OBJECTIVES: One of the main objectives of the Targeted Subsidies Law (TSL) in Iran was to improve equity in healthcare financing. This study aimed at measuring the effects of the TSL, which was implemented in Iran in 2010, on equity in healthcare financing. METHODS: Segmented regression analysis was applied to assess the effects of TSL implementation on the Gini and Kakwani indices of outcome variables in Iranian households. Data for the years 1977-2014 were retrieved from formal databases. Changes in the levels and trends of the outcome variables before and after TSL implementation were assessed using Stata version 13. RESULTS: In the 33 years before the implementation of the TSL, the Gini index decreased from 0.401 to 0.381. The Gini index and its intercept significantly decreased to 0.362 (p < 0.001) 5 years after the implementation of the TSL. There was no statistically significant change in the gross domestic product or inflation rate after TSL implementation. The Kakwani index significantly increased from -0.020 to 0.007 (p < 0.001) before the implementation of the TSL, while we observed no statistically significant change (p=0.81) in the Kakwani index after TSL implementation. CONCLUSIONS: The TSL reform, which was introduced as part of an economic development plan in Iran in 2010, led to a significant reduction in households’ income inequality. However, the TSL did not significantly affect equity in healthcare financing. Hence, while measuring the long-term impact of TSL is paramount, healthcare decision-makers need to consider the efficacy of the TSL in order to develop plans for achieving the desired equity in healthcare financing.
Delivery of Health Care* ; Economic Development ; Family Characteristics ; Gross Domestic Product ; Healthcare Financing* ; Inflation, Economic ; Iran ; Jurisprudence ; Regression Analysis* ; Social Justice ; Socioeconomic Factors

Objectives: Protecting people against financial hardship caused by illness stands as a fundamental obligation within healthcare systems and constitutes a pivotal component in achieving universal health coverage. The objective of this study was to analyze the prevalence and determinants of catastrophic health expenditures (CHE) in Iran, over the period of 2013 to 2019. Methods: Data were obtained from 7 annual national surveys conducted between 2013 and 2019 on the income and expenditures of Iranian households. The prevalence of CHE was determined using a threshold of 40% of household capacity to pay for healthcare. A binary logistic regression model was used to identify the determinants influencing CHE. Results: The prevalence of CHE increased from 3.60% in 2013 to 3.95% in 2019. In all the years analyzed, the extent of CHE occurrence among rural populations exceeded that of urban populations. Living in an urban area, having a higher wealth index, possessing health insurance coverage, and having employed family members, an employed household head, and a literate household head are all associated with a reduced likelihood of CHE (p<0.05). Conversely, the use of dental, outpatient, and inpatient care, and the presence of elderly members in the household, are associated with an increased probability of facing CHE (p<0.05). Conclusions Throughout the study period, CHE consistently exceeded the 1% threshold designated in the national development plan. Continuous monitoring of CHE and its determinants at both household and health system levels is essential for the implementation of effective strategies aimed at enhancing financial protection.

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.