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The use of injury surveillance databases to identify emerging injury hazards

Stokes, Mark, van Leeuwen, Paul and Ozanne-Smith, Joan 2005, The use of injury surveillance databases to identify emerging injury hazards, Injury control and safety promotion, vol. 12, no. 1, pp. 1-7, doi: 10.1080/17457300512331342180.

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Title The use of injury surveillance databases to identify emerging injury hazards
Author(s) Stokes, MarkORCID iD for Stokes, Mark orcid.org/0000-0001-6488-4544
van Leeuwen, Paul
Ozanne-Smith, Joan
Journal name Injury control and safety promotion
Volume number 12
Issue number 1
Start page 1
End page 7
Publisher Taylor & Francis The Netherlands
Place of publication Lisse, Netherlands
Publication date 2005-03
ISSN 1745-7300
Keyword(s) injury trends
trend forecasting
injury surveillance
Summary Among the many valuable uses of injury surveillance is the potential to alert health authorities and societies in general to emerging injury trends, facilitating earlier development of prevention measures. Other than road safety, to date, few attempts to forecast injury data have been made, although forecasts have been made of other public health issues. This may in part be due to the complex pattern of variance displayed by injury data. The profile of many injury types displays seasonality and diurnal variance, as well as stochastic variance. The authors undertook development of a simple model to forecast injury into the near term. In recognition of the large numbers of possible predictions, the variable nature of injury profiles and the diversity of dependent variables, it became apparent that manual forecasting was impractical. Therefore, it was decided to evaluate a commercially available forecasting software package for prediction accuracy against actual data for a set of predictions. Injury data for a 4-year period (1996 to 1999) were extracted from the Victorian Emergency Minimum Dataset and were used to develop forecasts for the year 2000, for which data was also held. The forecasts for 2000 were compared to the actual data for 2000 by independent t-tests, and the standard errors of the predictions were modelled by stepwise hierarchical multiple regression using the independent variables of the standard deviation, seasonality, mean monthly frequency and slope of the base data (R = 0.93, R2 = 0.86, F(3, 27) = 55.2, p < 0.0001). Significant contributions to the model included the SD (β = 1.60, p < 0.001), mean monthly frequency (β =  - 0.72, p < 0.002), and the seasonality of the data (β = 0.16, p < 0.02). It was concluded that injury data could be reliably forecast and that commercial software was adequate for the task. Variance in the data was found to be the most important determinant of prediction accuracy. Importantly, automated forecasting may provide a vehicle for identifying emerging trends.
Notes Online Publication Date: 01 March 2005
Language eng
DOI 10.1080/17457300512331342180
Field of Research 111799 Public Health and Health Services not elsewhere classified
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2005, Taylor & Francis Group Ltd
Persistent URL http://hdl.handle.net/10536/DRO/DU:30003077

Document type: Journal Article
Collection: School of Psychology
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