The evolving HIV epidemic in Ontario, Canada: A retrospective analysis of new HIV diagnoses to identify subpopulations with persistent risk of HIV transmission
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to characterize new HIV diagnoses in key Ontario cities, in order to understand current drivers of continued HIV transmission to inform HIV testing and prevention efforts. METHODS: Chart reviews were carried out at four clinical sites in Ontario, Canada. The study population included individuals who were diagnosed with HIV in Ottawa, Hamilton, and Toronto between January 1, 2018, and December 31, 2020, and had no previous evidence of HIV documented. RESULTS: The total number of persons in this analysis was 359, from Toronto (n = 201), Ottawa (n = 88), and Hamilton (n = 70). More than half of the diagnoses were among those who immigrated to Canada, and many were diagnosed (11%) during the year they arrived. Many participants experienced a late diagnosis (43.2%), and while 116 (32.3%) had HIV testing history in Ontario, 155 did not (43.2%). Many participants were men who have sex with men (MSM) and had a previously recorded diagnosis of gonorrhea or chlamydia (n = 27; 7.5%) or syphilis (n = 39; 10.9%). Among women and heterosexual men, a diagnosis of hepatitis C (n = 18; 5.0%) appeared to signal a risk of HIV diagnosis. CONCLUSION: These data show that HIV testing and prevention strategies should be targeted to (1) MSM with a history of syphilis, gonorrhea, or chlamydia; (2) heterosexual men and women with a history of hepatitis C; and (3) immigrants within the first 5 years of migration. To address the evolving epidemic, it will be necessary to employ targeted HIV screening and prevention measures.
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 | 0.001 | 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 | 0.001 | 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".