INTRODUCTION

Improving population health and reducing disparities across socioeconomic groups and geographic areas are central objectives of global public health. A critical step toward these objectives is assessing the overall health of populations and examining how health outcomes vary across socioeconomic strata [1,2]. Health-related quality of life (HRQoL) has become an increasingly important measure for evaluating health inequalities across socioeconomic groups. It offers a comprehensive view of health, encompassing both physical and mental well-being, as well as the impact of health on quality of life [3,4]. Socioeconomic status (SES) is a key determinant of health throughout life and strongly influences the health of communities [1,2]. Previous research has consistently shown a positive association between SES and various health outcomes, including HRQoL [5–7]. Factors such as age, sex, healthcare coverage, financial status, educational attainment, chronic illness, and behavioral characteristics, including physical activity, alcohol consumption, and tobacco use, have been shown to affect HRQoL. Therefore, identifying the variables that contribute to socioeconomic disparities in HRQoL is particularly important for Iran’s population

Socioeconomic inequalities in HRQoL and their primary determinants have been extensively examined in developed countries [3,6–8]. However, there remains a notable research gap in Iran. A 2018 Iranian study reported pro-rich inequality in HRQoL, with poorer HRQoL more concentrated among lower socioeconomic groups [9]. The present study differs from earlier Iranian research in several key ways. First, it investigated socioeconomic inequalities in HRQoL at the national level, using a larger and more geographically diverse sample drawn from multiple provinces, whereas previous studies were limited to single provinces [10,11]. Second, this study evaluated socioeconomic-related inequality in HRQoL using the EuroQoL 5-Dimension 5-Level (EQ-5D-5L) and its individual dimensions, applying multiple analytical approaches, while earlier studies considered only overall HRQoL with a single method. Most importantly, this study aimed to identify the principal factors driving observed HRQoL inequalities [11]. Given the scarcity of research on this topic, the objective of the present study was to measure socioeconomic-related inequalities in HRQoL across 9 Iranian provinces using the EQ-5D-5L, its 5 dimensions, and the EuroQol visual analogue scale (EQ-VAS), applying the concentration index (CI) approach. A decomposition analysis was then performed to determine the factors contributing to these inequalities. The results of this study can support health policymakers in designing and implementing targeted interventions to reduce health inequalities among Iranian adults.

METHODS

This cross-sectional study involved 3518 Iranian adults aged 18 and over from 9 provinces (Supplemental Material 1A). The primary aim was to examine socioeconomic-related inequalities in HRQoL and to identify the main determinants of these inequalities. Information was collected on participants’ socio-demographic characteristics, SES, behavioral risk factors, and HRQoL. Based on projections from the Iranian Statistical Center [12], Iran’s total population during the study period was estimated at 85 961 000, with the 9 participating provinces representing approximately 34 876 000 residents, or 41% of the national population. Sampling was conducted using a multistage approach. First, Iran was divided into 9 regions, and 1 province was randomly selected from each region. Within each selected province, the capital city was chosen and subdivided into 4 geographic areas: north, south, west, and east. From each area, 100 samples were collected using convenience sampling. Due to its larger population, Tehran was divided into 5 regions —north, south, west, east, and central—with 100 samples collected from each. In total, 3710 questionnaires were obtained. Of these, 44 were excluded due to incomplete reporting in 1 or more EuroQoL 5-Dimension (EQ-5D) dimensions, 19 were excluded for missing EQ-VAS scores, and 129 were excluded because the reported age was under 18 years. This yielded a final analytical sample of 3518 participants.

Data collection was performed via face-to-face interviews using a researcher-developed questionnaire along with the validated Iranian versions of the EQ-5D-5L and EQ-VAS [13]. The researcher-developed questionnaire consisted of 11 items designed to collect demographic and health-related information, including age, sex, marital status, health insurance coverage, education level, place of birth, household economic status (monthly income), presence of chronic disease(s), and lifestyle behaviors such as physical activity, smoking, and alcohol consumption.

