The Paradoxical Impact of Socioeconomic Status on Chronic Kidney Disease in Northwestern Iran: Exploring the Interaction ofEthnicity and Social Class

The Paradoxical Impact of Socioeconomic Status on Chronic Kidney Disease in Northwestern Iran: Exploring the Interaction ofEthnicity and Social Class | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Paradoxical Impact of Socioeconomic Status on Chronic Kidney Disease in Northwestern Iran: Exploring the Interaction ofEthnicity and Social Class Vahid Masoudi Mamakani, Mohammad Heidari, Rasul Entezarmahdi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8677897/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background Chronic kidney disease (CKD) is a major global public health concern, influenced not only by biological but also by social determinants of health. Socioeconomic status (SES) is a key determinant of CKD, yet its interaction with ethnicity remains underexplored. This study aimed to assess the association between SES and CKD prevalence and to examine ethnic differences in this relationship in a multiethnic population in northwestern Iran. Methods A cross-sectional study was conducted using population base data from the integrated CKD screening within the IraPEN program in Naqadeh County, West Azerbaijan Province, Iran (2018–2019). A total of 7,771 adults aged ≥30 years were analyzed. SES was derived from household asset indicators using polychoric principal component analysis and categorized into tertiles (low, middle, high). Logistic regression models were fitted to estimate adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for CKD, testing interactions between SES and ethnicity. Results The prevalence of CKD was 10.2% (95% CI: 9.5–10.9). Higher SES was associated with a lower likelihood of CKD (middle SES: OR=0.77, 95% CI: 0.62–0.95; high SES: OR=0.59, 95% CI: 0.44–0.79). Women had more than twice the odds of CKD compared to men (OR=2.10, 95% CI: 1.68–2.63). A significant interaction between SES and ethnicity was identified: CKD prevalence decreased with higher SES among Turks but not among Kurds. Model discrimination was excellent (AUC=0.83). Conclusions SES is strongly associated with CKD, but this relationship varies by ethnicity. Context-sensitive, equity-focused interventions are essential to address kidney health disparities in multiethnic settings. Chronic kidney disease Socioeconomic status Ethnicity Health inequity Social determinants of health Iran Interaction effect Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Chronic kidney disease (CKD) is one of the most significant public health challenges of the twenty-first century, being associated with increasing prevalence, considerable economic and social costs, and adverse clinical outcomes (1, 2). Across global data, CKD is recognised as a principal contributor to mortality and disability worldwide, and its upward trend—especially in low- and middle-income countries—has raised serious concerns among health-policy makers (3, 4). The global prevalence of CKD has risen markedly in recent decades. Studies suggest that approximately 10% to 15% of the adult population worldwide are affected by some stage of CKD, and this figure has been reported to exceed the global average in some countries, including Iran (5, 6). In Iran, the estimated prevalence of CKD across various studies ranges from 11% to 27%, with women being more likely than men to be affected (6, 7). Several factors—including advancing age, high prevalence of diabetes, hypertension, obesity, and unhealthy lifestyles—contribute to this trend(8). One less-explored dimension of CKD is the pivotal role of social-economic determinants of health (SDH). Growing evidence indicates that lower socioeconomic status (SES)—including income, education and occupation—is directly associated with an increased risk of CKD incidence, faster disease progression, and worse outcomes among CKD patients(9, 10). Individuals with lower SES often have reduced access to health services, preventive resources and education, placing them at higher risk for developing and advancing CKD (11). International and national studies emphasis that socioeconomic inequalities not only influence the prevalence and severity of CKD, but also the therapeutic outcomes, quality of life and mortality rates among patients(10, 12). For example, in both developed and developing countries, individuals residing in deprived areas or having low income experience higher rates of CKD and more severe outcomes (13). The World Health Organization defines social determinants of health as the economic, social and environmental conditions in which people are born, grow, live, work and age (14). These factors include income, education, occupation, health-insurance status, housing, food security and social support, all of which can impact the risk and outcomes of CKD(15). Evidence suggests that socioeconomic deprivation, chronic stress, limited access to healthcare, and lack of social support may accelerate CKD onset and progression via behavioral and biological pathways (16). Moreover, psychosocial factors such as social isolation, lack of family support and psychological distress may further augment the disease burden and reduce quality of life in CKD patients(17). This is particularly important among older adults and vulnerable population groups(18). Understanding the role of socioeconomic and social-determinant factors in CKD incidence and progression can provide a basis for targeted interventions and effective policy-making aimed at reducing health inequalities and improving patient outcomes (19). Health-policy makers should identify high-risk groups and adopt preventive and therapeutic strategies tailored to socioeconomic circumstances to help reduce disease burden and promote equity in health(20). Accordingly, recent studies propose that improving health literacy, increasing access to healthcare, supporting vulnerable groups and enhancing socioeconomic conditions can have a substantial impact on lowering CKD prevalence and its outcomes(21). CKD is more than merely a medical issue—it reflects broader social and economic inequalities within communities. Attention to socioeconomic dimensions and social determinants of health is not only essential for effective prevention and management of CKD, but may also lead to enhanced health equity and improved population health(15). Therefore, research and policy-making in this field must adopt a multidimensional, evidence-based approach to identify and address the socioeconomic factors influencing CKD(22). The county of Naghedeh has a population characterised by a mix of ethnic, religious and cultural groups, making it a representative setting for the north-western and western regions of the country. Moreover, these differences have resulted in varied socioeconomic conditions within the population, which to date have not been thoroughly investigated. On this basis, this study was conducted with the aim of examining, for the first time in the country, the association between CKD and socioeconomic status and ethnicity. Methods Study design and population This cross-sectional study utilized population base data from the integrated CKD screening component of the national IraPEN program, conducted under the supervision of Urmia University of Medical