Longitudinal patterns of illicit drug use, antiretroviral therapy exposure and plasma HIV-1 RNA viral load among HIV-positive people who use illicit drugs
Bibliographic record
Abstract
OBJECTIVES: HIV-positive people who use illicit drugs (PWUD) experience elevated rates of HIV-associated morbidity and mortality compared with members of other key affected populations. Although suboptimal levels of access and adherence to antiretroviral therapy (ART) are common among HIV-positive PWUD, there is a need for studies investigating the possible biological impacts of noninjection illicit drug use among people living with HIV in real-world settings. METHODS: We accessed data from the ACCESS study, an ongoing prospective cohort of illicit drug users with systematic HIV viral load monitoring in a setting with universal care and ART dispensation records. We used multivariable generalized linear mixed models to estimate the longitudinal associations between noninjection use of crack cocaine, powder cocaine, opioids, methamphetamine, cannabis and alcohol on plasma HIV-1 RNA viral load, adjusted for ART exposure and relevant confounders. RESULTS: Between 2005 and 2018, 843 individuals from the ACCESS cohort were included and contributed to 8698 interviews. At baseline, the mean age was 43 years, 566 (67%) reported male sex and 659 (78%) used crack cocaine in the previous 6 months. In multivariable models adjusted for ART exposure, only crack cocaine use in the last 6 months was found to be significantly associated with higher HIV viral load. CONCLUSION: We observed significantly higher HIV viral load during periods of crack cocaine use independent of ART exposure. Our findings support further research to investigate the possible biological mechanisms of this effect.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".