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 Global Food System & Policy Research, School of Global Health, York University, Toronto, ON, Canada

2 Global Food System & Policy Research, School of Global Health, York University, Toronto, ON, Canada

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

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

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

6 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 policy-makers 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 
We searched academic databases (Medline, Scopus, Web of Science, EMBASE) for all publications indexed by October 13, 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 decisionmaking; 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 policy-making, 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

References

  1. Lich KH, Ginexi EM, Osgood ND, Mabry PL. A call to address complexity in prevention science research. Prev Sci. 2013;14(3):279-289. doi:1007/s11121-012-0285-2
  2. Rusoja E, Haynie D, Sievers J, et al. Thinking about complexity in health: a systematic review of the key systems thinking and complexity ideas in health. J Eval Clin Pract. 2018;24(3):600-606. doi:1111/jep.12856
  3. Gates EF. Making sense of the emerging conversation in evaluation about systems thinking and complexity science. Eval Program Plann. 2016;59:62-73. doi:1016/j.evalprogplan.2016.08.004
  4. Meadows DH. Thinking in Systems: A Primer. Chelsea Green Publishing; 2008.
  5. Sterman JD. Learning from evidence in a complex world. Am J Public Health. 2006;96(3):505-514. doi:2105/ajph.2005.066043
  6. de Savigny D, Adam T. Systems Thinking for Health Systems Strengthening. Alliance for Health Policy and Systems Research, World Health Organization; 2009.
  7. Rutter H, Savona N, Glonti K, et al. The need for a complex systems model of evidence for public health. Lancet. 2017;390(10112):2602-2604. doi:1016/s0140-6736(17)31267-9
  8. Vennix JA, Akkermans HA, Rouwette EA. Group model-building to facilitate organizational change: an exploratory study. Syst Dyn Rev. 1996;12(1):39-58. doi:1002/(sici)1099-1727(199621)12:1<39::aid-sdr94>3.0.co;2-k
  9. Rouwette EA, Vennix JA, van Mullekom T. Group model building effectiveness: a review of assessment studies. Syst Dyn Rev. 2002;18(1):5-45. doi:1002/sdr.229
  10. Swinburn BA, Kraak VI, Allender S, et al. The global syndemic of obesity, undernutrition, and climate change: the Lancet Commission report. Lancet. 2019;393(10173):791-846. doi:1016/s0140-6736(18)32822-8
  11. Butland B, Jebb S, Kopelman P, et al. Tackling Obesities: Future Choices-Project Report. 2nd ed. London: Department of Innovation, Universities and Skills; 2007:164. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/287937/07-1184x-tackling-obesities-future-choices-report.pdf.
  12. World Health Organization (WHO). Global Action Plan on Physical Activity 2018-2030: More Active People for a Healthier World. WHO; 2018. p. 104. https://apps.who.int/iris/bitstream/handle/10665/272722/9789241514187-eng.pdf.
  13. Office of Behavioral and Social Sciences Research. https://obssr.od.nih.gov/.
  14. Wutzke S, Rowbotham S, Haynes A, et al. Knowledge mobilisation for chronic disease prevention: the case of the Australian Prevention Partnership Centre. Health Res Policy Syst. 2018;16(1):109. doi:1186/s12961-018-0379-9
  15. Atkinson JA, Page A, Prodan A, McDonnell G, Osgood N. Systems modelling tools to support policy and planning. Lancet. 2018;391(10126):1158-1159. doi:1016/s0140-6736(18)30302-7
  16. Atkinson JA, Wells R, Page A, Dominello A, Haines M, Wilson A. Applications of system dynamics modelling to support health policy. Public Health Res Pract. 2015;25(3):e2531531. doi:17061/phrp2531531
  17. Langellier BA, Yang Y, Purtle J, Nelson KL, Stankov I, Diez Roux AV. Complex systems approaches to understand drivers of mental health and inform mental health policy: a systematic review. Adm Policy Ment Health. 2019;46(2):128-144. doi:1007/s10488-018-0887-5
  18. Carey G, Malbon E, Carey N, Joyce A, Crammond B, Carey A. Systems science and systems thinking for public health: a systematic review of the field. BMJ Open. 2015;5(12):e009002. doi:1136/bmjopen-2015-009002
  19. World Health Organization (WHO). World Health Assembly Resolution WHA51. 12 Health Promotion. WHO; 1998. 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). 2013;35(4):486-487. doi:1093/pubmed/fdt113
  21. Petticrew M, Whitehead M, Macintyre SJ, Graham H, Egan M. Evidence for public health policy on inequalities: 1: the reality according to policymakers. J Epidemiol Community Health. 2004;58(10):811-816. doi:1136/jech.2003.015289
  22. McGill E, Egan M, Petticrew M, et al. Trading quality for relevance: non-health decision-makers' use of evidence on the social determinants of health. BMJ Open. 2015;5(4):e007053. doi:1136/bmjopen-2014-007053
  23. Barbrook-Johnson P, Proctor A, Giorgi S, Phillipson J. How do policy evaluators understand complexity? Evaluation. 2020;26(3):315-332. doi:1177/1356389020930053
  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. 2020;9(2):65-76. doi:15171/ijhpm.2019.86
  25. Knai C, Petticrew M, Mays N, et al. Systems thinking as a framework for analyzing commercial determinants of health. Milbank Q. 2018;96(3):472-498. doi:1111/1468-0009.12339
