Participatory simulation modelling to inform public health policy and practice: Rethinking the evidence hierarchies
- 165 Downloads
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.
Keywordsdynamic simulation modelling participatory modelling evidence hierarchy evidence synthesis systems science health policy
- 2.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.
- 3.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).
- 14.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.
- 15.Brockway, I. (2012) Risk factors contributing to chronic disease. Canberra, Australia.: Australian Institute of Health and Welfare 2012 Contract No.: Cat no. PHE 157.Google Scholar
- 16.Wilcox, S. (2014) Chronic diseases in Australia: the case for changing course. Melbourne, Australia: Australian Health Policy Collaboration. Report No.: Report no. 2014-02.Google Scholar
- 17.Cadilhac, DA. (2009) The Health and Economic Benefits of Reducing Disease Risk Factors: Research Report. Melbourne, Australia.Google Scholar
- 20.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.
- 24.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.Google Scholar
- 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.CrossRefGoogle Scholar
- 28.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.Google Scholar
- 31.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.Google Scholar
- 33.Shpitser, I. and Pearl, J. (2008) Complete identification methods for the causal hierarchy. Journal of Machin Learning Research 9: 1941–1979.Google Scholar