Global estimate of the prevalence of post-traumatic stress disorder among adults living with HIV: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVES: Although people living with HIV (PLWH) have been disproportionately affected by post-traumatic stress disorder (PTSD), the global prevalence of PTSD among PLWH is unknown. This study aimed to systematically review the prevalence of PTSD among PLWH worldwide and explore variation in prevalence across sociodemographic and methodological factors. DESIGN: A meta-analysis using a random-effects model was conducted to pool the prevalence estimated from individual studies, and subgroup analyses were used to analyse heterogeneities. SETTING, PARTICIPANTS AND MEASURES: Observational studies providing PTSD prevalence data in an adult HIV population were searched from January 2000 to November 2019. Measurements were not restricted, although the definition of PTSD had to align with the Diagnostic and Statistical Manual of Mental Disorders or the International Classification of Diseases diagnostic criteria. RESULTS: A total of 38 articles were included among 2406 records identified initially. The estimated global prevalence of PTSD in PLWH was 28% (95% CI 24% to 33%). Significant heterogeneity was detected in the proportion of PLWH who reported PTSD across studies, which was partially explained by geographic area, population group, measurement and sampling method (p<0.05). CONCLUSION: PTSD among PLWH is common worldwide. This review highlights that PTSD should be routinely screened for and that more effective prevention strategies and treatment packages targeting PTSD are needed in PLWH.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.024 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".