  26. Canty-Waldron J. Using systems thinking to create more impactful social policy. J Futures Stud. 2014;19(2):61-86.
  27. Forrester JW. System dynamics—the next fifty years. Syst Dyn Rev. 2007;23(2-3):359-370. doi:1002/sdr.381
  28. Ghaffarzadegan N, Lyneis J, Richardson GP. How small system dynamics models can help the public policy process. Syst Dyn Rev. 2011;27(1):22-44. doi:10.1002/sdr.442
  29. Clifford Astbury C, McGill E, Egan M, Penney TL. Systems thinking and complexity science methods and the policy process in non-communicable disease prevention: a systematic scoping review protocol. BMJ Open. 2021;11(9):e049878. doi:1136/bmjopen-2021-049878
  30. Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. doi:1136/bmj.n71
  31. Arksey H, O'Malley L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol. 2005;8(1):19-32. doi:1080/1364557032000119616
  32. Levac D, Colquhoun H, O'Brien KK. Scoping studies: advancing the methodology. Implement Sci. 2010;5:69. doi:1186/1748-5908-5-69
  33. Colquhoun HL, Levac D, O'Brien KK, et al. Scoping reviews: time for clarity in definition, methods, and reporting. J Clin Epidemiol. 2014;67(12):1291-1294. doi:1016/j.jclinepi.2014.03.013
  34. Jenkins WI. Policy Analysis: A Political and Organisational Perspective. Wiley-Blackwell; 1978.
  35. Howlett M, Cashore B. Conceptualizing public policy. In: Engeli I, Allison CR, eds. Comparative Policy Studies: Conceptual and Methodological Challenges. London: Palgrave Macmillan; 2014:17-33. doi:1057/9781137314154_2
  36. Centers for Disease Control and Prevention. Overview of CDC’s Policy Process. 2012. https://www.cdc.gov/policy/analysis/process/docs/CDCPolicyProcess.pdf.
  37. Aromataris E, Munn Z. JBI Manual for Evidence Synthesis. The Joanna Briggs Institute; 2020. doi:46658/jbimes-20-01
  38. Peters MDJ, Godfrey CM, Khalil H, McInerney P, Parker D, Soares CB. Guidance for conducting systematic scoping reviews. JBI Evid Implement. 2015;13(3):141-146. doi:1097/xeb.0000000000000050
