Global overview of injecting drug use and HIV infection among injecting drug users
Bibliographic record
Abstract
OBJECTIVE: To provide global estimates of the prevalence of injecting drug use (IDU) and HIV prevalence among IDU, in particular to provide estimates for developing and transitional countries. METHODS: Collation and review of existing estimates of IDU prevalence and HIV prevalence from published and unpublished documents for the period 1998-2003. The strength of evidence for the information was assessed based on the source and type of study. RESULTS: Estimates of IDU prevalence were available for 130 countries. The number of IDU worldwide was estimated as approximately 13.2 million. Over ten million (78%) live in developing and transitional countries (Eastern Europe and Central Asia, 3.1 million; South and South-east Asia, 3.3 million; East-Asia and Pacific, 2.3 million). Estimates of HIV prevalence were available for 78 countries. HIV prevalence among IDU of over 20% was reported for at least one site in 25 countries and territories: Belarus, Estonia, Kazakhstan, Russia, Ukraine, Italy, Netherlands, Portugal, Serbia and Montenegro, Spain, Libya, India, Indonesia, Malaysia, Myanmar, Nepal, Thailand, Viet Nam, China, Argentina, Brazil, Uruguay, Puerto Rico, USA and Canada. CONCLUSIONS: These findings update previous assessments of the number of countries with IDU and HIV-infected IDU, and the previous quantitative global estimates of the prevalence of IDU. However, gaps remain in the information and the strength of the evidence often was weak.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".