Disparities in the Burden of HIV/AIDS in Canada
Bibliographic record
Abstract
BACKGROUND: We aimed to characterize changes in patterns of new HIV diagnoses, HIV-related mortality, and HAART use in Canada from 1995 to 2008. METHODS: Data on new HIV diagnoses were obtained from Health Canada, HIV-related mortality statistics were obtained from Statistics Canada, and information on the number of people on HAART was obtained from the single antiretroviral distribution site in British Columbia (BC), and the Intercontinental Marketing Services Health for Ontario and Quebec. Trends of new HIV-positive tests were assessed using Spearman rank correlations and the association between the number of individuals on HAART and new HIV diagnoses were estimated using generalized estimating equations (GEE). RESULTS: A total of 34,502 new HIV diagnoses were observed. Rates of death in BC are higher than those in Ontario and Quebec with the rate being 2.03 versus 1.06 and 1.21 per 100,000 population, respectively. The number of HIV-infected individuals on HAART increased from 2,081 in 1996 to 20,431 in 2008 in the three provinces (nearly 10-fold increase). BC was the only province with a statistically significant decrease (trend test p<0.0001) in the rate of new HIV diagnoses from 18.05 to 7.94 new diagnoses per 100,000 population. Our analysis showed that for each 10% increment in HAART coverage the rate of new HIV diagnoses decreased by 8% (95% CI: 2.4%, 13.3%) INTERPRETATION: Except for British Columbia, the number of new HIV diagnoses per year has remained relatively stable across Canada over the study period. The decline in the rate of new HIV diagnoses per year may be in part attributed to the greater expansion of HAART coverage in this province.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".