Prevalence of depression or depressive symptoms among people living with HIV/AIDS in China: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background The number of people living with HIV/AIDS (PLHA) in China continues to increase. Depression, a common mental disorder in this population, may confer a higher likelihood of worse health outcomes. An estimate of the prevalence of this disorder among PLHA is required to guide public health policy, but the published results vary widely and lack accuracy in China. The goal of this study was to estimate the pooled prevalence of depression or depressive symptoms among PLHA in China. Methods A systematic literature search of several databases was conducted from inception to June 2017, focusing on studies reporting on depression or depressive symptoms among PLHA in China. The risk of bias of individual studies was assessed using a modified version of the Newcastle-Ottawa scale. The overall prevalence estimates were pooled using random-effects meta-analysis. Differences according to study-level characteristics were examined using stratified meta-analysis and meta-regression. Results Seventy-four observational studies including a total of 20,635 PLHA were included. The pooled prevalence of depression or depressive symptoms was 50.8% (95% CI: 46.0–55.5%) among general PLHA, 43.9% (95% CI: 36.2–51.9%) among HIV-positive men who have sex with men, 85.6% (95% CI: 64.1–95.2%) among HIV-positive former blood/plasma donors, and 51.6% (95% CI: 31.9–70.8%) among other HIV-positive populations. Significant heterogeneity was detected across studies regarding these prevalence estimates. Heterogeneity in the prevalence of depression among the general population of PLHA was partially explained by the geographic location and baseline survey year. Conclusions Because of the significant heterogeneity detected across studies regarding these prevalence estimates of depression or depressive symptoms, the results must be interpreted with caution. Our findings suggest that the estimates of depression or depressive symptoms among PLHA in China are considerable, which highlights the need to integrate screening and providing treatment for mental disorders in the treatment package offered to PLHA, which would ultimately lead to better health outcomes in PLHA.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.032 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| 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".