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Vol. 225. Issue 9.
(November 2025)
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No association between socioeconomic deprivation and cardiovascular risk or damage in systemic lupus erythematosus within a universal healthcare system: a cohort study from the Basque Country

No hay asociacion ente la privación socioeconómica y el riesgo o el daño cardiovascular en pacientes con lupus eritematoso sistémico dentro de un sistema con cobertura sanitaria universal: un estudio de cohortes del País Vasco
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Halbert Hernández-Negrina,b,c, Diana Paredes-Ruiza, Víctor Moreno-Torresa,d,e, Ioana Ruiz-Arruzaa,f, Guillermo Ruiz-Irastorzaa,f,
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guillermo.ruiz@ehu.eus

Corresponding author.
a Biobizkaia Health Research Institute, Autoimmune Diseases Research Unit, Department of Internal Medicine, Hospital Universitario Cruces, 48903 The Basque Country, Spain
b Internal Medicine Clinical Management Unit, Hospital Regional Universitario de Málaga, Instituto de Investigación Biomédica de Málaga (IBIMA-Plataforma BIONAND), 29009 Malaga, Spain
c Faculty of Medicine, Universidad de Málaga, 29071 Malaga, Spain
d UNIR Health Sciences School, 28224 Madrid, Spain
e Hospital Universitario Puerta de Hierro Majadahonda, 28222 Madrid, Spain
f UPV/EHU-University of The Basque Country, Bizkaia, 48940 The Basque Country, Spain
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Table 1. SLE Patients' baseline characteristics according to socioeconomic deprivation.
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Table 2. Multivariate analysis of cardiovascular risk factors and damage in SLE patients.
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Abstract
Background

Socioeconomic deprivation is a well-recognized determinant of cardiovascular health. We evaluated its influence on cardiovascular risk and damage in patients with systemic lupus erythematosus (SLE) in the Basque Country, where universal healthcare coverage is guaranteed.

Methods

Observational cohort study including 293 SLE patients with a 5-year follow-up. The association between the Basque Country’s Socioeconomic Deprivation Index and cardiovascular risk factors and damage (SLICC index) was analyzed using multilevel generalized linear mixed models.

Results

No significant associations were found between deprivation levels and the number of cardiovascular risk factors at diagnosis or at 5 years, nor with cardiovascular damage. Age at diagnosis and disease activity were the main predictors of cardiovascular outcomes.

Conclusion

In a universal healthcare setting, socioeconomic deprivation was not associated with worse cardiovascular risk or damage in SLE patients. These findings do not establish causality but are consistent with the hypothesis that universal healthcare may mitigate socioeconomic gradients in SLE cardiovascular outcomes.

Keywords:
Systemic Lupus Erythematosus
Cardiovascular Diseases
Health Equity
Social Determinants of Health
Universal Health Coverage
Abbreviations:
SLE
Q
SLICC
SLEDAI-2K
HCQ
Resumen
Introducción

La privación socioeconómica es un determinante de salud cardiovascular bien establecido. Evaluamos su influencia en el riesgo y daño cardiovascular en pacientes con lupus eritematoso sistémico (LES) en el País Vasco, donde existe cobertura sanitaria universal.

Métodos

Cohorte observacional de 293 pacientes con LES con un seguimiento de 5 años. Se analizó la asociación entre el Índice de Privación Socioeconómica del País Vasco y los factores de riesgo y daño cardiovascular (índice SLICC) utilizando modelos lineales mixtos generalizados multinivel.

Resultados

No se encontraron asociaciones significativas entre la privación y el número de factores de riesgo cardiovascular al diagnóstico ni a los 5 años, ni con el daño cardiovascular. La edad y la actividad del LES fueron los principales determinantes.

Conclusiones

En un entorno de asistencia sanitaria universal, la privación socioeconómica no se asoció con riesgo o daño cardiovascular en pacientes con LES. Estos hallazgos no permiten inferir causalidad, pero son compatibles con la hipótesis de que la sanidad universal podría atenuar los gradientes socioeconómicos en los resultados cardiovasculares del LES.

