Abstract
Objective
To explore the association between social determinants of health (SDoH) and chronic kidney disease (CKD) prevalence and prognosis in the US population.
Methods
Data were sourced from the US National Health and Nutrition Examination Survey 2003–2018, including 32,389 participants aged ≥20 years. Unfavorable SDoH included unemployment, low income, food insecurity, low education, lack of healthcare access, lack of health insurance, housing instability, and not being married or living with a partner. CKD prevalence and poor prognosis were the primary outcomes. A cumulative SDoH score assessed the overall association with CKD, while individual scores were examined for their independent associations. Multivariable logistic regression, restricted cubic splines, and subgroup analysis were conducted.
Results
Cumulative SDoH scores were associated with CKD prevalence (OR = 1.14, 95 %CI: 1.11–1.16) and poor prognosis (OR = 1.17, 95 %CI: 1.12–1.22). A nonlinear relationship existed between Cumulative SDoH scores and prevalence, while the association with prognosis was linear. Unemployment (OR = 1.13, 95 %CI: 1.01–1.27), low income (OR = 1.38, 95 %CI: 1.25–1.52), low education (OR = 1.15, 95 %CI: 1.05–1.27), lack of health insurance (OR = 1.11, 95 %CI: 1.01–1.21), housing instability (OR = 1.17, 95 %CI: 1.05–1.29), and not married nor living with a partner (OR = 1.12, 95 %CI: 1.01–1.25) were associated with prevalence. Unemployment (OR = 1.42, 95 %CI: 1.16–1.74), low income (OR = 1.48, 95 %CI: 1.28–1.71), low education (OR = 1.19, 95 %CI: 1.04–1.38), and housing instability (OR = 1.42, 95 %CI: 1.23–1.66) were associated with poor prognosis.
Conclusions
Unfavorable SDoH are positively associated with both CKD prevalence and poor prognosis.
Keywords: Social determinants of health, Chronic kidney disease, Prevalence, Prognosis, NHANES
Highlights
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Unfavorable social factors are linked to increased chronic kidney disease prevalence.
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Unfavorable social factors are associated with poor chronic kidney disease prognosis
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Low income is the strongest factor associated with chronic kidney disease.
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Addressing social factors may improve chronic kidney disease screening and management.
1. Introduction
Chronic kidney disease (CKD) is a progressive condition marked by irreversible nephron loss, eventually leading to end-stage renal disease. The global prevalence of CKD has risen in recent decades, affecting approximately 9.1 % of the population, and is associated with substantial morbidity and mortality, placing considerable strain on healthcare systems (Lancet, 2020). In the US, an estimated 37 million adults are affected by CKD, representing more than one in seven individuals (Chronic Kidney Disease in the United States: Centers for Disease Control, 2021). CKD exhibits notable sex and gender differences in both prevalence and prognosis (Carrero et al., 2018). Globally, women tend to have a higher prevalence of CKD, while men typically experience faster progression, leading to a greater proportion of men initiating renal replacement therapy (Carrero et al., 2018). The prevalence of CKD increases with age, particularly in individuals over 65, due to the gradual decline in glomerular filtration rate. In terms of prognosis, older adults generally experience slower progression to end-stage renal disease compared to younger individuals (Carrero et al., 2018).
In addition to age and sex, social determinants of health (SDoH), which are indicators of health equity, are associated with healthcare outcomes. The SDoH framework offers a comprehensive assessment of how the environments in which individuals are born, live, learn, work, play, worship, and age influence health outcomes (Gómez et al., 2021; Bundy et al., 2023). Emerging evidence suggests that unfavorable SDoH are associated with increased rates of premature mortality and contribute to racial disparities in all-cause premature mortality within the US population (Bundy et al., 2023). Furthermore, unfavorable SDoH are associated with higher mortality rates in adults with CKD and diabetes (Ozieh et al., 2021). Recent evidence from the China Health and Retirement Longitudinal Study (CHARLS) demonstrated that when the number of unfavorable SDoH equaled or exceeded four, there was a significant increase in the risk of CKD (Li et al., 2025). These findings highlight the importance of addressing health disparities and enhancing the well-being of individuals affected by CKD. However, the applicability of these findings to other populations is not always clear. This is especially true for countries with differing healthcare systems, social structures, and demographic characteristics.
