HIV/AIDS in Vancouver, British Columbia: a growing epidemic
Bibliographic record
Abstract
The prevalence of HIV in Vancouver, British Columbia was subject to two distinct periods of rapid increase. The first occurred in the 1980s due to high incidence among men who have sex with men (MSM), and the second occurred in the 1990s due to high incidence among injection drug users (IDU). The purpose of this study was to estimate and model the trends in HIV prevalence in Vancouver from 1980 to 2006. HIV prevalence data were entered into the UNAIDS/WHO Estimation and Projection Package (EPP) where prevalence trends were estimated by fitting an epidemiological model to the data. Epidemic curves were fit for IDU, MSM, street-based female sex trade workers (FSW), and the general population. Using EPP, these curves were then aggregated to produce a model of Vancouver's overall HIV prevalence. Of the 505 000 people over the age of 15 that reside in Vancouver, 6108 (ranging from 4979 to 7237) were living with HIV in the year 2006, giving an overall prevalence of 1.21 percent (ranging from 0.99 to 1.43 percent). The subgroups of IDU and MSM account for the greatest proportion of HIV infections. Our model estimates that the prevalence of HIV in Vancouver is greater than one percent, roughly 6 times higher than Canada's national prevalence. These results suggest that HIV infection is having a relatively large impact in Vancouver and that evidence-based prevention and harm reduction strategies should be expanded.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".