Psychosocial factors associated with depression and sleep quality in patients with tuberculosis: a multicenter cross-sectional study

Introduction

Background

Tuberculosis (TB) remains a major public health challenge in China (1). Beyond its physical health impacts, patients with TB frequently experience substantial psychological distress, including depression and a range of sleep-related problems (2). Depression has been reported in approximately 16.8–70% of patients with TB, particularly among older adults (3,4), and is associated with impaired medication adherence (5,6), prolonged treatment courses (7,8), and reduced quality of life (9). Sleep-related problems are also highly prevalent in this population. Previous studies have documented sleep deprivation, sleep disturbances, and poor subjective sleep experiences in 50–70% of patients with TB (10-12). These sleep-related difficulties may compromise immune function and exacerbate TB-related symptoms, thereby complicating recovery and treatment outcomes (12). Among these sleep-related manifestations, sleep quality captures patients’ overall subjective evaluation of sleep continuity, depth, and restoration, and has been increasingly used to reflect sleep-related well-being in TB research.

Rationale and knowledge gap

Patients with TB are exposed to multiple psychosocial stressors, including prolonged treatment regimens, physical discomfort, and disease-related stigma (13). These stressors may increase vulnerability to depression and impaired sleep quality, which in turn can undermine treatment adherence and overall quality of life (9,14). Although the bidirectional relationship between depression and sleep has been well documented in the general population (15,16), patients with TB face additional challenges related to the infectious nature of the disease, such as prolonged isolation and strict infection control measures (13). These factors may intensify psychological distress and further disrupt sleep, potentially amplifying the reciprocal interplay between depression and sleep quality.

TB-related stigma represents a major psychological challenge for patients with TB and has been linked to adverse mental health and sleep-related outcomes (15). Stigma-related experiences are associated with elevated perceived stress (14,17,18), and both perceived stress and anxiety have been implicated in depression and poorer sleep quality among TB patients (13,19). Despite these observations, few studies have simultaneously examined how stigma, perceived stress, and anxiety interact to influence depression and sleep quality within a single analytic framework in TB populations (14,18,20,21). A clearer understanding of these interrelationships is essential for developing targeted psychosocial interventions aimed at improving mental health, sleep quality, and treatment outcomes in this vulnerable group.

Theoretical framework

To better understand how these factors may be interconnected, the present study drew on the stress–coping perspective (22). This perspective suggests that psychosocial stressors may influence psychological and behavioral outcomes through individuals’ appraisal processes and emotional responses. In patients with TB, stigma can represent a persistent source of stress in the context of prolonged treatment and social isolation. When stigma-related experiences are perceived as threatening and coping resources are limited, elevated perceived stress and anxiety may occur, which may be associated with more severe depressive symptoms and poorer sleep quality (2,23). This perspective provides a conceptual basis for considering the interrelationships among stigma, emotional distress, and sleep problems in patients with TB.

Objective

This study aimed to examine the interrelationships among stigma, perceived stress, anxiety, depression, and sleep quality in Chinese patients with TB. A non-recursive structural equation modeling approach was used to quantify the direct, indirect, and reciprocal pathways linking these factors. The findings provide empirical evidence to inform integrated interventions addressing both psychological well-being and sleep quality in patients with TB.

Research model and hypotheses

Based on the preceding discussion, the research model including stigma, perceived stress, anxiety, depression, and sleep quality in patients with TB is illustrated in Figure 1. The hypotheses derived from this model are:

• H1: TB-related stigma is significantly associated with depression and sleep quality in patients with TB;
• H2: the association between TB-related stigma and depression is indirectly mediated by perceived stress and anxiety;
• H3: the association between TB-related stigma and sleep quality is indirectly mediated by perceived stress and anxiety;
• H4: depression and sleep quality exhibit a bidirectional relationship in patients with TB.

Figure 1 Conceptual diagram for the proposed model concerning structural relations of the study variables.

We present this article in accordance with the STROBE reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2026-1-0017/rc).

