### INTRODUCTION

### METHODS

### Study Setting

### Study Design

### Variables

*GI*represents the aggregated

*GI*;

*y*is the

_{i}*i*

^{th}decile of household income,

*y*

_{i+1}is the (i+1)

^{th}decile of the next household income,

*χ*is the number of households, and

_{i}*χ*

_{i+1}is the number of the next households in the (i+1)

^{th}decile [22].

*CI*is the concentration index and

*GI*is the Gini index. The CI, which is a widely applied technique in health economic studies [23], was calculated to measure the inequality in total household expenditures on healthcare from 1988 to 2014. This period was used because household expenditures on healthcare data as deciles were available starting in 1988. This index corresponds to the area between the concentration curve and the perfect equality line (45°). A CI of 0 shows an absence of inequality between the rich and the poor; a positive value indicates that the concentration curve lies below the perfect equality line, implying a pro-rich inequality; and a negative value shows inequality in favor of the poor [24]. Overall, the CI can be represented mathematically as follows [25]:

*P*represents the cumulative percentage declines in population income and

*L*household expenditures on healthcare, respectively.

### Control Variables

### Modeling the Segmented Regression Analysis

*y*represents the

_{GIt}*GI*at year t,

*t*is the

_{befor}*GI*trend before the intervention,

*cc*is the change in the intercept changes of the

_{after}*GI*after intervention,

*tc*is the trend changes after the intervention,

_{after}*GDP*represents the gross domestic product,

*InR*is the inflation rate, and et is the random variability that is not explained by the model.

*y*is the KIakwani index as an indicator of healthcare financing equity indicator at year

_{KIt}*t*;

*IMR*is the infant mortality rate, and

*PoU*60 is the ratio of the population over the age of 60 years.

### Statistical Analysis

_{0}) of this test was that the variances of the variables were same, and the decision was made at p-values less than 0.05. Finally, the Shapiro-Wilk W test was performed to assess the normality of the residuals.

### RESULTS

*F*statistic value (31.17) and adjusted coefficient of determination (R

^{2}=73.3%) in this model were higher than in the other models. There was a statistically significant (

*p*<0.001) decrease in the slope of the mean GI before the intervention (1977 to 2010). Immediately after the intervention of the TSL, the level of the mean GI dropped by -0.032, showing a statistically significant (

*p*<0.001) immediate reduction in income inequality, although there was no significant change in the slope of the mean GI during the period after the intervention (2010 to 2014,

*p*=0.08) (Table 2). Thus, the TSL implementation led to a reduction of the GI by 0.08, but the effect of the TSL on reducing income inequality was very small (Figure 1).

### Effect of the Targeted Subsidies Law on Equity in Healthcare Financing

*F*statistic of the KI model D showed it to be a well-fitted model (

*F*=31.17,

*p*< 0.001) and the index before the intervention was decreasing to a significant extent (

*p*<0.001), by 0.07 annually. Our study revealed no statistically significant change in the trend of KI after the implementation of the TSL (Figure 2). Over time, there was a negligible but statistically significant (

*p*<0.001) positive effect of the InR on the KI.