Statistical Analysis

The primary outcome variable, HRQoL, was measured with the EQ-5D-5L and converted into cardinal values (health utilities) using the social value set validated for Iran in 2023 [13]. The EQ-5D-5L comprises 5 dimensions—mobility, self-care, usual activities, pain/discomfort, and anxiety/depression—each with 5 levels: no problems (level 1), slight problems (level 2), moderate problems (level 3), severe problems (level 4), and extreme problems (level 5). In total, the instrument can describe 3125 unique health states, from 11111 (perfect health) to 55555 (worst health) [14]. According to the Iranian value set [14], utility scores range from −1.19 (worst state, 55555) to 1.00 (full health, 11111). Consequently, the outcome variable may include negative values, which poses a challenge when estimating the CI [15]. Similar to previous studies [5], to address this issue, utility scores were converted into disutility scores (worse HRQoL), representing the reduction in valued quality of life due to a given symptom, condition, or complication, calculated as:

Disutility=1-utility

Income-related health inequalities were analyzed in terms of both gaps and gradients. First, absolute and relative (20:20) gaps were calculated. Next, inequality gradients were visualized using concentration curves (CCs). Finally, the magnitude of inequality in disutility was quantified using the CI and CC. The CI is a well-established tool in health economics for quantifying socioeconomic-related inequality in health variables such as HRQoL [6,16]. Unlike simple measures like ranges or ratios, the CI incorporates the entire population distribution and is sensitive to both the extent and distribution of inequality across SES groups, making it particularly appropriate for HRQoL.

A CC is a tool for measuring the degree of inequality associated with SES in the distribution of a health variable. It plots the cumulative percentage of the health variable (disutility) on the y-axis against the cumulative percentage of the population ranked by household income (poorest to richest) on the x-axis. If the distribution is equal, the curve coincides with the 45° line. A curve below the line indicates a positive CI, meaning disutility is more concentrated among wealthier groups, while a curve above the line indicates a negative CI, meaning disutility is more concentrated among poorer groups. The CI, defined as twice the area between the CC and the 45° line, ranges from −1 (perfectly pro-poor inequality) to +1 (perfectly pro-rich inequality), with 0 representing perfect equality [15].

The CI was calculated using the following formula:

2δr2 (disutilityiμ)=α+φri+ɛi

where μ represents the mean of the outcome variable (in this case, disutility) for the entire sample, disutility_i is the outcome variable for individual i, and r_i is the fractional rank of individual i in the SES distribution ( ri=in, where n is the total number of individuals in the sample). The term 2δr2 denotes twice the variance of the fractional rank. The estimate of ϕ, obtained through ordinary least squares estimation, corresponds to the CI and its standard error [15].

The absolute and relative gaps, CI, and CC for the EQ-VAS score and the 5 dimensions of the EQ-5D-5L (mobility, self-care, usual activities, pain/discomfort, and anxiety/depression) were calculated in the same manner as for disutility. Each of the EQ-5D-5L’s dimensions has 5 levels, ranging from “no problems” to “extreme problems.” For each dimension, responses were dichotomized: “no problems”=0, and “slight,” “moderate,” “severe,” or “extreme/unable”=1. Because binary outcomes constrain the CI range, Wagstaff’s normalization [17] was applied by multiplying the estimated CI by 11-μ, where μ represents the mean of the reported problems in each dimension.

Decomposition Approach

To determine the contribution of explanatory variables to income-related inequalities in disutility (1–utility), the CI was decomposed into contributing factors, including socio-demographic characteristics, SES, and lifestyle variables. The following linear model was estimated:

disutility=α=Σk βk xk+ɛ

The CI for disutility, can be decomposed as follows [18,19]:

CI=Σk(βkx¯kμ) Ck+GCɛμ

In this equation, CI represents the CI for disutility, χ̄_k is the mean of explanatory variable x_k ,C_k s the CI for x_k , similarly to the overall CI. The term βkx¯kμ is the elasticity of disutility with respect to the explanatory variable x_k. The summation term Σk(βkx¯kμ)Ck reflects the contribution of each explanatory factor x_k to the CI. The final term, GCɛμ, represents the residual component and captures the income-related inequality in disutility that cannot be explained by the variation in the included explanatory variables among individuals with different levels of SES.

