دوره 13، شماره 3 - ( 6-1404 )
جلد 13 شماره 3 صفحات 0-0 |
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Ethics code: IR.GOUMS.REC.1402.127
Clinical trials code: https://ethics.research.ac.ir/ IR.GOUMS.REC.1402.127. .
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Babanezhad M, khorshah H, mohajervatan A. Comparative Analysis of Mortality Rates in Urban and Suburban Areas Due to Road Traffic Accidents by Time Series Forecasting Models: A Case Study in Golestan Province. Iran J Health Sci 2025; 13 (3)
URL: http://jhs.mazums.ac.ir/article-1-1018-fa.html
URL: http://jhs.mazums.ac.ir/article-1-1018-fa.html
Comparative Analysis of Mortality Rates in Urban and Suburban Areas Due to Road Traffic Accidents by Time Series Forecasting Models: A Case Study in Golestan Province. علوم بهداشتی ایران. 1404; 13 (3)
چکیده: (41 مشاهده)
Background and Purpose: The increase in road traffic accidents (RTAs) is driven by urban and suburban expansion and rising vehicle numbers, complicating mortality trend analysis. This study evaluates demographic mortality patterns using time series forecasting models.
Materials and Methods: This retrospective study examines all traffic incidents documented by the Emergency Medical Services between 2021 and 2023 on urban and suburban roadways in Golestan province, northern Iran. This study employs forecasting models such as Multiple Logistic Regression (MLR), Autoregressive Integrated Moving Average (ARIMA), and Propensity Score Matching (PSM) within time series data framework, to evaluate their effectiveness in comparing road traffic accidents across these regions and analyzing demographic patterns in mortality rates.
Results: This study analyzed daily time-series data consisting of 37,409 records of RTAs from March 2021 to March 2023. Out of these incidents, an overwhelming 98.7% resulted in injuries, while only 1.3% were fatal. We calculated the root mean square error (RMSE) and the mean absolute percentage error (MAPE) for each model used. Notably, the PSM model outperformed the others based on the results presented in Table 5. Moreover, our findings highlighted a concerning trend: over the two-year study period, patients involved in accidents who were transferred by the emergency center had a significant higher mortality rate in suburban areas (1.6%), which is twice the fatality rate from traffic incidents in urban areas (0.8%) (P=0.001).
Conclusion: Monitoring blood oxygen saturation level (SPO₂) is vital in trauma care, helping teams quickly find high-risk patients needing urgent help. Low SPO₂ levels (<90%) indicate a need for immediate assistance, especially in mass casualty situations, guiding treatment priorities. Findings can improve emergency care policies to save lives.
Materials and Methods: This retrospective study examines all traffic incidents documented by the Emergency Medical Services between 2021 and 2023 on urban and suburban roadways in Golestan province, northern Iran. This study employs forecasting models such as Multiple Logistic Regression (MLR), Autoregressive Integrated Moving Average (ARIMA), and Propensity Score Matching (PSM) within time series data framework, to evaluate their effectiveness in comparing road traffic accidents across these regions and analyzing demographic patterns in mortality rates.
Results: This study analyzed daily time-series data consisting of 37,409 records of RTAs from March 2021 to March 2023. Out of these incidents, an overwhelming 98.7% resulted in injuries, while only 1.3% were fatal. We calculated the root mean square error (RMSE) and the mean absolute percentage error (MAPE) for each model used. Notably, the PSM model outperformed the others based on the results presented in Table 5. Moreover, our findings highlighted a concerning trend: over the two-year study period, patients involved in accidents who were transferred by the emergency center had a significant higher mortality rate in suburban areas (1.6%), which is twice the fatality rate from traffic incidents in urban areas (0.8%) (P=0.001).
Conclusion: Monitoring blood oxygen saturation level (SPO₂) is vital in trauma care, helping teams quickly find high-risk patients needing urgent help. Low SPO₂ levels (<90%) indicate a need for immediate assistance, especially in mass casualty situations, guiding treatment priorities. Findings can improve emergency care policies to save lives.
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