Sciences. The study covered adults aged 30 years and older residing in Naqadeh County, West Azerbaijan Province, during 2018–2019. Ethical approval was obtained from the institutional ethics committee IR.UMSU.REC.1403.285. Data sources and variables Data were extracted from the CKD screening database merged with IraPEN health records. The dependent variable was CKD status, determined according to estimated glomerular filtration rate (eGFR) criteria. Independent variables included demographic, clinical, and socioeconomic indicators such as age, sex, body mass index (BMI), fasting blood sugar (FBS), cholesterol level, blood pressure, educational attainment, marital status, place of residence (urban/rural), smoking, and ethnicity (Turk or Kurd). Socioeconomic status measurement SES was assessed using an asset-based index following World Health Organization (WHO) recommendations for LMICs. Household ownership of durable goods (e.g., private car, air conditioner, washing machine, personal computer) and housing characteristics (e.g., dwelling area) were analyzed using polychoric principal component analysis (PCA), which accounts for binary, ordinal, and continuous variables. The resulting SES index was categorized into three tertiles: low, middle, and high. Statistical analysis Descriptive analyses were performed using SPSS version 26. Continuous variables were summarized as means ± standard deviations (SD), and categorical variables as frequencies and percentages. Differences between CKD and non-CKD groups were evaluated using t-tests and chi-square tests, as appropriate. Multivariable logistic regression models were fitted in Stata version 18 to estimate adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for the association between SES and CKD, controlling for age, sex, hypertension, diabetes, and other covariates. Interaction terms were introduced to assess the modifying effects of ethnicity × SES and sex × SES. Model performance was evaluated using the receiver operating characteristic (ROC) curve, and area under the curve (AUC) values above 0.80 were considered indicative of strong discriminative ability. Results Descriptive results A total of 7,771 adults aged 30 years or older were screened and included in the analysis. Of these, 791 participants were identified as having chronic kidney disease (CKD), corresponding to a crude prevalence of 10.18% (95% CI: 9.52–10.88%). The analytic sample used for multivariable models included 5,463 observations after excluding records with missing covariate data where applicable. Mean (± SD) estimated glomerular filtration rate (eGFR) and other key continuous variables differed significantly between CKD and non-CKD groups. Participants with CKD were older (62.72 ± 11.24 vs. 48.40 ± 10.61 years; p < 0.001), had higher fasting blood sugar (FBS) (117.75 ± 45.94 vs. 103.70 ± 30.52 mg/dL; p < 0.001), higher total cholesterol (199.93 ± 42.19 vs. 191.38 ± 40.98 mg/dL; p < 0.001), and higher systolic and diastolic blood pressure (128.40 ± 20.65 vs. 120.66 ± 18.53 mmHg; p < 0.001 and 79.72 ± 12.10 vs. 76.96 ± 11.65 mmHg; p < 0.001, respectively). Body mass index (BMI) was marginally higher among CKD cases (28.81 ± 4.63 vs. 28.44 ± 4.59; p = 0.038) Categorical variables also showed marked differences (Table 1 ). A larger proportion of CKD cases belonged to the lowest SES tertile (43.1% of CKD cases vs. 29.5% of non-CKD; p < 0.001). Women comprised 69.7% of CKD cases compared with 57.0% of non-CKD participants (p < 0.001). By ethnicity, Turk participants represented a higher share of CKD cases (73.8% of CKD vs. 60.2% of non-CKD; p < 0.001) compared with Kurdish participants. Additional descriptive findings included lower current smoking prevalence among CKD cases (4.8% vs. 7.8%; p = 0.003), higher prevalence of self-reported hypertension (60.4% vs. 23.4%; p < 0.001) and diabetes (23.2% vs. 7.4%; p < 0.001) among CKD patients, and greater frequency of widowed marital status in the CKD group (22.4% vs. 5.0%; p < 0.001). Urban residency was more common among CKD cases (74.5% vs. 69.7%; p = 0.006). Table 1 Baseline demographic and clinical characteristics of participants by CKD status Variable No CKD (N = 6,980) CKD (N = 791) P-Value Age, Mean (Sd) 48.40 (10.61) 62.72 (11.24) < 0.001 BMI, Mean (Sd) 28.44 (4.59) 28.81 (4.63) 0.038 FBS, Mean (Sd) 103.70 (30.52) 117.75 (45.94) < 0.001 Cholesterol, Mean (Sd) 191.38 (40.98) 199.93 (42.19) < 0.001 Systolic Bp, Mean (Sd) 120.66 (18.53) 128.40 (20.65) < 0.001 Diastolic Bp, Mean (Sd) 76.96 (11.65) 79.72 (12.10) < 0.001 SES - Poor, % 29.5% 43.1% < 0.001 SES - Moderate, % 49.0% 37.9% SES - Rich, % 21.5% 19.0% Sex - Female, % 57.0% 69.7% < 0.001 Ethnicity - Turk, % 60.2% 73.8% < 0.001 Ethnicity - Kurd, % 39.8% 26.2% Current Smoker, % 7.8% 4.8% 0.003 Diabetes History, % 7.4% 23.2% < 0.001 Hypertension History, % 23.4% 60.4% < 0.001 Residence - Urban, % 69.7% 74.5% 0.006 Marital Status - Widowed, % 5.0% 22.4% < 0.001 Multivariable analysis In multivariable logistic regression models (N = 5,463), SES remained independently associated with CKD after adjusting for age, sex, ethnicity, residence, education, marital status, history of diabetes and hypertension, smoking, BMI, FBS, and cholesterol. Compared with the poorest tertile, participants in the middle SES group had 23% lower odds of CKD (adjusted OR = 0.77; 95% CI: 0.62–0.95; p = 0.014), and those in the richest tertile had 41% lower odds (adjusted OR = 0.59; 95% CI: 0.44–0.79; p < 0.001). Age was a strong predictor: each additional year of age was associated with a 12% increase in the odds of CKD (adjusted OR = 1.12; 95% CI: 1.108–1.131; p < 0.001). Female sex was associated with substantially higher odds of CKD compared with male (adjusted OR = 2.10; 95% CI: 1.68–2.63; p < 0.001) (Table 2 ). Kurdish ethnicity showed a non-significant trend towards lower odds compared with Turk ethnicity (adjusted OR = 0.83; 95% CI: 0.67–1.03; p = 0.096). Overall model fit was acceptable with a pseudo R-squared of 0.230. The final model demonstrated excellent discrimination (AUC = 0.8348). Table 2 Multivariable logistic regression for factors associated with CKD Variable Reference Adjusted Or Std. Error P-Value 95% Ci Ses - Poor Base Ses - Moderate Poor 0.77 0.082 0.014 0.623–0.947 Ses - Rich Poor 0.59 0.089 < 0.001 0.438–0.794 Age (Per Year) 1.12 0.006 < 0.001 1.108–1.131 Sex - Female Male 2.10 0.241 < 0.001 1.677–2.631 Ethnicity - Kurd Turk 0.83 0.091 0.096 0.672–1.033 Residence - Urban Rural 1.11 0.127 0.364 0.887–1.388 Education - High School Elementary 1.30 0.066 0.983–1.714 Education - University Elementary 1.60 0.056 0.988–2.579 Interaction analyses Multiplicative interaction terms for SES×ethnicity and SES×sex were tested. A statistically significant interaction between SES and ethnicity was observed: among Turk participants, CKD prevalence decreased markedly across SES strata (approximately 14% in the poorest group to ~ 8% in the richest), whereas among Kurdish participants the SES gradient was absent and prevalence remained stable or slightly increased with higher SES. This indicates that the protective effect of higher SES on CKD risk is modified by ethnic background (Fig. 1 ). An interaction between sex and SES was also identified. While CKD prevalence declined with increasing SES among men, prevalence among women