NCD drugs have a critical role in attaining universal health coverage, which ensures access to effective, highquality and affordable health services (3). According to the framework introduced by WHO, there are 4 factors that affect access to medicines: rational selection and

Background: The health insurance and family physician reform in Iran were implemented in 2005. This study was conducted to assess the effect of these reforms on avoidable hospitalizations among the rural population of Eslam-shahr County, Iran. Methods: We conducted a before-after study in Eslam-shahr County’s single existing hospital. This county is a part of the Tehran Province of Iran. The demographic characteristics and diagnostic codes of the rural population that were hospitalized during the 2 years leading to, and after the reforms were extracted from the hospital’s electronic information system. A list of 61 three-character and 131 four-character AHs codes were developed based on the literature review. We estimated a logistic regression model which included gender and age as independent variables to assess changes in the probability of avoidable hospitalizations following reform implementation. Analyses were carried out using STATA version 13. Results: We recorded 817 rural hospitalizations before and 967 hospitalizations after reform implementation, suggesting that hospitalization growth after the reforms was almost 18.4%. The logistic regression results show that the probability of avoidable hospitalizations after the interventions had decreased compared to before the interventions were put into place (OR: 0.46; 95% CI: 0.24-0.88). Also, the probability of AHs among the 60< year-old age group was considerably higher compared to other age groups. No statistical relationship was found between avoidable hospitalizations and gender. Conclusion: The reforms may have had a mixed effect on hospitalization. They may result in increased hospitalizations due to responding to the unmet needs of the population, and simultaneously they may lead to a decrease in avoidable hospitalizations and eliminate the costs imposed by them upon the health system.

The diffi culty in gas price forecasting has attracted much attention of academic researchers and business practitioners. Various methods have been tried to solve the problem of forecasting gas prices however, all of the existing models of prediction cannot meet practical needs. In this paper, a novel hybrid intelligent framework is developed by applying a systematic integration of GMDH neural networks with GA and Rule-based Exert System (RES) employs for gas price forecasting. In this paper we use a new method for extract the rules and compare different methods for gas price forecasting. Our research reveals that during the recent fi nancial crisis period by employing hybrid intelligent framework for gas price forecasting, we obtain better forecasting results compared to the GMDH neural networks and MLF neural networks and results will be so better when we employ hybrid intelligent system with for gas price volatility forecasting.

Studies show neural networks have better results in predicting of financial time series in comparison to any linear or non-linear functional form to model the price movement. Neural networks have the advantage of simulating the non-linear models when little a priori knowledge of the structure of problem domains exist or the number of immeasurable input variables are great and system has a chaotic characteristics. Among different methods, MLFF neural network with back-propagation learning algorithm and GMDH neural network with genetic learning algorithms are used to predict gas price of the Henry Hob database covering 01/01/2004-13/7/2009 period. This paper uses moving average crossover inputs and the results confirms (1) the fact there is short-term dependence in gas price movements, (2) the EMA moving average has better result and also (3) by means of the GMDH neural networks, prediction accuracy in comparison to MLFF neural networks, can be improved.

Aim This study estimated the GDP share of pharmaceuticals in Iran based on the drivers of pharmaceutical expenditure and compared it with that of 31 members of the Organisation for Economic Cooperation and Development (OECD). Subject and methods The factors contributing to pharmaceutical expenditure were identified through literature review and studied by 8 experts to classify the factors. Then, using the panel data method, a model was built to estimate the GDP share of pharmaceutical expenditure based on the extracted factors of the selected countries in Iran’s model. To explain the observed differences, several regression analyses were performed based on cross-sectional data. The analyses were performed using EVIEWS software, version 10. Results The explanatory variables for the selected countries in the panel model (R2 = 0.98) were specified. Government health expenditure (β = 0.1432), the share of generic drugs (β = − 0.0143), gross domestic product (GDP) per capita (β = − 0.0058) and the rate of disability-adjusted life-years (DALY) (β = 0.0028) contributed most to pharmaceutical expenditure. In comparison, in the Iranian estimation model (R2 = 0.84), government health expenditure (β = 0.0536) and the share of generic drugs (β = 0.0369) had a significant impact on pharmaceutical expenditure. In the estimation model with more estimators for Iran (R2 = 0.99), government health expenditure (β = 0.1694), disease prevalence (β = 0.0537), the share of generic drugs (β = 0.0102), the DALY rate (β = 0.0039), GDP per capita (β = − 0.0033), and the drug price index (β = 0.0007) contribute most to pharmaceutical expenditure. Conclusion In the models of the study, factors related to the structure of the healthcare system and the pharmaceutical system contributed most to pharmaceutical expenditure as a share of GDP. Moreover, disease profiles show its predictive role in the second model for Iran.

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Objective: Rheumatoid arthritis is a chronic disease with various clinical characteristics. The introduction of biological drugs has enhanced the efficacy and increased diversity of treatment options. Considering the patients’ preferences in decision-making about treatment can improve their adherence. A discrete choice experiment is a type of conjoint method that can elicit preferences in more realistic scenarios. This article reviewed discrete choice experiment (DCE) studies to extract which attributes and levels were included in surveys. In addition, we focused on the process of designing surveys and the method that they used. Method: PubMed, EMBASE, Web of Science, Scopus, Ovid (Medline) and ProQuest were systematically searched in order to find studies that evaluated rheumatoid arthritis patients’ preferences about biological medicines. Studies published in peer-reviewed journals between 1/1/1990 and 12/31/2019 were included. The included studies were analyzed using a narrative synthesis method and descriptive statistics. Results: A total of 7124 studies were initially found. After deleting irrelevant and duplicate studies, 15 studies were included. The most common attributes that were used in surveys were efficacy, adverse effect, route of administration, frequency of administration, and cost. Most studies used a literature review for developing attributes and levels. The median number of included attributes and levels were seven and three, respectively. Eight studies explained their experimental design while seven studies did not. Conditional logit and mixed logit were the most common methods for modeling reciprocally. Conclusion: Several aspects of DCE studies investigating biological drugs in RA were assessed. Explaining the sample size, experimental design, and qualitative work for developing attributes can improve this type of study.

Objective:In this study, we aimed to assess comparative productivity of 21 pharmaceutical companies in Iran during 2000–2013.Methods:To evaluate the productivity trend of pharmaceutical companies in Iran, we used data envelopment analysis-based Malmquist index. “Total assets” and “capital stock” as inputs and “net sales” and “net profit” as outputs extracted from Tehran stock exchange, were selected to be included in the analysis. This method provides the possibility for analyzing the performance of each company in term of productivity changes over time. We also used an estimation generalized least square panel data model to identify the factors that might affect productivity of pharmaceutical companies in Iran using EViews 7 and Deep 2.1 software.Findings:The mean total productivity during all years of the study was 0.9829, which indicates the improvement in their overall productivity. The results, over the 13-year period, indicated that the range of productivity changes in pharmaceutical companies, that were included in this study, was between 0.884 and 1.098. Panel data model indicated that age of company could positively (t = 4.765978, P < 0.001) and being located in cities other than Tehran (the capital) could negatively (t = −5.369549, P < 0.001) affect the productivity of pharmaceutical companies. The analysis showed the new policy (brand-generic scheme) and also the type of ownership did not have a significant effect on the productivity of pharmaceutical companies.Conclusion:In this study, pharmaceutical productivity trends were fluctuated that could be due to the sub-optimal attention of policy makers and managers of pharmaceutical companies toward long-term strategic planning, focusing on productivity improvement.