Identifying which SDoH require targeted attention might inform more effective screening and prevention strategies. While the evidence gap regarding the impact of socioeconomic factors on CKD has been discussed, it may be overstated or oversimplified, as existing research already demonstrates strong links between lower socioeconomic status and CKD outcomes (Quiñones and Hammad, 2020; Nicholas et al., 2015; Grant et al., 2023). Inspired by Zhu et al.'s study (Zhu et al., 2024), which provides valuable insights into the multifaceted ways unfavorable SDoH influence the prevalence of cardiovascular-kidney-metabolic syndrome in US adults, this study aims to examine the cumulative effects of multiple unfavorable forms of SDoH and their association with CKD prevalence and prognosis, as well as the association between individual types of unfavorable SDoH and CKD, in a large, nationally representative sample of US adults. We further conducted subgroup analyses based on age, sex, race and ethnicity, and body mass index (BMI), which allow for the identification of potential variations in the association between SDoH and CKD. The added value of this study lies in its focus on understanding the complex associations of both cumulative and individual SDoH with CKD, and how cultural and healthcare system differences shape these associations in comparison to findings from studies in other global contexts. This nuanced understanding helps inform more targeted interventions for diverse populations and highlights the need for tailored policy approaches.
2. Methods
2.1. Study population
This cross-sectional study used data from the National Health and Nutrition Examination Survey (NHANES). Conducted biennially by the National Center for Health Statistics, NHANES collects nationally representative data from the non-institutionalized US population using a complex survey design and population-specific sample weights (Johnson et al., 2013). The study protocol was approved by the National Center for Health Statistics Institutional Review Board (Protocol #98-12; Protocol #2005-06; Protocol #2011-17, #2018-01), and informed consent was obtained from all participants. The NHANES dataset is publicly available at https://wwwn.cdc.gov/nchs/nhanes/Default.aspx.
This study used data from eight NHANES cycles (from 2003–2004 to 2017–2018). A total of 80,312 participants were initially considered. We excluded participants who were under 20 years of age, had incomplete SDoH data, lacked CKD diagnosis and prognosis data, or had missing information on age, sex, race, BMI, smoking status, or alcohol consumption. Finally, 32,389 participants were included in the analysis (Fig. 1).
Fig. 1.
Flowchart of participants selection: National Health and Nutrition Examination Survey 2003–2018 for United States adults. NHANES National Health and Nutrition Examination Survey; SDoH social determinants of health; CKD chronic kidney disease; BMI body mass index.
2.2. Assessment of social determinants of health
The SDoH scoring system was developed based on the definitions outlined in the Healthy People 2030 initiative by the US Department of Health and Human Services (Gómez et al., 2021; Bundy et al., 2023). Eight SDoH factors were recorded using standardized questionnaires: employment status, family poverty to income ratio, food security, education level, healthcare access, health insurance status, homeownership, and marital status. An unfavorable SDoH factor is defined as the presence of conditions that indicate a more disadvantaged socioeconomic position. Specifically, unfavorable SDoH factors include being unemployed, having a family poverty to income ratio <300 %, experiencing marginal, low, or very low food security, having an education level lower than high school, having no access to healthcare or only emergency room care, lacking health insurance or having government insurance, living in rental or other non-homeownership housing arrangements, and being not married nor living with a partner (Bundy et al., 2023). An unfavorable SDoH factor was assigned a score of one, with the cumulative score ranging from zero to eight (a higher SDoH score represents a more unfavorable condition). Detailed descriptions of the SDoH-related questions and their definitions are available in the published literature (Bundy et al., 2023).
2.3. Definition of chronic kidney disease
Since most participants had only a single measurement in the survey, and the timing of the second urine sample collection varied across selected NHANES cycles or subsamples, we used a one-time urinary albumin-to-creatinine ratio as a substitute for 24-h persistent proteinuria to minimize bias (Chen et al., 2023; Murphy et al., 2016). The Chronic Kidney Disease Epidemiology Collaboration equation was used to estimate glomerular filtration rate (Levey et al., 2009). According to the Kidney Disease: Improving Global Outcomes 2024 Clinical Practice Guideline, CKD is defined as a urinary albumin-to-creatinine ratio ≥ 30 mg/g and/or an estimate glomerular filtration rate < 60 mL/min/1.73 m2 (KDIGO, 2024).