remained relatively constant across SES groups, suggesting that socioeconomic gains confer smaller reductions in CKD risk for women than for men in this population (Fig. 2 ). The Fig. 3 illustrates the adjusted prevalence of chronic kidney disease (CKD) across different age groups, stratified by socioeconomic status (SES). The association between age and CKD prevalence appears positive across all SES categories, indicating that CKD prevalence increases markedly with advancing age. Individuals in the low SES (poor) and moderate SES groups show a consistently higher adjusted prevalence of CKD compared to those in the high SES (rich) group, particularly after age 60. The divergence between SES groups becomes more evident in older ages, suggesting that socioeconomic disparities in CKD risk widen with age. The error bars represent 95% confidence intervals, indicating increasing uncertainty in prevalence estimates among the oldest age groups due to smaller sample sizes. Overall, the Fig. 3 demonstrates a significant interaction effect between age and socioeconomic status on CKD prevalence, where older adults with lower SES exhibit a disproportionately higher burden of CKD The ROC curve (Fig. 4 ) analysis demonstrated strong model performance, with an area under the curve (AUC) of 0.8348, indicating excellent discrimination between CKD and non-CKD participants. Discussion The present study clearly demonstrated that socioeconomic status (SES) is one of the strongest determinants of chronic kidney disease (CKD) risk in the studied population. Individuals with lower SES levels were significantly more likely to develop CKD, and this association persisted even after adjusting for potential confounders such as age, sex, and blood pressure. These results are consistent with findings from meta-analyses and international studies conducted in low- and middle-income countries, reaffirming the pivotal role of social inequalities in the development of chronic diseases(23–26). A particularly novel finding of this study was the significant interaction between ethnicity and SES. Among the Turkish population, higher SES was associated with a marked reduction in CKD prevalence, whereas this declining pattern was not observed among Kurdish participants. In fact, among higher SES classes, the relationship appeared either stable or even reversed. This strong interaction challenges the simplistic assumption of “the poorer, the sicker,” and instead directs attention to the structural roots of inequality. In epidemiological literature, this phenomenon is referred to as the “legacy of inequality” or “residual social effects(27, 28).” In a multiethnic context such as Naqadeh County, this disparity can stem from a combination of structural and cultural factors. Structurally, differences in access to healthcare, levels of trust in the health system, language barriers, and variations in healthcare-seeking behaviors may explain why Kurdish individuals, even in higher SES groups, remain at elevated risk. Culturally, ethnic-specific lifestyles, dietary habits, and behavioral patterns may further modulate the pathway through which SES influences kidney health. For instance, in the Turkish population, higher SES may translate into healthier nutrition, increased physical activity, and better healthcare utilization, while among Kurdish participants, it might lead to lifestyle changes such as decreased activity or increased consumption of calorie-dense foods. From a social epidemiological perspective, these findings highlight the concept of “inequality within inequality”—that is, the impact of SES on health varies across ethnic groups, representing a more complex form of health injustice. Under such circumstances, a uniform “one-size-fits-all” intervention is likely to be ineffective or even inequitable. Therefore, public health policies should be designed with sensitivity to ethnic and cultural differences to ensure maximum effectiveness(28). In addition to the SES–ethnicity interaction, the study revealed that CKD prevalence was significantly higher among women than men, and this gender gap persisted across all social strata. Interestingly, while CKD prevalence decreased with higher SES in men, this trend was not observed among women. This divergence may reflect biological differences (e.g., hormonal influences) or social factors (e.g., household roles, psychological stress, or healthcare utilization patterns). The persistence of these disparities underscores the moderating role of gender in the SES–health relationship(29, 30). Furthermore, advancing age was found to be a significant determinant of CKD risk. The positive association between aging and CKD is well-documented and can be attributed to physiological changes such as reduced glomerular filtration rate, accumulation of renal damage, and increased comorbidities with age. The sharp rise in CKD prevalence after the sixth decade of life emphasizes the need for age-targeted interventions. Therefore, systematic screening of kidney function among elderly populations—particularly those with low SES and limited education—should be considered a priority in national prevention policies. Lifestyle-oriented interventions, including hypertension control, balanced diet, and regular physical activity, can play an essential role in preventing disease progression(21, 31, 32) From a public health perspective, this study conveys several key implications. First, socioeconomic inequality in kidney health is a structural, not merely individual, problem—requiring intersectoral policies that improve living conditions, reduce poverty, and strengthen healthcare infrastructure in underserved areas. Second, interventions must be ethnicity-specific and culturally grounded, as strategies effective in one group (e.g., Turkish) may not yield the same outcomes in another (e.g., Kurdish). Finally, prioritizing health equity through tailored screening and prevention programs can help reduce ethnic and socioeconomic disparities in kidney health outcomes across multiethnic populations. Conclusion The present study revealed that lower socioeconomic status (SES) is a significant and independent determinant of an increased risk of chronic kidney disease (CKD). This association remained significant after adjusting for age, sex, and other potential confounders. Furthermore, a significant interaction between ethnicity and SES was observed; specifically, the protective effect of higher SES in reducing the risk of CKD was evident among the Turk population, whereas this pattern was not present among the Kurdish population. These findings indicate that the impact of socioeconomic factors on kidney health is shaped by ethnic and cultural contexts. Accordingly, health policies and preventive interventions should adopt a multidimensional, ethnicity-sensitive, and socially contextualized approach to effectively reduce health inequalities and promote equity in kidney health. Abbreviations CKD Chronic Kidney Disease SDH Social Determinant Health Declarations Ethics approval and consent to participate The Ethics Committee of the Urmia University of Medical Sciences in Urmia,Iran approved the protocol of this study (Code number: IR.UMSU.REC.1403.285). Written informed consent is taken from each participant. In this study, all methods were carried out in accordance with the Declaration of Helsinki. