06 (95% CI: 1.05–1.08). Age over 35 years, residing in urban areas or in the Auckland region, riding in a bunch, using a road bike and history of a crash at baseline predicted a higher risk whereas being overweight or obese, cycling off-road and using lights in the dark lowered the risk. Bicycle commuting, however, did not increase the risk. There were 10 collisions per 1000 person-years or 38 collisions per million hours spent road cycling per year (Table 4). The adjusted HR for one click here hour increase in average time spent
cycling each week was 1.08 (95% CI: 1.05–1.12). Due to a very small number of events, “overweight” and “obese” categories were combined and helmet use was excluded in the multivariate models. Residing in urban areas, riding a road bike and having a crash history were associated with an increased risk. There were 50 crashes per 1000 person-years (Table 5). The risk was lower in university graduates, overweight or obese
cyclists and less experienced cyclists but higher in those who cycled in the dark or in a bunch and those who had a crash history. The effect estimates mentioned above were similar to those obtained from complete case analyses. Potential misclassification of crash outcomes during the linkage process may underestimate the actual incidence rate and may bias the hazard ratios to the null (Appendix A). Likewise, potential misclassification of exposures check details (due to changes over time) may underestimate the risk estimates in most cases (Appendix B). In this study, cyclists experienced 116 crashes attended medically or by police per 1000 person-years, of which 66 occurred on the road and 10 involved a collision PD184352 (CI-1040) with a motor vehicle. There were 240 on-road crashes and 38 collisions per million hours spent road cycling and the risk increased by 6% and 8% respectively for one hour increase in cycling each week.
After adjusting for all covariates, participants’ age, body mass index, urbanity, region of residence, cycling off road, in the dark or in a bunch, type of bicycle used and prior crash history predicted the crash risk with variations in effect estimates by crash type. This is one of the very few prospective cohort studies involving cyclists and used record linkage to obtain objective information on bicycle crashes from multiple databases. This resource efficient method of data collection was also designed to minimise potential biases associated with loss to follow-up (Greenland, 1977) and self-reports (af Wåhlberg et al., 2010, Jenkins et al., 2002 and Tivesten et al., 2012). While emigration during follow-up is a potential issue in using the linked data, this accounted for less than 2% of the participants resurveyed in 2009 and may not substantially influence outcome occurrences (Kristensen and Bjerkedal, 2010).