ArticleOpen access emergency medicine : OAEM2026
Forecasting Road Traffic Injuries in North-Eastern Iran: The Effects of COVID-19 and Time Series Analysis (2009-2023).
Article in Open access emergency medicine : OAEM, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Road Traffic Injuries (RTI) represent a major public health issue in the current century. Examining accident data is essential for developing effective strategies and measures aimed at minimizing these incidents and protecting lives. Methods: This study investigates the impact of the COVID-19 pandemic on RTI in northeast Iran, particularly in Mashhad, a city characterized by a high number of pilgrims and suburban commuters. We employed Seasonal Autoregressive Integrated Moving Average (ARIMA) models to analyze time‑series data collected between 2009 and 2023. The dataset included RTI records from official national sources. The seasonal ARIMA approach was selected to capture seasonal variations, long‑term trends, and the effects of pandemic‑related mobility restrictions on RTI patterns. All analyses were performed by statistical R software version 4.5.1. Results: The average monthly RTI decreased from 2385 (95% CI: 2055-2794) in 2019 to 2035 (95% CI: 1707-2362) in 2020. This reduction of approximately 14.8% was statistically significant (p < 0.05), highlighting the impact of pandemic-related traffic restrictions on road traffic injuries in Iran. The study employed detailed time series analysis, including seasonal ARIMA modeling, to forecast future RTI trends, which is crucial for informing effective prevention strategies. Conclusion: The analysis reveals seasonal patterns in RTI data, confirming the presence of non-stationarity. The results indicate a projected upward trend in RTI incidence, driven largely by increased traffic volumes in the post-pandemic period. These findings highlight the urgent need for targeted interventions to address this rising burden. Overall, the study offers valuable insights for policymakers, supporting more effective strategies to improve road safety and help mitigate the ongoing public health challenge posed by RTIs.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.