ABSTRACT
Introduction: Major part of health expenditure is paid directly by households in most of developing countries. This catastrophic expenditure is entailed on families. Assessing important determinants of Out Of Pocket Payment [OOP] is one of the key issues in health care finance
Methods: This study uses Iranian household income expenditure in 2005-2010 annually provided by statistical center of Iran using a two part model for assessing these factors. This was a cross sectional analytical study in which sampling was based on a multi-stage method. The sample size changed from 26882 to 38260 households
Result: The results showed that out of Pocket payments up to 74% increased during this period. High income households paid 2.5 times more than low income groups while probability of utilization for them was 17% more. Confidence of insurance indicates insurance systems succeeded in reducing only 15% of household insure expenditure whereas the probability of using health services increased only by 2%. Social demographic variables such as the number of the elderly, household size and urbanization increased OOP by 20%, 23% and 54%, respectively
Conclusion: During the study period, there was substantial inequality among different income groups. As if better off households had a higher chance of paying and using health care services. Moreover, the elderly, household dimension, urbanization and not having insurance coverage are the most important factors responsible for OOP. To reduce inequality, policy makers must provide the conditions to increase the role of social insurance in health finance and to make the services less expensive for the elderly
ABSTRACT
The relationship between population health and income inequality has attracted much attention during the last two decades. But the question on which there is no consensus is that whether fluctuations in economic inequality would lead to changes in population health indices. In response to this question, life expectancy and mortality rate as health indicators and Gini coefficient of inequality were used. A cross sectional data was used to survey the relationship between income inequality and population health over a period of 12 years [1995-2007] in 125 countries. There was no significant relationship between inequality and population health when panel data method was used and the heterogeneity and unobservable differences among countries in the form of fixed effects were considered. An income increase would enhance life expectancy and decrease mortality rate. Contrary to the findings of many studies using time series data, it appears that there is no significant relationship between inequality and health indicators