Dose-response Effect of Incarceration Events on Nonadherence to HIV Antiretroviral Therapy Among Injection Drug Users
Bibliographic record
Abstract
BACKGROUND: Although some studies have identified impressive clinical gains for incarcerated HIV-seropositive injection drug users (IDUs) undergoing antiretroviral therapy (ART), the effect of incarceration on adherence to ART remains undetermined. METHODS: We used data from a long-term community-recruited cohort of HIV-seropositive IDUs, including comprehensive ART dispensation records, in a setting where HIV care is free. We estimated the relationship between the cumulative burden of incarceration, measured longitudinally, and the odds of < 95% adherence to ART, with use of multivariate modeling. RESULTS: From 1996 through 2008, 490 IDUs were recruited and contributed 2220 person-years of follow-up; 271 participants (55.3%) experienced an incarceration episode, with the number of incarcerations totaling 1156. In a multivariate model, incarceration had a strong dose-dependent effect on the likelihood of nonadherence to ART: 1-2 incarceration events (adjusted odds ratio [AOR], 1.49; 95% confidence interval [95% CI], 1.03-2.05), 3-5 events (AOR, 2.48; 95% CI, 1.62-3.65), and > 5 events (AOR, 3.11; 95% CI, 1.86-4.95). CONCLUSIONS: Among HIV-seropositive IDUs receiving ART, an increasing burden of incarceration was associated with poorer adherence in a dose-dependent fashion. Our findings support improved adherence support for HIV-seropositive IDUs experiencing incarceration.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".