2.1. Study design

This cross-sectional study was conducted between 13 January 2025 and 22 June 2025 in Bandar Abbas County, the capital of Hormozgan Province, southern Iran. According to the 2016 national census, the county’s population was 680,366 [11]. The study followed the STROBE guidelines for reporting cross-sectional research [12].

2.2. Setting and eligibility criteria

Patients with end-stage kidney disease (ESKD) undergoing maintenance HD for more than three months were recruited from the dialysis units of Shahid Mohammadi, Khaleej Fars, and the Children’s Hospital in Bandar Abbas. The inclusion criteria were age over 18 years and willingness to participate. The exclusion criteria included age under 18 years, refusal to participate, acute kidney injury, severe cognitive impairment, advanced comorbidities (e.g., class IV heart failure, metastatic cancer, decompensated cirrhosis), or a history of untreated major psychiatric disorders. The average dialysis frequency in the dialysis centers is three sessions per week. The dialyzer filters used are PS130 and PS160, manufactured by Meditechsys (Iran), made of polysulfone, and sterilized with ethylene oxide.

2.3. Study outcomes, predictors, and confounders

2.4. Data sources and measurement

2.5. Ethical considerations

Ethical approval was obtained on 10 November 2024 from the Ethics Committee of Hormozgan University of Medical Sciences (IR.HUMS.REC.1403.253). All participants were informed about the objectives and procedures of the study prior to participation. Written informed consent was obtained from all participants before data collection. Participation was entirely voluntary, and respondents were assured of the confidentiality and anonymity of their information. The study did not include individuals under 18 years of age; therefore, no parental or guardian consent was required. The ethics committee did not waive the requirement for consent.

2.6. Bias considerations

To minimize bias and enhance validity, a simple random sampling method was used to obtain a representative sample and reduce selection bias. Standardized, validated instruments were employed uniformly to limit information bias. To control for response bias, all the interviews were conducted by three trained interviewers with final-year medical students and master’s-level nursing students. The data collection process was closely supervised to ensure consistency and adherence to the protocol.

2.7. Sample size determination and sampling

2.8. Handling of quantitative variables

The duration of dialysis was divided into two groups, < 5 years and ≥5 years, to differentiate the short-term and long-term effects on HRQoL. Additionally, to examine differences in HRQoL among patients, age was categorized into three groups: young (<40 years), middle-aged (40–60 years), and elderly (>60 years). These categorizations were selected to create a clear distinction between groups and are based on previous studies [24–27]. Educational level was classified as illiterate, less than 6 classes, 6–12 classes, or more than 12 years. Comorbidities were categorized as 0, 1, or ≥2 conditions. Blood pressure was classified as normal (≤120/80 mmHg), seminormal (121–139/81–89 mmHg), or high (≥140/90 mmHg).

2.9. Statistical analysis

Continuous variables are reported as the means ± standard deviations (SDs) if normally distributed and as medians with interquartile ranges (IQRs) otherwise. Categorical variables were summarized using frequencies and percentages. Group comparisons were conducted via independent t tests and one-way ANOVA for normally distributed variables and Mann‒Whitney U or Kruskal‒Wallis tests for non-normally distributed data. Chi-square tests were used to compare EQ-5D dimension responses, dichotomized as “no problem” vs. “any problem”.

Univariate linear regressions were first performed to examine the associations between each independent variable and HRQoL outcomes (EQ-5D index, EQ-VAS, PCS, and MCS). All variables were then included in the initial multivariable models, regardless of univariate significance. The final models were selected using backward elimination based on the Akaike Information Criterion (AIC). This procedure began with full models containing all pre-specified variables and iteratively removed the least contributory variable at each step, continuing only if removal resulted in a lower AIC. The process terminated when no further variable removal could reduce the AIC. To assess the robustness of our model selection, we performed a sensitivity analysis using the Bayesian Information Criterion (BIC), which applies a stronger penalty for model complexity. The consistency of key predictors across both selection approaches supports the stability of our findings. This strategy allowed for the identification of the most relevant predictors while adjusting for all measured covariates. Approximately 3% of the participants had substantial missing data and were excluded from the analyses. Statistical significance was defined as p < 0.05. All analyses were performed via R software (version 4.5.1).

3.1. Recruitment

Of 221 eligible patients, 13 were excluded because of ineligibility or refusal, and 5 were excluded because of incomplete data. A total of 203 participants were included in the final analysis (Fig 1).

