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Affiliation
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School of Rural Medicine, University of New England, Armidale, NSW 2351, Australia rmcclure@une.edu.au. |
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Abstract
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In its modern form, injury prevention is about 60 years old.1 2 For all of this time injury prevention has been a leader in the field of academic public health. Injury prevention has led the way in multidisciplinary and transdisciplinary thinking, has been an early adopter of technologies supporting the data revolution and has pioneered the development of implementation science to achieve population-level improvements.
In the 1960s, Haddon was explicitly linking the principles of engineering to those of epidemiology, in defining kinetic energy as the causal agent of injury (which he recommended be managed using a host-agent-environment/primary-secondary-tertiary prevention model).3 In the 1980s, authors in the USA were describing trauma deaths as a system performance issue,4 and in the 1990s the Swedish Government was articulating a systems solution to the escalating problem of road crash injury.5 With its foundations in injury surveillance and frequent use of routinely collected administrative data for injury-specific purposes, modern injury prevention was quick to incorporate ideas from the developing field of data science for data linkage,6 narrative text mining7 and data visualisation.8 Evidence of the development of implementation epidemiology in injury prevention is readily available,9 but best illustrated in the dramatic reduction in road crash deaths in high-income countries from the 1970s.
Arguably, this leadership has been driven by fortunate necessity. From the beginning injury prevention researchers have needed to go where many health fields are now only just starting to tread. Engineering, psychological and social sciences are implicit in the causal models that explain injury as a medical outcome. The nature of injury prevention frames problems in a way that encourages health researchers to push the boundaries of medical model thinking and traditional health research methods. The quotidian public presence of injury ensures research is coproduced with the public and focused on implementation usefulness.
A signature benefit of the needs-based incentive for scientific innovation is the efficiency of natural selection. Innovations are not pursued because they are bright and shiny but because they provide practical solutions to problems. A new technology without a purpose, like academic vanity, is quickly passed over in the search for something of real use. It is a simple approach: define the problem; set the research question; choose the scientific tool you need to help you answer the question. If the right tool does not exist—invent one that does the task required ...
Language: en |