Medication nonadherence, multitablet regimens, and food insecurity are key experiences in the pathway to incomplete HIV suppression
Bibliographic record
Abstract
OBJECTIVE: To identify potential pathways by which a variety of factors act to lead to unsuppressed viral load. DESIGN: A prospective cohort of HIV-HCV co-infected adults receiving care from 18 HIV clinics across Canada was followed every 6 months between November 2012 and October 2015. Participants with at least two visits while receiving combined antiretroviral treatment (cART) were included. METHODS: A path analysis was conducted on the basis of ordered sequences of multivariate logistic regressions using generalized estimating equations. The first regression model used incomplete viral suppression (viral load >50 copies/ml) as the outcome of interest and all other variables (i.e. nonadherence, food insecurity, treatment attributes, and other sociodemographic, behavioural, and clinical factors) as potential predictors. Any variable determined to be a statistically significant predictor of incomplete viral suppression was then used as the next outcome of interest in the subsequent regression, until all predictors of each selected outcome were purely explanatory variables. RESULTS: A total of 566 participants had at least two visits. Drivers of incomplete viral suppression included injection drug use, age 45 years or less, living alone, poor health status, longer duration of HIV infection and baseline CD4 cell count less than 200 cells/μl. Nonadherence, food insecurity, and the use of multitablet regimens mediated the effects of these factors on incomplete viral suppression. CONCLUSION: Our results suggest that nonadherence, multitablet regimens, and food insecurity are key points in the pathway to incomplete HIV suppression. These are potentially amenable intervention targets that would not be revealed using traditional regression analyses.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".