The Real‐World Effectiveness of Human Immunodeficiency Virus Pre‐Exposure Prophylaxis in Adults in Alberta, Canada: A Retrospective Population‐Based Cohort Study
Bibliographic record
Abstract
Background: In clinical trials, pre‐exposure prophylaxis (PrEP) with tenofovir disoproxil fumarate‐emtricitabine (TDF/FTC) or tenofovir alafenamide‐emtricitabine (TAF/FTC) is up to 99% efficacious in preventing human immunodeficiency virus (HIV) infection. The real‐world effectiveness of PrEP has not been extensively evaluated in Canada. Methods: This population‐based cohort included adults without HIV as determined by viral serology and ICD‐9/ICD‐10 codes from Alberta with ≥ 3 months of PrEP prescriptions. It used provincial administrative data. Patients were followed from their first PrEP prescription until diagnosed HIV infection or censoring. Cox proportional hazard models were used to identify independent predictors of HIV infection. Results: A total of 4750 adults with a mean (SD) age of 35.9 (11) years of which 8% were female were prescribed PrEP including TDF/FTC (97.5%) or TAF/FTC (2.5%). There were 335 HIV infections (92.9% effectiveness) over median cohort follow‐up of 1.0 years (IQR 1.9) with 4.89 (95% CI 4.38, 5.44) HIV infections per 100 patient years. Age (HR 1.04, 95% CI 1.03–1.05 per 1 year increase), male sex (HR 0.34, 95% CI 0.27–0.44), CKD Stage G3 (HR 2.39, 95% CI 1.82, 3.14), SES (4th and 5th quintiles versus 1st quintile), drug use (HR 2.11, 95% 1.45, 3.08), and history of STI (HR 0.45, 95% CI 0.29, 0.72) were independent predictors of HIV infection. The HIV incidence decreased to 1.5 (95% CI 1.2, 1.8) and 0.6 (95% CI 0.4, 0.9) per 100 patient years in cohorts with negative baseline HIV serology with 180 and 30 days prior to index PrEP prescription. Conclusion: HIV PrEP appears to be effective for preventing HIV infection in this real‐world population‐based study in Alberta, Canada. Strategies to mitigate residual HIV risk in PrEP users are needed.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".