Environmental Factors Associated with Severe Motorcycle Crash Injury in University Neighborhoods: A Multicenter Study in Taiwan.
Heng-Yu LinJian-Sing LiChih-Wei PaiWu-Chien ChienWen-Cheng HuangChin-Wang HsuChia-Chieh WuShih-Hsiang YuWen-Ta ChiuCarlos LamPublished in: International journal of environmental research and public health (2022)
University neighborhoods in Taiwan have high-volume traffic, which may increase motorcyclists' risk of injury. However, few studies have analyzed the environmental factors affecting motorcycle crash injury severity in university neighborhoods. In this multicenter cross-sectional study, we explored the factors that increase the severity of such injuries, especially among young adults. We retrospectively connected hospital data to the Police Traffic Accident Dataset. Areas within 500 m of a university were considered university neighborhoods. We analyzed 4751 patients, including 513 with severe injury (injury severity score ≥ 8). Multivariate analysis revealed that female sex, age ≥ 45 years, drunk driving, early morning driving, flashing signals, and single-motorcycle crashes were risk factors for severe injury. Among patients aged 18-24 years, female sex, late-night and afternoon driving, and flashing signals were risk factors. Adverse weather did not increase the risk. Time to hospital was a protective factor, reflecting the effectiveness of urban emergency medical services. Lifestyle habits among young adults, such as drunk driving incidents and afternoon and late-night driving, were also explored. We discovered that understanding chaotic traffic in the early morning, flashing signals at the intersections, and roadside obstacles is key for mitigating injury severity from motorcycle crashes in university neighborhoods.
Keyphrases
- young adults
- healthcare
- risk factors
- air pollution
- end stage renal disease
- early onset
- metabolic syndrome
- systematic review
- randomized controlled trial
- type diabetes
- primary care
- emergency department
- cardiovascular disease
- chronic kidney disease
- peritoneal dialysis
- prognostic factors
- big data
- patient safety
- clinical trial
- electronic health record
- health insurance
- human health
- deep learning