Methods

Study setting, design, and period

This multicenter cross-sectional study was conducted between July 1, 2023, and October 31, 2024, at three hospitals in China: West China Hospital, Guangyuan Mental Health Center of Sichuan Province, and The Fourth People Hospital of Nanning, China. A total of 584 patients with TB were enrolled. All participants provided demographic information and completed self-report questionnaires assessing stigma, perceived stress, anxiety, depression, and sleep quality. All instruments used are well-validated and widely applied in clinical research.

Participants

Patients were recruited consecutively from the participating centers during the study period. Inclusion criteria: (I) aged 18 years or older; (II) diagnosed with pulmonary TB (either bacteriologically confirmed or clinically diagnosed according to national guidelines) by a treating physician; (III) able to read and understand Chinese to complete self-report questionnaires; (IV) provided written informed consent. Exclusion criteria: (I) severe cognitive impairment (e.g., dementia) or acute psychiatric condition (e.g., active psychosis, delirium) that would preclude reliable questionnaire completion; (II) severe comorbid physical illness (e.g., terminal cancer, major organ failure); (III) pregnancy; (IV) experience of a major life event (other than TB diagnosis; e.g., death of an immediate family member, recent divorce or job loss) within the past 6 months that was likely to substantially affect mental health or sleep.

Sample size estimate

The minimum required sample size was estimated using the formula for cross-sectional studies:

N=(μα/2}2×π×(1−π))/δ2[1]

where N represents the sample size required for the study, μα/2=1.96 (corresponding to α=0.05, two-tailed), π is the expected prevalence, and δ is the margin of error. Based on a pilot study at West China Hospital, the prevalence of sleep problems among TB patients was estimated at 50% (π=0.5). With a margin of error (δ) at 0.05 and the significance level (α) at 0.05, the minimum required sample size was calculated to be 385. Ultimately, 604 patients were enrolled, and after excluding 20 with missing data, 584 were included in the final analysis.

Instruments

TB-related stigma was assessed using the Tuberculosis-related Stigma Scale (TRSS), which was developed and validated for the Chinese population (23). The TRSS comprises nine items rated on a 4-point Likert scale (0= strongly disagree to 3= strongly agree), with total scores ranging from 0 to 27. Higher scores indicate greater levels of TB-related stigma. In the present study, the Cronbach’s alpha coefficient for the TRSS was 0.925.

Perceived stress over the past month was measured using the 14-item Perceived Stress Scale (PSS-14) (24,25). Each item is rated on a 5-point Likert scale, with reverse scoring applied to items 4, 5, 6, 7, 9, 10, and 13. Total scores range from 0 to 56, with higher scores indicating greater perceived stress. The Cronbach’s alpha for the PSS-14 in this study was 0.874.

Anxiety symptoms during the past two weeks were assessed using the 7-item Generalized Anxiety Disorder scale (GAD-7) (26). Total scores range from 0 to 21, with higher scores indicating more severe anxiety symptoms. The Cronbach’s alpha coefficient for the GAD-7 in the current sample was 0.938.

Depressive symptoms were evaluated using the 9-item Patient Health Questionnaire (PHQ-9) (27), which assesses symptom severity over the past two weeks. Total scores range from 0 to 27, with higher scores reflecting more severe depressive symptoms. In this study, depression refers to these measured depressive symptoms. The Cronbach’s alpha for the PHQ-9 in this study was 0.901.

Sleep quality over the past month was evaluated using the Pittsburgh Sleep Quality Index (PSQI) (24,28,29). The scale comprises 7 items, including sleep quality, sleep onset latency, sleep duration, sleep efficiency, sleep disturbances, use of sleep medication, and daytime dysfunction. Each item is rated on a scale from 0 to 3, and the total score ranges from 0 to 21, with higher scores indicating poorer sleep quality. In this study, the Cronbach’s alpha for the PSQI was 0.837.

Data collection

A standardized protocol was implemented across all centers. Trained research assistants explained the study purpose and procedures to participants, who then completed the questionnaires independently in a designated area. For participants with reading or visual difficulties, items were read aloud neutrally. All questionnaires were checked for completeness on-site upon completion, with any missing items clarified immediately with the participant to ensure data integrity.

