Fall- and collision-related injuries among pedestrians in road traffic environment – A Swedish national register-based study
Bibliographic record
Abstract
OBJECTIVE: To investigate the burden of pedestrian injuries, including pedestrian fall injuries (PFI), compared to other transport-related injuries in Sweden and document their characteristics in terms of demographics, causes, type of injuries, and severity level with a focus on long-term consequences. METHODS: Data were retrieved from the national Swedish Traffic Accident Data Acquisition register. A total of 361,531 fatalities and injuries were reported by emergency hospitals during 2010-2019, of which 127,804 were pedestrians (35%). We assessed the magnitude of PFIs and conducted comparative analyses to assess differences compared to other types of road users regarding sex, age, severity level, injury circumstances, hospital care, causes of accidents, and type of injuries. RESULTS: Pedestrians were the second largest group of traffic-related deaths in Sweden after car occupants and accounted for just over a quarter of all fatal accidents in the road traffic environment. Of the total number of pedestrian fatalities, three out of four have been in collision accidents and the others in fall-related accidents. In terms of injuries, pedestrians were the largest group among all road users, regardless of the type of accident. PFIs accounted for a third of all injuries in the road traffic environment and nearly half of all injuries resulting in permanent medical impairment (i.e., 2.2 times more long-term consequences among PFIs compared to injured car occupants). Females (particularly middle-aged and older) and older adults were overrepresented, and most PFIs occurred on urban and municipal roads. The causes were often related to maintenance (e.g., slippery surfaces such as ice, snow, leaves or gravel together with uneven pavements and roads are the cause three out of four of PFIs). Among collision injuries, the representation was almost equal for sex and age. CONCLUSIONS: Injuries and fatalities among pedestrians are a considerable issue in the road traffic environment in Sweden. Contrary to other traffic groups, the incidence has not decreased over time, meaning that this issue must be met with specific measures and address the specific risk factors they are associated with. PRACTICAL APPLICATION: Including fall accidents in the definition of traffic accidents increases the chances of getting better information about the accidents and taking preventive measures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".