Mortality in HIV-Infected Alcohol and Drug Users in St. Petersburg, Russia
Bibliographic record
Abstract
In Russia, up to half of premature deaths are attributed to hazardous drinking. The respective roles of alcohol and drug use in premature death among people with HIV in Russia have not been described. Criminalization and stigmatization of substance use in Russia may also contribute to mortality. We explored whether alcohol, drug use and risk environment factors are associated with short-term mortality in HIV-infected Russians who use substances. Secondary analyses were conducted using prospective data collected at baseline, 6 and 12-months from HIV-infected people who use substances recruited between 2007-2010 from addiction and HIV care settings in a single urban setting of St. Petersburg, Russia. We used Cox proportional hazards models to explore associations between 30-day alcohol hazardous drinking, injection drug use, polysubstance use and environmental risk exposures (i.e. past incarceration, police involvement, selling sex, and HIV stigma) with mortality. Among 700 participants, 59% were male and the mean age was 30 years. There were 40 deaths after a median follow-up of 12 months (crude mortality rate 5.9 per 100 person-years). In adjusted analyses, 30-day NIAAA hazardous drinking was significantly associated with mortality compared to no drinking [adjusted Hazard Ratio (aHR) 2.60, 95% Confidence Interval (CI): 1.24-5.44] but moderate drinking was not (aHR 0.95, 95% CI: 0.35-2.59). No other factors were significantly associated with mortality. The high rates of short-term mortality and the strong association with hazardous drinking suggest a need to integrate evidence-based alcohol interventions into treatment strategies for HIV-infected Russians.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".