Teschke et al. BMC Public Health 2012, 12:765 http://www.biomedcentral.com/1471-2458/12/765 safety measures, there is a range of possible options, in- cluding those aimed at crash prevention (e.g., lights) and those focussed on injury mitigation (e.g., helmets). A number of studies have measured use of individual- based safety equipment by cyclists [10-25], though few have documented use of multiple types of equipment in the same population. Fewer still have examined charac- teristics (e.g., weather conditions, cyclist age) associated with use of safety equipment [10-12,14-18,20,22,25]. Bet- ter understanding could help inform priorities for im- provement, interventions to improve uptake, and the relative safety potential of these individual-based mea- sures vis-à-vis population-based alternatives such as bicycle-dedicated infrastructure. As part of a study of 690 cyclists injured in two of Canada’s largest cities, Toronto and Vancouver, we col- lected data on use of lights, high conspicuity clothing on the torso, and helmets. In addition, we collected data on trip and personal characteristics that allowed us to examine factors associated with use of these types of safety equipment. Methods The study methods were reviewed and approved by the human subjects ethics review boards of the University of British Columbia, the University of Toronto, St. Paul’s Hospital, Vancouver General Hospital, St. Michael’s Hos- pital, and the University Health Network (Toronto Gen- eral Hospital and Toronto Western Hospital). All participants gave informed consent before taking part in the study. Methods of study conduct have been described in de- tail elsewhere [26]. The study population consisted of adult (≥ 19 years) residents of Toronto and Vancouver who were injured while riding a bicycle in the city and treated within 24 hours in the emergency departments of the hospitals listed above between May 18, 2008 and November 30, 2009. Eligible participants were interviewed in person by trained interviewers, using a structured questionnaire (http://cyclingincities.spph.ubc.ca/files/2011/10/Inter- viewFormFinal.pdf ) as soon as possible after the injury to maximize recall. Questions related to safety equip- ment use were the following (asked in this order): Did you have a back light that was turned on during this trip? Did you have a front light that was turned on during this trip? What colour was the clothing on your upper body? What colour was the helmet you were wearing? Questions about front and back lights were combined and if at least one light was turned on, assigned a “yes”. Page 2 of 9 The following torso clothing colours were classified as highly visible based on evidence of conspicuity from the study of Hagel et al. [18]: white, yellow, orange, and red. Those who reported a helmet colour were classified as wearing a helmet. “Don’t know” or “refused” responses for all questions were grouped with the “no” category, to provide a conservative estimate of the prevalences of safety equipment use. Only highly visible clothing on the torso had large numbers of “don’t know” responses; we repeated analyses (removing “don’t know” responses) to determine whether this conservative classification had an impact on the results. The interview also collected data on trip characteris- tics (weather conditions, time of day, day of week, sea- son, trip distance, trip purpose, bike type used, alcohol use in the 6 hours prior to the trip, drug use in the 6 hours prior, sleep in the 24 hours prior, and whether the participant was cycling with a companion) and per- sonal characteristics (age, sex, education, income, em- ployment, cycling frequency, and whether the participant had a driver’s license, was an experienced cyclist, had taken a cycling training course, and had chil- dren in the household). Unconditional multiple logistic regression was used to examine associations between the use of each type of safety equipment (bike lights, highly visible clothing on the torso, or helmet; as dependent variables) and the fol- lowing independent variables: city; all trip and personal characteristics listed above; and the two types of safety equipment that were not the dependent variable in the analysis. We used backwards selection to construct mul- tiple logistic regression models, starting by offering all variables of interest. Based on results of the Wald test for each variable, the variable with the highest p-value was removed and the model refit with the remaining variables until all variables in the model were statistically significant at the p < 0.05 level. Data analyses were per- formed using SAS 9.2 (SAS Institute, Cary, NC). In this paper we present the unadjusted and adjusted results for the variables in the final models. The results for full models with all variables included (prior to backwards selection) are available from the authors. Results Details on the recruitment process are available else- where [26]. In brief, 2,335 injured cyclists attended one of the five study emergency departments during the study period. Of these, 927 were deemed ineligible, 741 deemed eligible and 690 participated (414 in Vancouver, 276 in Toronto). There were 667 with unknown eligibil- ity (543 not contacted, 124 refusals). Participants repre- sented 93.1% of those confirmed to be eligible and 66.5% of those estimated to be eligible (based on the propor- tion eligible among those contacted). The most common