摘要
Aims: Establish profiles of young “at risk” of injuries, first, on an overall point of view and, secondly, for some types of injuries (sport, home, road traffic, school and work injuries). Methods: We have taken nearly 50 variables into consideration: 17 variables for construction of the socioeconomic status, 9 variables for the investigation of symptoms, 11 concerning drugs, 5 for healthy habits, 3 for investigating the violence behavior, 4 concerning the school, 3 for subjective health and finally 3 for social network. We have used the principal component analysis, the multiple correspondence analysis and the weighted-frequency score for reducing the number of them. After these reductions, 15 variables were available for analyses. The relationship between injuries and investigated factors was assessed using the Pearson’s chi-square test. We also calculated odds ratio (OR) with their 95% confidence intervals (95%CI) to estimate the strengths of the associations. To further assess these relationships but also for taking into account the potentials confounding effects of some variables, logistic regression model and multinomial logistic regression model were applied. Results: The whole injury prevalence was equal to 45.6% and among the injured, the proportion of the several types was equal to 33.8% for sport injuries, 32.2% for home injuries, 16.6% for traffic injuries, 11.6% for school injuries and 5.7% for work injuries. We can say that, in light of the variables studied, the “at risk” profile for having reported an injury is being a boy, being younger, having drug experiences, with the violent profile, and declaring several symptoms. There are no consistent and marked deviations in this study from the findings obtained in previous studies. Conclusion: Analyzing injuries in general is interesting but for preventing them it is important to know in which activities children and students are engaged when they are injured.
Aims: Establish profiles of young “at risk” of injuries, first, on an overall point of view and, secondly, for some types of injuries (sport, home, road traffic, school and work injuries). Methods: We have taken nearly 50 variables into consideration: 17 variables for construction of the socioeconomic status, 9 variables for the investigation of symptoms, 11 concerning drugs, 5 for healthy habits, 3 for investigating the violence behavior, 4 concerning the school, 3 for subjective health and finally 3 for social network. We have used the principal component analysis, the multiple correspondence analysis and the weighted-frequency score for reducing the number of them. After these reductions, 15 variables were available for analyses. The relationship between injuries and investigated factors was assessed using the Pearson’s chi-square test. We also calculated odds ratio (OR) with their 95% confidence intervals (95%CI) to estimate the strengths of the associations. To further assess these relationships but also for taking into account the potentials confounding effects of some variables, logistic regression model and multinomial logistic regression model were applied. Results: The whole injury prevalence was equal to 45.6% and among the injured, the proportion of the several types was equal to 33.8% for sport injuries, 32.2% for home injuries, 16.6% for traffic injuries, 11.6% for school injuries and 5.7% for work injuries. We can say that, in light of the variables studied, the “at risk” profile for having reported an injury is being a boy, being younger, having drug experiences, with the violent profile, and declaring several symptoms. There are no consistent and marked deviations in this study from the findings obtained in previous studies. Conclusion: Analyzing injuries in general is interesting but for preventing them it is important to know in which activities children and students are engaged when they are injured.