All analyses were performed using Microsoft Excel (Microsoft, Redmond, WA, USA) and Stata version 17.0 (StataCorp., College Station, TX, USA) and the significance level was set at p-value<0.05.

Ethics Statement

The research protocol was approved by the Research Deputy of Kermanshah University of Medical Sciences, with the approval No. IR.KUMS.REC.1403.497. The study adhered to the ethical principles outlined in the Declaration of Helsinki. Before data collection, the researchers provided a verbal explanation of the study’s purpose to each potential participant, and informed consent was obtained from all subjects. Participants were also informed of their right to withdraw from the study at any time without facing any consequences.

RESULTS

A total of 3518 individuals aged 18 years and older from 9 provinces in Iran were included. The mean±standard deviation (SD) age of participants was 38.19±14.65 years, with a slightly higher proportion of females (51.3%). Most participants (58.4%) were married, and nearly half had attained a bachelor’s degree as their highest level of education. The majority (82.0%) were born in urban areas, and 89.8% reported having basic health insurance coverage. Additionally, 25.0% reported at least 1 chronic condition. Regarding monthly household income, 20.6% earned between 10 million Iranian rial (IRR) and 15 million IRR, and 27.8% earned between 15 million IRR and 20 million IRR. Self-rated health status indicated that most participants considered their health good (46.1%) or fair (29.0%). More than half (58.2%) engaged in sufficient physical activity. Smoking status revealed that 17.0% were current smokers and 8.8% were past smokers. The descriptive characteristics of the study population are presented in Table 1.

Supplemental Material 1B illustrates the average disutility values by household income quintile, revealing a clear social gradient: lower HRQoL scores were concentrated among individuals in lower socioeconomic groups. Absolute and relative differences were calculated for the total sample and for the poorest versus richest quintiles across multiple HRQoL indicators (Table 2). Mean±SD values for disutility and EQ-VAS score were 0.21±0.26 and 74.34±18.67, respectively. The percentages of respondents reporting at least 1 problem in the EQ-5D-5L dimensions were: mobility (17.79%), self-care (5.77%), usual activities (12.11%), pain/discomfort (45.28%), and anxiety/depression (49.20%). Significant disparities in HRQoL indicators were observed between the poorest and richest quintiles. Mean disutility in the poorest quintile (0.43) was nearly 3 times that of the richest (0.15), yielding an inequality ratio of 2.89 and a gap of 0.28. The EQ-VAS score was substantially lower in the poorest group (58.03 vs. 77.63), corresponding to an inequality ratio of 0.75 and a gap of -19.60. Across the 5 EQ-5D-5L dimensions, individuals in the poorest quintile consistently reported more problems for pain/discomfort (62.13 vs. 31.55%, inequality ratio 1.97, gap 30.58%), mobility (36.89 vs. 10.69%), self-care (12.62 vs. 4.81%), usual activities (25.24 vs. 8.02%), and anxiety/depression (66.01 vs. 42.24%).

The CIs for multiple HRQoL indicators are presented in Table 3. The CI for disutility was −0.116 (p<0.001), indicating a significant concentration of worse HRQoL among disadvantaged socioeconomic groups. Conversely, the EQ-VAS score showed a pro-rich inequality, with a CI of 0.027 (p<0.001), meaning higher HRQoL scores were more common among higher-income individuals. For the EQ-5D-5L dimensions, most CIs were negative, indicating pro-poor inequality in problem reporting; individuals from lower SES backgrounds were more likely to report problems in all dimensions. Among dimensions, pain/discomfort had the largest CI magnitude, while self-care had the smallest.

Figure 1 illustrates the CCs for disutility, EQ-VAS score, and the 5 dimensions of EQ-5D-5L. The curve for EQ-VAS lies below the 45° line, confirming pro-rich inequality, while curves for disutility and all 5 dimensions lie above the line, reflecting pro-poor inequality, meaning that problems were disproportionately concentrated among lower-income individuals.