  39. Covidence - Better Systematic Review Management. https://www.covidence.org/home.
  40. Dixon-Woods M, Cavers D, Agarwal S, et al. Conducting a critical interpretive synthesis of the literature on access to healthcare by vulnerable groups. BMC Med Res Methodol. 2006;6:35. doi:1186/1471-2288-6-35
  41. Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol. 2006;3(2):77-101. doi:1191/1478088706qp063oa
  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, Kernoghan A, Riley B, Popp J, Best A, Milward HB. Outcomes of interorganizational networks in Canada for chronic disease prevention: insights from a concept mapping study, 2015. Prev Chronic Dis. 2015;12:E199. doi:5888/pcd12.150297
  44. Stankov I, Howard NJ, Daniel M, Cargo M. Policy, research and residents' perspectives on built environments implicated in heart disease: a concept mapping approach. Int J Environ Res Public Health. 2017;14(2):170. doi:3390/ijerph14020170
  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. Int J Health Geogr. 2020;19(1):24. doi:1186/s12942-020-00217-1
  46. Giles BG, Findlay CS, Haas G, LaFrance B, Laughing W, Pembleton S. Integrating conventional science and aboriginal perspectives on diabetes using fuzzy cognitive maps. Soc Sci Med. 2007;64(3):562-576. doi:1016/j.socscimed.2006.09.007
  47. Auchincloss AH, Riolo RL, Brown DG, Cook J, Diez Roux AV. An agent-based model of income inequalities in diet in the context of residential segregation. Am J Prev Med. 2011;40(3):303-311. doi:1016/j.amepre.2010.10.033
  48. Gerritsen S, Harré S, Swinburn B, et al. Systemic barriers and equitable interventions to improve vegetable and fruit intake in children: interviews with national food system actors. Int J Environ Res Public Health. 2019;16(8):1387. doi:3390/ijerph16081387
  49. Guariguata L, Rouwette EA, Murphy MM, 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. 2020;12(2):384. doi:3390/nu12020384
  50. Mazzocchi G, Marino D. Rome, a policy without politics: the participatory process for a metropolitan scale food policy. Int J Environ Res Public Health. 2020;17(2):479. doi:3390/ijerph17020479
  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 Res Policy Syst. 2018;16(1):126. doi:1186/s12961-018-0394-x
  52. Witter S, Zou G, Diaconu K, 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. Confl Health. 2020;14:3. doi:1186/s13031-019-0248-3
  53. Roblin L, Truscott R, Boddy MR. The development of a provincial food and nutrition strategy through cross-sector collaboration. Can J Diet Pract Res. 2018;79(1):28-34. doi:3148/cjdpr-2017-033
  54. Nelson DA, Simenz CJ, OʼConnor SP, et al. Using group model building to understand factors that influence childhood obesity in an urban environment. J Public Health Manag Pract. 2015;21 Suppl 3:S74-78. doi:1097/phh.0000000000000219
  55. Nau T, Lee K, Smith BJ, et al. Toward whole-of-system action to promote physical activity: a cross-sectoral analysis of physical activity policy in Australia. J Phys Act Health. 2019;16(11):1029-1038. doi:1123/jpah.2019-0122
  56. Malhi L, Karanfil O, Merth T, Acheson M, Palmer A, Finegood DT. Places to intervene to make complex food systems more healthy, green, fair, and affordable. J Hunger Environ Nutr. 2009;4(3-4):466-476. doi:1080/19320240903346448
  57. Buck C, Loyen A, Foraita R, et al. Factors influencing sedentary behaviour: a system based analysis using Bayesian networks within DEDIPAC. PLoS One. 2019;14(1):e0211546. doi:1371/journal.pone.0211546
  58. Chao D, Hashimoto H, Kondo N. Dynamic impact of social stratification and social influence on smoking prevalence by gender: an agent-based model. Soc Sci Med. 2015;147:280-287. doi:1016/j.socscimed.2015.08.041
  59. Stillman F, Hoang M, Linton R, Ritthiphakdee B, Trochim W. Mapping tobacco industry strategies in South East Asia for action planning and surveillance. Tob Control. 2008;17(1):e1. doi:1136/tc.2006.017988
  60. Tan DT, Siri JG, Gong Y, et al. Systems approaches for localising the SDGs: co-production of place-based case studies. Global Health. 2019;15(1):85. doi:1186/s12992-019-0527-1
