High rates of midazolam injection among drug users in Bangkok, Thailand
Bibliographic record
Abstract
BACKGROUND: Reports from Thailand suggest that a growing number of people who inject drugs (IDU) are now injecting midazolam, a legal benzodiazepine with potent amnestic and ventilatory depressant effects. We therefore sought to examine midazolam injection among a community-recruited sample of Thai IDU. METHODS: We examined the prevalence and correlates of midazolam injection among 252 IDU participating in the Mitsampan Community Research Project, Bangkok, using multivariate logistic regression. We also examined the use of midazolam in combination with other drugs. RESULTS: 252 IDU participated in this study, including 66 (26.2%) women. In total, 170 (67.5%) participants reported ever having injected midazolam, and 144 (57.1%) reported daily midazolam injection in the past six months. In multivariate analyses, a history of midazolam injection was independently associated with using drugs in combination (adjusted odds ratio [AOR] = 5.86; 95% confidence interval [CI]: 2.96-11.60), younger age (AOR = 0.43; 95%CI: 0.22-0.83), having a history of methadone treatment (AOR = 3.12, 95%CI: 1.55-6.90), and binge drug use (AOR = 2.25, 95%CI: 1.09-4.63). The drugs most commonly used in combination with midazolam were heroin (72.3%) and yaba (methamphetamine) (30.5%). CONCLUSION: We observed a high rate of midazolam injection among Thai IDU. Midazolam injection was strongly associated with polysubstance use and binge drug use, and was most commonly used in combination with both opiates and methamphetamines. Our findings suggest that midazolam injection has become increasingly common within Thailand. Evidence-based approaches for reducing harms associated with midazolam injection are needed.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".