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Author Contribution VMM and MH contributed to the conception and design of the study.VMM drafted the first version of the manuscript. VMM, MH and RE revised themanuscript. MH and RE critically reviewed the manuscript for important intellectualcontent. All authors approved the final version. 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Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 23 Mar, 2026 Reviewers invited by journal 17 Mar, 2026 Editor invited by journal 06 Feb, 2026 Editor assigned by journal 05 Feb, 2026 Submission checks completed at journal 05 Feb, 2026 First submitted to journal 23 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8677897","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":607882154,"identity":"7286cfcf-f6b8-4eea-8e18-c4c9c46d1c3d","order_by":0,"name":"Vahid Masoudi Mamakani","email":"","orcid":"","institution":"Urmia University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Vahid","middleName":"Masoudi","lastName":"Mamakani","suffix":""},{"id":607882156,"identity":"7c25773a-0d43-4983-9865-1fc2cee3a288","order_by":1,"name":"Mohammad Heidari","email":"","orcid":"","institution":"Urmia University of Medical 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10:09:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8677897/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8677897/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104971341,"identity":"fda3a91e-c912-4785-968d-54cd4cc8499d","added_by":"auto","created_at":"2026-03-19 11:08:01","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":89398,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction between socioeconomic status and ethnicity in relation to CKD prevalence\u003c/p\u003e","description":"","filename":"roc.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8677897/v1/6b29cc371022214b984d5f17.jpg"},{"id":104971338,"identity":"46c67b8b-2c68-44c4-9e07-1e7b1dedf0fb","added_by":"auto","created_at":"2026-03-19 11:07:59","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":102788,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction between socioeconomic status and sex in relation to CKD prevalence\u003c/p\u003e","description":"","filename":"sesage.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8677897/v1/1295beaa255f71b38b1001a9.jpg"},{"id":105035266,"identity":"a4327053-9bdc-4a0d-8cd2-2cd1911aa8e3","added_by":"auto","created_at":"2026-03-20 07:25:46","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":85571,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction between socioeconomic status and age in relation to CKD prevalence\u003c/p\u003e","description":"","filename":"SESETHNIC.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8677897/v1/06e30897dcdb58fd0ea565b6.jpg"},{"id":104971340,"identity":"4ed637d1-4be8-417f-ac35-12c5168a62da","added_by":"auto","created_at":"2026-03-19 11:07:59","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":80188,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve for CKD prediction model (AUC = 0.8348)\u003c/p\u003e","description":"","filename":"SESSEX.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8677897/v1/ed5a163801f2ccd2894356e7.jpg"},{"id":105036730,"identity":"c8209494-c716-46d1-9367-993cb92f92d0","added_by":"auto","created_at":"2026-03-20 07:35:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":918242,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8677897/v1/b59aec17-6766-48b4-97ef-fb2ce09206a8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Paradoxical Impact of Socioeconomic Status on Chronic Kidney Disease in Northwestern Iran: Exploring the Interaction ofEthnicity and Social Class","fulltext":[{"header":"Background","content":"\u003cp\u003eChronic kidney disease (CKD) is one of the most significant public health challenges of the twenty-first century, being associated with increasing prevalence, considerable economic and social costs, and adverse clinical outcomes (1, 2). Across global data, CKD is recognised as a principal contributor to mortality and disability worldwide, and its upward trend\u0026mdash;especially in low- and middle-income countries\u0026mdash;has raised serious concerns among health-policy makers (3, 4).\u003c/p\u003e \u003cp\u003eThe global prevalence of CKD has risen markedly in recent decades. Studies suggest that approximately 10% to 15% of the adult population worldwide are affected by some stage of CKD, and this figure has been reported to exceed the global average in some countries, including Iran (5, 6). In Iran, the estimated prevalence of CKD across various studies ranges from 11% to 27%, with women being more likely than men to be affected (6, 7). Several factors\u0026mdash;including advancing age, high prevalence of diabetes, hypertension, obesity, and unhealthy lifestyles\u0026mdash;contribute to this trend(8).\u003c/p\u003e \u003cp\u003eOne less-explored dimension of CKD is the pivotal role of social-economic determinants of health (SDH). Growing evidence indicates that lower socioeconomic status (SES)\u0026mdash;including income, education and occupation\u0026mdash;is directly associated with an increased risk of CKD incidence, faster disease progression, and worse outcomes among CKD patients(9, 10). Individuals with lower SES often have reduced access to health services, preventive resources and education, placing them at higher risk for developing and advancing CKD (11). International and national studies emphasis that socioeconomic inequalities not only influence the prevalence and severity of CKD, but also the therapeutic outcomes, quality of life and mortality rates among patients(10, 12).\u003c/p\u003e \u003cp\u003eFor example, in both developed and developing countries, individuals residing in deprived areas or having low income experience higher rates of CKD and more severe outcomes (13). The World Health Organization defines social determinants of health as the economic, social and environmental conditions in which people are born, grow, live, work and age (14). These factors include income, education, occupation, health-insurance status, housing, food security and social support, all of which can impact the risk and outcomes of CKD(15). Evidence suggests that socioeconomic deprivation, chronic stress, limited access to healthcare, and lack of social support may accelerate CKD onset and progression via behavioral and biological pathways (16). Moreover, psychosocial factors such as social isolation, lack of family support and psychological distress may further augment the disease burden and reduce quality of life in CKD patients(17). This is particularly important among older adults and vulnerable population groups(18). Understanding the role of socioeconomic and social-determinant factors in CKD incidence and progression can provide a basis for targeted interventions and effective policy-making aimed at reducing health inequalities and improving patient outcomes (19). Health-policy makers should identify high-risk groups and adopt preventive and therapeutic strategies tailored to socioeconomic circumstances to help reduce disease burden and promote equity in health(20). Accordingly, recent studies propose that improving health literacy, increasing access to healthcare, supporting vulnerable groups and enhancing socioeconomic conditions can have a substantial impact on lowering CKD prevalence and its outcomes(21).