CKD prognosis is classified based on estimate glomerular filtration rate category (G1–G5) and albuminuria category (A1–A3) (KDIGO, 2024). According to the guidelines, CKD is divided into four prognostic categories: low risk (G1/2-A1; if no other markers of kidney disease are present, CKD is not diagnosed), moderate risk (G2–A1, G1/2–A2), high risk (G3b–A1, G3a–A2, G1/2–A3), and very high risk (G3a–A3, G3b–A2/3, G4, G5). The definitions of the estimate glomerular filtration rate and albuminuria categories are detailed in Supplementary Table S1. In this study, individuals classified as having high or very high risk (G1/2–A3, G3-G5) were defined as having a poor prognosis.
2.4. Covariates
Potential confounding factors were considered as covariates, including age, sex, race and ethnicity, BMI, alcohol consumption, and smoking status. We focused on Mexican American, Non-Hispanic White, and Non-Hispanic Black groups due to their larger sample sizes. Other ethnicities with smaller sample sizes were classified as ‘Other Race.’ Smoking status was categorized as never (smoked fewer than 100 cigarettes in their lifetime), former (smoked more than 100 cigarettes in their lifetime but do not currently smoke), and current (smoke ≥ one cigarette daily) (Xie et al., 2024). Alcohol consumption was classified as never (< 12 drinks in lifetime), former (consumed ≥12 drinks in a single year or lifetime but did not drink in the past year), and current (≥ 12 drinks in the past year).
2.5. Statistical analysis
Appropriate weighting methodologies were employed in accordance with NHANES guidelines (Johnson et al., 2013). Continuous variables are presented as the weighted means with 95 % confidence intervals (CI), while categorical variables are expressed as the percentages (%). Chi-square tests and analysis of variance were utilized to assess differences in baseline characteristics. A multivariable logistic regression model was used to estimate the associations between cumulative SDoH scores and the prevalence and prognosis of CKD, with results presented as odds ratios (OR) and 95 % CI. The fully adjusted model was adjusted for age, sex, race/ethnicity, BMI, alcohol consumption, and smoking status. A restricted cubic spline model was used to graphically represent the dose-response relationships between cumulative SDoH scores and CKD outcomes. Subgroup analyses and interaction tests were conducted to explore potential confounding effects. For individual forms of SDoH, multivariable logistic regression was used to estimate their independent associations with CKD prevalence and prognosis, adjusting for age, sex, race and ethnicity, and other SDoH. A p-value less than 0.05 was considered statistically significant. All statistical analyses were performed using R software version 4.3.3.
3. Results
3.1. Baseline characteristics
As shown in Table 1, the mean age of total participants was 46 years, with 16,471 females and 15,918 males. The racial and ethnic composition included 14,864 Non-Hispanic White, 6530 Non-Hispanic Black, 5205 Mexican American, and 5790 identifying as other races. Compared to participants without CKD, those with CKD were older and more likely to be female, obese, former smokers, and former drinkers. Regarding SDoH variables, participants with CKD were more likely to be have low income, lower educational levels, lack of healthcare access, lack health insurance, experience housing instability, and report not married nor living with a partner. The baseline characteristics of participants categorized by prognosis are shown in Supplementary Table S2.
Table 1.
Baseline characteristics stratified by chronic kidney disease among United States adults from National Health and Nutrition Examination Survey 2003–2018.
