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Methods: We conducted an analysis using NPS law enforcement MVC data. An MVC is a collision involving an in-transit motor vehicle that occurred or began on a public roadway. An AVC is characterized as a collision between a motor vehicle and an animal. A non-AVC is a crash between a motor vehicle and any object other than an animal or noncollision event (e.g., rollover crash). The final data for analysis included 54,068 records from 51 NPS units during 1990–2013. Counts and proportions were calculated for categorical variables and medians and ranges were calculated for continuous variables. We used Pearson’s chi-square to compare circumstances of AVCs and non-AVCs. Data were compiled at the park regional level; NPS parks are assigned to 1 of 7 regions based on the park’s location.
Results: AVCs accounted for 10.4% (5,643 of 54,068) of all MVCs from 51 NPS units. The Northeast (2,021 of 5,643; 35.8%) and Intermountain (1,180 of 5,643; 20.9%) regions had the largest percentage of the total AVC burden. November was the peak month for AVCs across all regions (881 of 5,643; 15.6%); however, seasonality varied by park geographic regions. The highest counts of AVCs were reported during fall for the National Capital, Northeast/Southeast, and Northeast regions; winter for the Southeast region; and summer for Intermountain and Pacific West regions.
Conclusions: AVCs represent a public health and wildlife safety concern for NPS units. AVCs in select NPS units were approximately 2-fold higher than the national percentage for AVCs. The peak season for AVCs varied by NPS region. Knowledge of region-specific seasonality patterns for AVCs can help NPS staff develop mitigation strategies for use primarily during peak AVC months. Improving AVC data collection might provide NPS with a more complete understanding of risk factors and seasonal trends for specific NPS units. By collecting information concerning the animal species hit, park managers can better understand the impacts of AVC to wildlife population health. 相似文献
Methods: We analyzed 138 road suicides (2011–2016) involving a passenger car crashing into a heavy vehicle. We used in-depth road crash investigation data from the Finnish Crash Data Institute.
Results: The results showed that all but 2 crashes were head-on collisions. Almost 30% of truck drivers were injured, but only a few suffered serious injuries. More than a quarter reported sick leave following their crash. Injury insurance compensation to heavy-vehicle drivers was just above €9,000 on average. Material damage to heavy vehicles was significant, with average insurance compensation paid being €70,500. Three out of 4 truck drivers reported that drivers committing suicide acted abruptly and left them little opportunity for preventive action.
Conclusions: Suicides by crashing into heavy vehicles can have an impact on drivers’ well-being; however, it is difficult to see how heavy-vehicle drivers could avoid a suicide attempt involving their vehicle. 相似文献
Methods: Police-reported child PMVCs during school travel times from 2000 to 2011 were mapped within 200 m of 118 elementary schools. Observers measured dangerous student morning car drop-off behaviors and number of children walking to school during one day in 2011. A composite score of school social disadvantage was obtained from the Toronto District School Board. Built environment and traffic features were mapped and included as covariates. A multivariate Poisson regression was used to model the rates of PMVC/number of children walking and dangerous student car drop-off behaviors, adjusting for the built environment and social disadvantage.
Results: There were 45 child PMVCs, with 29 (64%) sustaining minor injuries resulting in emergency department visits. The mean collision rate was 2.9/10,000 children walking/year (SD = 6.7). Dangerous drop-off behaviors were observed in 104 schools (88%). In the multivariate analysis, each additional dangerous drop-off behavior was associated with a 45% increase in collision rates (incident rate ratio [IRR] = 1.45, 95% confidence interval [CI], 1.02, 2.07). Higher speed roads (IRR = 1.27, 95% CI, 1.13, 1.44) and social disadvantage (IRR = 2.99, 95% CI, 1.03, 8.68) were associated with higher collision rates.
Conclusions: Dangerous student car drop-off behaviors were associated with historical nonfatal child PMVC rates during school travel times near schools. Some caution must be taken in interpreting these results due small number of events and limitations in the data collection, because collision data were collected historically over a 12-year period, whereas driving behavior was only observed on a single day in 2011. Targeted multifaceted intervention approaches related to the built environment, enforcement, and education could address dangerous drop-off behaviors near schools to reduce child PMVCs and promote safe walking to school. 相似文献