Palabras clave:
Lupus eritematoso sistémico
Enfermedades cardiovasculares
Equidad en salud
Determinantes sociales de la salud
Cobertura sanitaria universal
Graphical abstract
Full Text
Introduction

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease characterized by a wide range of clinical manifestations and a significant burden of comorbidities, particularly cardiovascular disease.1,2 Cardiovascular risk in SLE patients is notably higher compared to the general population, driven by both traditional and disease-specific risk factors, such as chronic inflammation and prolonged glucocorticoid use.3 The literature consistently highlights that social determinants of health, including socioeconomic deprivation, significantly influence health outcomes in various chronic diseases.4 Socioeconomic status has been linked to disparities in SLE outcomes, with lower socioeconomic status associated with higher morbidity and mortality, partly due to limited access to healthcare, lower health literacy, and increased prevalence of unhealthy behaviors.5,6

Most research on health disparities in SLE has focused on contexts with significant variations in healthcare access, such as low- and middle-income countries or regions with fragmented healthcare systems.5,7 Studies have demonstrated that, in these settings, socioeconomic deprivation exacerbates health inequities, leading to poorer outcomes among SLE patients.6,7 These findings underscore the critical role of social determinants in shaping health trajectories and highlight the need for targeted interventions to mitigate these effects.4,6

However, the impact of socioeconomic deprivation in regions with universal healthcare coverage, such as the Basque Country/Euskadi,8 in Spain, remains underexplored. The Basque Country provides a unique context due to its comprehensive public healthcare system, universal and free to access, with a widespread net of primary care and extra-hospital specialized services, interconnected with county and university hospitals, some of them with reference units for autoimmune diseases, such as Cruces University Hospital.8 Patients with SLE from all over the Basque Country can easily access to our referral unit. Besides, the small size and good communications of our country make it easy for patients to keep close contact both with primary and specialized health providers. This study aimed to investigate the impact of socioeconomic deprivation on cardiovascular risk and damage in SLE patients within a universal healthcare setting in the Basque Country, Spain.

Material and methodsPatients

This observational fixed cohort study included 293 patients with SLE in prospective follow-up from the Autoimmune Diseases Research Unit at Hospital Universitario Cruces, the Basque Country, Spain. All patients met the 1997 American College of Rheumatology criteria for SLE,9 and were enrolled at the time of diagnosis with a minimum follow-up period of 5 years. No formal a priori sample-size calculation was performed. Informed consent was obtained from all participants, and the study protocol was approved by the local institutional review board in accordance with the Declaration of Helsinki.

Socioeconomic deprivation

Socioeconomic deprivation was assessed using the Basque Socioeconomic Deprivation Index (B-SDI), which comprises seven indicators: unemployment, manual worker population, temporary worker population, low educational attainment in the population aged 16 and over, low educational attainment in the population aged 16 to 29, foreign-born population from low-income countries, and recipients of income support. It does not include individual/household annual income, and therefore our deprivation measure reflects contextual, multidimensional disadvantage rather than personal income. Each patient was assigned a deprivation quintile (Q1 to Q5), with Q1 representing the most favorable socioeconomic status and Q5 the most deprived.10 Our justification for using B-SDI is based on validation in our environment and complete geocoded coverage throughout the study period.

Cardiovascular outcomes

The number of cardiovascular risk factors, including dyslipidemia, smoking, diabetes mellitus and hypertension, were assessed at diagnosis and at 5 years. Obesity was not evaluated due to the lack of data to calculate BMI in all participants. Cardiovascular damage was evaluated using the following multi-domains definition based on the Systemic Lupus International Collaborating Clinics (SLICC) Damage Index,11 as defined in previous studies by our group: cerebrovascular accident, angina or coronary bypass, myocardial infarction, ventricular dysfunction, or claudication lasting ≥6 months.12

Data collection

Demographic, clinical, analytical, and therapeutic data were collected at diagnosis and at 5 years of follow-up. Variables included age at diagnosis, sex, origin (European vs non-European), SLE Disease Activity Index (SLEDAI-2 K) score.13 The cumulative dose of prednisone and the number of months on treatment with hydroxychloroquine (HCQ) was assessed during the first year of follow-up.

