Time trends in gender-specific incidence rates of road traffic injuries in Iran

Foroutaghe, Milad Delavary; Moghaddam, Abolfazl Mohammadzadeh; Fakoor, Vahid · 2019 · Crossref

DOI: 10.1371/journal.pone.0216462

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Summary

This study addresses the public health challenge of road traffic injuries (RTIs) in Iran by modeling and validating gender-specific incidence rates to inform traffic safety policymaking. Motivated by the high prevalence of RTIs and the lack of accessible, detailed statistical reports, the authors aimed to determine time trends for total, male, female, and male-to-female RTI incidence rates. The research specifically sought to evaluate the efficiency of Seasonal Auto-Regressive Integrated Moving Average (SARIMA) models in predicting these trends and to analyze gender disparities in injury patterns. The researchers utilized monthly data from the Legal Medicine Organization of Iran, covering total RTIs from March 2005 to February 2016, and gender-specific data from March 2009 to February 2016. Incidence rates were calculated per 100,000 population. The study employed time domain analysis, specifically SARIMA models, selected based on the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Model validity was assessed using Ljung-Box tests for residual un-correlation, residual plots for zero mean and stationarity, and Auto-Correlation Function (ACF) and Partial Auto-Correlation Function (PACF) plots. Exponential smoothing methods were used to validate out-of-range predictions. The results identified specific optimal SARIMA models for each series: SARIMA (2,1,0)(0,1,1)12 for total RTIs, SARIMA (0,1,1)(0,1,1)12 for males, SARIMA (1,1,1)(0,0,1)12 for females, and SARIMA (2,0,0)(1,0,0)12 for the male-to-female ratio. Residual analysis confirmed that the models met the white noise assumption. Predictions for 2016–2018 indicated no declining trend in injury incidence. Notably, male incidence rates were two to three times higher than female rates throughout the study period. While male trends remained non-increasing, female incidence rates showed an increasing trend from 2009 to 2012, which the authors attribute to increased outdoor activities and a rise in issued driving licenses for women during that period. The study concludes that SARIMA models are highly efficient for predicting RTIs and can serve as valuable tools for simulating the impact of future traffic enforcement interventions. The findings highlight a persistent gender disparity, with men experiencing significantly higher injury rates, while women’s injury rates are rising. This suggests that traffic safety strategies in Iran must account for these distinct gender-specific trends, particularly addressing the growing risk for female drivers as their participation in traffic increases.

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Topics

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• fatality injury trends
• incidence prevalence
• sex gender

Information type

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• Empirical Findings: crash risk outcomes