\u003c/p\u003e \u003cp\u003eCKD is more than merely a medical issue\u0026mdash;it reflects broader social and economic inequalities within communities. Attention to socioeconomic dimensions and social determinants of health is not only essential for effective prevention and management of CKD, but may also lead to enhanced health equity and improved population health(15). Therefore, research and policy-making in this field must adopt a multidimensional, evidence-based approach to identify and address the socioeconomic factors influencing CKD(22).\u003c/p\u003e \u003cp\u003eThe county of Naghedeh has a population characterised by a mix of ethnic, religious and cultural groups, making it a representative setting for the north-western and western regions of the country. Moreover, these differences have resulted in varied socioeconomic conditions within the population, which to date have not been thoroughly investigated. On this basis, this study was conducted with the aim of examining, for the first time in the country, the association between CKD and socioeconomic status and ethnicity.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy design and population\u003c/p\u003e \u003cp\u003eThis cross-sectional study utilized population base data from the integrated CKD screening component of the national IraPEN program, conducted under the supervision of Urmia University of Medical Sciences. The study covered adults aged 30 years and older residing in Naqadeh County, West Azerbaijan Province, during 2018\u0026ndash;2019. Ethical approval was obtained from the institutional ethics committee IR.UMSU.REC.1403.285.\u003c/p\u003e \u003cp\u003eData sources and variables\u003c/p\u003e \u003cp\u003eData were extracted from the CKD screening database merged with IraPEN health records. The dependent variable was CKD status, determined according to estimated glomerular filtration rate (eGFR) criteria. Independent variables included demographic, clinical, and socioeconomic indicators such as age, sex, body mass index (BMI), fasting blood sugar (FBS), cholesterol level, blood pressure, educational attainment, marital status, place of residence (urban/rural), smoking, and ethnicity (Turk or Kurd).\u003c/p\u003e \u003cp\u003eSocioeconomic status measurement\u003c/p\u003e \u003cp\u003eSES was assessed using an asset-based index following World Health Organization (WHO) recommendations for LMICs. Household ownership of durable goods (e.g., private car, air conditioner, washing machine, personal computer) and housing characteristics (e.g., dwelling area) were analyzed using polychoric principal component analysis (PCA), which accounts for binary, ordinal, and continuous variables. The resulting SES index was categorized into three tertiles: low, middle, and high.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eDescriptive analyses were performed using SPSS version 26. Continuous variables were summarized as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations (SD), and categorical variables as frequencies and percentages. Differences between CKD and non-CKD groups were evaluated using t-tests and chi-square tests, as appropriate. Multivariable logistic regression models were fitted in Stata version 18 to estimate adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for the association between SES and CKD, controlling for age, sex, hypertension, diabetes, and other covariates. Interaction terms were introduced to assess the modifying effects of ethnicity \u0026times; SES and sex \u0026times; SES. Model performance was evaluated using the receiver operating characteristic (ROC) curve, and area under the curve (AUC) values above 0.80 were considered indicative of strong discriminative ability.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive results\u003c/h2\u003e \u003cp\u003eA total of 7,771 adults aged 30 years or older were screened and included in the analysis. Of these, 791 participants were identified as having chronic kidney disease (CKD), corresponding to a crude prevalence of 10.18% (95% CI: 9.52\u0026ndash;10.88%). The analytic sample used for multivariable models included 5,463 observations after excluding records with missing covariate data where applicable.\u003c/p\u003e \u003cp\u003eMean (\u0026plusmn;\u0026thinsp;SD) estimated glomerular filtration rate (eGFR) and other key continuous variables differed significantly between CKD and non-CKD groups. Participants with CKD were older (62.72\u0026thinsp;\u0026plusmn;\u0026thinsp;11.24 vs. 48.40\u0026thinsp;\u0026plusmn;\u0026thinsp;10.61 years; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), had higher fasting blood sugar (FBS) (117.75\u0026thinsp;\u0026plusmn;\u0026thinsp;45.94 vs. 103.70\u0026thinsp;\u0026plusmn;\u0026thinsp;30.52 mg/dL; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), higher total cholesterol (199.93\u0026thinsp;\u0026plusmn;\u0026thinsp;42.19 vs. 191.38\u0026thinsp;\u0026plusmn;\u0026thinsp;40.98 mg/dL; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and higher systolic and diastolic blood pressure (128.40\u0026thinsp;\u0026plusmn;\u0026thinsp;20.65 vs. 120.66\u0026thinsp;\u0026plusmn;\u0026thinsp;18.53 mmHg; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and 79.72\u0026thinsp;\u0026plusmn;\u0026thinsp;12.10 vs. 76.96\u0026thinsp;\u0026plusmn;\u0026thinsp;11.65 mmHg; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, respectively). Body mass index (BMI) was marginally higher among CKD cases (28.81\u0026thinsp;\u0026plusmn;\u0026thinsp;4.63 vs. 28.44\u0026thinsp;\u0026plusmn;\u0026thinsp;4.59; p\u0026thinsp;=\u0026thinsp;0.038) Categorical variables also showed marked differences (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A larger proportion of CKD cases belonged to the lowest SES tertile (43.1% of CKD cases vs. 29.5% of non-CKD; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Women comprised 69.7% of CKD cases compared with 57.0% of non-CKD participants (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). By ethnicity, Turk participants represented a higher share of CKD cases (73.8% of CKD vs. 60.2% of non-CKD; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared with Kurdish participants. Additional descriptive findings included lower current smoking prevalence among CKD cases (4.8% vs. 7.8%; p\u0026thinsp;=\u0026thinsp;0.003), higher prevalence of self-reported hypertension (60.4% vs. 23.4%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and diabetes (23.2% vs. 7.4%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) among CKD patients, and greater frequency of widowed marital status in the CKD group (22.4% vs. 5.0%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Urban residency was more common among CKD cases (74.5% vs. 69.7%; p\u0026thinsp;=\u0026thinsp;0.006).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline demographic and clinical characteristics of participants by CKD status\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo CKD (N\u0026thinsp;=\u0026thinsp;6,980)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCKD (N\u0026thinsp;=\u0026thinsp;791)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, Mean (Sd)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48.40 (10.