Trends in HIV prevalence, new diagnoses and mortality of persons with HIV who have entered care in Ontario, 1996 to 2009: a population-based study
Bibliographic record
Abstract
Background Population-based estimates of HIV prevalence, rates of new HIV diagnoses and mortality rates among persons with HIV who have entered care are needed to optimize health service delivery and improve health outcomes of these individuals. However, these data are presently lacking for Ontario. Methods Using a validated case-finding algorithm, we conducted a population-based study using linked administrative healthcare databases to determine the prevalence of HIV and rates of new HIV diagnoses among adults aged 18 years and older in Ontario between 1996 and 2009, as well as all-cause mortality rates among persons with HIV over this same period. Results Between 1996 and 2009, the number of adults living with HIV increased by 98.6% and the age- and sex-standardized HIV prevalence increased by 52.8% (p < 0.001). Women and individuals 50 years and older accounted for an increasing proportion of persons with HIV, increasing from 12.8% to 19.7% (p < 0.001) and 10.4% to 29.9% (p < 0.001), respectively, between 1996 and 2009. Age and sex-standardized rates of new HIV diagnoses and mortality rates among persons with HIV decreased 32.5% (p < 0.001) and 71.9% (p < 0.001), respectively, during the study period. Interpretation The prevalence of HIV infection in Ontario has increased considerably between 1996 and 2009, with a greater relative burden being assumed by women and individuals aged 50 years and older. However, we estimate that only 55% of those infected have entered care. Interventions are required to link infected individuals to appropriate care.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".