| Variable | Total, n (%) (n = 32,389) |
Non-CKD, n (%) (n = 26,659) |
CKD, n (%) (n = 5730) |
P-value |
|---|---|---|---|---|
| Age, years (95 %CI) | 46 (33, 59) | 44 (32, 56) | 64 (49, 75) | < 0.01 |
| Sex | < 0.01 | |||
| Female | 16,471 (51.2) | 13,498 (50.3) | 2973 (56.6) | |
| Male | 15,918 (48.8) | 13,161 (49.7) | 2757 (43.4) | |
| Race/ethnicity | < 0.01 | |||
| Mexican American | 5205 (8.0) | 4412 (8.3) | 793 (6.7) | |
| Non-Hispanic Black | 6530 (10.3) | 5303 (10.0) | 1227 (11.7) | |
| Non-Hispanic White | 14,864 (70.1) | 11,970 (69.9) | 2894 (71.4) | |
| Other | 5790 (11.6) | 4974 (11.8) | 816 (10.2) | |
| Body mass index category | < 0.01 | |||
| Underweight | 474 (1.5) | 383 (1.4) | 91 (1.7) | |
| Normal | 8836 (28.5) | 7512 (29.3) | 1324 (23.4) | |
| Overweight | 10,830 (33.1) | 9012 (33.6) | 1818 (30.2) | |
| Obese | 12,249 (36.9) | 9752 (35.7) | 2497 (44.7) | |
| Alcohol consumption | < 0.01 | |||
| Former | 5518 (13.8) | 3984 (12.4) | 1534 (22.8) | |
| Never | 4491 (10.6) | 3497 (10.0) | 994 (14.8) | |
| Now | 22,380 (75.5) | 19,178 (77.6) | 3202 (62.4) | |
| Smoking status | < 0.01 | |||
| Former | 8072 (25.1) | 6158 (23.8) | 1914 (33.1) | |
| Never | 17,572 (54.3) | 14,736 (54.9) | 2836 (50.4) | |
| Now | 6745 (20.7) | 5765 (21.3) | 980 (16.6) | |
| Employment | 0.3 | |||
| Employed, student, retired | 24,620 (79.7) | 20,254 (79.8) | 4366 (79.0) | |
| Not employed | 7769 (20.3) | 6405 (20.2) | 1364 (21.0) | |
| Family poverty to income ratio | < 0.01 | |||
| ≥ 300 % | 12,277 (51.1) | 10,534 (52.7) | 1743 (41.5) | |
| < 300 % | 20,112 (48.9) | 16,125 (47.3) | 3987 (58.5) | |
| Food security | 0.6 | |||
| Full food security | 22,977 (78.1) | 18,797 (78.1) | 4180 (78.5) | |
| Marginal, low, or very low | 9412 (21.9) | 7862 (21.9) | 1550 (21.5) | |
| Education | < 0.01 | |||
| High school or more | 24,638 (84.8) | 20,709 (85.9) | 3929 (78.2) | |
| Less than high school | 7751 (15.2) | 5950 (14.1) | 1801 (21.8) | |
| Access to healthcare | < 0.01 | |||
| Regular health-care facility | 26,353 (82.7) | 21,237 (81.6) | 5116 (89.6) | |
| None or emergency room | 6036 (17.3) | 5422 (18.4) | 614 (10.4) | |
| Health insurance | < 0.01 | |||
| Private insurance | 17,392 (64.2) | 14,611 (65.2) | 2781 (57.8) | |
| Government or none | 14,997 (35.8) | 12,048 (34.8) | 2949 (42.2) | |
| Housing instability | < 0.01 | |||
| Own home | 20,354 (68.6) | 16,488 (67.9) | 3866 (72.8) | |
| Rent or other arrangement | 12,035 (31.4) | 10,171 (32.1) | 1864 (27.2) | |
| Marital status | < 0.01 | |||
| Married or living with a partner | 19,656 (64.5) | 16,511 (65.3) | 3145 (59.4) | |
| Not married nor living with a partner | 12,733 (35.5) | 10,148 (34.7) | 2585 (40.6) | |
| Cumulative SDoH score | < 0.01 | |||
| 0–1 | 10,206 (43.2) | 8658 (44.1) | 1548 (37.2) | |
| 2–3 | 10,085 (30.0) | 8081 (29.3) | 2004 (34.6) | |
| 4–5 | 8695 (20.1) | 7094 (19.9) | 1601 (21.5) | |
| ≥ 6 | 3403 (6.7) | 2826 (6.7) | 577 (6.6) | |
| CKD prognosis | < 0.01 | |||
| Low risk | 26,659 (86.2) | 26,659 (100.0) | 0 | |
| Moderate risk | 3925 (10.1) | 0 | 3925 (73.1) | |
| High risk | 1117 (2.5) | 0 | 1117 (17.8) | |
| Very high risk | 688 (1.3) | 0 | 688 (9.1) |
The data are presented as the mean (95 % CI) or number (%). P-values were obtained from Kruskal-Wallis tests for continuous variables and from Chi-square tests for categorical variables.
CKD chronic kidney disease; SDoH social determinants of health; CI confidence intervals.
CKD prognosis is classified based on estimate glomerular filtration rate category (G1–G5) and albuminuria category (A1–A3): low risk (G1/2-A1; if no other markers of kidney disease are present, CKD is not diagnosed), moderate risk (G2–A1, G1/2–A2), high risk (G3b–A1, G3a–A2, G1/2–A3), and very high risk (G3a–A3, G3b–A2/3, G4, G5). Cumulative SDoH score: an unfavorable SDoH factor was assigned a score of one, with the cumulative score ranging from zero to eight.
