M. Integrative system dynamics: analysis of policy options for tobacco control in New Zealand. Systems Research and Behavioral Science 25, 675–694 (2008).
  93. Cavana, R. Y. & Clifford, L. V. Demonstrating the utility of system dynamics for public policy analysis in New Zealand: the case of excise tax policy on tobacco. System Dynamics Review 22, 321–348 (2006).
  94. Hammond, R. A. et al. Development of a computational modeling laboratory for examining tobacco control policies: Tobacco Town. Health & Place 61, 102256 (2020).
  95. Roberts, E. B., Homer, J., Kasabian, A. & Varrell, M. A systems view of the smoking problem: Perspective and limitations of the role of science in decision-making. International Journal of Bio-Medical Computing 13, 69–86 (1982).
  96. Tengs, T. O., Ahmad, S., Moore, R. & Gage, E. Federal policy mandating safer cigarettes: A hypothetical simulation of the anticipated population health gains or losses. Journal of Policy Analysis and Management 23, 857–872 (2004).
  97. Tengs, T. O., Ahmad, S., Savage, J. M., Moore, R. & Gage, E. The AMA proposal to mandate nicotine reduction in cigarettes: a simulation of the population health impacts. Preventive Medicine 40, 170–180 (2005).
  98. Tobias, M. I., Cavana, R. Y. & Bloomfield, A. Application of a System Dynamics Model to Inform Investment in Smoking Cessation Services in New Zealand. Am J Public Health 100, 1274–1281 (2010).
  99. Zwald, M. L. et al. Network influences on the development and implementation of active transportation policies in six U.S. cities. Preventive Medicine 118, 176–183 (2019).
  100. Peters, D. T. J. M., Verweij, S., Grêaux, K., Stronks, K. & Harting, J. Conditions for addressing environmental determinants of health behavior in intersectoral policy networks: A fuzzy set Qualitative Comparative Analysis. Social Science & Medicine 195, 34–41 (2017).
  101. Freebairn, L. et al. Knowledge mobilisation for policy development: Implementing systems approaches through participatory dynamic simulation modelling. Health Research Policy and Systems 15, (2017).
  102. McGetrick, J. A., Raine, K. D., Wild, T. C. & Nykiforuk, C. I. J. Advancing Strategies for Agenda Setting by Health Policy Coalitions: A Network Analysis of the Canadian Chronic Disease Prevention Survey. Health Communication 34, 1303–1312 (2019).
  103. Garney, W. R., Patterson, M. S., Garcia, K., Muraleetharan, D. & McLeroy, K. Interorganizational network findings from a nationwide cardiovascular disease prevention initiative. Evaluation and Program Planning 79, 101771 (2020).
  104. Beaton, A. et al. He Pikinga Waiora: Supporting Māori health organisations to respond to pre-diabetes. International Journal for Equity in Health 18, (2019).
  105. Baker, P. et al. Generating political commitment for ending malnutrition in all its forms: A system dynamics approach for strengthening nutrition actor networks. Obesity Reviews 20, 30–44 (2019).
  106. Cullerton, K., Donnet, T., Lee, A. & Gallegos, D. Joining the dots: the role of brokers in nutrition policy in Australia. BMC Public Health 17, 307 (2017).
  107. Peters, D. T. J. M., Raab, J., Grêaux, K. M., Stronks, K. & Harting, J. Structural integration and performance of inter-sectoral public health-related policy networks: An analysis across policy phases. Health Policy 121, 1296–1302 (2017).
  108. Heo, H.-H., Jeong, W., Che, X. H. & Chung, H. A stakeholder analysis of community-led collaboration to reduce health inequity in a deprived neighbourhood in South Korea: Global Health Promotion (2018) doi:10.1177/1757975918791517.
  109. Scheele, C. E., Little, I. & Diderichsen, F. Governing health equity in Scandinavian municipalities: The inter-sectorial challenge. Scand J Public Health 46, 57–67 (2018).
  110. Clarke, B., Swinburn, B. & Sacks, G. Understanding Health Promotion Policy Processes: A Study of the Government Adoption of the Achievement Program in Victoria, Australia. International Journal of Environmental Research and Public Health 15, 2393 (2018).
  111. Clarke, B., Swinburn, B. & Sacks, G. Understanding the LiveLighter® obesity prevention policy processes: An investigation using political science and systems thinking. Social Science and Medicine 246, (2020).
  112. Pérez-Escamilla, R. et al. Prevention of childhood obesity and food policies in Latin America: from research to practice. Obesity Reviews 18, 28–38 (2017).
  113. Waqa, G. et al. Exploring the dynamics of food-related policymaking processes and evidence use in Fiji using systems thinking. Health Research Policy and Systems 15, 74 (2017).