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Fig 1. Study flow diagram.

https://doi.org/10.1371/journal.pone.0342155.g001

3.2. Patient characteristics

The mean age of the participants was 55.26 years (SD = 15.76), with 41% aged >60 years. Males accounted for 53% of the participants. Most were married (82%), urban residents (71%), and had ≤ 12 years of education (91%). Comorbidities ≥2 were reported in 51% of the patients, and 48% had supplemental insurance. Missing data were minimal (<3%), primarily related to HRQoL and laboratory variables (Table 1).

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Table 1. Sociodemographic, lifestyle, clinical and biochemical characteristics of the study population (n = 203).

https://doi.org/10.1371/journal.pone.0342155.t001

3.3. Health-related quality of life scores

3.3.1. EQ-5D-5 L Index.

The mean EQ-5D-5 L index score was 0.50. Scores were statistically and clinically significantly higher among participants aged <40 years (0.67), those employed (0.67), and those without recent hospitalization (0.62). statistically and clinically significantly lower scores were observed among divorced or widowed individuals (−0.05), illiterate individuals (0.21), and those with ≥2 comorbidities (0.37) (Table 2).

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Table 2. Statistical comparison of mean HRQoL Scores (EQ-5D-5L, EQ-VAS, PCS, MCS) according to sociodemographic and clinical characteristics of study population (n = 203).

https://doi.org/10.1371/journal.pone.0342155.t002

3.4. Predictors of health-related quality of life

According to the initial regression model, individuals with fewer than six years of education exhibited a statistically significant increase of 0.22 in EQ-5D index scores (95% CI: 0.01 to0.43). Dialysis duration was significantly associated with a decrease of 0.17 in EQ-5D index scores (95% CI: −0.34 to −0.01). In addition, being disabled or retired was associated with a statistically significant reduction of 26.14 points in EQ-VAS scores (95% CI: −50.11 to −2.17). Unemployment was significantly associated with a 4.72point decrease in Physical Component Summary (PCS) scores (95% CI: −8.65 to −0.79), whereas having supplemental insurance was associated with a statistically significant increase of 2.62 points in PCS scores (95% CI: 0.34 to4.89) (S3 Table). All reported associations were statistically significant.

To improve model parsimony, a final model was selected using the Akaike information criterion (AIC) (Table 3). In this model, all education levels remained positively associated with the EQ-5D scores. EQ-VAS scores were significantly negatively associated with divorced (β = −20.5, 95% CI = [−35.7 to −5.3]) and retired or disabled status (β = −22.5, 95% CI = [−41.6 to −3.3]). The PCS was significantly positively associated with having supplemental insurance (β = 2.4, 95% CI = [0.11 to 4.6]), whereas the MCS was significantly negatively associated with current tobacco use (β = −3.7, 95% CI = [−7.4 to −0.02]).

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Table 3. Final multivariate regression model selected by akaike information criterion (AIC) (n = 203).

https://doi.org/10.1371/journal.pone.0342155.t003

4.1. Comparisons with previous studies and interpretation

The mean HRQoL in the present study was low among HD patients (EQ-5D-5L index: 0.50; EQ-VAS: 64.9; PCS/MCS: 42.3). Comparable findings were observed across the MENA region, where HRQoL ranged from moderate to poor, including Palestine (0.37; 59.4) [21], Jordan (0.44; 62.1) [28], Saudi Arabia (36.5 VAS) [29], and Turkey (66.7 VAS) [30]. In Asia, South Korea reported relatively higher PCS and MCS scores (61.9; 55.5) [31], while Japan showed pronounced physical impairment (37.1) with similar mental levels (49.6) [32]. Africa and the United States demonstrated substantial variability (South Africa: PCS 65.4, MCS 74.6; United States: PCS 48.8, MCS 51.7) [33,34], whereas Latin American countries reported notably higher HRQoL (Colombia EQ-5D-5L: 0.9; VAS 69.6; Vietnam 0.766) [35,36]. These cross-country differences likely reflect heterogeneity in healthcare systems, the extent of financial coverage for HD, and socio-cultural contexts. Available evidence suggests that stronger universal health and social support is associated with a lower economic and psychological burden of disease and higher HRQoL [37]; therefore, cross-country comparisons should be interpreted within the context of health policies and socio-economic conditions.Furthermore, Mean EQ-5D index scores were higher among younger patients (<40 years), those who were employed, patients without hospitalization in the past year, and those with a dialysis duration of less than five years. In contrast, patients with two or more comorbidities, illiterate individuals, and those who were divorced or widowed had lower scores. These differences were both statistically and clinically meaningful, exceeding the MCID for the EQ-5D index. These findings indicate that HRQoL results from the interaction between clinical factors and social determinants of health and emphasize the need for interventions that go beyond purely clinical care.