3.2. Association between social determinants of health and chronic kidney disease prevalence
As shown in Table 2, weighted multivariable logistic regression revealed positive associations between cumulative SDoH scores and CKD prevalence. In fully adjusted model, each unit increase in SDoH score was associated with a 13.7 % increase in the prevalence of CKD (OR = 1.14, 95 %CI: 1.11–1.16). A nonlinear association between cumulative SDoH scores and CKD prevalence was found in both models (all p-overall <0.01 and all p-nonlinear <0.01). After fully adjusting for covariates, the risk of CKD increased significantly when the cumulative SDoH scores was less than four (Supplementary Fig. S1B). To determined whether the association between cumulative SDoH scores and CKD prevalence persists across specific populations, subgroups were stratified by age, sex, race and ethnicity, and BMI. A consistently positive association was observed among all subgroups (Supplementary Table S3).
Table 2.
The association between social determinants of health and chronic kidney disease in United States adults from National Health and Nutrition Examination Survey 2003–2018.
| Cumulative SDoH score | Unadjusted model |
Fully adjusted model |
|---|---|---|
| OR (95 %CI) | OR (95 %CI) | |
| Prevalence | ||
| Per unit increase | 1.05 (1.03, 1.07) | 1.14 (1.11, 1.16) |
| Poor prognosis | ||
| Per unit increase | 1.10 (1.06, 1.13) | 1.17 (1.12, 1.22) |
All estimates were obtained from complex survey designs.
SDoH social determinants of health; OR odds ratio; CI confidence intervals.
Prevalence: chronic kidney disease is defined as a urinary albumin-to-creatinine ratio ≥ 30 mg/g and/or an estimate glomerular filtration rate < 60 mL/min/1.73 m2. Poor prognosis: based on estimate glomerular filtration rate category (G1–G5) and albuminuria category (A1–A3), individuals classified as high or very high risk (G1/2–A3, G3–G5) were defined as having poor prognosis.
Fully adjusted model: adjusted for age, sex, race/ethnicity, body mass index, alcohol consumption, and smoking status.
3.3. Association between social determinants of health and chronic kidney disease prognosis
As shown in Table 2, in fully adjusted model, each unit increase in cumulative SDoH scores was associated with a 16.9 % increase in the risk of poor prognosis (OR = 1.17, 95 %CI: 1.12–1.22). As shown in Supplementary Fig. S1D, after fully adjusting for covariates, the association between cumulative SDoH scores and CKD prognosis was linear (p-overall <0.01 and p-nonlinear = 0.46). Subgroup analysis showed a consistently positive association between cumulative SDoH scores and CKD prognosis among individuals aged ≥40 years, regardless of sex, race, and ethnicity, as well as those with a BMI ≥ 18.5 kg/m2 (Supplementary Table S3).
3.4. Association between each social determinants of health and chronic kidney disease prevalence
After adjusting for age, sex, race/ethnicity, and other SDoH, the following factors were independently associated with CKD prevalence (Table 3): not employed (OR = 1.13, 95 %CI: 1.01–1.27), a family poverty to income ratio < 300 % (OR = 1.38, 95 %CI: 1.25–1.52), an educational level lower than high school (OR = 1.15, 95 %CI: 1.05–1.27), government or no health insurance (OR = 1.11, 95 %CI: 1.01–1.21), renting or having other housing arrangements (OR = 1.17, 95 %CI: 1.05–1.29), and not married nor living with a partner (OR = 1.12, 95 %CI: 1.01–1.25).
Table 3.
