Multiple attribute entropy classification of school-age injuries

Incidence and outcomes of school-based injuries during four academic years in Kocaeli, Turkey

BACKGROUND

School-based injuries are an important public health problem. The aim of this study was to determine the incidence as well as outcomes of school-based injuries at a public primary and secondary school.

METHODS

This retrospective cohort study involved 2956 students enrolled at a public primary and secondary school in the central district of Kocaeli province in Turkey from the academic year 2010-2011 to 2013-2014. The data were collected by intern public health nurses at school health units using a school-based injury form.

RESULTS

The total incidence of school-based injuries during 4 academic years was 10.52% person-year. The majority of the injured students were aged 10-15 years (73.3%), and boys (65%). Around one-tenth of the injuries were serious, such as burn, fracture-dislocation and soft-tissue injuries. Outdoor injuries were more prevalent for girls while boys had more indoor injuries (50.5%, 53.0%, P > 0.05). The number of the incidence of ≥two injuries increased as the academic year progressed, and there was a weak positive association between the variables (P < 0.05, Cramer's V = 0.311). One out of six injuries were intentional, with the highest incidence in the 6-9 year age group (20.5%) and boys sex (19.8%; P > 0.05).

CONCLUSIONS

School-based injuries at primary and secondary school still challenge the health as well as the security of the students. © 2016 Japan Pediatric Society.

Applying data mining techniques to explore factors contributing to occupational injuries in Taiwan's construction industry

Construction accident research involves the systematic sorting, classification, and encoding of comprehensive databases of injuries and fatalities. The present study explores the causes and distribution of occupational accidents in the Taiwan construction industry by analyzing such a database using the data mining method known as classification and regression tree (CART). Utilizing a database of 1542 accident cases during the period 2000-2009, the study seeks to establish potential cause-and-effect relationships regarding serious occupational accidents in the industry. The results of this study show that the occurrence rules for falls and collapses in both public and private project construction industries serve as key factors to predict the occurrence of occupational injuries. The results of the study provide a framework for improving the safety practices and training programs that are essential to protecting construction workers from occasional or unexpected accidents.

Industrial and occupational ergonomics in the petrochemical process industry: a regression trees approach

This work is an attempt to apply classification tree methods to data regarding accidents in a medium-sized refinery, so as to identify the important relationships between the variables, which can be considered as decision-making rules when adopting any measures for improvement. The results obtained using the CART (Classification And Regression Trees) method proved to be the most precise and, in general, they are encouraging concerning the use of tree diagrams as preliminary explorative techniques for the assessment of the ergonomic, management and operational parameters which influence high accident risk situations. The Occupational Injury analysis carried out in this paper was planned as a dynamic process and can be repeated systematically. The CART technique, which considers a very wide set of objective and predictive variables, shows new cause-effect correlations in occupational safety which had never been previously described, highlighting possible injury risk groups and supporting decision-making in these areas. The use of classification trees must not, however, be seen as an attempt to supplant other techniques, but as a complementary method which can be integrated into traditional types of analysis.

Analyses of rear-end crashes based on classification tree models

OBJECTIVE

Signalized intersections are accident-prone areas especially for rear-end crashes due to the fact that the diversity of the braking behaviors of drivers increases during the signal change. The objective of this article is to improve knowledge of the relationship between rear-end crashes occurring at signalized intersections and a series of potential traffic risk factors classified by driver characteristics, environments, and vehicle types.

METHODS

Based on the 2001 Florida crash database, the classification tree method and Quasi-induced exposure concept were used to perform the statistical analysis. Two binary classification tree models were developed in this study. One was used for the crash comparison between rear-end and non-rear-end to identify those specific trends of the rear-end crashes. The other was constructed for the comparison between striking vehicles/drivers (at-fault) and struck vehicles/drivers (not-at-fault) to find more complex crash pattern associated with the traffic attributes of driver, vehicle, and environment.

RESULTS

The modeling results showed that the rear-end crashes are over-presented in the higher speed limits (45-55 mph); the rear-end crash propensity for daytime is apparently larger than nighttime; and the reduction of braking capacity due to wet and slippery road surface conditions would definitely contribute to rear-end crashes, especially at intersections with higher speed limits. The tree model segmented drivers into four homogeneous age groups: < 21 years, 21-31 years, 32-75 years, and > 75 years. The youngest driver group shows the largest crash propensity; in the 21-31 age group, the male drivers are over-involved in rear-end crashes under adverse weather conditions and the 32-75 years drivers driving large size vehicles have a larger crash propensity compared to those driving passenger vehicles.

CONCLUSIONS

Combined with the quasi-induced exposure concept, the classification tree method is a proper statistical tool for traffic-safety analysis to investigate crash propensity. Compared to the logistic regression models, tree models have advantages for handling continuous independent variables and easily explaining the complex interaction effect with more than two independent variables. This research recommended that at signalized intersections with higher speed limits, reducing the speed limit to 40 mph efficiently contribute to a lower accident rate. Drivers involved in alcohol use may increase not only rear-end crash risk but also the driver injury severity. Education and enforcement countermeasures should focus on the driver group younger than 21 years. Further studies are suggested to compare crash risk distributions of the driver age for other main crash types to seek corresponding traffic countermeasures.