Objective: This study aligns to the body of research dedicated to estimating the underreporting of road crash injuries and adds the perspective of understanding individual and crash factors contributing to the decision to report a crash to the police, the hospital, or both.
Method: This study focuses on road crash injuries that occurred in the province of Funen, Denmark, between 2003 and 2007 and were registered in the police, the hospital, or both authorities. Underreporting rates are computed with the capture–recapture method, and the probability for road crash injuries in police records to appear in hospital records (and vice versa) is estimated with joint binary logit models.
Results: The capture–recapture analysis shows high underreporting rates of road crash injuries in Denmark and the growth of underreporting not only with the decrease in injury severity but also with the involvement of cyclists (reporting rates of about 14% for serious injuries and 7% for slight injuries) and motorcyclists (reporting rates of about 35% for serious injuries and 10% for slight injuries). Model estimates show that the likelihood of appearing in both data sets is positively related to helmet and seat belt use, number of motor vehicles involved, alcohol involvement, higher speed limit, and females being injured.
Conclusions: This study adds significantly to the literature about underreporting by recognizing that understanding the heterogeneity in the reporting rate of road crashes may lead to devising policy measures aimed at increasing the reporting rate by targeting specific road user groups (e.g., males, young road users) or specific situational factors (e.g., slight injuries, arm injuries, leg injuries, weekend). 相似文献
ABSTRACT: A regional adjustment relationship was developed to estimate long-term (30-year) monthly median discharges from short term (three-year) records. This method differs from traditional approaches in that it is based on site-specific discharge data but does not require correlation of these data with discharges from a single hydrologically similar long-term gage. The method is shown to be statistically robust, and applicable to statistics other than the median. 相似文献
Abstract: The successful invasion of exotic plants is often attributed to the absence of coevolved enemies in the introduced range (i.e., the enemy release hypothesis). Nevertheless, several components of this hypothesis, including the role of generalist herbivores, remain relatively unexplored. We used repeated censuses of exclosures and paired controls to investigate the role of a generalist herbivore, white‐tailed deer (Odocoileus virginianus), in the invasion of 3 exotic plant species (Microstegium vimineum, Alliaria petiolata, and Berberis thunbergii) in eastern hemlock (Tsuga canadensis) forests in New Jersey and Pennsylvania (U.S.A.). This work was conducted in 10 eastern hemlock (T. canadensis) forests that spanned gradients in deer density and in the severity of canopy disturbance caused by an introduced insect pest, the hemlock woolly adelgid (Adelges tsugae). We used maximum likelihood estimation and information theoretics to quantify the strength of evidence for alternative models of the influence of deer density and its interaction with the severity of canopy disturbance on exotic plant abundance. Our results were consistent with the enemy release hypothesis in that exotic plants gained a competitive advantage in the presence of generalist herbivores in the introduced range. The abundance of all 3 exotic plants increased significantly more in the control plots than in the paired exclosures. For all species, the inclusion of canopy disturbance parameters resulted in models with substantially greater support than the deer density only models. Our results suggest that white‐tailed deer herbivory can accelerate the invasion of exotic plants and that canopy disturbance can interact with herbivory to magnify the impact. In addition, our results provide compelling evidence of nonlinear relationships between deer density and the impact of herbivory on exotic species abundance. These findings highlight the important role of herbivore density in determining impacts on plant abundance and provide evidence of the operation of multiple mechanisms in exotic plant invasion.相似文献
Applied tracer tests provide a means to estimate aquifer parameters in fractured rock. The traditional approach to analysing these tests has been using a single fracture model to find the parameter values that generate the best fit to the measured breakthrough curve. In many cases, the ultimate aim is to predict solute transport under the natural gradient. Usually, no confidence limits are placed on parameter values and the impact of parameter errors on predictions of solute transport is not discussed. The assumption inherent in this approach is that the parameters determined under forced conditions will enable prediction of solute transport under the natural gradient. This paper considers the parameter and prediction uncertainty that might arise from analysis of breakthrough curves obtained from forced gradient applied tracer tests. By adding noise to an exact solution for transport in a single fracture in a porous matrix we create multiple realisations of an initial breakthrough curve. A least squares fitting routine is used to obtain a fit to each realisation, yielding a range of parameter values rather than a single set of absolute values. The suite of parameters is then used to make predictions of solute transport under lower hydraulic gradients and the uncertainty of estimated parameters and subsequent predictions of solute transport is compared. The results of this study show that predictions of breakthrough curve characteristics (first inflection point time, peak arrival time and peak concentration) for groundwater flow speeds with orders of magnitude smaller than that at which a test is conducted can sometimes be determined even more accurately than the fracture and matrix parameters. 相似文献
ABSTRACT: Missing rainfall data from a time series or a spatial field of observations can present a serious obstacle to data analysis, modeling studies and operational forecasting in hydrology. Numerous schemes for replacing missing data have been proposed, ranging from simple weighted averages of data points that are nearby in time and space to complex statistically-based interpolation methods and function fitting schemes. This paper presents a technique for replacing missing spatial data using a backpropagation neural network applied to concurrent data from nearby gauges. Tests performed on a sample of gauges in the Middle Atlantic region of the United States show that this technique produces results that compare favorably to simple techniques such as arithmetic and distance-weighted averages of the values from nearby gauges, and also to linear optimization methods such as regression. 相似文献