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Methods: A cross-sectional study was used to interview 1,422 farm vehicle drivers in southern China. Farm vehicle–related road traffic crashes that occurred from December 2013 to November 2014 were investigated. Data on farm vehicle–related road traffic crashes and related factors were collected by face-to-face interviews.
Results: The prevalence of farm vehicle–related road traffic crashes among the investigated drivers was 7.2%. Farm vehicle–related road traffic crashes were significantly associated with self-reported vision problem (adjusted odds ratio [AOR] = 6.48, 95% confidence interval [CI], 3.86–10.87), self-reported sleep disorders (AOR = 10.03, 95% CI, 6.28–15.99), self-reported stress (AOR = 20.47, 95% CI, 9.96–42.08), reported history of crashes (AOR = 5.40, 95% CI, 3.47–8.42), reported history of drunk driving (AOR = 5.07, 95% CI, 2.97–8.65), and reported history of fatigued driving (AOR = 5.72, 95% CI, 3.73–8.78). The number of road traffic crashes was highest in the daytime and during harvest season. In over 96% of farm vehicle–related road traffic crashes, drivers were believed to be responsible for the crash. Major crash-causing factors included improper driving, careless driving, violating of traffic signals or signs, and being in the wrong lane.
Conclusion: Findings of this study suggest that farm vehicle–related road traffic crashes have become a burgeoning public health problem in China. Programs need to be developed to prevent farm vehicle–related road traffic crashes in this emerging country. 相似文献
Methods: Weighted data from the 2012 and 2013 Monitoring the Future surveys were combined and analyzed. We imputed missing values using fully conditional specification multiple imputation methods. Multivariate logistic regression modeling was conducted to explore associations among race/ethnicity, socioeconomic factors, and driving status, while accounting for selected student behaviors and location. Lastly, odds ratios were converted to prevalence ratios.
Results: 23% of high school seniors did not drive during an average week; 14% of white students were nondrivers compared to 40% of black students. Multivariate analysis revealed that minority students were 1.8 to 2.5 times more likely to be nondrivers than their white counterparts, and students who had no earned income were 2.8 times more likely to be nondrivers than those earning an average of ≥$36 a week. Driving status also varied considerably by student academic performance, number of parents in the household, parental education, census region, and urbanicity.
Conclusions: Our findings suggest that resources—both financial and time—influence when or whether a teen will learn to drive. Many young people from minority or lower socioeconomic families who learn to drive may be doing so after their 18th birthday and therefore would not take advantage of the safety benefits provided by graduated driver licensing. Innovative approaches may be needed to improve safety for these young novice drivers. 相似文献