The associations between each social determinants of health and chronic kidney disease in United States adults from National Health and Nutrition Examination Survey 2003–2018.
| Prevalence (95 %CI), % | Prevalence Adjusted* OR (95 %CI) |
Poor prognosis (95 %CI), % | Poor prognosis Adjusted* OR (95 %CI) |
|
|---|---|---|---|---|
| Employment | ||||
| Employed, student, retired | 13.7 (13.1, 14.4) | 1.00 | 3.7 (3.4, 4.0) | 1.00 |
| Not employed | 14.3 (13.3, 15.2) | 1.13 (1.01, 1.27) | 3.9 (3.4, 4.4) | 1.42 (1.16, 1.74) |
| Family poverty to income ratio | ||||
| ≥ 300 % | 11.2 (10.5, 12.0) | 1.00 | 2.5 (2.2, 2.8) | 1.00 |
| < 300 % | 16.6 (15.9, 17.3) | 1.38 (1.25, 1.52) | 5.0 (4.7, 5.3) | 1.48 (1.28, 1.71) |
| Food security | ||||
| Full food security | 13.9 (13.2, 14.6) | 1.00 | 3.8 (3.5, 4.0) | 1.00 |
| Marginal, low, or very low | 13.6 (12.8, 14.4) | 1.09 (0.97, 1.22) | 3.6 (3.2, 4.1) | 1.16 (0.97, 1.38) |
| Education | ||||
| High school or more | 12.8 (12.2, 13.4) | 1.00 | 3.2 (3.0, 3.4) | 1.00 |
| Less than high school | 19.9 (18.7, 21.0) | 1.15 (1.05, 1.27) | 6.7 (6.1, 7.3) | 1.19 (1.04, 1.38) |
| Access to healthcare | ||||
| Regular health-care facility | 15.0 (14.4, 15.6) | 1.00 | 4.2 (3.9, 4.5) | 1.00 |
| None or emergency room | 8.3 (7.4, 9.2) | 0.89 (0.78, 1.02) | 1.5 (1.1, 1.8) | 0.77 (0.58, 1.02) |
| Health insurance | ||||
| Private insurance | 12.5 (11.7, 13.2) | 1.00 | 3.2 (2.9, 3.5) | 1.00 |
| Government or none | 16.3 (15.6, 17.0) | 1.11 (1.01, 1.21) | 4.7 (4.3, 5.0) | 1.01 (0.88, 1.16) |
| Housing instability | ||||
| Own home | 14.7 (14.0, 15.4) | 1.00 | 3.9 (3.6, 4.2) | 1.00 |
| Rent or other arrangement | 12.0 (11.1, 12.8) | 1.17 (1.05, 1.29) | 3.4 (3.0, 3.8) | 1.42 (1.23, 1.66) |
| Marital status | ||||
| Married or living with a partner | 12.8 (12.1, 13.4) | 1.00 | 3.2 (2.9, 3.5) | 1.00 |
| Not married nor living with a partner | 15.8 (14.9, 16.7) | 1.12 (1.01, 1.25) | 4.7 (4.3, 5.1) | 1.06 (0.92, 1.23) |
All estimates were obtained from complex survey designs.
OR odds ratio; CI confidence intervals.
Prevalence: chronic kidney disease is defined as a urinary albumin-to-creatinine ratio ≥ 30 mg/g and/or an estimate glomerular filtration rate < 60 mL/min/1.73 m2. Poor prognosis: based on estimate glomerular filtration rate category (G1–G5) and albuminuria category (A1–A3), individuals classified as high or very high risk (G1/2–A3, G3–G5) were defined as having poor prognosis.
Adjusted*: adjusted for age, sex, race/ethnicity, and other social determinants of health.
3.5. Association between each social determinants of health and chronic kidney disease prognosis
As shown in Table 3, after adjusting for age, sex, race/ethnicity, and other SDoH, the following factors were independently associated with CKD prognosis: not employed (OR = 1.42, 95 %CI: 1.16–1.74), a family poverty to income ratio <300 % (OR = 1.48, 95 %CI: 1.28–1.71), an educational level lower than high school (OR = 1.19, 95 %CI: 1.04–1.38), and renting or having other housing arrangements (OR = 1.42, 95 %CI: 1.23–1.66).
4. Discussion
To our knowledge, this is the first study to investigate the associations between SDoH and the prevalence and prognosis of CKD in US population. The findings indicated that unfavorable SDoH were significantly positively associated with both the prevalence and poor prognosis of CKD. Specifically, factors such as unemployment, low family income, low educational levels, and housing instability were significantly associated with increased CKD prevalence and poor prognosis. In addition, the absence of health insurance and being unmarried or not living with a partner were associated with a higher prevalence of CKD.
