Injury Patterns and Contributing Factors in Two-Wheeler Road Traffic Accidents: A Cross-Sectional Analysis from a Tertiary Care Hospital in Chengalpattu District, Tamil Nadu
Year: 2024
Type: article
Study design: Observational Study
Abstract: Road traffic accidents are one of the world's major sources of death and injury, with low-and middleincome nations suffering the most from them, despite having 60% of the world's automobiles.Twowheeler accidents lead to a high rate of fatalities and severe injuries, disproportionately affecting young, economically active individuals and resulting in a considerable loss of productive life years.The impact of this death toll on society's socioeconomic output is valuable.This study evaluated the causes and patterns of injuries associated with two-wheeler road traffic accidents in the Chengalpattu region.In a Tertiary Care Hospital in Chengalpattu district, 296 two-wheeler RTA victims participated in six-month cross-sectional research from January to June 2023.Interviews with accident victims were conducted using a pre-tested, semi-structured questionnaire intended for injury patterns in road traffic accidents.Data were analysed using SPSS version 22.Among the participants in the study, 17.1% of the cases were linked to traffic accidents; the majority 51.7% occurred in the under-32 age group, and 80.7% of the cases involved males.Head and neck injuries accounted for 54.4% of all injuries, with upper extremities coming in second with 28.4%.During a collision, alcohol was consumed by 25.7% of the drivers who were involved.The majority of accidents, 37.8%, occur during the night hours from 6 p.m. to 12 midnight.The data reveals a concerning trend of head and limb injuries predominating, highlighting the vulnerability of motorcyclists and scooter riders to severe trauma, particularly to the head and extremities.
Language: English
Topic: Traffic and Road Safety
Subfield: Safety, Risk, Reliability and Quality
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4403103570
