Drawing on the long tradition of evidence-based medicine that aims to improve the efficiency and effectiveness of clinical practice, the field of public health has sought to apply ‘hierarchies of evidence’ to appraise and synthesise public health research. Various critiques of this approach led to the development of synthesis methods that include broader evidence typologies and more ‘fit for purpose’ privileging of methodological designs. While such adaptations offer great utility for evidence-informed public health policy and practice, this paper offers an alternative perspective on the synthesis of evidence that necessitates a yet more egalitarian approach. Dynamic simulation modelling is increasingly recognised as a valuable evidence synthesis tool to inform public health policy and programme planning for complex problems. The development of simulation models draws on and privileges a wide range of evidence typologies, thus challenging the traditional use of ‘hierarchies of evidence’ to support decisions on complex dynamic problems.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
McGuire, W.L. (2005) Beyond EBM: New directions for evidence-based public health. Perspectives in Biology and Medicine Autumn 48(4): 557–569.
Abeysinghe, S. and Parkhurst, J. (2013) ‘Good’ evidence for improved policy making: from hierarchies to appropriateness. London: London School of Hygiene and Tropical Medicine, http://www.lshtm.ac.uk/groups/griphealth/resources/better_evidence_for_policy:_from_hierarchies_to_appropriateness.pdf.
Nutley, S.M. and Powell, A.E. (2013) What counts as good evidence? London: Alliance for Useful Evidence. Retrieved from http://www.alliance4usefulevidence.org/assets/What-Counts-as-Good-Evidence-WEB.pdf (Archived by WebCite® at http://www.webcitation.org/6ZTa2uMEb).
Petticrew, M. and Roberts, H. (2003) Evidence, hierarchies, and typologies: Horses for courses. Journal of Epidemiology and Community Health 57(7): 527–529.
Milat, A.J., Bauman, A.E., Redman, S. and Curac, N. (2011) Public health research outputs from efficacy to dissemination: A bibliometric analysis. BMC Public Health 11: 934.
Cunningham, J.A. and Van Mierlo, T. (2009) Methodological issues in the evaluation of internet-based interventions for problem drinking. Drug and Alcohol Review 28(1): 12–17.
Ogilvie, D., Foster, C.E., Rothnie, H., Cavill, N., Hamilton, V., Fitzsimons, C.F. et al (2007). Interventions to promote walking: Systematic review. BMJ 334(7605): 1204.
Laws, R.A., St George, A.B., Rychetnik, L. and Bauman, A.E. (2012) Diabetes prevention research: A systematic review of external validity in lifestyle interventions. American Journal of Preventive Medicine 43(2): 205–214.
Glasgow, R.E., Lichtenstein, E. and Marcus, A.C. (2003) Why don’t we see more translation of health promotion research to practice? Rethinking the efficacy-to-effectiveness transition. American Journal of Public Health 93(8): 1261–1267.
Green, L.W. (2008) Making research relevant: If it is an evidence-based practice, where’s the practice-based evidence? Family Practice 25(1): i20–i24.
Ammerman, A., Smith, T.W. and Calancie, L. (2014) Practice-based evidence in public health: Improving reach, relevance, and results. Annual Review of Public Health 35: 47–63.
Hansen, H.F. (2014) Organisation of evidence-based knowledge production: Evidence hierarchies and evidence typologies. Scandinavian Journal of Public Health 42(13): 11–17.
Green, L.W. and Glasgow, R.E. (2006) Evaluating the relevance, generalization, and applicability of research: Issues in external validation and translation methodology. Evaluation and the Health Professions 29: 126–153.
Australian Health Survey: first results, 2011–2012 [database on the Internet] 2012, http://www.abs.gov.au/AUSSTATS/abs@.nsf/DetailsPage/4364.0.55.0012011-12?OpenDocument.
Brockway, I. (2012) Risk factors contributing to chronic disease. Canberra, Australia.: Australian Institute of Health and Welfare 2012 Contract No.: Cat no. PHE 157.
Wilcox, S. (2014) Chronic diseases in Australia: the case for changing course. Melbourne, Australia: Australian Health Policy Collaboration. Report No.: Report no. 2014-02.
Cadilhac, DA. (2009) The Health and Economic Benefits of Reducing Disease Risk Factors: Research Report. Melbourne, Australia.
Rychetnik, L., Bauman, A., Laws, R., King, L., Rissel, C., Nutbeam, D. et al (2012). Translating research for evidence-based public health: Key concepts and future directions. Journal of Epidemiology and Community Health 66(12): 1187–1192.
Leeman, J. and Sandelowski, M. (2012) Practice-based evidence and qualitative inquiry. Journal of Nursing Scholarship: An Official Publication of Sigma Theta Tau International Honor Society of Nursing/Sigma Theta Tau 44(2): 171–179.