International studies has provided valuable insights into how socioeconomic status influences CKD progression and outcomes (Ritte et al., 2017; Ang et al., 2024; Choi et al., 2024). Ritte et al. (2017) conducted a study in Indigenous Australian populations and demonstrated that factors such as lower education, unemployment, and receiving welfare benefits are linked to poorer kidney function and higher cardio-metabolic risk. Unemployment, in particular, was strongly associated with lower estimate glomerular filtration rate, underscoring the health disadvantages experienced by those in economically vulnerable positions. Additionally, remote living and renting, compared to owning a home, were also linked to worse kidney outcomes, suggesting that broader SDoH, including housing stability and geographic location, exacerbate the burden of CKD. Furthermore, a Malaysian study by Ang et al. (2024) focused on advanced CKD patients and explored the role of work disability and employment status. This study revealed that 50 % of patients with advanced CKD were unemployed, with factors such as age, sex, and comorbidities like diabetes and dyslipidemia influencing employment status. Female patients, for example, had a significantly higher likelihood of being unemployed, potentially due to domestic responsibilities or health issues. A nationwide study in South Korea found a significant association between CKD and a higher likelihood of unemployment, with patients diagnosed with CKD being 1.70 times more likely to be unemployed than those without CKD (Choi et al., 2024). These international studies support our findings and emphasize the multifaceted challenges faced by individuals with CKD in maintaining stable employment.
US-based studies have shown that individuals with lower educational attainment and unstable housing tend to experience worse kidney outcomes (Nicholas et al., 2015; Novick et al., 2023). Along with lower income, Americans with fewer than 12 years of education were more likely to experience reduced kidney function and face a higher burden of CKD-related complications (Nicholas et al., 2015). Higher educational levels are associated with better health literacy, which in turn promotes greater awareness and adherence to CKD treatment protocols. Therefore, targeting educational interventions and improving access to health information for individuals with lower educational attainment could enhance early detection and management of CKD. Such initiatives may ultimately reduce the progression to end-stage renal disease, improving health outcomes and quality of life for those affected. In addition, unstable housing can significantly contribute to the poor management of kidney disease, exacerbating both the prevalence and prognosis of CKD (Novick and King, 2024). For individuals with kidney disease, a lack of stable housing can fragment care, leading to missed appointments and inconsistent adherence to essential treatments such as dialysis (Novick et al., 2023). Housing instability also disrupts medication regimens, particularly those requiring proper storage or regular access to healthcare services (Novick and King, 2024). As a result, unstable housing can hinder medication adherence, interrupt dialysis treatments, and increase the risk of poor health outcomes, including higher mortality and hospitalization rates.
Several studies have suggested that individuals without health insurance are less likely to access healthcare services for early detection of CKD (Ritte et al., 2017; Annadata et al., 2024). This lack of access contributes to a higher prevalence of undiagnosed cases, particularly among economically disadvantaged populations. An Indian study by Annadata et al. (2024) found that individuals with greater awareness of insurance coverage had better access to necessary healthcare, potentially reducing the prevalence of untreated CKD. Similarly, in Australia, uninsured individuals, often from lower socioeconomic backgrounds, are more likely to develop CKD and less likely to receive timely diagnosis and treatment (Ritte et al., 2017). In addition, health insurance status plays a critical role in the ability of end-stage renal disease patients to initiate or transition to peritoneal dialysis, with Medicare coverage facilitating earlier access and transition compared to limited insurance (Perez et al., 2018). Our study found that the absence of health insurance was associated with a higher prevalence of CKD, but did not show a significant impact on CKD prognosis. This suggests that while insurance may improve early detection, its impact on long-term outcomes in the US may be limited by other factors.
Marital status has been identified as a significant socioeconomic determinant influencing the prevalence and prognosis of CKD. In our study, individuals who were not married nor living with a partner exhibited a higher prevalence of CKD, a finding that aligns with other international studies (Nicholas et al., 2015; Molsted et al., 2021). Marital status can affect health outcomes through various mechanisms, such as emotional support, financial stability, and health-related behaviors. Unmarried individuals, especially those living alone, may also face challenges in managing complex medical regimens, including medication adherence and attending regular healthcare appointments, which can negatively affect CKD outcomes. Furthermore, individuals with CKD experience a higher rate of food insecurity than the general population at a rate of one in four (Quiñones and Hammad, 2020). Food insecurity is associated with unhealthier diets, which is then connected to higher rates of diabetes, hypertension, and CKD (Zhu et al., 2024). Lack of consistent healthcare access exacerbates delays in diagnosis, reduces adherence to treatment regimens, and increases the risk of disease. Studies have shown that CKD patients without stable healthcare access have lower rates of peritoneal dialysis initiation (Ang et al., 2024; Perez et al., 2018). However, in this study, we found no significant association between food insecurity or healthcare access and the prevalence or prognosis of CKD.
