Medical Journal of The Islamic Republic of Iran (MJIRI)

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Background: Based on the target income hypothesis, the economic behavior of physicians is mainly affected by their target income. This study aimed at designing an instrument to explain how general practitioners (GPs) set their desired income.
   Methods: A self-administered questionnaire of affecting factors on GPs' target income was extracted from literature reviews and a small qualitative study. Respondents were 666 GPs who completed the questionnaire (response rate= 52%) during 2 seasonal congresses of Iranian GPs. The principal component analysis (PCA) with varimax rotation was used to classify the variables and data reduction. Sample adequacy test, sphericity test, eigenvalues of components, and scree plot were evaluated for PCA. Cronbach's alpha was also checked to assess the internal consistency of the principal components.
   Results: The results of the KMO measure of sampling adequacy (0.657) and Bartlett’s test of sphericity (809.05, p<0.001) revealed that the collected data were suitable for PCA.
Based on the scree plot pattern and eigenvalues greater than 1, 6 components including perceived comparative income, importance of responsiveness to patients, perceived socioeconomic status, economic expectations, socioeconomic status of paternal family, and provision of luxury services were selected, which explained 65.19% of the total variance. Finally, only those with a Cronbach's alpha value higher than 0.6 were considered reliable (the first 4 components). 
   Conclusion: Based on the target income hypothesis, a physician’s desired level of income affects their behavior. Our developed instrument and its mentioned components can be used in future studies related to GPs' behavior, especially those studies related to the economic aspects of GPs’ behavior. It also helps formulate a better payment mechanism for primary care providers. 
 

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Background: Literature abounds with various techniques for efficiency measurement of health care organizations (HCOs), which should be used cautiously and appropriately. The present study aimed at discovering the rules regulating the interplay among the number of inputs, outputs, and decision- making units (DMUs) and identifying all methods used for the measurement of Iranian HCOs and critically appraising all DEA studies on Iranian HCOs in their application of such rules.
   Methods: The present study employed a systematic search of all studies related to efficiency measurement of Iranian HCOs. A search was conducted in different databases such as PubMed and Scopus between 2001 and 2015 to identify the studies related to the measurement in health care. The retrieved studies passed through a multi-stage (title, abstract, body) filtering process. Data extraction table for each study was completed and included method, number of inputs and outputs, DMUs, and their efficiency score.
   Results: Various methods were found for efficiency measurement. Overall, 122 studies were retrieved, of which 73 had exclusively employed DEA technique for measuring the efficiency of HCOs in Iran, and 23 with hybrid models (including DEA). Only 6 studies had explicitly used the rules of thumb.
   Conclusion: The number of inputs, outputs, and DMUs should be cautiously selected in DEA like techniques, as their proportionality can directly affect the discriminatory power of the technique. The given literature seemed to be, to a large extent, unsuccessful in attending to such proportionality. This study collected a list of key rules (of thumb) on the interplay of inputs, outputs, and DMUs, which could be considered by most researchers keen to apply DEA technique.
 
 

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Background: Nonmarket stated preferences valuation, especially discrete choice experiments (DCEs), is one of the commonly used techniques in the health sector. The primary purpose of this approach is to help select attributes and attributes-levels that are able to properly describe health care products or services. This study aimed at developing attributes and attributes-levels for basic health insurance system in Iran.
   Methods: This study was conducted in 3 phases. First, narrative review was performed to identify related attributes. Also, 9 experts were interviewed to identify relevant attributes of health insurance in context. Other 36 experts rated the attributes and levels. Then, the research team decided on the inclusion of attributes and levels in the final design. The design was constructed using generic and D-efficient method with SAS 9.1. The design was divided into 3 blocks, each having 8 choice sets. Finally, the choice set was piloted with 45 participants.
   Results: Public hospitals, and private hospitals benefits, dental insurance coverage, inpatient benefits, rehabilitation therapy, and paraclinical benefits, long-term care,  medical devices benefits (Ortez, Protez, etc.), and monthly premium were identified and included in the final attribute design (D-efficiency = 98.16). The pilot study revealed that participants could easily understand and answer all the choice sets.
   Conclusion: The results of our study indicated that health insurance service benefit packages and premium were among the most important attributes that need to be included in the final attribute design for Iranians. The policymakers and health insurance organizations should emphasize these attributes in the benefit packages to make improvements. The emphasis on these attributes can help elicit people’s preferences and willingness to pay for attributes.
 

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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. 
 

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Background: Considering the health economics aspect, the effect of population aging on the growth of medical expenditures is of great importance. Thus, the aim of this study was to investigate the growth rate of inpatient hospital expenditures (IHE) of older age groups compared to younger age groups or testing of steepening hypothesis.
   Methods: In this descriptive-analytic study, monthly IHE data of insured patients from April 2011 to March 2014 was collected from Iran Health Insurance Organization database. The sample consisted of 297,145 patients who were selected using stratified random sampling among the patients insured by different health insurance funds. Using the Age-cut method, patients were classified into two main age groups: older than 60 and under 60 years old. The average IHE of the two groups and the growth rate of their expenditures were estimated. At the 95% significance level, the difference in average growth rate of IHE was tested between the two age groups using t-statistic in Stata (version 13.0) software.
   Results: The findings of this study revealed that per capita IHE for the older and younger groups was $469 and $399, respectively. The monthly average growth rate of IHE for the older (60-90 yrs.) and younger groups (30-59 yrs.) was 2.43% and 2.38%, respectively. The difference between the monthly average growth rate of the older and younger groups was not statistically significant (t = - 0.0332; P value = 0.9736).
   Conclusion: Our study results rejected the steepening hypothesis. The monthly average growth rates of IHE in both older and younger age groups have risen to a similar extent over the period of 36 months. Further research should focus on the inpatient hospital expenditures of younger age groups to explain the causes of their increased expenditures.