Drug and alcohol use among people living with HIV in care in the United States by geographic region
Bibliographic record
Abstract
Substance use in the U.S. varies by geographic region. Opioid prescribing practices and marijuana, heroin, and methamphetamine availability are evolving differently across regions. We examined self-reported substance use among people living with HIV (PLWH) in care at seven sites from 2017-2019 to understand current regional substance use patterns. We calculated the percentage and standardized percentage of PLWH reporting current drug use and at-risk and binge alcohol use by U.S. Census Bureau geographic region and examined associations in adjusted logistic regression analyses. Among 7,686 PLWH, marijuana use was the most prevalent drug (30%), followed by methamphetamine/crystal (8%), cocaine/crack (7%), and illicit opioids (3%). One-third reported binge alcohol use (32%). Differences in percent of current use by region were seen for marijuana (24-41%) and methamphetamine/crystal (2-15%), with more use in the West and Northeast, and binge alcohol use (26-40%). In adjusted analyses, PLWH in the Midwest were significantly less likely to use methamphetamine/crystal (aOR: 0.13;0.06-0.25) or illicit opioids (aOR:0.16;0.05-0.53), and PLWH in the Northeast were more likely to use cocaine/crack (aOR:1.59;1.16-2.17), compared to PLWH in the West. Understanding differences in substance use patterns in the current era, as policies continue to evolve, will enable more targeted interventions in clinical settings among PLWH.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".