This study provides a unique opportunity to examine the association between SDoH and CKD in the US population, complementing the findings from the CHARLS (Li et al., 2025). Specifically, while the CHARLS study highlights the importance of factors such as financial support, education, pensions, health insurance, and marital status in predicting CKD risk in the Chinese context, the NHANES study adds depth to this understanding by focusing on the role of additional socioeconomic factors, such as family income, employment, and housing instability in the US context. Drawing on the insights from both studies, we can better understand how social disparities contribute to CKD risk and prognosis across different cultural and healthcare settings. This comparison also emphasizes the need for localized interventions and policies that address SDoH in order to mitigate CKD risk in diverse populations.
Our study has several limitations. First, as a cross-sectional analysis, it cannot establish a definitive causal relationship. Second, there is potential for misclassification in self-reported SDoH categories. Third, this study was unable to assess the impact of changes in SDoH over time, as these factors were only evaluated at baseline. Fourth, this study includes a limited number of racial and ethnic groups, which may not fully represent the diversity of the US population. Although the dataset is nationally representative, the findings may not apply to smaller or specific subpopulations underrepresented in the data. Fifth, the SDoH scoring system does not account for the varying degrees or nuances within each factor, which may influence outcomes in more complex ways than the scoring system captures. Sixth, residual confounding, such as dietary patterns, physical activity, and comorbidities, cannot be ruled out. Finally, considering that the Coronavirus Disease 2019 pandemic could introduce significant confounding factors and bias the interpretation of the relationship between SDoH and CKD (Abrams and Szefler, 2020; Long et al., 2022; Brogan and Ross, 2023; Mosnaim et al., 2023), we analyzed only pre-pandemic data to to ensure the reliability and validity of our analysis.
We recommend that future longitudinal studies examine changes in SDoH over time and their impact on CKD progression. Additionally, research on the cumulative effects of multiple SDoH factors, and their interactions with genetic and clinical variables, would enhance understanding of how these factors contribute to health disparities. Further studies should also explore community-based interventions addressing SDoH and their potential to improve kidney health outcomes in underserved populations.
5. Conclusion
In conclusion, unfavorable SDoH are positively associated with both the prevalence and poor prognosis of CKD. Specifically, employment status, income, education level, health insurance, housing instability, and marital status are linked to CKD prevalence, while employment status, income, education level, and housing instability are associated with CKD prognosis. These findings highlight the need to target SDoH disparities and promote health equity.
CRediT authorship contribution statement
Chengpeng Xie: Writing – original draft, Software, Investigation, Formal analysis, Data curation. Qian Wu: Writing – original draft, Methodology, Investigation, Formal analysis, Data curation. Chengxin Xie: Writing – review & editing, Visualization, Validation, Supervision, Resources, Project administration, Methodology, Investigation, Conceptualization. Zhien Shi: Writing – review & editing, Supervision, Project administration, Investigation, Conceptualization.
Ethical approval and consent to participate
The study protocol for the US National Health and Nutrition Examination Survey was approved by the National Center for Health Statistics Research ethics review board (Protocol #98-12; Protocol #2005-06; Protocol #2011-17, #201801). All participants provided written informed consent. Institutional review board approval was waived for this analysis because of the publicly available and deidentified data.
Funding
The authors declare no financial support was received for the research, authorship, or publication of this article.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Footnotes
Supplementary data to this article can be found online at https://doi.org/10.1016/j.pmedr.2025.103132.
Contributor Information
Chengxin Xie, Email: chengxin_xie@163.com.
Zhien Shi, Email: swimsmallfish@qq.com.
Appendix A. Supplementary data
Supplementary tables and figures.
Ethical exemption from our institutional review board.
Data availability
The datasets generated during and/or analyzed during the current study are available in the National Health and Nutrition Examination Survey, https://www.cdc.gov/nchs/nhanes/index.htm.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplementary tables and figures.
Ethical exemption from our institutional review board.
Data Availability Statement
The datasets generated during and/or analyzed during the current study are available in the National Health and Nutrition Examination Survey, https://www.cdc.gov/nchs/nhanes/index.htm.