title: Commuting accidents in the German chemical industry.
abstract: Due to accident severity and the extent of claim payments commuting accidents are a significant expense factor in the German industry. Therefore the aim of the present study was the identification of risk factors for commuting accidents in a German chemical company. A retrospective analysis of commuting accidents recorded between 1990 and 2003 was conducted in a major chemical company in Germany. A logistic regression-model was calculated in order to determine factors influencing the duration of work inability as a result of commuting accidents. The analysed data included 5,484 employees with commuting accidents. Cars (33.1%) and bicycles (30.5%) were the most common types of vehicles used by commuters who had an accident. The highest number of commuting accidents was observed in the age group under 26 yr. Accidents on the route from the work site to the worker's residence were less frequently observed, but they caused longer periods of work inability than accidents on the way to the work site. The longest periods of work inability were found in the groups of motorcyclists and older employees. The present study identifies specific groups at risk for commuting accidents. The data of the present investigation also underline the need for developing group specific prevention strategies.
authors.affiliation: Institute of Occupational, Social and Environmental Medicine, Johannes Gutenberg University Mainz, Mainz, Germany. loefflek@uni-mainz.de
authors.forename: Kirsten Isabel
authors.initials: KI
authors.lastname: Zepf
authors.forename: Stephan
authors.initials: S
authors.lastname: Letzel
authors.forename: Susanne
authors.initials: S
authors.lastname: Voelter-Mahlknecht
authors.forename: Ulrich
authors.initials: U
authors.lastname: Wriede
authors.forename: Britta
authors.initials: B
authors.lastname: Husemann
authors.forename: Luis Carlos
authors.initials: LC
authors.lastname: Escobar Pinzón
mesh_terms: D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D000293:Adolescent; D000328:Adult; D017677:Age Distribution; D000368:Aged; D001642:Bicycling / Q000706:statistics & numerical data; D002617:Chemical Industry / Q000706:statistics & numerical data*; D005260:Female; D005858:Germany / Q000453:epidemiology; D006801:Humans; D008297:Male; D018986:Motor Vehicles / Q000706:statistics & numerical data; D016272:Occupational Health / Q000706:statistics & numerical data; D009790:Occupations / Q000706:statistics & numerical data; D012189:Retrospective Studies; D012307:Risk Factors; D017678:Sex Distribution; D012959:Socioeconomic Factors