Supplemental Material 2 presents the decomposition analysis of socioeconomic inequality in worse HRQoL (disutility) in Iran. The table includes estimated regression coefficients for explanatory variables, elasticities, CIs (C_k) for each explanatory variable, and the contribution of each variable to the overall CI. The elasticity column reflects the responsiveness of worse HRQoL to changes in each explanatory variable. A positive elasticity indicates that disutility increases as the variable increases, whereas a negative elasticity indicates a reduction in disutility. Positive elasticities were observed for factors such as older age, female sex, marital status (married), urban place of birth, presence of a chronic condition, alcohol consumption, and smoking (current and past). In contrast, higher educational attainment, higher income, health insurance coverage, and sufficient physical activity were associated with negative elasticities.

Across age groups, coefficients generally increased with age, indicating that older individuals tended to have higher disutility. For instance, the coefficient for those aged 65 and above was 0.141, indicating a significant positive association with worse HRQoL. The elasticity for this group was 0.0403, suggesting moderate responsiveness of HRQoL disutility to changes in the size or characteristics of this group. For education, coefficients were negative and became more pronounced at higher levels, consistent with the protective effect of education on HRQoL. For example, the coefficient for individuals with a bachelor’s degree was -0.120, and for those with master’s or Ph.D. degrees, −0.110. Corresponding elasticities were also negative and relatively large (e.g., −0.2735 for bachelor’s degree), underscoring the substantial reduction in disutility associated with higher education.

The sign of C_k indicates the socioeconomic distribution of each variable: a negative value means the predictor is more concentrated among poorer respondents, while a positive value indicates concentration among wealthier respondents. As shown in Supplemental Material 2, higher education levels, health insurance coverage, alcohol consumption, sufficient physical activity, and urban place of birth were more prevalent among wealthier individuals. In contrast, female sex, younger or older age (relative to middle age), having at least 1 chronic condition, and smoking were more concentrated among poorer participants. Figure 2 visually depicts the absolute contribution of each explanatory variable to socioeconomic inequality in HRQoL in Iran. Together with Supplemental Material 2, it highlights that income (38.00%), education (31.53%), age (8.05%), and physical activity (7.30%) were the most influential drivers of inequality in disutility. If the contribution of variable X is x and positive (or negative), equalizing the distribution of that variable across socioeconomic groups would reduce (or increase) inequality in worse HRQoL by x%. For example, equalizing income across participants would reduce inequality in disutility by 38.00%, while equalizing physical activity would reduce it by 7.30%. The explanatory variables in the model accounted for 90.34% of the observed socioeconomic inequality in disutility, leaving approximately 9.66% unexplained. This residual suggests that additional unmeasured factors may also contribute to HRQoL disparities in Iran.

DISCUSSION

SES is a well-established predictor of lifetime health, with poorer individuals experiencing shorter lifespans and lower HRQoL. Reducing these disparities remains a central goal of global public health. However, evidence on socioeconomic inequalities in HRQoL remains limited in developing countries such as Iran. To address this gap, this study employed a large national sample to measure and decompose income-related inequality in disutility (worse HRQoL) in Iran for the 2024–2025 period.

The results revealed a CI of −0.116 for disutility and 0.027 for the EQ-VAS score. These findings indicate that worse HRQoL was more concentrated among lower socioeconomic groups, whereas better HRQoL (measured by EQ-VAS) was more prevalent among higher socioeconomic groups. An analysis of reported problems across the 5 HRQoL dimensions—mobility, self-care, usual activities, pain/discomfort, and anxiety/depression—showed that all dimensions exhibited disproportionate concentrations of problems among disadvantaged groups. The negative CI for disutility underscores the heavier burden of health problems among the poor, while the positive CI for EQ-VAS reflects a concentration of better self-perceived health among the more affluent. These results are consistent with evidence from other contexts. For example, a study in Chile reported a CI of −0.063, indicating worse HRQoL concentrated among disadvantaged socioeconomic groups [5]. Similarly, research in western Iran identified a pro-rich distribution of HRQoL among adults [9]. Additionally, the CI for HRQoL based on the EQ-5D was reported as 0.0103 in China [19] and 0.035 in Korea [20], highlighting comparable trends of inequality across different nations.