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62.72 (11.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, Mean (Sd)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.44 (4.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.81 (4.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFBS, Mean (Sd)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e103.70 (30.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e117.75 (45.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol, Mean (Sd)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e191.38 (40.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e199.93 (42.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic Bp, Mean (Sd)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e120.66 (18.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e128.40 (20.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic Bp, Mean (Sd)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e76.96 (11.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79.72 (12.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSES - Poor, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSES - Moderate, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSES - Rich, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex - Female, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity - Turk, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e60.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity - Kurd, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent Smoker, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes History, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension History, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence - Urban, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e74.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital Status - Widowed, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMultivariable analysis\u003c/h3\u003e\n\u003cp\u003eIn multivariable logistic regression models (N\u0026thinsp;=\u0026thinsp;5,463), SES remained independently associated with CKD after adjusting for age, sex, ethnicity, residence, education, marital status, history of diabetes and hypertension, smoking, BMI, FBS, and cholesterol. Compared with the poorest tertile, participants in the middle SES group had 23% lower odds of CKD (adjusted OR\u0026thinsp;=\u0026thinsp;0.77; 95% CI: 0.62\u0026ndash;0.95; p\u0026thinsp;=\u0026thinsp;0.014), and those in the richest tertile had 41% lower odds (adjusted OR\u0026thinsp;=\u0026thinsp;0.59; 95% CI: 0.44\u0026ndash;0.79; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eAge was a strong predictor: each additional year of age was associated with a 12% increase in the odds of CKD (adjusted OR\u0026thinsp;=\u0026thinsp;1.12; 95% CI: 1.108\u0026ndash;1.131; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Female sex was associated with substantially higher odds of CKD compared with male (adjusted OR\u0026thinsp;=\u0026thinsp;2.10; 95% CI: 1.68\u0026ndash;2.63; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Kurdish ethnicity showed a non-significant trend towards lower odds compared with Turk ethnicity (adjusted OR\u0026thinsp;=\u0026thinsp;0.83; 95% CI: 0.67\u0026ndash;1.03; p\u0026thinsp;=\u0026thinsp;0.096).\u003c/p\u003e \u003cp\u003eOverall model fit was acceptable with a pseudo R-squared of 0.230. The final model demonstrated excellent discrimination (AUC\u0026thinsp;=\u0026thinsp;0.8348).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable logistic regression for factors associated with CKD\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdjusted Or\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% Ci\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSes - Poor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSes - Moderate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.623\u0026ndash;0.947\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSes - Rich\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.438\u0026ndash;0.794\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (Per Year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.108\u0026ndash;1.131\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex - Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.677\u0026ndash;2.631\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity - Kurd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTurk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.672\u0026ndash;1.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence - Urban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.887\u0026ndash;1.388\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation - High School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElementary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.983\u0026ndash;1.714\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation - University\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElementary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.988\u0026ndash;2.579\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eInteraction analyses\u003c/h3\u003e\n\u003cp\u003eMultiplicative interaction terms for SES\u0026times;ethnicity and SES\u0026times;sex were tested. A statistically significant interaction between SES and ethnicity was observed: among Turk participants, CKD prevalence decreased markedly across SES strata (approximately 14% in the poorest group to ~\u0026thinsp;8% in the richest), whereas among Kurdish participants the SES gradient was absent and prevalence remained stable or slightly increased with higher SES. This indicates that the protective effect of higher SES on CKD risk is modified by ethnic background (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAn interaction between sex and SES was also identified. While CKD prevalence declined with increasing SES among men, prevalence among women remained relatively constant across SES groups, suggesting that socioeconomic gains confer smaller reductions in CKD risk for women than for men in this population (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the adjusted prevalence of chronic kidney disease (CKD) across different age groups, stratified by socioeconomic status (SES). The association between age and CKD prevalence appears positive across all SES categories, indicating that CKD prevalence increases markedly with advancing age.\u003c/p\u003e \u003cp\u003eIndividuals in the low SES (poor) and moderate SES groups show a consistently higher adjusted prevalence of CKD compared to those in the high SES (rich) group, particularly after age 60. The divergence between SES groups becomes more evident in older ages, suggesting that socioeconomic disparities in CKD risk widen with age.\u003c/p\u003e \u003cp\u003eThe error bars represent 95% confidence intervals, indicating increasing uncertainty in prevalence estimates among the oldest age groups due to smaller sample sizes.\u003c/p\u003e \u003cp\u003eOverall, the Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e demonstrates a significant interaction effect between age and socioeconomic status on CKD prevalence, where older adults with lower SES exhibit a disproportionately higher burden of CKD\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe ROC curve (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) analysis demonstrated strong model performance, with an area under the curve (AUC) of 0.8348, indicating excellent discrimination between CKD and non-CKD participants.