Ethical considerations

This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study protocol was approved by the Biomedical Research Ethics Committee of West China Hospital, Sichuan University (approval No. 2022(1391)), and the other two participating hospitals granted local administrative permissions in recognition of this approval. All participants provided written informed consent prior to data collection.

Statistical analysis

Continuous variables are presented as mean with standard deviation (SD) or median with interquartile range (IQR), as appropriate. Pearson’s correlation analysis was performed to examine bivariate associations among study variables. Potential common method bias was examined using Harman’s single-factor test.

Given the complex interrelationships among TB-related stigma, perceived stress, anxiety, depression, and sleep quality, the SEM analysis was conducted in an exploratory manner. The goal was to investigate potential reciprocal associations and indirect pathways, rather than to test a pre-specified causal model.

To examine the hypothesised reciprocal relationship between depression and sleep quality, a non-recursive structural equation model was specified. Non-recursive SEM models involve the specification of reciprocal paths between variables. In our model, identification was achieved by constraining the reciprocal paths between depression and sleep quality to be equal (W1 = W2) (30). Importantly, because the data are cross-sectional, the estimated reciprocal associations reflect statistical relationships rather than temporal or causal effects. Longitudinal data would be required to confirm the directionality of these associations.

Overall model fit was assessed using the chi-square to degrees of freedom ratio (χ²/df), Comparative Fit index (CFI), Tucker-Lewis index (TLI), root mean square error of approximation (RMSEA). Model fit was considered acceptable if χ²/df <3, CFI and TLI ≥0.90, and RMSEA ≤0.08 (31,32). The significance of direct and indirect (mediation) effects was tested using the bias-corrected percentile bootstrap method with 5,000 resamples. Effects were considered statistically significant if the corresponding 95% confidence intervals did not include zero.

All statistical analyses were conducted using R (http://www.R-project.org) and Free Statistics software (version 2.3). Structural equation modelling was performed using AMOS software (version 24.0; IBM Corp., Armonk, NY, USA). A two-sided P value <0.05 was considered statistically significant.

Results

Common method bias

To address potential common method bias, we conducted the Harman 1-factor test. The results of principal component factor analysis without rotation revealed 4 factors with eigenvalues greater than 1, among which the variation explained by the first factor was only 38.36%, which is less than the critical standard of 40%. Thus, there was no substantial common method bias in our study.

Correlational analysis

Correlational analysis showed that stigma was positively correlated with perceived stress, anxiety symptoms, depression, and sleep quality, with correlation coefficients ranging from 0.318 to 0.573 (P<0.01) (Table 1).

Structural equation modeling

Based on a priori, single-direction structural equation models were first estimated to examine the effect of depression on sleep quality (Figure 2A) and the effect of sleep quality on depression (Figure 2B). Both models showed acceptable fit to the data. Subsequently, the non-recursive structural equation model demonstrated a good fit to the data: χ²/df =2.857, NFI =0.981, CFI =0.987, GFI =0.985. RMSEA =0.056 (Figure 2C). All standardized direct path coefficients in the model were significant (P<0.05; Table 2).

Figure 2 Standardized path coefficients of the single-directional and non-recursive structural equation models of depression and sleep quality in TB patients. (A) Standardized model paths for when sleep quality is the dependent variable; (B) standardized model paths for when depression is the dependent variable; (C) standardized model paths and standardized parameter estimates for the non-recursive variables. Fit indices: GFI, AGFI, IFI, CFI (>0.9 good, 0.8–0.9 acceptable), RMSEA (<0.05 good, 0.05–0.08 acceptable), Chi-squared/df (<3 good, <5 acceptable). AGFI, Adjusted Goodness-of-Fit index; CFI, Comparative Fit index; Chi-squared/df, Chi-squared to degrees of freedom ratio (χ²/df); GFI, Goodness-of-Fit index; IFI, Incremental Fit index; NFI, Normed Fit index; TB, tuberculosis; TLI, Tucker-Lewis index.

TB-related stigma was directly associated with perceived stress (β=0.576), anxiety (β=0.359), depression (β=0.165), and sleep quality (β=0.323). Perceived stress was directly associated with anxiety (β=0.186), depression (β=0.143), and sleep quality (β=0.235). Anxiety was directly associated with depression (β=0.233) and sleep quality (β=0.178).