Decomposition analysis identified household income as the largest contributor to the concentration of worse HRQoL among poorer groups, explaining 38% of the observed inequality. This suggests that equalizing income across the population could reduce inequality in worse HRQoL by 38%. Income plays a pivotal role in accessing health-promoting resources, including nutritious food, quality healthcare, safe housing, and opportunities for education and recreation. Comparable findings have been reported elsewhere: in China, income accounted for 15.3% of HRQoL inequality [19], and in Australia, SES explained more than 80% of inequality in HRQoL [6]. Another study found SES to be the primary driver of health inequality, explaining 45.50% of the EQ-5D index inequality and 41.39% of EQ-VAS inequality [21]. In addition to income, education, age, and physical activity also contributed significantly to HRQoL inequality in Iran. Differences in educational attainment emerged as a particularly influential factor, accounting for 31.53% of observed inequality. Education is a critical determinant of health, equipping individuals with the knowledge and skills to adopt healthier behaviors, access healthcare, and engage in preventive activities. Higher educational attainment is also linked to increased income, greater health expectations, and a higher likelihood of engaging in physical activity, all of which contribute to widening disparities. Similar associations between education and HRQoL inequality have been reported in multiple studies [9,19,22].

From a policy perspective, these findings have important implications for promoting health equity in Iran. The Ministry of Health and Medical Education has incorporated social determinants of health into primary healthcare through national guidelines, provincial networks, and intersectoral collaborations [23,24]. The results of this study reinforce the importance of these efforts and suggest that targeted policies to reduce income inequality and expand access to education could substantially decrease HRQoL disparities. Measures such as social protection programs, targeted subsidies, and job creation could strengthen income security, while expanding access to quality education would enable individuals to adopt healthier lifestyles and improve long-term health outcomes. Furthermore, increasing opportunities for physical activity and enhancing chronic disease management—especially in disadvantaged areas—should be integral to community health strategies. These priorities align with Iran’s Health System Vision for 2025, which emphasizes health equity and the integration of health considerations into all policy sectors [25]. Using HRQoL inequality data in national and regional health planning can help policymakers allocate resources more effectively and design interventions that address the root causes of disparities, thereby advancing universal health coverage and sustainable health equity.

This study has several limitations that should be considered when interpreting the findings. First, the sample included only participants aged 18 years and older, limiting the generalizability of the results to younger populations. Adolescents and children may have different health-related behaviors and outcomes; thus, our findings may not be directly applicable to these age groups. Second, convenience sampling was employed at the sub-provincial level. Although a multistage sampling design was used to enhance geographic and demographic diversity, the reliance on convenience sampling may have introduced selection bias and reduced the representativeness of the sample. Third, income and other socioeconomic variables were self-reported, which may have led to misclassification or reporting bias. It is well established that survey respondents often underreport their income, which can distort socioeconomic classification and affect the accuracy of inequality estimates. Finally, the cross-sectional design limits the ability to establish causal or temporal relationships between variables. Reverse causality is possible—for example, lower HRQoL could lead to reduced income rather than income solely influencing HRQoL—because exposure and outcome were measured simultaneously, leaving the directionality of associations uncertain.

In conclusion, this study identified a pro-poor distribution of worse HRQoL and reported problems across all 5 EQ-5D dimensions in Iran. Decomposition analysis revealed that economic position accounted for a substantial portion of the observed inequality (38.00%), with additional contributions from education level (31.53%), age (8.05%), and physical activity (7.30%). These findings improve understanding of HRQoL inequalities in Iran and provide valuable guidance for strategies aimed at reducing these disparities, which is essential for improving overall population health. By identifying key determinants of HRQoL inequality, policymakers can design more targeted and effective interventions. Furthermore, these results offer a robust measure of health inequality for monitoring progress toward social justice in health policy and serve as a benchmark for international comparisons, particularly with other middle-income and low-income countries facing similar challenges.

Supplemental Materials

Notes

Conflict of Interest

The author has no conflicts of interest associated with the material presented in this paper.

Funding

This research was funded by Kermanshah University of Medical Sciences (grant no. 4030833).

