Incarceration history is associated with HIV infection among community‐recruited people who inject drugs in Europe: A propensity‐score matched analysis of cross‐sectional studies
Bibliographic record
Abstract
AIMS: We measured the association between a history of incarceration and HIV positivity among people who inject drugs (PWID) across Europe. DESIGN, SETTING AND PARTICIPANTS: This was a cross-sectional, multi-site, multi-year propensity-score matched analysis conducted in Europe. Participants comprised community-recruited PWID who reported a recent injection (within the last 12 months). MEASUREMENTS: Data on incarceration history, demographics, substance use, sexual behavior and harm reduction service use originated from cross-sectional studies among PWID in Europe. Our primary outcome was HIV status. Generalized linear mixed models and propensity-score matching were used to compare HIV status between ever- and never-incarcerated PWID. FINDINGS: Among 43 807 PWID from 82 studies surveyed (in 22 sites and 13 countries), 58.7% reported having ever been in prison and 7.16% (n = 3099) tested HIV-positive. Incarceration was associated with 30% higher odds of HIV infection [adjusted odds ratio (aOR) = 1.32, 95% confidence interval (CI) = 1.09-1.59]; the association between a history of incarceration and HIV infection was strongest among PWID, with the lowest estimated propensity-score for having a history of incarceration (aOR = 1.78, 95% CI = 1.47-2.16). Additionally, mainly injecting cocaine and/or opioids (aOR = 2.16, 95% CI = 1.33-3.53), increased duration of injecting drugs (per 8 years aOR = 1.31, 95% CI = 1.16-1.48), ever sharing needles/syringes (aOR = 1.91, 95% CI = 1.59-2.28) and increased income inequality among the general population (measured by the Gini index, aOR = 1.34, 95% CI = 1.18-1.51) were associated with a higher odds of HIV infection. Older age (per 8 years aOR = 0.84, 95% CI = 0.76-0.94), male sex (aOR = 0.77, 95% CI = 0.65-0.91) and reporting pharmacies as the main source of clean syringes (aOR = 0.72, 95% CI = 0.59-0.88) were associated with lower odds of HIV positivity. CONCLUSIONS: A history of incarceration appears to be independently associated with HIV infection among people who inject drugs (PWID) in Europe, with a stronger effect among PWID with lower probability of 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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".