Depression and sleep quality showed a significant bidirectional association. More severe depression were associated with poorer sleep quality (β=0.179, 95% CI: 0.124–0.246), and conversely, poorer sleep quality was associated with more severe depression (β=0.089, 95% CI: 0.062–0.121). The model explained 27% of depression variance (R²=0.27) and 52% of sleep quality variance (R²=0.52).

Effect decomposition showed that the total effect of stigma on depressive symptoms was β=0.410, with indirect effects accounting for 59.8% (β=0.245). The total effect of stigma on sleep quality was β=0.615, with indirect effects accounting for 47.5% (β=0.292) (Table 3).

Bootstrap analyses identified multiple statistically significant indirect pathways linking stigma to depression and sleep quality (Table S1). For depression, indirect effects via perceived stress (β=0.081, 20.4%) and anxiety (β=0.083, 20.7%) were both statistically significant. For sleep quality, indirect effects via perceived stress (β=0.139, 23.1%) and anxiety (β=0.065, 10.9%) were also statistically significant. Pairwise comparisons showed no statistically significant differences between the indirect effects via perceived stress and anxiety on depression (Ind1 vs. Ind2, P>0.05, Table S2), nor between those on sleep quality (Ind8 vs. Ind9, P>0.05, Table S2). Additional serial mediation pathways reached statistical significance but showed smaller effect sizes.

Discussion

This study examined the interrelationships among TB-related stigma, perceived stress, anxiety, depressive symptoms, and sleep quality in Chinese patients with TB using a non-recursive structural equation model. Stigma was associated with both depressive symptoms and sleep quality through direct and indirect pathways, highlighting its central role in the psychosocial burden of TB. Concerns about social judgment, illness disclosure, and fear of discrimination may directly compromise emotional well-being and disrupt sleep, particularly in the Chinese cultural context, where emphasis on ‘saving face’ may discourage seeking psychological support (13,19,33).

The mediation analyses clarified the mechanisms linking stigma to these outcomes. For sleep quality, the indirect effect via perceived stress was numerically larger than that via anxiety, but pairwise comparisons indicated no statistically significant difference, suggesting that both pathways contribute meaningfully. For depressive symptoms, the indirect effects through perceived stress and anxiety were of similar magnitude. Together, these results indicate that stigma affects mental health and sleep through multiple overlapping psychological pathways rather than a single linear mechanism (14,17,18,20,21). These findings imply that interventions targeting stress appraisal and management may be relevant for improving sleep quality, whereas strategies addressing both stress and anxiety could be important for mitigating depressive symptoms.

Depressive symptoms and sleep quality were reciprocally associated within the integrated model, indicating that these outcomes are closely interrelated within the broader psychosocial context of TB. This observation aligns with previous studies reporting bidirectional associations between sleep and depression in general and clinical populations (15,16). Sleep disturbances are not only symptoms of depression but also risk factors for its onset (16,34), with disrupted circadian rhythms and increased stress contributing to depressive mood and symptoms such as anhedonia, fatigue, and impaired concentration (35-37). Approximately 20% of individuals with initial insomnia or non-restorative sleep exhibit depressive symptoms, and insomnia is associated with a twofold higher risk of subsequent depression (16,38). Conversely, depression can impair sleep patterns and is a risk factor for insomnia, with up to 90% of patients with depression experiencing sleep disturbances (39,40). Abnormalities in sleep architecture, such as prolonged sleep latency and increased nighttime awakenings, are frequently observed in depressed individuals and may serve as potential biomarkers of depression (41,42). In our sample, the standardized association from depressive symptoms to sleep quality was somewhat larger than the reverse, indicating that emotional distress may have a stronger concurrent link to sleep disruption, which in turn may exacerbate depressive symptoms through fatigue and reduced coping capacity.

From a clinical perspective, these findings highlight the potential value of integrated psychosocial support for patients with TB. For example, TB services could incorporate stigma-reduction counseling employing cognitive-behavioral techniques to challenge negative illness-related beliefs (43); brief stress management programs incorporating mindfulness or relaxation training; and behavioral sleep interventions such as sleep hygiene education or stimulus control therapy adapted for medically ill populations. Delivering these components systematically within routine TB care settings, such as during follow-up or medication visits, may help address the multiple interconnected psychosocial factors identified in our analysis.