Acknowledgements

The authors would like to express their heartfelt gratitude to all those who contributed to the completion of this research, particularly Dr. Edris Kakemam from Qazvin University of Medical Sciences, Dr. Mohammad Bazyar from Ilam University of Medical Sciences, Dr. Eshagh Barfar from Zahedan University of Medical Sciences, Dr. Seyed Fahim Irandoost from Urmia University of Medical Sciences, Dr. Javad Moghri from Mashhad University of Medical Sciences, Dr. Hedayat Salari from Bushehr University of Medical Sciences, Dr. Mohammad Ranjbar from Shahid Sadoughi University of Medical Sciences, and Ms. Nasim Badiee, a Ph.D. student in Health Policy at Tehran University of Medical Sciences. Additionally, I extend my sincere appreciation to all the participants who took part in this study and completed the questionnaires.

The funding body had no role in the study’s design, data collection, analysis, interpretation, or manuscript preparation.

Author Contributions

All work was done by SR.

Figure 1

Concentration curves for disutility, the EuroQol visual analogue scale (EQ-VAS) score, and the 5 dimensions of the EuroQoL 5-Dimension 5-Level (EQ-5D-5L) in Iran. (A) Mobility, (B) self-care, (C) usual activities, (D) pain/discomfort, (E) anxiety/depression, (F) disutility, and (G) EQ-VAS score.

Figure 2

Absolute contribution of each factor to socioeconomic inequality in disutility in Iran, 2024–2025. The total of all absolute contributions equals the concentration index for disutility.

