Prediction of the burden of road traffic injuries in Iran by 2030: Prevalence, death, and disability-adjusted life years.

PURPOSE: Road traffic accidents pose a global challenge with substantial human and economic costs. Iran experiences a high incidence of road traffic injuries, leading to a significant burden on society. This study aims to predict the future burden of road traffic injuries in Iran until 2030, providing valuable insights for policy-making and interventions to improve road safety and reduce the associated human and economic costs. METHODS: This analytical study utilized time series models, specifically autoregressive integrated moving average (ARIMA) and artificial neural networks (ANNs), to predict the burden of road traffic accidents by analyzing past data to identify patterns and trends in Iran until 2030. The required data related to prevalence, death, and disability-adjusted life years (DALYs) rates were collected from the Institute for Health Metrics and Evaluation database and analyzed using R software and relevant modeling and statistical analysis packages. RESULTS: Both prediction models, ARIMA and ANNs indicate that the prevalence rates (per 100,000) of all road traffic injuries, except for motorcyclist road injuries which have an almost flat trend, remaining at around 430, increase by 2030. Based on estimations of both models, the rates of death and DALYs due to motor vehicle and pedestrian road traffic injuries decrease. For motor vehicle road injuries, estimated trends decrease to approximately 520 DALYs and 10 deaths. Also, for pedestrian road injuries these rates reached approximately 300 DALYs and 6 deaths, according to the models. For cyclists and other road traffic injuries, the predicted DALY rates by the ANN model increase to almost 50 and 8, while predictions conducted by the ARIMA model show a static trend, remaining at 40 and approximately 6.5. Moreover, these rates for the prediction of death rate by the ANN model increased to 0.6 and 0.1, while predictions conducted by the ARIMA model show a static trend, remaining at 0.43 and 0.07. According to the ANN model, the predicted rates of DALY and death for motorcyclists decrease to 100 and approximately 2.7, respectively. On the other hand, predictions made by the ARIMA model show a static trend, with rates remaining at 200 and approximately 3.2, respectively. CONCLUSION: The prevalence of road traffic injuries is predicted to increase, while the death and DALY rates of road traffic injuries show different patterns. Effective intervention programs and safety measures are necessary to prevent and reduce road traffic accidents. Different interventions should be designed and implemented specifically for different groups of pedestrians, cyclists, motorcyclists, and motor vehicle drivers.

Vaccination and its impact on healthcare utilization in two groups of vaccinated and unvaccinated patients with COVID-19: A cross-sectional study in Iran between 2021 and 2022.

Background and Aims: One of the main responsibilities of health systems impacted by the global Coronavirus disease 2019 (COVID-19) pandemic, where the first case was discovered in Wuhan, China, in December 2019, is the provision of medical services. The current study looked into the impact of vaccination on the utilization of services provided to COVID-19 patients. Methods: This study was conducted in Iran between 2021 and 2022, utilizing a cross-sectional research design. The research team collected data on the utilization of provided services and the number of COVID-19 vaccines administered to 1000 patients in Iran through a random sampling approach. The data were analyzed with statistical methods, including the mean difference test, and multiple linear regression. Results: Regression estimates show that after controlling for confounding variables like age, type of admission, and comorbidities, vaccination reduces the utilization of healthcare services in the general majority of services. The study's results reveal a fall in COVID-19 patients' utilization of services, specifically in patients administered two or three doses of the vaccine. However, the reduction is not statistically significant. Regression models are in contrast to univariate analysis findings that vaccination increases the mean utilization of healthcare services for COVID-19 patients in general. Comorbidities are a crucial factor in determining the utilization of diagnostic and treatment services for COVID-19 patients. Conclusion: Full COVID-19 vaccination and other implementations, including investing in public health, cooperating globally, and vaccinating high-risk groups for future pandemics, are essential as a critical response to this pandemic as they reduce healthcare service utilization to alleviate the burden on healthcare systems and allocate resources more efficiently.