Long-term, bidirectional associations between depressive symptom severity and opioid use among people with HIV: A prospective cohort study
Bibliographic record
Abstract
Background: The bidirectional relationships between opioid use and depressive symptom severity among people living with HIV (PLHIV) are poorly understood. We hypothesized that higher opioid use frequency would be associated with greater subsequent depressive symptom severity and that greater depressive symptom severity would be associated with higher subsequent opioid use frequency. Methods: We analyzed data from the Veterans Aging Cohort Study (VACS) - survey sample, a prospective cohort including PLHIV receiving care at 8 US Veterans Health Administration sites. From 2002 to 2018, we assessed past year opioid use frequency based on self-reported heroin and/or prescription opioid use at study entry and follow-up, as well as depressive symptom severity. Time-lagged, generalized estimating equation models were used to construct estimates of the association between opioid use frequency and subsequent depressive symptom severity, and vice versa, adjusting for key sociodemographic and clinical characteristics. Results: In final adjusted models that included 2033 PLHIV (98 % male), subsequent depressive symptom severity was greater (adjusted odds ratio [aOR] = 1.44, 95 % CI: 1.22,1.70) for those who used opioids at least monthly compared to those who never used, and the association between these variables appeared to follow a dose-response pattern. Similarly, subsequent opioid use frequency was higher (aOR = 1.38, 95 % CI: 1.17,1.62) for those with moderate depressive symptom severity compared to those with none. Conclusions: Enhanced access to screening for substance use disorders, harm reduction services, and medications for opioid use disorder may be warranted in settings that serve veteran PLHIV; strategies expanding access to mental health services may also be promising.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".