  114. Sturgiss, E., Luig, T., Campbell-Scherer, D. L., Lewanczuk, R. & Green, L. A. Using Concept Maps to compare obesity knowledge between policy makers and primary care researchers in Canada. BMC Research Notes 12, (2019).
  115. De Bruin, W. E., Stayner, C., Lange, M. de & Taylor, R. W. Who Are the Key Players Involved with Shaping Public Opinion and Policies on Obesity and Diabetes in New Zealand? Nutrients 10, 1592 (2018).
  116. Loitz, C. C., Stearns, J. A., Fraser, S. N., Storey, K. & Spence, J. C. Network analysis of inter-organizational relationships and policy use among active living organizations in Alberta, Canada. BMC Public Health 17, 649 (2017).
  117. Barnes, M., MacLean, J. & Cousens, L. Understanding the structure of community collaboration: the case of one Canadian health promotion network. Health Promotion International 25, 238–247 (2010).
  118. Bergeron, K. & Lévesque, L. Designing Active Communities: A Coordinated Action Framework for Planners and Public Health Professionals. Journal of Physical Activity and Health 11, 1041–1051 (2014).
  119. Buchthal, O. V., Taniguchi, N., Iskandar, L. & Maddock, J. Assessing State-Level Active Living Promotion Using Network Analysis. Journal of Physical Activity and Health 10, 19–32 (2013).
  120. Noël Racine, A. et al. Perceptions of barriers and levers of health-enhancing physical activity policies in mid-size French municipalities. Health Research Policy and Systems 18, (2020).
  121. Spitters, H. P. E. M. et al. Unravelling networks in local public health policymaking in three European countries – a systems analysis. Health Research Policy and Systems 15, 5 (2017).
  122. Leppin, A. L. et al. Applying Social Network Analysis to Evaluate Implementation of a Multisector Population Health Collaborative That Uses a Bridging Hub Organization. Front. Public Health 6, (2018).
  123. Fisher, M., Milos, D., Baum, F. & Friel, S. Social determinants in an Australian urban region: a ‘complexity’ lens. Health Promot Int 31, 163–174 (2016).
  124. Hoeijmakers, M., De Leeuw, E., Kenis, P. & De Vries, N. K. Local health policy development processes in the Netherlands: An expanded toolbox for health promotion. Health Promotion International 22, 112–121 (2007).
  125. Leider, J. P., Castrucci, B. C., Harris, J. K. & Hearne, S. The Relationship of Policymaking and Networking Characteristics among Leaders of Large Urban Health Departments. International Journal of Environmental Research and Public Health 12, 9169–9180 (2015).
  126. Merrill, J., Keeling, J. W. & Carley, K. M. A comparative study of 11 local health department organizational networks. Journal of Public Health Management and Practice 16, 564–576 (2010).
  127. Oliver, K., Everett, M., Verma, A. & de Vocht, F. The human factor: Re-organisations in public health policy. Health Policy 106, 97–103 (2012).
  128. Oliver, K., De Vocht, F., Money, A. & Everett, M. Who runs public health? A mixed-methods study combining qualitative and network analyses. Journal of Public Health (United Kingdom) 35, 453–459 (2013).
  129. Pineo, H., Zimmermann, N. & Davies, M. Integrating health into the complex urban planning policy and decision-making context: a systems thinking analysis. Palgrave Communications 6, 1–14 (2020).
  130. Mareeuw, F. van den D., Vaandrager, L., Klerkx, L., Naaldenberg, J. & Koelen, M. Beyond bridging the know-do gap: a qualitative study of systemic interaction to foster knowledge exchange in the public health sector in The Netherlands. Bmc Public Health 15, 922 (2015).
  131. Harris, J. K. Communication ties across the national network of local health departments. American Journal of Preventive Medicine 44, 247–253 (2013).
  132. Harris, J. K., Luke, D. A., Burke, R. C. & Mueller, N. B. Seeing the forest and the trees: Using network analysis to develop an organizational blueprint of state tobacco control systems. Social Science & Medicine 67, 1669–1678 (2008).
  133. Luke, D. A., Wald, L. M., Carothers, B. J., Bach, L. E. & Harris, J. K. Network Influences on Dissemination of Evidence-Based Guidelines in State Tobacco Control Programs: Health Education & Behavior (2013) doi:10.1177/1090198113492760.
  134. Moreland-Russell, S. & Carothers, B. J. An Examination of Two Policy Networks Involved in Advancing Smokefree Policy Initiatives. International Journal of Environmental Research and Public Health 12, 11117–11131 (2015).
  135. Weishaar, H., Amos, A. & Collin, J. Capturing complexity: mixing methods in the analysis of a European tobacco control policy network. International Journal of Social Research Methodology 18, 175–192 (2015).