  61. Langellier BA, Kuhlberg JA, Ballard EA, et al. Using community-based system dynamics modeling to understand the complex systems that influence health in cities: the SALURBAL study. Health Place. 2019;60:102215. doi:1016/j.healthplace.2019.102215
  62. Yang Y, Diez-Roux AV. Using an agent-based model to simulate children's active travel to school. Int J Behav Nutr Phys Act. 2013;10:67. doi:1186/1479-5868-10-67
  63. Atkinson JA, Knowles D, Wiggers J, et al. Harnessing advances in computer simulation to inform policy and planning to reduce alcohol-related harms. Int J Public Health. 2018;63(4):537-546. doi:1007/s00038-017-1041-y
  64. Castillo-Carniglia A, Pear VA, Tracy M, Keyes KM, Cerdá M. Limiting alcohol outlet density to prevent alcohol use and violence: estimating policy interventions through agent-based modeling. Am J Epidemiol. 2019;188(4):694-702. doi:1093/aje/kwy289
  65. Holder HD, Blose JO. Reduction of community alcohol problems: computer simulation experiments in three counties. J Stud Alcohol. 1987;48(2):124-135. doi:15288/jsa.1987.48.124
  66. Kang H, Nembhard HB, Ghahramani N, Curry W. A system dynamics approach to planning and evaluating interventions for chronic disease management. J Oper Res Soc. 2018;69(7):987-1005. doi:1057/s41274-017-0279-3
  67. Hirsch G, Homer J, Evans E, Zielinski A. A system dynamics model for planning cardiovascular disease interventions. Am J Public Health. 2010;100(4):616-622. doi:2105/ajph.2009.159434
  68. Li Y, Kong N, Lawley M, Weiss L, Pagán JA. Advancing the use of evidence-based decision-making in local health departments with systems science methodologies. Am J Public Health. 2015;105(Suppl 2):S217-222. doi:2105/ajph.2014.302077
  69. Loyo HK, Batcher C, Wile K, Huang P, Orenstein D, Milstein B. From model to action: using a system dynamics model of chronic disease risks to align community action. Health Promot Pract. 2013;14(1):53-61. doi:1177/1524839910390305
  70. Yarnoff B, Bradley C, Honeycutt AA, Soler RE, Orenstein D. Estimating the relative impact of clinical and preventive community-based interventions: an example based on the community transformation grant program. Prev Chronic Dis. 2019;16:E87. doi:5888/pcd16.180594
  71. Freebairn L, Atkinson JA, Qin Y, et al. 'Turning the tide' on hyperglycemia in pregnancy: insights from multiscale dynamic simulation modeling. BMJ Open Diabetes Res Care. 2020;8(1):e000975. doi:1136/bmjdrc-2019-000975
  72. Freebairn L, Atkinson JA, Osgood ND, Kelly PM, McDonnell G, Rychetnik L. Turning conceptual systems maps into dynamic simulation models: an Australian case study for diabetes in pregnancy. PLoS One. 2019;14(6):e0218875. doi:1371/journal.pone.0218875
  73. Li Y, Padrón NA, Mangla AT, Russo PG, Schlenker T, Pagán JA. Using systems science to inform population health strategies in local health departments: a case study in San Antonio, Texas. Public Health Rep. 2017;132(5):549-555. doi:1177/0033354917722149
  74. Li Y, Zhang D, Thapa JR, et al. Assessing the role of access and price on the consumption of fruits and vegetables across New York City using agent-based modeling. Prev Med. 2018;106:73-78. doi:1016/j.ypmed.2017.10.014
  75. Widener MJ, Metcalf SS, Bar-Yam Y. Agent-based modeling of policies to improve urban food access for low-income populations. Appl Geogr. 2013;40:1-10. doi:1016/j.apgeog.2013.01.003
  76. Abdollahiasl A, Kebriaeezadeh A, Dinarvand R, et al. A system dynamics model for national drug policy. Daru. 2014;22(1):34. doi:1186/2008-2231-22-34
  77. Honeycutt AA, Wile K, Dove C, Hawkins J, Orenstein D. Strategic planning for chronic disease prevention in rural America: looking through a PRISM lens. J Public Health Manag Pract. 2015;21(4):392-399. doi:1097/phh.0000000000000062
  78. Signal LN, Walton MD, Ni Mhurchu C, et al. Tackling 'wicked' health promotion problems: a New Zealand case study. Health Promot Int. 2013;28(1):84-94. doi:1093/heapro/das006
  79. Zhang D, Giabbanelli PJ, Arah OA, Zimmerman FJ. Impact of different policies on unhealthy dietary behaviors in an urban adult population: an agent-based simulation model. Am J Public Health. 2014;104(7):1217-1222. doi:2105/ajph.2014.301934
  80. Carrete L, Arroyo P, Villaseñor R. A socioecological view toward an understanding of how to prevent overweight in children. J Consum Mark. 2017;34(2):156-168. doi:1108/jcm-01-2016-1660