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study clearly demonstrated that socioeconomic status (SES) is one of the strongest determinants of chronic kidney disease (CKD) risk in the studied population. Individuals with lower SES levels were significantly more likely to develop CKD, and this association persisted even after adjusting for potential confounders such as age, sex, and blood pressure. These results are consistent with findings from meta-analyses and international studies conducted in low- and middle-income countries, reaffirming the pivotal role of social inequalities in the development of chronic diseases(23\u0026ndash;26).\u003c/p\u003e \u003cp\u003eA particularly novel finding of this study was the significant interaction between ethnicity and SES. Among the Turkish population, higher SES was associated with a marked reduction in CKD prevalence, whereas this declining pattern was not observed among Kurdish participants. In fact, among higher SES classes, the relationship appeared either stable or even reversed. This strong interaction challenges the simplistic assumption of \u0026ldquo;the poorer, the sicker,\u0026rdquo; and instead directs attention to the structural roots of inequality. In epidemiological literature, this phenomenon is referred to as the \u0026ldquo;legacy of inequality\u0026rdquo; or \u0026ldquo;residual social effects(27, 28).\u0026rdquo;\u003c/p\u003e \u003cp\u003eIn a multiethnic context such as Naqadeh County, this disparity can stem from a combination of structural and cultural factors. Structurally, differences in access to healthcare, levels of trust in the health system, language barriers, and variations in healthcare-seeking behaviors may explain why Kurdish individuals, even in higher SES groups, remain at elevated risk. Culturally, ethnic-specific lifestyles, dietary habits, and behavioral patterns may further modulate the pathway through which SES influences kidney health. For instance, in the Turkish population, higher SES may translate into healthier nutrition, increased physical activity, and better healthcare utilization, while among Kurdish participants, it might lead to lifestyle changes such as decreased activity or increased consumption of calorie-dense foods.\u003c/p\u003e \u003cp\u003eFrom a social epidemiological perspective, these findings highlight the concept of \u0026ldquo;inequality within inequality\u0026rdquo;\u0026mdash;that is, the impact of SES on health varies across ethnic groups, representing a more complex form of health injustice. Under such circumstances, a uniform \u0026ldquo;one-size-fits-all\u0026rdquo; intervention is likely to be ineffective or even inequitable. Therefore, public health policies should be designed with sensitivity to ethnic and cultural differences to ensure maximum effectiveness(28).\u003c/p\u003e \u003cp\u003eIn addition to the SES\u0026ndash;ethnicity interaction, the study revealed that CKD prevalence was significantly higher among women than men, and this gender gap persisted across all social strata. Interestingly, while CKD prevalence decreased with higher SES in men, this trend was not observed among women. This divergence may reflect biological differences (e.g., hormonal influences) or social factors (e.g., household roles, psychological stress, or healthcare utilization patterns). The persistence of these disparities underscores the moderating role of gender in the SES\u0026ndash;health relationship(29, 30).\u003c/p\u003e \u003cp\u003eFurthermore, advancing age was found to be a significant determinant of CKD risk. The positive association between aging and CKD is well-documented and can be attributed to physiological changes such as reduced glomerular filtration rate, accumulation of renal damage, and increased comorbidities with age. The sharp rise in CKD prevalence after the sixth decade of life emphasizes the need for age-targeted interventions. Therefore, systematic screening of kidney function among elderly populations\u0026mdash;particularly those with low SES and limited education\u0026mdash;should be considered a priority in national prevention policies. Lifestyle-oriented interventions, including hypertension control, balanced diet, and regular physical activity, can play an essential role in preventing disease progression(21, 31, 32)\u003c/p\u003e \u003cp\u003eFrom a public health perspective, this study conveys several key implications. First, socioeconomic inequality in kidney health is a structural, not merely individual, problem\u0026mdash;requiring intersectoral policies that improve living conditions, reduce poverty, and strengthen healthcare infrastructure in underserved areas. Second, interventions must be ethnicity-specific and culturally grounded, as strategies effective in one group (e.g., Turkish) may not yield the same outcomes in another (e.g., Kurdish). Finally, prioritizing health equity through tailored screening and prevention programs can help reduce ethnic and socioeconomic disparities in kidney health outcomes across multiethnic populations.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe present study revealed that lower socioeconomic status (SES) is a significant and independent determinant of an increased risk of chronic kidney disease (CKD). This association remained significant after adjusting for age, sex, and other potential confounders. Furthermore, a significant interaction between ethnicity and SES was observed; specifically, the protective effect of higher SES in reducing the risk of CKD was evident among the Turk population, whereas this pattern was not present among the Kurdish population.\u003c/p\u003e \u003cp\u003eThese findings indicate that the impact of socioeconomic factors on kidney health is shaped by ethnic and cultural contexts. Accordingly, health policies and preventive interventions should adopt a multidimensional, ethnicity-sensitive, and socially contextualized approach to effectively reduce health inequalities and promote equity in kidney health.\u003c/p\u003e"},{"header":"Abbreviations","content":" \u003cp\u003eCKD Chronic Kidney Disease\u003c/p\u003e \u003cp\u003eSDH Social Determinant Health\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e The Ethics Committee of the Urmia University of Medical Sciences in Urmia,Iran approved the protocol of this study (Code number: IR.UMSU.REC.1403.285). Written informed consent is taken from each participant. In this study, all methods were carried out in accordance with the Declaration of Helsinki.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eVMM and MH contributed to the conception and design of the study.VMM drafted the first version of the manuscript. VMM, MH and RE revised themanuscript. MH and RE critically reviewed the manuscript for important intellectualcontent. All authors approved the final version.