The final multivariate regression model showed that compared to illiterate individuals, those with <6, 6–12, and >12 years of education had 0.24, 0.22, and 0.33 higher EQ-5D index scores, respectively, which were statistically significant and clinically meaningful. Similarly, in China, patients with higher education levels reported significantly better HRQoL, a difference attributed to greater disease awareness and broader social support [20]. In Palestine, university education was significantly associated with higher EQ-5D and EQ-VAS scores, with health literacy and active participation in disease management identified as key contributing factors [21]. In the Netherlands, higher education was significantly linked to better HRQoL, which has been explained by more favourable socioeconomic conditions, healthier lifestyles, and greater access to healthcare services [38]. In Iran, a significant association between education level and the mental health status of dialysis patients has also been reported, an association that may partly reflect differences in coping abilities among individuals with higher levels of education. The observed differences in the association between education and HRQoL among HD patients across countries likely reflect heterogeneity in healthcare systems, health literacy levels, cultural norms, and socio-economic conditions [39].

Each additional year of dialysis duration was associated with a 0.17 decrease in EQ-5D-Index, which was statistically significant and clinically meaningful. A similar association was reported in Japan, although the effect size was considered mild, and the reduction was attributed to psychological adaptation, life satisfaction, and the structural support of the healthcare system [10]. In Pakistan, extended time on dialysis was linked to a significant and substantial decline in physical, mental, social, and environmental functioning, which was explained by chronic stress due to repetitive treatments, functional limitations, and impaired social interactions [40]. In China, more years of dialysis were significantly associated with lower KDQOL-36 scores [41]. In contrast, a study from Poland reported no significant association between dialysis duration and patients’ HRQoL [42]. Similarly, an Iranian study reported no statistically significant difference in HRQoL scores between patients who had received dialysis for less than or more than 36 months [43]. The observed differences in the association between dialysis duration and HRQoL are likely due to heterogeneity in healthcare systems, access to support, psychological adaptation, and management of patient complications, and may also be influenced by patients’ mean age. The results show that divorced or widowed individuals scored 20.52 units lower on the EQ-VAS than single individuals, with statistical significance. In China, a significant relationship was also observed between marital status and HRQoL [44]. In Iran, married individuals reported significantly higher scores than single individuals did, a difference attributed to greater social support and higher psychological resilience among those who are married [18]. In contrast, no significant associations between marital status and HRQoL were reported in studies from Palestine and Pakistan [21,45]. These findings suggest that the effect of marital status on HRQoL may be partially influenced by social support, psychological resilience, and cultural context, which may explain the observed cross-country differences [46]. Disabled or retired individuals scored 22.45 units lower on the EQ-VAS than housekeepers, with statistical significance. In Kazakhstan, employed patients appeared to have higher HRQoL in both physical and mental health domains, which may be related to better physical functioning and a more positive mood [47]. Similarly, a study in India showed that employed individuals had higher scores across physical, psychological, and environmental domains—differences that were attributed to financial independence, greater mobility, and fewer limitations in daily activities [48]. In Northern Cyprus, employment was identified as a significant and independent predictor of QoL, with unemployed patients reporting lower scores across most KDQOL-36 dimensions; this disparity was linked to financial dependency, social isolation, and negative psychological consequences among unemployed individuals [49]. In contrast, a study from Bahrain reported no significant association between employment status and QoL among HD patients [50]. This difference between countries may be due to variations in insurance coverage, socio-economic support, cultural norms, rehabilitation programs, and access to supportive services; in Bahrain, comprehensive universal insurance may mitigate financial dependency, social isolation, and the psychological consequences of unemployment, whereas in other countries, employment may enhance quality of life to some extent by providing financial independence, physical mobility, social interaction, and a sense of purpose.

Individuals with supplementary insurance had a 2.37 higher PCS than those without, with statistical significance. Similarly, a study conducted in Nepal demonstrated that patients with health insurance reported higher scores in both physical and mental components of quality of life (PCS and MCS), a difference attributed to reduced treatment-related expenses and greater independence in accessing healthcare services [51]. Findings from a study in China corroborated these results; in that study, the type of medical insurance was significantly associated with patients’ HRQoL, and low reimbursement coverage was identified as an independent predictor of poorer outcomes in HD patients [52]. Supplemental insurance improves HRQoL in HD patients by reducing financial burden and increasing access to care, with its effect varying according to the coverage and support provided by the country’s healthcare system.