  81. El-Sayed AM, Seemann L, Scarborough P, Galea S. Are network-based interventions a useful antiobesity strategy? An application of simulation models for causal inference in epidemiology. Am J Epidemiol. 2013;178(2):287-295. doi:1093/aje/kws455
  82. Johnston LM, Matteson CL, Finegood DT. Systems science and obesity policy: a novel framework for analyzing and rethinking population-level planning. Am J Public Health. 2014;104(7):1270-1278. doi:2105/ajph.2014.301884
  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. J Oper Res Soc. 2016;67(5):708-721. doi:1057/jors.2015.99
  84. Orr MG, Kaplan GA, Galea S. Neighbourhood food, physical activity, and educational environments and Black/White disparities in obesity: a complex systems simulation analysis. J Epidemiol Community Health. 2016;70(9):862-867. doi:1136/jech-2015-205621
  85. Powell KE, Kibbe DL, Ferencik R, et al. Systems thinking and simulation modeling to inform childhood obesity policy and practice. Public Health Rep. 2017;132(2_Suppl):33S-38S. doi:1177/0033354917723601
  86. Roberts N, Li V, Atkinson J-A, et al. Can the target set for reducing childhood overweight and obesity be met? A system dynamics modelling study in New South Wales, Australia. Syst Res Behav Sci. 2019;36(1):36-52. doi:1002/sres.2555
  87. Bellew W, Smith BJ, Nau T, Lee K, Reece L, Bauman A. Whole of systems approaches to physical activity policy and practice in Australia: the ASAPa project overview and initial systems map. J Phys Act Health. 2020;17(1):68-73. doi:1123/jpah.2019-0121
  88. Browne J, de Leeuw E, Gleeson D, Adams K, Atkinson P, Hayes R. A network approach to policy framing: a case study of the National Aboriginal and Torres Strait Islander Health Plan. Soc Sci Med. 2017;172:10-18. doi:1016/j.socscimed.2016.11.011
  89. Brennan LK, Brownson RC, Kelly C, Ivey MK, Leviton LC. Concept mapping: priority community strategies to create changes to support active living. Am J Prev Med. 2012;43(5 Suppl 4):S337-350. doi:1016/j.amepre.2012.07.015
  90. Yang Y, Auchincloss AH, Rodriguez DA, Brown DG, Riolo R, Diez-Roux AV. Modeling spatial segregation and travel cost influences on utilitarian walking: towards policy intervention. Comput Environ Urban Syst. 2015;51:59-69. doi:1016/j.compenvurbsys.2015.01.007
  91. Ahmad S, Franz GA. Raising taxes to reduce smoking prevalence in the US: a simulation of the anticipated health and economic impacts. Public Health. 2008;122(1):3-10. doi:1016/j.puhe.2007.02.020
  92. Cavana RY, Tobias M. Integrative system dynamics: analysis of policy options for tobacco control in New Zealand. Syst Res Behav Sci. 2008;25(5):675-694. doi:1002/sres.934
  93. Cavana RY, Clifford LV. Demonstrating the utility of system dynamics for public policy analysis in New Zealand: the case of excise tax policy on tobacco. Syst Dyn Rev. 2006;22(4):321-348. doi:1002/sdr.347
  94. Hammond RA, Combs TB, Mack-Crane A, et al. Development of a computational modeling laboratory for examining tobacco control policies: Tobacco Town. Health Place. 2020;61:102256. doi:1016/j.healthplace.2019.102256
  95. Roberts EB, Homer J, Kasabian A, Varrell M. A systems view of the smoking problem: perspective and limitations of the role of science in decision-making. Int J Biomed Comput. 1982;13(1):69-86. doi:1016/0020-7101(82)90051-4
  96. Tengs TO, Ahmad S, Moore R, Gage E. Federal policy mandating safer cigarettes: a hypothetical simulation of the anticipated population health gains or losses. J Policy Anal Manage. 2004;23(4):857-872. doi:1002/pam.20051
  97. Tengs TO, Ahmad S, Savage JM, Moore R, Gage E. The AMA proposal to mandate nicotine reduction in cigarettes: a simulation of the population health impacts. Prev Med. 2005;40(2):170-180. doi:1016/j.ypmed.2004.05.017
  98. Tobias MI, Cavana RY, Bloomfield A. Application of a system dynamics model to inform investment in smoking cessation services in New Zealand. Am J Public Health. 2010;100(7):1274-1281. doi:2105/ajph.2009.171165
  99. Zwald ML, Eyler AA, Haire-Joshu D, et al. Network influences on the development and implementation of active transportation policies in six U.S. cities. Prev Med. 2019;118:176-183. doi:1016/j.ypmed.2018.10.026
