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American Journal of Epidemiology Advance Access published online on November 23, 2005
American Journal of Epidemiology, doi:10.1093/aje/kwj015
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American Journal of Epidemiology Copyright © 2005 by the Johns Hopkins Bloomberg School of Public Health All rights reserved; printed in U.S.A.
Received February 8, 2005
Accepted August 4, 2005

PRACTICE OF EPIDEMIOLOGY

Comparison between Two Quasi-Induced Exposure Methods for Studying Risk Factors for Road Crashes

Pablo Lardelli-Claret 1 *, José Juan Jiménez-Moleón 2, Juan de Dios Luna-del-Castillo 3, Miguel García-Martín 2, Obdulia Moreno-Abril 1, and Aurora Bueno-Cavanillas 2

1 Department of Preventive Medicine and Public Health, Faculty of Pharmacy, University of Granada, Granada, Spain
2 Department of Preventive Medicine and Public Health, Faculty of Medicine, University of Granada, Granada, Spain
3 Department of Statistics, Faculty of Medicine, University of Granada, Granada, Spain

* To whom correspondence should be addressed.
Pablo Lardelli-Claret, E-mail: lardelli{at}ugr.es


  Abstract

This study was designed to compare estimates from two quasi-induced exposure methods of the effects of driver- and vehicle-related conditions on the risk of causing a road crash for drivers of vehicles with four or more wheels. From the Spanish register of road crashes with victims, the authors selected, for 1993-2002, all 755,329 drivers of ≥4-wheeled vehicles involved in single-vehicle crashes or in two-vehicle collisions in which only one of the drivers was considered responsible. Multinomial and logistic regression models were used to obtain the odds ratio for each driver- and vehicle-related variable. Construction of these models was based on the assumptions of classical quasi-induced exposure methods and on the method (a paired-by-collision analysis of responsible and nonresponsible drivers) proposed by Perneger and Smith (Am J Epidemiol 1991;134:1138-45). The main driver-dependent conditions for any type of crash were psychophysical circumstances (alcohol use and sleepiness). The effect of most driver- and vehicle-related characteristics was higher for single-vehicle crashes than for two-vehicle collisions. Furthermore, both classical and paired-by-collision analyses yielded similar estimates and can be considered equally useful alternatives for assessing the effect of driver and vehicle characteristics on the risk of causing a collision between two vehicles.

Keywords: accidents, traffic; automobile driving; epidemiologic methods; risk factors; vehicles.
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