### INTRODUCTION

^{2}, respectively. That study found an increasing trend in the prevalence of obesity in Iran [15]. The results of an assessment of the prevalence of obesity and its determinants in females residing in Kermanshah showed that the prevalence of overweight or obesity was 39.4 and 21.9%, respectively. That study found lower economic status and illiteracy to be significantly associated with obesity [16]. Recently, a national study based on the Iranian surveillance system for the risk factors of non-communicable diseases reported a prevalence of obesity of 17.5% in Kermanshah in 2005 [17]. Obesity is a complex and multifactorial problem that is affected by factors at both the macro-level (e.g., economic and nutritional transitions) and the individual level (e.g., genetic, psychosocial, lifestyle, and socioeconomic factors) [18]. It should be noted that socioeconomic status (SES) has potential effects on individuals’ lifestyle, eating behaviors, and caloric intake [19,20]. Some ‘unhealthy’ lifestyles and health conditions such as obesity and overweight, particularly in developing countries, tend to be more widespread in specific socioeconomic groups [19-21].

### METHODS

### Study Setting

*khaneye behdasht*) in rural areas. The RaNCD cohort comprised 10 086 individuals aged 35-65 years old.

### Dependent and Independent Variables

^{2}. Anthropometric parameters were measured using automated bioelectric impedance with integrated automatic stadiometer (InBody 770, BSM350; InBody, Seoul, Korea). The explanatory variables used in the analyses were sex, age, marital status, education, number of members in the household, place of residence, wealth index as an indicator of economic status, and smoking behavior as a lifestyle factor.

#### Socioeconomic status measure

### Statistical Analysis

*y*-axis) against the cumulative percentage of the population, ranked by SES from poorest to richest (

*x*-axis). A line of 45° shows perfect equality. If the health outcome variable is concentrated in lower socioeconomic groups, the CC lies above the 45° line (line of perfect equality) and vice versa. The farther the CC is under or above the line of equality, the higher the inequality in the health variable of interest. The CI is directly related to the CC, which quantifies the degree of socioeconomic-related inequality in health outcomes and is defined as twice the space between the CC and the line of perfect equality. This parameter indicates whether the health outcome is concentrated more among people of lower or higher socioeconomic groups. The value of the CI ranges from -1 to +1, and a negative value indicates that the health outcome is more concentrated in groups with lower SES, and vice versa for a positive value. If the CI equals zero, the health outcome is equally distributed among populations [29]. The CI is defined as follows:

*μ*is the mean or the proportion of the health variable and

*y*and

_{i}*r*represent the variable of interest and fractional rank in the socioeconomic distribution for the

_{i}*i*individual, respectively. Additionally, the individuals were ranked according to their SES, from the richest to the poorest [28,30]. The bounds of the CI for a binary variable are not +1 and -1, and instead depend on the mean (

_{th}*μ*) of the variable [28]. To this end, different correction methods were proposed by Wagstaff [28] and Erreygers [31] to address this issue. Hence, according to the results of previous studies [32-34], the method proposed by Wagstaff was employed to normalize the CI. This solution helps to correctly quantify the degree of inequality within the range of -1 to +1. According to the Wagstaff [28] approach, the

*CI*is normalized as follows:

*μ*is the mean of the health variable and

*CI*represents the conventional

*CI*.

*y*), such as:

*CI*is the overall concentration index,

*μ*indicates the mean of

*y*(health outcome variable),

*x*(determinants),

_{k}*Ck*is the concentration index for

*x*, and

_{k}*GC*denotes the generalized concentration index for

_{ε}*ε*. It should be noted that

*CI*is equal to the weighted sum of the

*CIs*of the

*k*determinants, where the weight of

*x*is the elasticity of

_{k}*y*with respect to

*x*.

_{k}*CI*has 2 components: the explained component (

*CI*(

*y*), and the unexplained (residual) component (

*GC*/

_{ε}*ε*), which is the socioeconomic inequality not explained by systematic variation in the determinants across socioeconomic groups.

*β*based on the logic model were estimated, and these marginal effects were then used to compute the contributions of the explanatory variables [30]. Below, the linear approximation of the non-linear estimations is given by equation (5).

_{k}*x*. In summary, the contribution of the determinants is calculated as follows: first, the regression model of the health outcome variable is performed for all

*x*to obtain the marginal effects of determinants (

_{k}*β*). In the second step, the mean of the health outcome (

_{k}*μ*) and each of the determinants (

*x*) and the elasticity of the health variable are calculated with respect to each

_{k}*x*(

*x*). In the third step, the

_{k}*CIs*are calculated for the health outcome and each explanatory variable. In the fourth step, the contribution of each

*x*to the

*CI*is calculated by multiplying the elasticity of each determinant by its

### RESULTS

*p*=0.002).