Impact of mood/anxiety and substance use disorders on the HIV continuum of care and healthcare utilization of people living with HIV
Bibliographic record
Abstract
Introduction: Substance use disorder (SUD), mood, and anxiety disorders disproportionately affect people living with HIV (PLWH). However, comprehensive research on the effects of these disorders on HIV care engagement and healthcare utilization of PLWH is lacking. This thesis aimed to assess the impact of the syndemic between SUD and mood/anxiety disorders on the HIV continuum of care for PLWH in British Columbia (BC), Canada, and identify the stage with the highest attrition in the continuum. Additionally, this thesis intended to examine the effects of SUD, mood/anxiety disorders, and disengagement from HIV care (at the stage with the highest attrition) on healthcare utilization of PLWH. Methods: This retrospective population-based cohort study utilized data from the Comparative Outcomes And Service Utilization Trends (COAST) study that contains data on all diagnosed PLWH in BC. Eligible individuals were ≥19 years of age and were followed during 2001-2019. First, I identified the stage with the highest attrition in the HIV continuum of care. Using statistical models, I measured the associations between SUD and mood/anxiety disorders and the achievement of each stage of the continuum. I then measured the effects of these disorders and disengagement from HIV care (at the stage with the highest attrition) on counts of physician visits, hospitalization, and length of stay (LOS). Results: The stage of sustained viral suppression exhibited the highest attrition. SUD was associated with incomplete antiretroviral therapy adherence (adjusted Odds Ratio (aOR) 0.47, 95% CI: 0.42-0.53) and unsustained suppression (aOR 0.58, 95% CI: 0.53-0.63). Mood/anxiety disorders were also associated with incomplete adherence (aOR 0.78, 95% CI: 0.71-0.87) and unsustained suppression (aOR 0.82, 95% CI: 0.77-0.88). Having both disorders was associated with over four times more acute care hospitalizations, three times longer length of stay and almost twice the physician visits. Unsustained suppression was strongly and independently associated with more physician visits, hospitalizations and longer LOS. Conclusion: SUD and mood/anxiety disorders contribute to attritions across the HIV continuum of care and high healthcare utilization among PLWH. Therefore, this thesis emphasizes the need for integrated mental health and substance use services to support HIV care.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".