  100. Peters D, 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. Soc Sci Med. 2017;195:34-41. doi:1016/j.socscimed.2017.08.036
  101. Freebairn L, Rychetnik L, Atkinson JA, et al. Knowledge mobilisation for policy development: implementing systems approaches through participatory dynamic simulation modelling. Health Res Policy Syst. 2017;15(1):83. doi:1186/s12961-017-0245-1
  102. McGetrick JA, Raine KD, Wild TC, Nykiforuk CIJ. Advancing strategies for agenda setting by health policy coalitions: a network analysis of the Canadian chronic disease prevention survey. Health Commun. 2019;34(11):1303-1312. doi:1080/10410236.2018.1484267
  103. Garney WR, Patterson MS, Garcia K, Muraleetharan D, McLeroy K. Interorganizational network findings from a nationwide cardiovascular disease prevention initiative. Eval Program Plann. 2020;79:101771. doi:1016/j.evalprogplan.2019.101771
  104. Beaton A, Manuel C, Tapsell J, Foote J, Oetzel JG, Hudson M. He Pikinga Waiora: supporting Māori health organisations to respond to pre-diabetes. Int J Equity Health. 2019;18(1):3. doi:1186/s12939-018-0904-z
  105. Baker P, Brown AD, Wingrove K, et al. Generating political commitment for ending malnutrition in all its forms: a system dynamics approach for strengthening nutrition actor networks. Obes Rev. 2019;20 Suppl 2:30-44. doi:1111/obr.12871
  106. Cullerton K, Donnet T, Lee A, Gallegos D. Joining the dots: the role of brokers in nutrition policy in Australia. BMC Public Health. 2017;17(1):307. doi:1186/s12889-017-4217-8
  107. Peters D, Raab J, Grêaux KM, Stronks K, Harting J. Structural integration and performance of inter-sectoral public health-related policy networks: an analysis across policy phases. Health Policy. 2017;121(12):1296-1302. doi:1016/j.healthpol.2017.10.001
  108. Heo HH, Jeong W, Che XH, Chung H. A stakeholder analysis of community-led collaboration to reduce health inequity in a deprived neighbourhood in South Korea. Glob Health Promot. 2020;27(2):35-44. doi:1177/1757975918791517
  109. Scheele CE, Little I, Diderichsen F. Governing health equity in Scandinavian municipalities: the inter-sectorial challenge. Scand J Public Health. 2018;46(1):57-67. doi:1177/1403494816685538
  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. Int J Environ Res Public Health. 2018;15(11):2393. doi:3390/ijerph15112393
  111. Clarke B, Swinburn B, Sacks G. Understanding the LiveLighter® obesity prevention policy processes: an investigation using political science and systems thinking. Soc Sci Med. 2020;246:112757. doi:1016/j.socscimed.2019.112757
  112. Pérez-Escamilla R, Lutter CK, Rabadan-Diehl C, et al. Prevention of childhood obesity and food policies in Latin America: from research to practice. Obes Rev. 2017;18 Suppl 2:28-38. doi:1111/obr.12574
  113. Waqa G, Moodie M, Snowdon W, et al. Exploring the dynamics of food-related policymaking processes and evidence use in Fiji using systems thinking. Health Res Policy Syst. 2017;15(1):74. doi:1186/s12961-017-0240-6
  114. Sturgiss E, Luig T, Campbell-Scherer DL, Lewanczuk R, Green LA. Using concept maps to compare obesity knowledge between policy makers and primary care researchers in Canada. BMC Res Notes. 2019;12(1):23. doi:1186/s13104-018-4042-x
  115. de Bruin WE, Stayner C, Lange M, Taylor RW. Who are the key players involved with shaping public opinion and policies on obesity and diabetes in New Zealand? Nutrients. 2018;10(11):1592. doi:3390/nu10111592
  116. Loitz CC, Stearns JA, Fraser SN, Storey K, Spence JC. Network analysis of inter-organizational relationships and policy use among active living organizations in Alberta, Canada. BMC Public Health. 2017;17(1):649. doi:1186/s12889-017-4661-5
  117. Barnes M, Maclean J, Cousens L. Understanding the structure of community collaboration: the case of one Canadian health promotion network. Health Promot Int. 2010;25(2):238-247. doi:1093/heapro/daq002
  118. Bergeron K, Lévesque L. Designing active communities: a coordinated action framework for planners and public health professionals. J Phys Act Health. 2014;11(5):1041-1051. doi:1123/jpah.2012-0178