Statistical analysis

Descriptive analyses were performed to compare demographic, clinical, analytical, and therapeutic characteristics across the deprivation quintiles. Categorical variables were expressed as absolute and relative frequencies and compared using the chi-square test. Continuous variables were expressed as medians and interquartile ranges (IQR) and compared using the Kruskal-Wallis test due to their non-parametric distribution.

To evaluate the effect of socioeconomic deprivation on cardiovascular outcomes, we fitted multilevel generalized linear mixed models. For the outcomes ‘number of cardiovascular risk factors at diagnosis’ and ‘at 5 years’ (count, range 0–4), we used Poisson regression with a log link, reporting rate ratios (incidence rate ratios, RR) with 95% CIs. For ‘cardiovascular damage at 5 years’ (binary), we used multilevel logistic regression, reporting odds ratios (OR) with 95% CIs. Models were adjusted for age at diagnosis, sex, origin, SLEDAI-2 K, first-year cumulative prednisone dose (mg), and months on hydroxychloroquine.14 We assessed (over)dispersion using Pearson/deviance statistics and applied a scale (robust) adjustment to standard errors when indicated.

Data analysis was performed using IBM SPSS Statistics software (version 25), and significance was set at p < 0.05.

ResultsPatient characteristics by deprivation quintile

The baseline characteristics of the 293 SLE were stratified according to socioeconomic deprivation quintiles. As shown in Table 1, there were no significant differences in most demographic, clinical, laboratory, or therapeutic characteristics across the deprivation quintiles (all p-values > 0.05), except for the presence of anti-DNA antibodies. For other variables, the median age at diagnosis ranged from 31 to 37 years across quintiles, and the percentage of female patients varied from 81.8% to 94.4%. The prevalence of non-European patients ranged from 1.6% to 7.3%.

Table 1.

SLE Patients' baseline characteristics according to socioeconomic deprivation.

