The utility of a workplace injury instrument in prediction of workplace injury

Workplace injuries are common and destructive to persons, organisations, and society. Various instruments presently exist that are designed to assess the factors underlying workplace injury. The study reports on the construct and predictive validity of a 46-item instrument, the safety perception survey (SPS), currently used to assess safety climate in industrial organisations throughout Australia. Initially, factor analysis was conducted on the data from a sample of 1238 employees from nine organisations, which indicated a one-factor solution, was the best fit. A structural equation model (SEM) linking injury rates to the safety climate measure for 16 sub-groups of six industrial organisations indicated that the measure contributed just 23% of the variance in injury rates. Interestingly, the results indicated that the number of employees was a better and more significant predictor of injury (R² = 0.48). It is proposed that the SPS as is would need to be modified significantly from its current form to produce improvements in validity, as in its current form the survey is no more predictive of injury than organisational size. Future research into safety climate measures should incorporate predictive validity analysis on injury rates, as for many organisations; this is a performance outcome measure.

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, 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.