Healthcare utilisation in people living with HIV: the role of substance use, mood/anxiety disorders and unsustained viral suppression – a retrospective cohort study in British Columbia, Canada, 2001–2019
Bibliographic record
Abstract
OBJECTIVE: People living with HIV (PLWH) are disproportionately affected by substance use disorder (SUD) and mood/anxiety disorders, which are barriers to sustained viral suppression and can contribute to increased healthcare utilisations. This study examined the impact of SUD and mood/anxiety disorders on healthcare utilisation of PLWH with sustained and unsustained viral suppression. DESIGN AND PARTICIPANTS: This retrospective population-based cohort study used administrative data from 9757 antiretroviral-treated PLWH (83% men, median age 40 years). Eligible PLWH were≥19 years of age, followed during 2001-2019, and achieved viral suppression at least once during follow-up. SETTING: This study was conducted in British Columbia, Canada. MEASUREMENTS: The exposure variable consisted of eight levels and included (1) sustained suppression, (2) SUD and mood/anxiety disorder diagnoses and the interaction between (1) and (2). Outcome variables included annual counts of primary care and specialist physician visits, laboratory visits, acute care hospitalisation, day surgery episodes and hospital length of stay (LOS). Statistical count models were used to determine the effect of exposure variables on each healthcare utilisation outcome while adjusting for socioeconomic confounders. RESULTS: In the presence of sustained suppression, having both disorders was significantly associated with over four times more acute-care hospitalisations (0.28 vs 0.05), three times longer LOS (9.1 vs 3.0 days) and almost double primary care physician (13.1 vs 6.9) and specialist (7.9 vs 4.0) visits. Overall, SUD alone was associated with increased use of all healthcare services (except day surgery). Regardless of disorder diagnoses, unsustained suppression was associated with higher healthcare utilisation (except day surgery). CONCLUSION: In this study, SUD, mood/anxiety disorders and unsustained suppression, when combined, resulted in the highest healthcare utilisation among PLWH. The results suggest that providing comprehensive mental health and substance use services to PLWH and addressing barriers to sustained suppression could reduce the healthcare burden within this population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| 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".