  119. Buchthal OV, Taniguchi N, Iskandar L, Maddock J. Assessing state-level active living promotion using network analysis. J Phys Act Health. 2013;10(1):19-32. doi:1123/jpah.10.1.19
  120. Noël Racine A, Garbarino JM, Corrion K, D'Arripe-Longueville F, Massiera B, Vuillemin A. Perceptions of barriers and levers of health-enhancing physical activity policies in mid-size French municipalities. Health Res Policy Syst. 2020;18(1):62. doi:1186/s12961-020-00575-z
  121. Spitters HP, Lau CJ, Sandu P, et al. Unravelling networks in local public health policymaking in three European countries - a systems analysis. Health Res Policy Syst. 2017;15(1):5. doi:1186/s12961-016-0168-2
  122. Leppin AL, Okamoto JM, Organick PW, et al. Applying social network analysis to evaluate implementation of a multisector population health collaborative that uses a bridging hub organization. Front Public Health. 2018;6:315. doi:3389/fpubh.2018.00315
  123. Fisher M, Milos D, Baum F, Friel S. Social determinants in an Australian urban region: a 'complexity' lens. Health Promot Int. 2016;31(1):163-174. doi:1093/heapro/dau071
  124. Hoeijmakers M, De Leeuw E, Kenis P, De Vries NK. Local health policy development processes in the Netherlands: an expanded toolbox for health promotion. Health Promot Int. 2007;22(2):112-121. doi:1093/heapro/dam009
  125. Leider JP, Castrucci BC, Harris JK, Hearne S. The relationship of policymaking and networking characteristics among leaders of large urban health departments. Int J Environ Res Public Health. 2015;12(8):9169-9180. doi:3390/ijerph120809169
  126. Merrill J, Keeling JW, Carley KM. A comparative study of 11 local health department organizational networks. J Public Health Manag Pract. 2010;16(6):564-576. doi:1097/PHH.0b013e3181e31cee
  127. Oliver K, Everett M, Verma A, de Vocht F. The human factor: re-organisations in public health policy. Health Policy. 2012;106(1):97-103. doi:1016/j.healthpol.2012.03.009
  128. Oliver K, de Vocht F, Money A, Everett M. Who runs public health? A mixed-methods study combining qualitative and network analyses. J Public Health (Oxf). 2013;35(3):453-459. doi:1093/pubmed/fdt039
  129. Pineo H, Zimmermann N, Davies M. Integrating health into the complex urban planning policy and decision-making context: a systems thinking analysis. Palgrave Commun. 2020;6(1):21. doi:1057/s41599-020-0398-3
  130. van den Driessen Mareeuw F, 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. 2015;15:922. doi:1186/s12889-015-2271-7
  131. Harris JK. Communication ties across the national network of local health departments. Am J Prev Med. 2013;44(3):247-253. doi:1016/j.amepre.2012.10.028
  132. Harris JK, Luke DA, Burke RC, Mueller NB. Seeing the forest and the trees: using network analysis to develop an organizational blueprint of state tobacco control systems. Soc Sci Med. 2008;67(11):1669-1678. doi:1016/j.socscimed.2008.07.013
  133. Luke DA, Wald LM, Carothers BJ, Bach LE, Harris JK. Network influences on dissemination of evidence-based guidelines in state tobacco control programs. Health Educ Behav. 2013;40(1 Suppl):33S-42S. doi:1177/1090198113492760
  134. Moreland-Russell S, Carothers BJ. An examination of two policy networks involved in advancing smokefree policy initiatives. Int J Environ Res Public Health. 2015;12(9):11117-11131. doi:3390/ijerph120911117
  135. Weishaar H, Amos A, Collin J. Capturing complexity: mixing methods in the analysis of a European tobacco control policy network. Int J Soc Res Methodol. 2015;18(2):175-192. doi:1080/13645579.2014.897851
  136. Weishaar H, Amos A, Collin J. Best of enemies: using social network analysis to explore a policy network in European smoke-free policy. Soc Sci Med. 2015;133:85-92. doi:1016/j.socscimed.2015.03.045
  137. Macmillan A, Smith M, Witten K, et al. Suburb-level changes for active transport to meet the SDGs: causal theory and a New Zealand case study. Sci Total Environ. 2020;714:136678. doi:1016/j.scitotenv.2020.136678
  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 Res Policy Syst. 2019;17(1):102. doi:1186/s12961-019-0509-z
  139. Shankardass K, Muntaner C, Kokkinen L, et al. The implementation of Health in All Policies initiatives: a systems framework for government action. Health Res Policy Syst. 2018;16(1):26. doi:1186/s12961-018-0295-z
