The Prevalence of Mental Health Disorders in people with HIV and the effects on the HIV Care Continuum
Bibliographic record
Abstract
ABSTRACT Objective To describe the prevalence of diagnosed depression, anxiety, bipolar disorder, and schizophrenia in people with HIV (PWH) and the differences in HIV care continuum outcomes in those with and without mental health disorders (MHD). Design Observational study of participants in the NA-ACCORD. Methods PWH (≥18 years) contributed data on prevalent schizophrenia, anxiety, depressive, and bipolar disorders from 2008-2018 based on ICD code mapping. MH multimorbidity was defined as having ≥ 2 MHD. Log binomial models with generalized estimating equations estimated adjusted prevalence ratios (aPR) and 95% confidence intervals for retention in care (≥ 1 visit/year) and viral suppression (HIV RNA ≤ 200 copies/mL) by presence vs. absence of each MHD between 2016-2018. Results Among 122,896 PWH, 67,643 (55.1%) were diagnosed with ≥ 1 MHD: 39% with depressive disorders, 28% with anxiety disorders, 10% with bipolar disorder, and 5% with schizophrenia. The prevalence of depressive and anxiety disorders increased between 2008-2018, while bipolar disorder and schizophrenia remained stable. MH multimorbidity affected 24% of PWH. From 2016-2018 (N=64,684), retention in care was marginally lower among PWH with depression or anxiety, however those with MH multimorbidity were more likely to be retained in care. PWH with bipolar disorder had marginally lower prevalence of viral suppression (aPR=0.98 [0.98-0.99]) as did PWH with MH multimorbidity (aPR=0.99 [0.99-1.00]) compared with PWH without MHD. Conclusion The prevalence of MHD among PWH was high, including MH multimorbidity. Although retention and viral suppression were similar to people without MHD, viral suppression was lower in those with bipolar disorder and MH multimorbidity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".