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X Demographics
Mendeley readers
| Title |
Understanding traffic crash under-reporting: Linking police and medical records to individual and crash characteristics
|
|---|---|
| Published in |
Traffic Injury Prevention, June 2016
|
| DOI | 10.1080/15389588.2015.1128533 |
| Pubmed ID | |
| Authors |
Kira H. Janstrup, Sigal Kaplan, Tove Hels, Jens Lauritsen, Carlo G. Prato |
| Abstract |
This study aligns to the body of research dedicated to estimating the under-reporting 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. 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. Under-reporting 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. The capture-recapture analysis shows high under-reporting rates of road crash injuries in Denmark, and the growth of under-reporting not only with the decrease of injury severity, but also with the involvement of cyclists (reporting rates about 14% for serious injuries and 7% for slight injuries) and motorcyclists (reporting rates about 35% for serious injuries and 10% for slight injuries). Model estimates show that the likelihood of appearing in both datasets is positively related to helmet and seat-belt use, number of motor vehicles involved, alcohol involvement, higher speed limit, and females being injured. This study adds significantly to the literature about under-reporting by recognizing that understanding the heterogeneity in the reporting rate of a road crash 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). |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 125 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| South Africa | 1 | <1% |
| Unknown | 124 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 28 | 22% |
| Student > Ph. D. Student | 25 | 20% |
| Researcher | 18 | 14% |
| Student > Doctoral Student | 13 | 10% |
| Student > Bachelor | 5 | 4% |
| Other | 9 | 7% |
| Unknown | 27 | 22% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 51 | 41% |
| Medicine and Dentistry | 10 | 8% |
| Social Sciences | 8 | 6% |
| Computer Science | 4 | 3% |
| Nursing and Health Professions | 4 | 3% |
| Other | 19 | 15% |
| Unknown | 29 | 23% |