Extending these clinical considerations to a broader service and policy level, these findings support the integration of psychosocial assessment and support into routine TB programs as part of a more holistic, patient-centered care model. Embedding structured psychosocial screening and low-intensity interventions within existing TB services may enhance the comprehensiveness of care while remaining feasible in resource-constrained settings. Future longitudinal and implementation studies are needed to further evaluate feasibility and long-term effectiveness.

There are several limitations in this study. The cross-sectional design precludes causal or temporal inferences, and the findings should be interpreted as structural associations rather than causal effects. Although a non-recursive structural equation model was used to specify reciprocal paths, such modeling does not establish directionality or causality in the absence of longitudinal data. The bidirectional paths identified in this study represent statistical associations within the observed data structure rather than temporal sequences. Prospective longitudinal studies are needed to verify the direction and stability of these relationships over time. All measures were based on patient self-report and may not fully reflect objective sleep quality or depressive symptoms. In addition, potential selection bias cannot be entirely excluded, as participation in the study was voluntary and limited to individuals who agreed to complete self-report questionnaires. Patients who declined participation or were unable to complete the survey may differ systematically from those included, which could have influenced the observed associations. Furthermore, several clinical characteristics of TB, including treatment phase, duration of illness, drug resistance status, and medication-related factors, may act as potential confounders in the associations examined. These factors could plausibly influence both depressive symptoms and sleep quality and therefore warrant consideration when interpreting the findings. To assess the potential impact of residual confounding, we conducted E-value sensitivity analyses. The results indicated that a relatively strong unmeasured confounder would be required to fully account for the observed associations (Table S3). Other potential contributors to depressive symptoms in TB patients extend beyond the clinical and psychosocial factors examined in our model. For example, disease-related characteristics such as symptom burden and treatment-related effects have been associated with increased psychological distress among individuals undergoing TB treatment (44). Broader social determinants, including financial strain, limited social support, and reduced access to healthcare, have also been linked to depression in TB populations and other chronic illness groups (45,46). Individual-level psychological factors such as coping styles and health literacy, as well as a prior history of mental health conditions, have been shown to influence depression risk in medically ill cohorts (47,48). Biological mechanisms, particularly systemic inflammation and immune dysregulation, have been implicated in the pathophysiology of depression in patients with infectious diseases including TB (49). These domains, though beyond our study scope, may independently affect depressive symptoms. Future research integrating clinical, social, psychological, and biological domains will be important to further clarify the complex etiology of depression in TB populations. Despite these limitations, the use of a non-recursive structural equation model allowed us to specify and evaluate a network of reciprocal associations rarely examined in TB research, generating empirically grounded hypotheses for future longitudinal studies.

Conclusions

In patients with TB, stigma was centrally associated with both depressive symptoms and sleep quality, largely through pathways involving perceived stress and anxiety. The observed reciprocal relationship between depression and sleep disturbances highlights the importance of integrated psychosocial interventions addressing stigma-related stress while concurrently targeting mood and sleep outcomes. Future longitudinal studies are needed to confirm the temporal direction of these associations and to assess the long-term effectiveness of such integrated interventions in TB populations.

Acknowledgments

We would like to thank all the participants and collaborating staff who made this study possible. We also thank Dr. Jie Liu of Department of Vascular and Endovascular Surgery, Chinese PLA General Hospital & Physician-Scientist Center of China for his contribution to the statistical support, study deign consultations and comments regarding the manuscript.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2026-1-0017/rc

Data Sharing Statement: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2026-1-0017/dss

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2026-1-0017/prf

Funding: This study was supported by the Ministry of Science and Technology of the People’s Republic of China (STI2030-Major Projects2021ZD0201900).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2026-1-0017/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study protocol was approved by the Biomedical Research Ethics Committee of West China Hospital, Sichuan University (approval No. 2022(1391)), and the other two participating hospitals granted local administrative permissions in recognition of this approval. All participants provided written informed consent prior to data collection.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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