DomainVariableTotal N = 293Socioeconomic deprivationp
Quintile 1 n = 54  Quintile 2 n = 61  Quintile 3 n = 62  Quintile 4 n = 61  Quintile 5 n = 55 
DemographicsAge at diagnosis, median (interquartile range)  34 (25 to 43.5)  32 (23.7 to 44.7  37 (27 to 4435.5 (27.7 to 46.5)  31 (24 to 40.5)  33.4 (24 to 430.362 
Female sex (%)  258 (88.1)  51 (94.4)  55 (90.2)  53 (85.5)  54 (88.5)  45 (81.8)  0.312 
Non-European (%)  13 (4.4)  2 (3.7)  4 (3.2)  2 (3.2)  1 (1.6)  4 (7.3)  0.542 
Disease CharacteristicsSkin involvement (%)  164 (5629 (53.7)  32 (52.5)  34 (54.8)  40 (65.6)  29 (52.7)  0.567 
Joint involvement (%)  174 (59.4)  29 (53.7)  38 (62.3)  40 (64.5)  35 (57.4)  32 (58.2)  0.785 
Serosal involvement (%)  59 (20.1)  16 (29.6)  13 (21.3)  11 (17.7)  9 (14.8)  10 (18.2)  0.341 
Hematological involvement (%)  197 (67.2)  40 (74.1)  39 (63.9)  41 (66.1)  43 (70.5)  34 (61.8)  0.648 
Renal involvement (%)  36 (12.3)  7 (137 (11.5)  5 (8.1)  9 (14.8)  8 (14.5)  0.791 
Central nervous system involvement (%)  12 (41 (1.8)  3 (4.9)  1 (1.6)  5 (8.2)  2 (3.6)  0.437 
Antiphospholipid síndrome (%)  13 (4.4)  3 (5.6)  2 (3.3)  2 (3.2)  2 (3.3)  4 (7.3)  0.811 
Immunological CharacteristicsAnti-DNA antibodies (%)  163 (55.6)  37 (68.5)  27 (44.3)  29 (46.8)  35 (57.4)  35 (63.6)  0.035 
Anti Sm antibodies (%)  48 (16.4)  9 (16.7)  8 (13.1)  5 (8.1)  13 (21.3)  13 (23.6)  0.147 
Anti Ro antibodies (%)  105 (35.8)  16 (29.6)  20 (32.8)  19 (30.6)  28 (45.9)  22 (400.292 
Anti La antibodies (%)  37 (12.6)  5 (9.3)  7 (11.5)  10 (16.1)  6 (9.8)  9 (16.4)  0.657 
Anti RNAP antibodies (%)  58 (19.8)  6 (11.1)  17 (27.9)  10 (16.1)  10 (16.4)  15 (27.3)  0.092 
Anticardiolipin antibodies (%)  70 (23.9)  16 (29.6)  15 (24.6)  13 (2110 (16.4)  16 (29.1)  0.409 
Lupus anticoagulant (%)  55 (18.8)  9 (16.7)  12 (19.7)  10 (16.1)  10 (16.4)  14 (25.5)  0.684 
Anti β2 glycoprotein 1 antibodies* (%)  7 (4.9)  0 (03 (8.8)  1 (33 (120 (00.148 
Hypocomplementemia (%)  165 (56.3)  24 (44.4)  35 (57.4)  35 (56.5)  37 (60.7)  34 (61.8)  0.374 
Disease activity  SLEDAI-2 K, median (interquartile range)  6 (4 to 10)  5 (4 to 12)  5 (3.5 to 105.5 (4 to 87 (4 to 11)  6 (2 to 10)  0.395 
Cumulative damage  Cardiovascular damage (%)  2 (0.7)  1 (1.9)  0 (0)  1 (1.6)  0 (0)  0 (0)  0.500 
Cardiovascular risk factorsArterial hypertension(%)  37 (12.6)  6 (11.1)  6 (9.8)  9 (14.5)  7 (11.5)  9 (16.4)  0.832 
Diabetes mellitus (%)  8 (2.7)  0 (0)  2 (3.3)  3 (4.8)  2 (3.3)  1 (1.8)  0.658 
Smoking (%)  69 (23.5)  14 (25.9)  13 (21.3)  13 (2113 (21.3)  16 (29.1)  0.800 
Dyslipidemia (%)  62 (21.2)  17 (31.5)  15 (24.6)  9 (14.5)  7 (11.5)  14 (25.5)  0.048 
Number of cardiovascular risk factors, median (interquartile range)  0 (0 to 1)  1 (0 to 1)  0 (0 to 1)  0 (0 to 1)  0 (0 to 1)  1 (0 to 1)  0.168 
Treatments receivedPrednisone cumulative dose, median (interquartile range)  1545 (0 to 3450)  1849 (399 to 4481)  1350 (0 to 2492)  1743 (338 to 3159)  1800 (0 to 3375)  1430 (630 to 3600)  0.489 
Methylprednisolone pulses (%)  63 (21.5)  11 (20.4)  9 (14.8)  16 (25.8)  11 (1816 (29.1)  0.325 
Months with hydroxychloroquine, median (interquartile range)  12 (0 to 12)  11.5 (0 to 12)  12 (0 to 12)  11 (0 to 12)  12 (0 to 12)  12 (0 to 12)  0.604 
Methotrexate (%)  18 (6.1)  2 (3.7)  3 (4.9)  5 (8.1)  1 (1.6)  7 (12.7)  0.140 
Azathioprine (%)  31 (10.6)  7 (133 (4.9)  7 (11.3)  8 (13.1)  6 (10.9)  0.592 
Mycophenolate mofetil (%)  14 (4.8)  2 (3.7)  2 (3.3)  3 (4.8)  3 (4.9)  4 (7.3)  0.899 
Cyclosporine (%)  1 (0.3)  0 (0)  0 (0)  0 (0)  0 (0)  1 (1.8)  0.370 
Cyclophosphamide (%)  32 (10.9)  8 (14.8)  5 (8.2)  5 (8.1)  5 (8.2)  9 (16.4)  0.418 
Vitamin D supplements (%)  114 (38.9)  17 (31.5)  25 (4124 (38.7)  19 (31.1)  29 (52.7)  0.120 
Bisphosphonates (%)  16 (5.4)  5 (9.2)  4 (6.6)  4 (6.5)  1 (1.6)  2 (3.6)  0.389 
Acetylsalicylic acid (%)  75 (25.6)  15 (27.8)  21 (34.4)  14 (22.6)  12 (19.7)  13 (23.6)  0.388 
Statins (%)  21 (7.1)  5 (9.2)  5 (8.2)  3 (4.8)  4 (6.6)  4 (7.3)  0.896 
*

