142nd APHA Annual Meeting and Exposition

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305366
System dynamic models: Useful decision-support tools for the development of injury prevention policy

142nd APHA Annual Meeting and Exposition (November 15 - November 19, 2014): http://www.apha.org/events-and-meetings/annual
Monday, November 17, 2014 : 8:30 AM - 8:45 AM

Rod McClure, PhD , Injury Research Institute, Monash University, Melbourne, Australia
Claudia Adriazola-Steil
Christine Mulvihill
Michael Fitzharris
Paul Salmon, PhD , Centre for Accident Research, University of the Sunshine Coast, Maroochydore DC, Australia
Paul Bonnington
Mark Stevenson, PhD , Accident Research Centre, Monash University, Melbourne, Australia
Audio (mp3) recording Recorded Presentation (mp4)
Background/Purpose: The aim of the research was to develop a decision-support tool based on a quantitative system dynamic model to address the policy question that lies at the heart of the United Nations Decade of Action for Road Safety; i.e. how do we stabilise and then reduce the forecast level of road traffic fatalities around the world by 2020?.

Methods: A mathematical representation of a land use/transport/population health causal loop diagram was developed as a dynamic model. The base model, and variants for each of five cities included in the study, was developed using exogenous inputs for the model parameters extracted from public access databases and published national reports. The model was verified and validated as far as practical and its properties described. Once the base model and international variants were developed, a ‘status quo’ simulation was performed for each city to establish baseline outcomes for the simulation period. An experiment was then undertaken to explore the results obtained by varying crash and injury risks and mode of transport distribution.

Results/Outcomes: Optimal reduction in the public health burden attributable to land transport was demonstrated by the models when transport safety risk reduction policies were combined with place-based land use and transport polices that minimise reliance on individual motorised transport and maximise use of active transport modes.

Conclusions: Quantitative systems dynamic models can be used to develop policy decision-support tools that, through analysis, demonstrate the opportunities potentially achieved by research and practice integration.

Learning Areas:

Systems thinking models (conceptual and theoretical models), applications related to public health

Learning Objectives:
Explain how to use system dynamic models as decision support tools for injury prevention Describe the strengths and limitations of this methodology Discuss a range of possible new applications of this methodology that have the potential to advance the field of injury prevention

Keyword(s): Public Policy, Methodology

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I have thirty years experience in the primary, secondary and tertiary prevention of injury across the research to practice continuum. I have received over $60 Million in competitive research grants for injury and published over 150 peer reviewed scientific journal articles on the topic.
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.

Back to: 3046.0: Innovations in injury research methods