The Pattern of Motorcyclists' Death Due to Accidents and a Three-year Forecast in East Azerbaijan Province, Iran: A Time Series Study

Abstract

Introduction: In low- and middle-income countries, a large proportion of road users include pedestrians, cyclists, and motorcyclists, and nearly half of road traffic fatalities occur among motorcyclists. This study aimed to examine the pattern of motorcyclists' death due to accidents in East Azerbaijan, Iran between 2006 and 2021 and present a forecast. Methods: We used death data due to motorcycle accidents of Legal Medicine Department between 2006 and 2021. For time series analysis, the Box-Jenkins model was used and three stages of identification, estimation, and diagnosis were successively performed and repeated several times to achieve the best prediction model. The Box-cox transformation method was used to stabilize the variance, and the first-order seasonal differential method with a period of 12 was used to control the seasonality. Due to seasonal variations, the Seasonality Auto-Regressive Integrated Moving Average model: SARIMA (p, d, q) (P, D, Q)s was employed and the death trend was predicted for 36 months. The candidate models were compared based on Log-likelihood, AIC, and BIC indices. STATA 17 was used for data analysis. Results: About 18.6% of all accident deaths are attributed to motorcycle accidents. The death rate for all causes of accidents and motorcycle accidents were 23.13 and 4.30 per 100,000 population, respectively. Seven models were considered as candidates. The SARIMA (0, 0, 0) (1, 1, 1)12 model was selected as the best model due to better fit and used to predict the number and trend of motorcycle accident deaths. Motorcycle accident deaths are predicted to decrease gradually in the next 36 months, from June 2021 to May 2024, affected by seasonal changes. Conclusion: The trend of death due to motorcycle accidents from 2006 to 2021 in East Azerbaijan was declining, and it is predicted to decrease slightly in the next three years as well. As this reduction may be attributed to many factors, it is recommended to investigate effective factors in future studies.