Frequent Disengagement and Subsequent Mortality Among People With HIV and Hepatitis C in Canada: A Prospective Cohort Study
Bibliographic record
Abstract
Background: The cascade of care, commonly used to assess HIV and hepatitis C (HCV) health service delivery, has limitations in capturing the complexity of individuals' engagement patterns. This study examines the dynamic nature of engagement and mortality trajectories among people with HIV and HCV. Methods: We used data from the Canadian HIV-HCV Co-Infection Cohort, which prospectively follows 2098 participants from 18 centers biannually. Markov multistate models were used to evaluate sociodemographic and clinical factors associated with transitioning between the following states: (1) lost-to-follow-up (LTFU), defined as no visit for 18 months; (2) reengaged (reentry into cohort after being LTFU); (3) withdrawn from the study (ie, moved); (4) death; otherwise remained (5) engaged-in-care. Results: A total of 1809 participants met the eligibility criteria and contributed 12 591 person-years from 2003 to 2022. LTFU was common, with 46% experiencing at least 1 episode, of whom only 57% reengaged. One in 5 (n = 383) participants died during the study. Participants who transitioned to LTFU were twice as likely to die as those who were consistently engaged. Factors associated with transitioning to LTFU included detectable HCV RNA (adjusted hazards ratio [aHR], 1.37; 95% confidence interval [CI], 1.13-1.67), evidence of HCV treatment but no sustained virologic response result (aHR, 1.99; 95% CI, 1.56-2.53), and recent incarceration (aHR, 1.94; 95% CI, 1.58-2.40). Being Indigenous was a significant predictor of death across all engagement trajectories. Interpretation: Disengagement from clinical care was common and resulted in higher death rates. People LTFU were more likely to require HCV treatment highlighting a priority population for elimination strategies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".