The Use of Multistate Models to Examine Associations of Stress and Adherence With Transitions Among HIV Care States Observed in a Clinical HIV Cohort
Bibliographic record
Abstract
BACKGROUND: The "cascade of care" is a framework for quantifying the trajectory of people with HIV along the continuum of HIV care. We extended this framework to recognize that individuals may transition back and forth between states of care and to identify factors associated with movement among states of care over time, with particular focus on stress, depression, and adherence. METHODS: The Ontario HIV Treatment Network Cohort Study is a multisite HIV clinical cohort. We analyzed data from participants who had initiated antiretroviral therapy, achieved virologic suppression, completed ≥1 study questionnaire including psychosocial data, and had ≥1 viral load (VL) result within 2 years of a questionnaire. Follow-up time from the first suppressed VL was divided into 6-month intervals and classified into 1 of 3 states for HIV care retention: (1) suppressed VL (VL <50 copies/mL), (2) unsuppressed VL (VL >50 copies/mL), and (3) unobserved. Multistate models were used to determine the association of transitioning between states and time-updated demographic and clinical characteristics. RESULTS: In total, 1842 participants were included. After multivariable adjustment, poor adherence [hazard ratio (HR) 1.88, 95% confidence interval (CI): 1.19 to 2.98) and stress (HR = 1.38; 95% CI: 1.04 to 1.83) were associated with transitions from suppressed to unsuppressed VL. Similarly, low adherence (HR = 1.52; 95% CI: 1.14 to 2.04) and stress (HR = 1.25; 95%: 1.03, 1.51) were associated with transitions from suppressed to unobserved states. CONCLUSIONS: Higher levels of stress and low adherence are associated with transitions to less favorable states of care. Interventions to manage stress and facilitate adherence may improve engagement in 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 | 0.021 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".