Status of Road Safety and Injury Burden
Bibliographic record
Abstract
Rapid industrialization and the subsequent economic growth have led to increasing motorization in China. In 1991, there were only 300,000 passenger vehicles in China.1 Today, China is the largest vehicle market in the world, with an average of 50,000 vehicles sold every day.1,2 Unsurprisingly, road traffic injuries (RTIs) have become the leading cause of injury-related death and a primary cause of disability in China.3 In 2009, there were 238,351 traffic accidents in China, causing 67,758 deaths and 275,125 injuries with a direct economic cost of 0.91 billion Chinese Yuan.4 The burden of injury in the younger population contributed to the loss of 3 million potentially productive life years in 1999.3 The number of traffic-related fatalities per population is higher in China than in many other nations. In 2007, there were 6.21 fatalities per 100,000 population in China compared with 4.50 in Japan and 5.00 in the United Kingdom.5 Pedestrians are the most likely to suffer fatalities (24.6%) followed by vehicle passengers (24.1%), motorcyclists (22.0%), bicyclists (15.6%), and vehicle drivers (12.2%).6 In addition, the introduction of new electric vehicles such as the E-bike has been associated with increases in RTIs and fatalities. The E-bike is a 2-wheeled bicycle propelled by a motor. Although E-bikes are not regulated as a motor vehicle, drivers often travel in motor vehicle lanes. Researchers believe that this has contributed to the 5-fold increase in E-biker fatalities and a 3-fold increase in nonfatal injuries during the period 2004–2008.7 An estimated 120 million E-bikes were on the road in China at the end of 2011, and the number of E-bikes on the road is expected to increase.7 Within China, differences in the geographic distribution of RTIs have been observed. Areas with more rapid economic growth such as Guangdong, Zhejiang, and Jiangsu report more traffic injuries compared with less developed regions or regions with established development.3 Finally, the majority of RTIs and fatalities occur in young males; nonetheless, traffic injury is also a serious concern for elderly because it is one of the top 3 causes of death in patients older than 60 years.6,8 Research investigating the causes of traffic accidents has identified several driver and pedestrian factors thought to contribute to the high fatality rate. Pedestrians and cyclists have been shown to frequently cross the road at any time, and cyclists often do not heed traffic lanes.5 Furthermore, drivers tend not to stop for pedestrians or cyclists, and aggressive driving in rush hour traffic is prevalent.5 In addition, seatbelt use is very low. In a survey conducted in 5 cities, participants estimated that seatbelt use was 54% for drivers, 12% for front seat passengers, and 1% for rear seat passengers.9 Finally, the prehospital and hospital emergency system may also contribute to the death and disability in road traffic accidents. There is no standardized prehospital medical response system, nor is there systematic professional training for hospital emergency personnel.10 In 2003, the Chinese government responded to the road traffic injury problem by reforming and implementing changes in the road safety law and regulation system and by establishing a federal organization called The Ministerial Coordination System on Road Safety.11 The rates of fatalities and nonfatal injuries sustained in road traffic crashes have shown progressive declines since 2002.3 There are several possible explanations for this, including the successful implementation of improvements in traffic safety, an increase in the number of experienced drivers, and differing data-reporting methods. Data regarding traffic fatalities can be obtained from police-reported data or death registration records collected by the Ministry of Health.2,12 Hu et al12 recently found inconsistencies in the fatality rates reported by the 2 data sources, with the Ministry of Health's fatality rate being 2 times greater than the police-reported data. These data discrepancies may prove problematic if safety policy is being defined by inaccurate estimates. RTIs are a leading cause of mortality and morbidity in China. The government has recognized the gravity of this problem and has developed multifaceted strategies for addressing the problem. Future targets for intervention include improving transportation infrastructure, refining and enforcing traffic laws, standardizing driver qualifications and training, promoting road traffic safety prevention initiatives, and developing evidence-based treatment protocols for prehospital and emergency personnel.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".