Morestin, F., Gauvin, F., Hogue, M., Benoit, F. Method for synthesizing knowledge about public policies Canada: National Collaborating Centre for Healthy Public Policy, http://www.ncchpp.ca/docs/MethodPP_EN.pdf2010.
Saul, J.E., Willis, C.D., Bitz, J. and Best, A. (2013) A time-responsive tool for informing policy making: Rapid realist review. Implementation Science: IS 8: 103.
Pawson, R. (2002). Evidence-based policy: The promise of ‘realist synthesis’. Evaluation 8(3): 340–358.
Homer, J.B. and Hirsch, G.B. (2006) System dynamics modeling for public health: Background and opportunities. American Journal of Public Health 96(3): 452–458.
Li, Y., Lawley, M.A., Siscovick, D.S., Zhang, D. and Pagán, J.A. (2016) Agent-based modeling of chronic diseases: A narrative review and future research directions. Preventing Chronic Disease 2016: 13.
Atkinson, J.A., Page, A., Wells, R., Milat, A. and Wilson, A. (2015) A modelling tool for policy analysis to support the design of efficient and effective policy responses for complex public health problems. Implementation Science: IS 10: 26.
Ip, E.H., Rahmandad, H., Shoham, D.A., Hammond, R., Huang, T.T., Wang, Y. et al (2013) Reconciling statistical and systems science approaches to public health. Health Education & Behavior : The Official Publication of the Society for Public Health Education 40(1 Suppl): 123S–131S.
Voinov, A. and Bousquet, F. (2010) Modelling with stakeholders. Environmental Modelling and Software 25(11): 1268–1281.
Rouwette, E.A.J.A., Korzilius, H., Vennix, J.A.M., and Jacobs, E. (2011) Modeling as persuasion: The impact of group model building on attitudes and behavior. System Dynamics Review 27(1): 1–21.
Hovmand, P. (2014) Community Based System Dynamics. New York: Springer.
Loyo, H.K., Batcher, C., Wile, K., Huang, P., Orenstein, D., and Milstein, B. (2013) From model to action: Using a system dynamics model of chronic disease risks to align community action. Health Promotion Practice 14(1): 53–61.
Atkinson, J., O’Donnell, E.M., Wiggers, J., McDonnell, G., Mitchell, J., Freebairn, L. et al (2017) A participatory dynamic simulation modelling approach to developing policy responses to reduce alcohol-related harms: rationale and procedure. Public Health Research and Practice 27(1): 2711707.
Sugihara, G., May, R., Ye, H., Hsieh, C.H., Deyle, E., Fogarty, M. et al (2012) Detecting causality in complex ecosystems. Science 338(6106): 496–500.
Shpitser, I. and Pearl, J. (2008) Complete identification methods for the causal hierarchy. Journal of Machin Learning Research 9: 1941–1979.
Michie, S., van Stralen, M.M. and West, R. (2011) The behaviour change wheel: A new method for characterising and designing behaviour change interventions. Implementation science 6: 42–49.
Marshall, D.A., Burgos-Liz, L., Pasupathy, K.S., Padula, W.V., Ijzerman, M.J., Wong, P.K. et al (2016) Transforming healthcare delivery: Integrating dynamic simulation modelling and big data in health economics and outcomes research. PharmacoEconomics 34(2): 115–126.
Gittelsohn, J., Mui, Y., Adam, A., Lin, S., Kharmats, A., Igusa, T. et al (2015) Incorporating systems science principles into the development of obesity prevention interventions: Principles, benefits, and challenges. Current Obesity Reports 4(2): 174–181.
This work was funded by the National Health and Medical Research Council of Australia (NHMRC) through its partnership centre grant scheme (Grant ID: GNT9100001). NSW Health, ACT Health, The Commonwealth Department of Health, The Hospitals Contribution Fund of Australia and HCF Research Foundation contributed funds to support this work as part of the NHMRC partnership centre grant scheme. The contents of this paper are solely the responsibility of the individual authors and do not reflect the views of the NHMRC or funding partners. The authors thank Geoff McDonnell for his review and valuable comments on the penultimate draft of this paper, and Sally Redman for her contributions to discussions during the conceptualisation of this work.
About this article
Cite this article
O’Donnell, E., Atkinson, J., Freebairn, L. et al. Participatory simulation modelling to inform public health policy and practice: Rethinking the evidence hierarchies. J Public Health Pol 38, 203–215 (2017). https://doi.org/10.1057/s41271-016-0061-9
- dynamic simulation modelling
- participatory modelling
- evidence hierarchy
- evidence synthesis
- systems science
- health policy