Binge Drug Use Independently Predicts HIV Seroconversion Among Injection Drug Users: Implications for Public Health Strategies
Bibliographic record
Abstract
Several studies have highlighted risk factors that cause HIV vulnerability among injection drug users (IDUs); these studies in turn have prompted public health officials to take action to minimize these risks. We sought to evaluate the potential association between binge drug use and HIV seroconversion and, subsequently, risk factors associated with binge drug use among a cohort of IDUs. To do this, we performed analyses of (1) associations with HIV seroconversion and (2) associations with binge drug use among participants enrolled in the Vancouver Injection Drug Users Study (VIDUS), a prospective cohort of IDU. Because serial measures for each individual were available, we undertook a time-updated Cox regression analysis to detect associations with HIV incidence and variables potentially associated with binge drug use were evaluated by using generalized estimating equations (GEE). Overall, 1548 IDU were enrolled into the VIDUS cohort between May 1996 and May 2003. There were 1013 individuals who were HIV seronegative at enrollment and had at least one follow-up visit; 125 (12%) became HIV positive during the study period for a cumulative incidence rate of 14% at 64 months after enrollment. In the final multivariate model, binge drug use [Adjusted Hazards Ratio: 1.61 (CI: 1.12, 2.31)] was independently associated with HIV seroconversion. In subanalyses, when we evaluated associations with binge drug use in GEE analyses, borrowing [Odds Ratio (OR): 153 (CI: 1.33-1.76)] and lending [OR: 1.73 (CI: 1.50-1.98)] syringes, sex trade work [OR: 1.14 (CI: 1.01-1.29)], frequent cocaine [OR: 2.34 (CI: 2.11-2.60)] and heroin [OR: 1.29 (CI: 1.17-1.43)] injection were independently associated with binge drug use and methadone [OR: 0.80 (CI: 0.71-0.89)] was protective against binge drug use. Our study identified an independent association between binge drug use and HIV incidence and demonstrated several high-risk drug practices associated with bingeing. Given the unaddressed public health risks associated with bingeing, a public health response protocol must be developed to minimize the personal and public health risks associated with the binge use of drugs.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".