Depression and Anxiety among Tuberculosis Patients: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Background: Tuberculosis (TB) patients often experience depressive and anxiety symptoms, which can significantly impact their quality of life, treatment adherence, and outcomes. Understanding the magnitude of these mental health issues is crucial for improving TB programs and achieving successful treatment outcomes. Materials and Methods: We conducted a systematic review and meta-analysis, to assess the prevalence of depressive and anxiety symptoms among TB patients. Relevant studies were identified through a search of the PubMed database. Studies were assessed for quality using the Newcastle–Ottawa Quality Assessment Scale (NOS). Data extraction was performed, and a random-effects meta-analysis was conducted to estimate pooled prevalence rates. Results: Forty studies were included in the analysis. The pooled estimated prevalence of depression among TB patients was 11% (95% confidence interval [CI]: 11–12), while the pooled estimated prevalence of anxiety was 28% (95% CI: 26–29). Subgroup analyses revealed variations in the prevalence rates among drug-sensitive (DS-TB), drug-resistant, and extensively drug-resistant patients, as well as across continents and settings. Conclusions: The review indicates that there was a considerable burden of depressive and anxiety symptoms among TB patients worldwide. The findings emphasize the need for routine screening, integrated care approaches, and targeted interventions to address the mental health needs of TB patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.026 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".