Tobacco users scored an average of 3.72 units lower on the MCS compared to non-smokers, which was statistically significant. Studies from Korea and China identified smoking as a potential factor linked to lower MCS, which may be associated with increased anxiety, depression, and psychological distress [9,53]. In Iran, smoking was associated with impaired sleep quality, potentially mediating its adverse effects on mental health [54]. Similarly, in Saudi Arabia, smoking correlated with lower PCS and MCS scores among hospitalized CKD patients [55]. In contrast, a study from Turkey reported no difference in smoking prevalence between dialysis patients and nondialysis patients and reported no association with HRQoL [56]. Tobacco use is associated with reduced mental health in HD patients, but the magnitude of its effect may depend on cultural factors, social support, access to mental health services, and differences in healthcare systems across countries, which may explain the inconsistent findings in cross-country studies.

Age, sex, and comorbidities remained in the final multivariable regression model but did not exhibit any statistically significant associations with HRQoL scores. While several studies have identified older age as a significant predictor of reduced HRQoL [20,21,57], other evidence suggests that, among HD patients, this relationship may be influenced by psychological adaptation, disease severity, and chronicity, resulting in a nonlinear or nonsignificant association [50,58,59]. Findings on gender are similarly inconsistent, whereas some studies have reported lower HRQoL scores in females [21,58], others have reported no significant differences [50]. These discrepancies may partly reflect the potential moderating role of cultural factors, social roles, and family support. Furthermore, although some previous studies have suggested that comorbidities may be associated with lower HRQoL [21,27,60,61], our findings, which are consistent with some prior reports [62], revealed no significant associations. Several contextual factors may explain our null findings. First, the substantial physical and psychosocial burden of long-term HD may exert a dominant influence on HRQoL, thereby diminishing the relative contribution of demographic characteristics. In our sample, HRQoL scores were consistently low across age groups and between men and women, indicating that dialysis-related constraints may overshadow demographic differences. Second, more than half of the participants had two or more comorbidities, resulting in a relatively homogeneous clinical profile and limited variability across subgroups. This lack of variation reduces statistical power to detect adjusted associations. Additionally, the absence of detailed information on comorbidity severity may have introduced misclassification, attenuating potential true effects. Collectively, these factors likely contributed to the absence of significant associations in multivariable analyses.

In the present study, the highest frequency of “any problem” was observed in the P/D dimension (61.6%), followed by MO and A/D (both 54.7%), whereas the lowest frequency was reported in SC (25.6%). This pattern aligns with findings from the studies by Wasserfallen, Zhang, and Zyoud, which similarly reported the highest burden in P/D and A/D and the lowest in SC [20,21,63]. Furthermore, older patients (over 60 years), those with two or more comorbidities, illiterate individuals, and divorced or widowed participants reported significantly more problems across most or all dimensions. These findings suggest that targeted management of pain and anxiety/depression, along with focused support for older adults and patients with higher disease burden or lower socioeconomic status, may be crucial for improving HRQoL in this population.

4.2. Implications for practice and policy

The findings of this study present a multidimensional roadmap for enhancing the HRQoL of HD patients. Redesigning health education for individuals with lower literacy—incorporating visual and interactive approaches—may foster better understanding and more active participation in self-care. Strengthening psychological and social support for those who have lost a partner can not only reduce feelings of loneliness but also bolster emotional resilience. Flexible employment opportunities tailored to patients’ physical capacities, alongside economic support, may help sustain their social connections and everyday roles. Expanding supplemental insurance coverage and reducing direct treatment costs can ease financial strain while improving access to high-quality care. For patients with longer dialysis histories, regular monitoring and fatigue management programs can help preserve both physical and mental capacity. Finally, integrating smoking cessation counselling and services into routine care offers an opportunity to simultaneously improve physical and mental health outcomes.

4.3. Strengths and limitations

This study benefits from a relatively large and diverse sample of HD patients, the use of validated instruments for assessing HRQoL, and robust multivariable regression modelling. The inclusion of a broad range of demographic, socioeconomic, behavioral, and clinical variables also allowed for a more comprehensive evaluation of factors influencing HRQoL.

However, the cross-sectional design limits causal inference, and reliance on self-reported data may introduce recall or social desirability bias. Conducting the study within a single regional context may also reduce the generalizability of the findings. Despite these limitations, the results provide a basis for designing targeted interventions to improve the quality of life of HD patients. In addition, there is a potential for residual confounding due to unmeasured clinical and socio-economic factors, including comorbidity severity and socio-economic conditions, which may have attenuated the observed associations between age, sex, and the number of comorbidities and HRQoL.