Prevalence of depression in people with tuberculosis in East Africa: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Depression is one of the most common mental health problems comorbid with tuberculosis. However, a consolidated picture of the prevalence of depression among tuberculosis patients in East Africa remains unknown. This systematic review and meta-analysis provide new understandings by systematically examining evidence concerning the prevalence of depression among tuberculosis patients in East Africa. Methods: Literature was found in a database of HINARI, SCOPUS, PubMed, Science Direct, and Google Scholar. The Newcastle-Ottawa quality assessment scale was used to appraise the quality of the selected studies. Then, the DerSimonian and Laird random-effects model was applied because of the presence of heterogeneity among studies. Results: A total of 409 studies were accessed. However, only 29 qualified for a full-text review, and 9 studies with a population of 2838 were included in the qualitative description and quantitative analysis. The pooled prevalence estimate of depression amongst tuberculosis patients was 43.03 % (34.93, 51.13). The highest prevalence was observed in Kenya, with 45.71% (29.26, 62.16); a similar rate was observed in Ethiopia, with 45.11 % (34.60, 55.62). Subgroup analysis based on screening tool was used: 45.71% with BDI and 41.53% with PHQ.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.023 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".