Increased PrEP uptake and PrEP-RN coincide with decreased HIV diagnoses in men who have sex with men in Ottawa, Canada
Bibliographic record
Abstract
Background: We sought to evaluate if increased uptake of HIV pre-exposure prophylaxis (PrEP) correlated to population-level changes in human immunodeficiency virus (HIV) epidemiology, in a setting with an integrated PrEP delivery system centred on a public health nurse-led PrEP clinic and referral process. Methods: This study was conducted in Ottawa, Canada, where all positive HIV test results are reported to the public health units. Risk factor information is also collected by nurses and subsequently entered into a provincial database. We extracted these data for Ottawa from 2017 to 2021 and restricted our analyses to first-time diagnoses. Results: We identified 154 persons with a new HIV diagnosis. Over this period, the number of new diagnoses among men who have sex with men, the group most targeted for PrEP, decreased by 50%-60%. We did not identify changes in the number of new diagnoses based on race, intravenous drug use or among women. Conclusion: Increasing PrEP uptake in Ottawa in 2017 to 2021 coincided with a significant decrease in new HIV diagnoses among men who have sex with men. PrEP uptake in Ottawa, particularly by those most at risk, is likely supported by an integrated approach via PrEP-RN, a nurse-led public health program where individuals diagnosed with syphilis or rectal gonorrhea or chlamydia receive an automatic offer of PrEP. While these findings cannot causally link PrEP-RN or PrEP with this reduction in new HIV diagnoses, these changes in HIV epidemiology in Ottawa occurred exclusively among the group targeted for PrEP. These data highlight the efficacy and importance of PrEP.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".