Factors Associated with Fatality from Motorcycle Accidents among Injured Patients Treated at Public Hospitals in Sisaket Province, 2024

Abstract

The objective of this study was to investigate the factors associated with fatality from motorcycle accidents among injured patients admitted to the emergency departments of public hospitals in Sisaket Province during 2024. The study was an analytical cross-sectional study and was conducted using secondary data. The sample consisted of 2,274 injured patients who received treatment at the emergency departments of public hospitals in Sisaket Province during 2024. Data were analyzed using descriptive and inferential statistics, including crude odds ratios (OR), adjusted odds ratios (AOR), and 95% confidence intervals (95%CI). The level of statistical significance was set at p < 0.05. The results revealed five statistically significant risk factors associated with motorcycle accident fatalities: being male (AOR = 2.21, 95%CI: 1.26–3.87); being 35 years or older (AOR = 1.73, 95%CI: 1.06–2.81); injuries from collisions with vehicles, people, animals, or objects (AOR = 2.62, 95%CI:1.38–4.98); head injuries (AOR = 1.83, 95%CI: 1.12–2.98); and injured patients transport by advanced life support (ALS) or basic life support (BLS) emergency medical service teams (AOR = 9.97, 95%CI: 4.93–20.17). From these findings, the data can be applied to risk prevention and fatality reduction strategies, such as developing fatality risk assessment tools and strengthening the capacity of emergency medical services (EMS) for high-risk patient medical care and transport. In addition, district-level multidisciplinary teams should be empowered to conduct in-depth fatality investigations of motor vehicle accidents for area-based risk management. The recommendation from this study is that a proactive data collection design should be implemented to capture a comprehensive range of risk factors across all dimensions. Furthermore, the quality of injury surveillance data in public hospitals should be improved by applying artificial intelligence technology to ensure data completeness, accuracy, and timeliness.