Prevalence and correlates of probable common mental disorders in a population with high prevalence of HIV in Zimbabwe
Bibliographic record
Abstract
BACKGROUND: In 2014 close to 10 million people living with HIV (PLWH) in sub-Saharan Africa were on highly active anti-retroviral therapy (HAART). The incidence of non-communicable diseases has increased markedly in PLWH as mortality is reduced due to use of HAART. Common mental disorders (CMD) are highly prevalent in PLWH. We aimed to determine factors associated with probable CMD and depression, assessed by 2 locally validated screening tools in a population with high prevalence of HIV in Harare, Zimbabwe. METHODS: We carried out a cross-sectional survey of a systematic random sample of patients utilizing the largest primary health care facility in Harare. Adults aged ≥18 years attending over a 2-week period were eligible, excluding those who were critically ill or unable to give written informed consent. Two locally validated screening tools the Shona symptom questionnaire (SSQ-14) and the Patient Health Questionnaire (PHQ-9) were administered by trained research assistants to identify probable CMD and depression. RESULTS: Of the 264 participants, 165 (62.5 %) were PLWH, and 92 % of these were on HAART. The prevalence of probable CMD (SSQ14 > = 9) and depression (PHQ9 > = 11) were higher among people living with HIV than among those without HIV (67.9 and 68.5 % vs 51.4 and 47.2 % respectively). Multivariable analysis showed female gender and recent negative life events to be associated with probable CMD and depression among PLWH (gender: OR = 2.32 95 % CI:1.07-5.05; negative life events: OR = 4.14; 95 % CI 1.17-14.49) and with depression (gender: OR = 1.84 95 % CI:0.85-4.02; negative life events: OR = 4.93.; 95 % CI 1.31-18.50) CONCLUSION: Elevated scores on self-report measures for CMD and depression are highly prevalent in this high HIV prevalence population. There is need to integrate packages of care for CMD and depression in existing primary health care programs for HIV/AIDS.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".