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Record W4365512794 · doi:10.4314/ahs.v23i1.25

Prevalence of depression in people with tuberculosis in East Africa: a systematic review and meta-analysis

2023· review· en· W4365512794 on OpenAlexaboutno aff
Wondale Getinet Alemu, Tadele Amare

Bibliographic record

VenueAfrican Health Sciences · 2023
Typereview
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineTuberculosisDepression (economics)Meta-analysisScopusPopulationMental healthPsychiatryDemographyMEDLINEEnvironmental healthInternal medicinePathology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.559
Threshold uncertainty score0.782

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0110.001
Bibliometrics0.0020.010
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.209
GPT teacher head0.447
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations10
Published2023
Admission routes1
Has abstractyes

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