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analyzed during the current research arenot publicly available as individual privacy could be compromised but areavailable from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLevey AS SA, Burrows NR, Williams DE, Stith KR. McClellan Comprehensive public health strategies for preventing the development, progression, and complications of chronic kidney disease: Report of the Expert Panel at the Centers of Disease Control and Prevention. Am J Kidney Dis. 2009. 2009.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEl Nahas AM BA. Chronic kidney disease: The global challenge. Lancet. 2005;365:331\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNugent RAFS, Feigl AB, Chyung D. The burden of chronic kidney disease on developing nations: A 21st century challenge in global health. Nephron Clin Pract. 2011;118:c269\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJha VWA, Wang H. The impact of CKD identification in large countries: The burden of illness. Nephrol Dial Transpl. 2012;27:iii32\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuyckx VATM, Stanifer JW. The global burden of kidney disease and the sustainable development goals. Lancet. 2018;392(10164):1288\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoazzeni SS ea. Prevalence of chronic kidney diseases and its determinants among Iranian population: a review. J Ren Inj Prev. 2022;11(2):e17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHosseinpanah FKF, Nassiri AA, Azizi F. High prevalence of chronic kidney disease in Iran: a large population-based study. 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Nephrol Dial Transplant. 2012;27:iii19\u0026ndash;iii26. 2012.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStanifer JW MA, Jafar TH, Patel UD. Chronic kidney disease in low- and middle-income countries. Nephrol Dial Transpl. 2016;31(6):868\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCollaboration GCKD. Global, regional, and national burden of chronic kidney disease, 1990\u0026ndash;2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2020;395(10225):709\u0026ndash;733. 2017.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrganization WH. Social determinants of health. [Internet]. Geneva: WHO; 2023. 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Am J Kidney Dis. 2014;63(3):429\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCukor DCJ, Brown C, et al. Depression and anxiety in urban hemodialysis patients. Clin J Am Soc Nephrol. 2007;2(3):484\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWhite SLCS, Jan S, Chapman JR, Cass A. How can we achieve global equity in provision of renal replacement therapy? Bull World Health Organ. 2008;86(3):229\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHsu RK PN. Recent trends in the prevalence of chronic kidney disease: not the same old song. Curr Opin Nephrol Hypertens. 2017;26(3):187\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHill NRFS, Oke JL, Hirst JA, O\u0026rsquo;Callaghan CA, Lasserson DS et al. Global prevalence of chronic kidney disease \u0026ndash; a systematic review and meta-analysis. BMC Public Health. 2020;2020;20(1):1\u0026ndash;19.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrganization WH. Closing the gap in a generation: health equity through action on the social(2) determinants of health. Final Report of the Commission on Social Determinants of Health. Geneva: WHO; 2008. [.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJha V, Garcia-Garcia G, Iseki K, Li Z, Naicker S, Plattner B, et al. Chronic kidney disease: global dimension and perspectives. Lancet. 2013;382(9888):260\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBr\u0026uuml;ck KJK, Zoccali C, et al. CKD: challenges and opportunities for population health. BMC Public Health. 2022;22:937.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBello AKOM, Jha V, et al. Inequities in kidney health: a global perspective. BMC Nephrol. 2020;21:214.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarmot MWR. 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BMC Public Health. 2019;2019;19(1):261.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Chronic kidney disease, Socioeconomic status, Ethnicity, Health inequity, Social determinants of health, Iran, Interaction effect","lastPublishedDoi":"10.21203/rs.3.rs-8677897/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8677897/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e Chronic kidney disease (CKD) is a major global public health concern, influenced not only by biological but also by social determinants of health. Socioeconomic status (SES) is a key determinant of CKD, yet its interaction with ethnicity remains underexplored. This study aimed to assess the association between SES and CKD prevalence and to examine ethnic differences in this relationship in a multiethnic population in northwestern Iran.\u003cbr\u003e\n \u003cstrong\u003eMethods\u003c/strong\u003e A cross-sectional study was conducted using population base data from the integrated CKD screening within the IraPEN program in Naqadeh County, West Azerbaijan Province, Iran (2018–2019). A total of 7,771 adults aged ≥30 years were analyzed. SES was derived from household asset indicators using polychoric principal component analysis and categorized into tertiles (low, middle, high). Logistic regression models were fitted to estimate adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for CKD, testing interactions between SES and ethnicity.\u003cbr\u003e\n \u003cstrong\u003eResults\u003c/strong\u003e The prevalence of CKD was 10.2% (95% CI: 9.5–10.9). Higher SES was associated with a lower likelihood of CKD (middle SES: OR=0.77, 95% CI: 0.62–0.95; high SES: OR=0.59, 95% CI: 0.44–0.79). Women had more than twice the odds of CKD compared to men (OR=2.10, 95% CI: 1.68–2.63). A significant interaction between SES and ethnicity was identified: CKD prevalence decreased with higher SES among Turks but not among Kurds. Model discrimination was excellent (AUC=0.83).\u003cbr\u003e\n \u003cstrong\u003eConclusions\u003c/strong\u003e SES is strongly associated with CKD, but this relationship varies by ethnicity. Context-sensitive, equity-focused interventions are essential to address kidney health disparities in multiethnic settings.\u003c/p\u003e","manuscriptTitle":"The Paradoxical Impact of Socioeconomic Status on Chronic Kidney Disease in Northwestern Iran: Exploring the Interaction ofEthnicity and Social Class","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-19 11:07:54","doi":"10.21203/rs.3.rs-8677897/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"101157507172613244492184083609882437432","date":"2026-03-23T06:27:18+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-17T12:57:39+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-06T05:52:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-06T04:05:46+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-06T04:05:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2026-01-23T09:48:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1451550b-09da-4c9e-81fc-fd7ab83f0d31","owner":[],"postedDate":"March 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-19T11:07:54+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-19 11:07:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8677897","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8677897","identity":"rs-8677897","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}