REFERENCES

• 1. Marmot M. Social determinants of health inequalities. Lancet 2005;365(9464):1099-1104. https://doi.org/10.1016/S0140-6736(05)71146-6ArticlePubMed
• 2. Marmot M. The health gap: the challenge of an unequal world. Lancet 2015;12. 386(10011):2442-2444. https://doi.org/10.1016/S0140-6736(15)00150-6ArticlePubMed
• 3. Ellina P, Middleton N, Lambrinou E, Kouta C. Investigation of socioeconomic inequalities in health-related quality of life across Europe: a systematic review. Divers Equal Health Care 2019;16(4):80-90. https://doi.org/10.36648/2049-5471.16.3.197Article
• 4. Karimi M, Brazier J. Health, health-related quality of life, and quality of life: what is the difference? Pharmacoeconomics 2016;34(7):645-649. https://doi.org/10.1007/s40273-016-0389-9ArticlePubMed
• 5. Severino R, Espinoza M, Cabieses B. Health-related quality of life by household income in Chile: a concentration index decomposition analysis. Int J Equity Health 2022;21(1):176. https://doi.org/10.1186/s12939-022-01770-wArticlePubMedPMC
• 6. Haque R, Alam K, Gow J, Neville C, Keramat SA. Socio-economic inequalities in health-related quality of life and the contribution of cognitive impairment in Australia: a decomposition analysis. Soc Sci Med 2024;361: 117399. https://doi.org/10.1016/j.socscimed.2024.117399ArticlePubMed
• 7. Djärv T, Wikman A, Johar A, Lagergren P. Poor health-related quality of life in the Swedish general population: the association with disease and lifestyle factors. Scand J Public Health 2013;41(7):744-753. https://doi.org/10.1177/1403494813491031ArticlePubMed
• 8. Rajmil L, Herdman M, Ravens-Sieberer U, Erhart M, Alonso J. European KIDSCREEN group. Socioeconomic inequalities in mental health and health-related quality of life (HRQOL) in children and adolescents from 11 European countries. Int J Public Health 2014;59(1):95-105. https://doi.org/10.1007/s00038-013-0479-9ArticlePubMed
• 9. Rezaei S, Hajizadeh M, Salimi Y, Moradi G, Nouri B. What explains socioeconomic inequality in health-related quality of life in Iran? A Blinder-Oaxaca decomposition. J Prev Med Public Health 2018;51(5):219-226. https://doi.org/10.3961/jpmph.18.012ArticlePubMedPMC
• 10. Rezaei S, Hajizadeh M, Khosravipour M, Khosravi F, Rezaeian S. Socioeconomic inequalities in poor health-related quality of life in Kermanshah, western Iran: a decomposition analysis. J Res Health Sci 2018;18(1):e00405PubMed
• 11. Maleki A, Faghihzadeh E, Youseflu S, Barjasteh SZ. Socio-economic inequalities in health-related quality of life among Iranian young people in the middle stage of adolescence: application of Health Equity Assessment Toolkit. BMC Pediatr 2023;23(1):16. https://doi.org/10.1186/s12887-022-03815-zArticlePubMedPMC
• 12. Statistical Center of Iran. Detailed results of the national population and housing census; 2016 [cited 2025 Mar 9]. Available from: https://amar.org.ir/statistical-information (Persian)
• 13. Afshari S, Daroudi R, Goudarzi R, Mahboub-Ahari A, Yaseri M, Sari AA, et al. A national survey of Iranian general population to estimate a value set for the EQ-5D-5L. Qual Life Res 2023;32(7):2079-2087. https://doi.org/10.1007/s11136-023-03378-1ArticlePubMed
• 14. Devlin N, Finch AP, Parkin D. Chapter 5. Guidance to users of EQ-5D-5L value sets. In: Devlin N, Roudijk B, Ludwig K, editors. Value sets for EQ-5D-5L: a compendium, comparative review & user guide. Cham: Springer; 2022. https://doi.org/10.1007/978-3-030-89289-0ArticlePubMed
• 15. O’Donnell O, Van Doorslaer E, Wagstaff A, Lindelow M. Analyzing health equity using household survey data: a guide to techniques and their implementation; 2008 [cited 2025 Mar 9]. Available from: https://cphs.huph.edu.vn/uploads/tainguyen/sachvabaocao/2008_AnalyzingHealthEquityUsingHouseholdSurveyData_234.pdf
• 16. O’Donnell O, O’Neill S, Van Ourti T, Walsh B. Conindex: estimation of concentration indices. Stata J 2016;16(1):112-138. https://doi.org/10.1177/1536867X1601600112ArticlePubMedPMC
• 17. Wagstaff A. The concentration index of a binary outcome revisited. Health Econ 2011;20(10):1155-1160. https://doi.org/10.1002/hec.1752ArticlePubMed
• 18. Wagstaff A, Van Doorslaer E, Watanabe N. On decomposing the causes of health sector inequalities with an application to malnutrition inequalities in Vietnam. J Econom 2003;112(1):207-223. https://doi.org/10.1016/S0304-4076(02)00161-6Article
• 19. Yao Q, Zhang X, Wu Y, Liu C. Decomposing income-related inequality in health-related quality of life in mainland China: a national cross-sectional study. BMJ Glob Health 2023;8(11):e013350. https://doi.org/10.1136/bmjgh-2023-013350ArticlePubMedPMC
• 20. Chung K, Lee JH. A decomposition of income-related inequality in EQ-5D: a South Korea study. Int J Public Policy 2012;8(1–3):53-68. https://doi.org/10.1504/IJPP.2012.045872Article
• 21. JieAnNaMu , Xu X, You H, Gu H, Gu J, Li X, et al. Inequalities in health-related quality of life and the contribution from socioeconomic status: evidence from Tibet, China. BMC Public Health 2020;20(1):630. https://doi.org/10.1186/s12889-020-08790-7ArticlePubMedPMC
• 22. Tang C, Wu X, Chen X, Pan B, Yang X. Examining income-related inequality in health literacy and health-information seeking among urban population in China. BMC Public Health 2019;19(1):221. https://doi.org/10.1186/s12889-019-6538-2ArticlePubMedPMC
• 23. Bahadori M, Sanaeinasab H, Ghanei M, Mehrabi Tavana A, Ravangard R, Karamali M. The social determinants of health (SDH) in Iran: a systematic review article. Iran J Public Health 2015;44(6):728-741PubMedPMC
• 24. World Health Organization. Iran advances health equity by addressing the social determinants of health through primary health care; [cited 2025 Mar 20]. Available from: https://www.emro.who.int/iran/news/iran-advances-health-equity-by-addressing-the-social-determinants-of-health-through-primary-health-care.html
• 25. Rostamigooran N, Esmailzadeh H, Rajabi F, Majdzadeh R, Larijani B, Dastgerdi MV. Health system vision of Iran in 2025. Iran J Public Health 2013;42(Supple 1):18-22PubMedPMC

Figure & Data

References

Citations

Citations to this article as recorded by