This determination was available for 143 patients.

The results of the multilevel generalized linear mixed models are presented in Fig. 1. Dispersion statistics did not indicate relevant overdispersion; we nonetheless used robust standard errors as a conservative approach; inferences were unchanged. The analysis indicated no significant association between socioeconomic deprivation and the number of cardiovascular risk factors at diagnosis or at 5 years. Specifically, the RR for the number of cardiovascular risk factors at diagnosis in the highest deprivation quintile (Q5) compared to the most favorable quintile (Q1) was 1.085 (95% CI: 0.713-1.654, p = 0.702). Similarly, at the 5-year follow-up, the RR for Q5 vs. Q1 was 0.977 (95% CI: 0.647-1.475, p = 0.911).

Fig. 1.

Socioeconomic deprivation and cardiovascular outcomes in SLE patients: multilevel generalized linear mixed models results.

(A and B) display rate ratios (incidence rate ratios) from Poisson models (log link) for the count of cardiovascular risk factors. (C) displays odds ratios from logistic models for cardiovascular damage at 5 years.

Additionally, no significant association was found between socioeconomic deprivation and cardiovascular damage at 5 years. The OR for cardiovascular damage at 5 years in Q5 vs. Q1 was 1.131 (95% CI: 0.162-7.898, p = 0.901). Cardiovascular damage at diagnosis was not evaluated due to its occurrence in only two patients.

Due to the low number of male and non-European patients, stratified analyses by these categories were not performed. Detailed results of the multivariate models are provided in Table 2.

Table 2.

Multivariate analysis of cardiovascular risk factors and damage in SLE patients.