  136. Weishaar, H., Amos, A. & Collin, J. Best of enemies: Using social network analysis to explore a policy network in European smoke-free policy. Social Science & Medicine 133, 85–92 (2015).
  137. Macmillan, A. et al. Suburb-level changes for active transport to meet the SDGs: Causal theory and a New Zealand case study. Science of the Total Environment 714, (2020).
  138. Kokkinen, L., Freiler, A., Muntaner, C. & Shankardass, K. How and why do win-win strategies work in engaging policy-makers to implement Health in All Policies? A multiple-case study of six state- and national-level governments. Health Research Policy and Systems 17, 102 (2019).
  139. Shankardass, K. et al. The implementation of Health in All Policies initiatives: a systems framework for government action. Health Research Policy and Systems 16, 26 (2018).
  140. Cambon, L., Minary, L., Ridde, V. & Alla, F. A tool to analyze the transferability of health promotion interventions. Bmc Public Health 13, 1184 (2013).
  141. MacDiarmid, J. I. et al. Developing a timeline for evaluating public health nutrition policy interventions. What are the outcomes and when should we expect to see them? Public Health Nutrition 14, 729–739 (2011).
  142. Conte, K. P. & Davidson, S. Using a ‘rich picture’ to facilitate systems thinking in research coproduction. Health Research Policy and Systems 18, (2020).
  143. Roussy, V., Riley, T., Livingstone, C. & Russell, G. A system dynamic perspective of stop–start prevention interventions in Australia. Health Promot Int doi:10.1093/heapro/daz098.
  144. Beets, M. W., Webster, C., Saunders, R. & Huberty, J. L. Translating Policies Into Practice: A Framework to Prevent Childhood Obesity in Afterschool Programs. Health Promotion Practice 14, 228–237 (2013).
  145. Knai, C. et al. The Public Health Responsibility Deal: Using a Systems-Level Analysis to Understand the Lack of Impact on Alcohol, Food, Physical Activity, and Workplace Health Sub-Systems. International Journal of Environmental Research and Public Health 15, 2895 (2018).
  146. Kuunders, T. J. M., van Bon-Martens, M. J. H., van de Goor, I. A. M., Paulussen, T. G. W. M. & van Oers, H. A. M. Towards local implementation of Dutch health policy guidelines: a concept-mapping approach. Health Promot Int 33, 635–647 (2018).
  147. Pagliccia, N. et al. Network analysis as a tool to assess the intersectoral management of health determinants at the local level: A report from an exploratory study of two Cuban municipalities. Social Science and Medicine 71, 394–399 (2010).
  148. Van Roode, T. et al. Values are not enough: Qualitative study identifying critical elements for prioritization of health equity in health systems. International Journal for Equity in Health 19, (2020).
  149. Terpstra, J. L., Best, A., Saul, J. & Leischow, S. J. The Complexity of Institutionalizing Evaluation as a Best Practice in North American Quitlines. American Journal of Evaluation 34, 356–371 (2013).
  150. Valente, T. W., Wipfli, H. & Vega Yon, G. G. Network influences on policy implementation: Evidence from a global health treaty. Social Science and Medicine 222, 188–197 (2019).
  151. Wen, W. et al. Public Reactions to the Cigarette Control Regulation on a Chinese Microblogging Platform: Empirical Analysis. Journal of Medical Internet Research 22, e14660 (2020).
  152. Cullerton, K., Donnet, T., Lee, A. & Gallegos, D. Exploring power and influence in nutrition policy in Australia. Obesity Reviews 17, 1218–1225 (2016).
  153. Fisher, M., Milos, D., Baum, F. & Friel, S. Social determinants in an Australian urban region: a ‘complexity’ lens. Health Promot Int 31, 163–174 (2016).
  154. Signal, L. N. et al. Tackling owicked’ health promotion problems: a New Zealand case study. Health Promotion International 28, 84–94 (2013).
  155. Castillo-Carandang, N. T. et al. Moving towards optimized noncommunicable disease management in the asean region: Recommendations from a review and multidisciplinary expert panel. Risk Management and Healthcare Policy 13, 803–819 (2020).
  156. Brown, V. et al. Better transport accessibility, better health: a health economic impact assessment study for Melbourne, Australia. Int J Behav Nutr Phys Act 16, 89 (2019).
  157. McGill, E. et al. Evaluation of public health interventions from a complex systems perspective: A research methods review. Social Science & Medicine 272, 113697 (2021).
  158. Anzola, D., Barbrook-Johnson, P. & Cano, J. I. Self-organization and social science. Computational and Mathematical Organization Theory 2, 221–257 (2016).

Articles in Press, Accepted Manuscript
Available Online from 18 January 2023
  • Receive Date: 10 September 2021
  • Revise Date: 24 June 2022
  • Accept Date: 14 January 2023
  • First Publish Date: 18 January 2023