  140. Cambon L, Minary L, Ridde V, Alla F. A tool to analyze the transferability of health promotion interventions. BMC Public Health. 2013;13:1184. doi:1186/1471-2458-13-1184
  141. Macdiarmid JI, Loe J, Douglas F, Ludbrook A, Comerford C, McNeill G. Developing a timeline for evaluating public health nutrition policy interventions. What are the outcomes and when should we expect to see them? Public Health Nutr. 2011;14(4):729-739. doi:1017/s1368980010002168
  142. Conte KP, Davidson S. Using a 'rich picture' to facilitate systems thinking in research coproduction. Health Res Policy Syst. 2020;18(1):14. doi:1186/s12961-019-0514-2
  143. Roussy V, Riley T, Livingstone C, Russell G. A system dynamic perspective of stop-start prevention interventions in Australia. Health Promot Int. 2020;35(5):1015-1025. doi:1093/heapro/daz098
  144. Beets MW, Webster C, Saunders R, Huberty JL. Translating policies into practice: a framework to prevent childhood obesity in afterschool programs. Health Promot Pract. 2013;14(2):228-237. doi:1177/1524839912446320
  145. Knai C, Petticrew M, Douglas N, 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. Int J Environ Res Public Health. 2018;15(12):2895. doi:3390/ijerph15122895
  146. Kuunders TJM, van Bon-Martens MJH, van de Goor IAM, Paulussen T, van Oers HAM. Towards local implementation of Dutch health policy guidelines: a concept-mapping approach. Health Promot Int. 2018;33(4):635-647. doi:1093/heapro/dax003
  147. Pagliccia N, Spiegel J, Alegret M, Bonet M, Martinez B, Yassi A. 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. Soc Sci Med. 2010;71(2):394-399. doi:1016/j.socscimed.2010.03.041
  148. van Roode T, Pauly BM, Marcellus L, et al. Values are not enough: qualitative study identifying critical elements for prioritization of health equity in health systems. Int J Equity Health. 2020;19(1):162. doi:1186/s12939-020-01276-3
  149. Terpstra JL, Best A, Saul J, Leischow SJ. The complexity of institutionalizing evaluation as a best practice in North American quitlines. Am J Eval. 2013;34(3):356-371. doi:1177/1098214013487426
  150. Valente TW, Pitts S, Wipfli H, Vega Yon GG. Network influences on policy implementation: evidence from a global health treaty. Soc Sci Med. 2019;222:188-197. doi:1016/j.socscimed.2019.01.008
  151. Wen W, Zhang Z, Li Z, et al. Public reactions to the cigarette control regulation on a Chinese microblogging platform: empirical analysis. J Med Internet Res. 2020;22(4):e14660. doi:2196/14660
  152. Cullerton K, Donnet T, Lee A, Gallegos D. Exploring power and influence in nutrition policy in Australia. Obes Rev. 2016;17(12):1218-1225. doi:1111/obr.12459
  153. Fisher M, Milos D, Baum F, Friel S. Social determinants in an Australian urban region: a 'complexity' lens. Health Promot Int. 2016;31(1):163-174. doi:1093/heapro/dau071
  154. Signal LN, Walton MD, Ni Mhurchu C, et al. Tackling 'wicked' health promotion problems: a New Zealand case study. Health Promot Int. 2013;28(1):84-94. doi:1093/heapro/das006
  155. Castillo-Carandang NT, Buenaventura RD, Chia YC, et al. Moving towards optimized noncommunicable disease management in the ASEAN region: recommendations from a review and multidisciplinary expert panel. Risk Manag Healthc Policy. 2020;13:803-819. doi:2147/rmhp.s256165
  156. Brown V, Barr A, Scheurer J, Magnus A, Zapata-Diomedi B, Bentley R. Better transport accessibility, better health: a health economic impact assessment study for Melbourne, Australia. Int J Behav Nutr Phys Act. 2019;16(1):89. doi:1186/s12966-019-0853-y
  157. McGill E, Er V, Penney T, et al. Evaluation of public health interventions from a complex systems perspective: a research methods review. Soc Sci Med. 2021;272:113697. doi:1016/j.socscimed.2021.113697
  158. Anzola D, Barbrook-Johnson P, Cano JI. Self-organization and social science. Comput Math Organ Theory. 2017;23(2):221-257. doi:1007/s10588-016-9224-2

Files

History

  • Receive Date: 10 September 2021
  • Revise Date: 24 June 2022
  • Accept Date: 14 January 2023
  • First Publish Date: 18 January 2023

Share

How to cite

Statistics