Outcome  Model  Variable  p  Association measure  Lower limit of the 95% confidence interval  Upper limit of the 95% confidence interval 
Number of cardiovascular risk factors at diagnosisWithout socioeconomic deprivationIntercept  0.000  0.215  0.119  0.388 
Age at diagnosis  0.000  1.029  1.019  1.038 
Female sex  0.009  0.628  0.443  0.891 
Non-European  0.778  1.089  0.602  1.970 
SLEDAI-2 K  0.000  1.046  1.023  1.069 
With socioeconomic deprivationIntercept  0.014  0.364  0.163  0.815 
Age at diagnosis  0.000  1.020  1.011  1.030 
Female sex  0.145  0.761  0.527  1.099 
Non-European  0.934  1.025  0.565  1.861 
SLEDAI-2 K  0.002  1.036  1.014  1.059 
Quintile 1  Reference
Quintile 2  0.882  0.969  0.638  1.471 
Quintile 3  0.520  0.871  0.572  1.328 
Quintile 4  0.743  0.932  0.613  1.419 
Quintile 5  0.702  1.085  0.713  1.654 
Number of cardiovascular risk factors at the 5-year follow-upWithout socioeconomic deprivationIntercept  0.001  0.376  0.207  0.682 
Age at diagnosis  0.000  1.027  1.018  1.037 
Female sex  0.001  0.570  0.409  0.796 
Non-European  0.893  0.959  0.518  1.775 
SLEDAI-2 K  0.000  1.051  1.027  1.076 
Prednisone cumulative dose  0.187  1.000  1.000  1.000 
Months with hydroxychloroquine  0.002  0.961  0.937  0.985 
With socioeconomic deprivationIntercept  0.065  0.520  0.259  1.043 
Age at diagnosis  0.000  1.019  1.010  1.029 
Female sex  0.072  0.722  0.506  1.030 
Non-European  0.759  0.907  0.485  1.698 
SLEDAI-2 K  0.001  1.040  1.016  1.064 
Prednisone cumulative dose  0.563  1.000  1.000  1.000 
Months with hydroxychloroquine  0.123  0.980  0.956  1.005 
Quintile 1  Reference
Quintile 2  0.532  0.878  0.584  1.321 
Quintile 3  0.757  0.940  0.635  1.392 
Quintile 4  0.843  0.843  0.560  1.270 
Quintile 5  0.911  0.977  0.647  1.475 
Cardiovascular damage at the 5-year follow-upWithout socioeconomic deprivationIntercept  0.005  0.001  0.000  0.100 
Age at diagnosis  0.007  1.096  1.026  1.171 
Female sex  0.910  0.855  0.056  12.980 
Non-European  0.999  0.000  0.000 
SLEDAI-2 K  0.815  1.026  0.829  1.269 
Prednisone cumulative dose  0.707  1.000  1.000  1.000 
Months with hydroxychloroquine  0.668  0.959  0.793  1.160 
With socioeconomic deprivationIntercept  0.007  0.011  0.000  0.285 
Age at diagnosis  0.075  1.037  0.996  1.079 
Female sex  0.944  0.941  0.167  5.288 
Non-European  0.742  0.584  0.024  14.491 
SLEDAI-2 K  0.919  1.006  0.895  1.131 
Prednisone cumulative dose  0.846  1.000  1.000  1.000 
Months with hydroxychloroquine  0.769  0.983  0.879  1.100 
Quintile 1  Reference
Quintile 2  0.999  1.001  0.151  6.620 
Quintile 3  0.672  1.447  0.261  8.035 
Quintile 4  0.951  1.060  0.160  7.050 
Quintile 5  0.901  1.131  0.162  7.898 

SLEDAI-2 K, SLE Disease Activity Index.

Discussion

This study aimed to investigate the influence of socioeconomic deprivation on cardiovascular risk and damage in SLE patients from the Lupus-Cruces Cohort in the Basque Country, Spain. By examining a cohort with free and universal access to primary and specialized healthcare, we sought to understand whether the well-documented associations between socioeconomic deprivation and adverse health outcomes persist in a context where healthcare accessibility is not a limiting factor.

Our results indicate no significant associations between socioeconomic deprivation and the number of cardiovascular risk factors at diagnosis or at 5 years. Additionally, there was no significant association between socioeconomic deprivation and cardiovascular damage at 5 years. Interestingly, significant variables in our models included age at diagnosis and SLEDAI-2 K score, which were consistently associated with cardiovascular risk factors both at diagnosis and at the 5-year follow-up. This highlights the critical role of disease activity and age in cardiovascular health among SLE patients.3,14

The lack of a significant association between socioeconomic deprivation and cardiovascular risk and damage is consistent with the hypothesis that the comprehensive and equitable healthcare system in the Basque Country may mitigate the effects of socioeconomic disparities4,8; however, causality cannot be inferred and alternative explanations (residual confounding, limited power) remain possible. Universal access to healthcare likely reduces barriers to receiving timely and adequate medical care, thereby leveling the playing field across different socioeconomic groups.4 This contrasts sharply with findings from studies conducted in regions with less equitable healthcare systems, such as the United States, where socioeconomic deprivation significantly exacerbates health disparities.2,5–7

Other factor that may influence our findings in that the socioeconomic the B-SDI might capture deprivation differently compared to indices used in other studies.15 The specific components of this index, such as unemployment rates and educational attainment, may not directly influence health outcomes in the same way across different regions.10 Additionally, the relatively small sample size and the potential homogeneity within the Basque population could have limited the variability needed to detect significant differences.

