Indicator Variables for Road Traffic Injury Severity in District Gujrat, Pakistan

Injury Severity in Road Traffic

Abstract

Road Traffic Injuries (RTIs) are unexpected and unpredicted events involving at least a single vehicle. They claim the lives of around 1.3 million people every year. Objective: To determine the indicator variables that play roles in forecasting the injury severity levels (slight, moderate, and severe) of road traffic accidents in the populous city of Gujrat and correspondingly design explicit health policies for the public health system. Methods: An analytical cross-sectional study was conducted for six months from June 2023 to December 2023 on 342 subjects with and without fatalities; excluded were the ones with non-RTA emergencies. The response variable, injury severity, was divided into three categories: slight, moderate, and severe. Chi-square and multiplicative logit tests were applied to determine a predictive model using Stata version 15, respectively. Results: Pearson X² associated p-values for ‘time lapse until help’ and marital statuses were 0.086 and 0.123, respectively. A multinomial logit model of road accident injury severity concluded that if distance from first aid increases along with foggy weather, the chances of severe injuries are e higher. The increase in education level will decrease the frequency of severe injuries. Licenses gained without training result in 116 times more severe injuries. Conclusions: This study showed that distance from first aid, weather, age, education, and license gained affect the risks of severe injuries. The public health care sector should take on recommended initiatives to manage the resource burden of RTA in tertiary care hospitals that could be used for combating other systemic illnesses.