The use of injury surveillance databases to identify emerging injury hazards.
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, R(2) = 0.86, F(3, 27) = 55.2, p < 0.0001). Significant contributions to the model included the SD (beta = 1.60, p < 0.001), mean monthly frequency (beta = -0.72, p < 0.002), and the seasonality of the data (beta = 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.
Stokes
Mark
M
Deakin University, School of Psychology, Australia. stokes@deakin.edu.au
van Leeuwen
Paul
P
Ozanne-Smith
Joan
J
D000056:Accident Prevention; D016208:Databases, Factual; D005544:Forecasting / Q000379:methods*; D006801:Humans; D011159:Population Surveillance; D015203:Reproducibility of Results; D012621:Seasons; D012984:Software; D014947:Wounds and Injuries / Q000453:epidemiology* / Q000517:prevention & control