Our study benefits from several methodological strengths that enhance the credibility and relevance of our findings. First, the longitudinal design allowed us to capture the evolution of cardiovascular risk and damage over time, providing a dynamic view of disease progression in SLE patients. The use of the Basque Socioeconomic Deprivation Index,10 tailored to the specific context of the Basque Country, enabled precise measurement of socioeconomic status, reflecting local social and economic conditions more accurately than broader indices.16 Furthermore, the incorporation of multilevel generalized linear mixed models facilitated a robust analysis by accounting for the hierarchical structure of the data and controlling for both individual-level and contextual variables. This comprehensive approach ensured that we could isolate the impact of socioeconomic deprivation on cardiovascular outcomes while considering a range of relevant clinical and demographic factors. Finally, our study's setting within a universal healthcare system offers unique insights into how equitable access to healthcare can influence health outcomes, adding a valuable dimension to the existing literature on health disparities.4–6,8,16

However, our study has also some limitations. This was a fixed, consecutive cohort with no a priori sample-size calculation; analyses are therefore precision-based, and the study was underpowered to detect small-to-moderate effects, as reflected by wide confidence intervals. Power was especially limited for key subgroups (e.g., men, migrants) and for finer deprivation strata, which restricts generalizability. BMI was not systematically recorded and could not be reliably retrieved; its omission may confound estimates, so future studies should include adiposity measures. Socioeconomic exposure was measured with the B-SDI, an area-level composite that includes “recipients of income support” but not individual/household income (nor personal education or occupation); thus, residual socioeconomic status confounding and non-differential exposure misclassification (likely biasing toward the null) are possible. The cohort’s relatively young age yielded few cardiovascular damage events over five years; we complemented event outcomes with a count of cardiovascular risk factors to capture earlier risk burden, yet modest associations may remain undetected. Finally, the observational design precludes causal inference; replication in larger, multicenter cohorts with longer follow-up, additional individual-level socioeconomic status dimensions, and in health systems with different financing models is warranted.6

The findings of this study have important implications for clinical practice and public health. They suggest that in regions with universal healthcare coverage, socioeconomic deprivation may not be a primary determinant of cardiovascular outcomes in SLE patients. This highlights the potential of comprehensive healthcare systems to mitigate health disparities and improve outcomes for disadvantaged populations, reinforcing the role of public universal health systems in the construction of more advanced and fair societies, in a time when health provisioning is being considered as a privilege and an opportunity for business in many parts of the world.4,8,16

In conclusion, in our cohort cared for within a universal healthcare system, socioeconomic deprivation was not associated with cardiovascular risk factors or damage over five years. These descriptive findings do not establish causality but are consistent with the hypothesis that universal access may mitigate socioeconomic gradients. Replication in larger, multicenter cohorts and other health-system contexts is needed.

Informed consent

Informed consent was obtained from all subjects involved in the study.

Ethics approval

This study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Research Ethics Committee of Hospital Universitario de Cruces (Spain). Ethics Committee code: E08/35.

Funding

G. Ruiz-Irastorza was supported by the Department of Education of the Basque Government, research grant IT 1512-22. H. Hernandez-Negrin was supported by Consejería de Transformación Económica, Industria, Conocimiento y Universidades, Junta de Andalucía-Sevilla (Spain), research grant PREDOC-00826.

Conflict of interests

The authors declare no conflict of interest.

Data availability

The datasets generated during the present study are not publicly available due to ethical or privacy restrictions but may be requested for reasonable reasons from the author for correspondence.

Acknowledgments

We acknowledge ADELES Gipuzkoa and Asociación de Lupus y Autoinmunes de Castilla-La Mancha for their support. We thank Dr. Carlos Saiz-Hernando for his aid in obtaining the Socioeconomic Deprivation Index of our Lupus-Cruces patients.

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