Trends in HIV pre-exposure prophylaxis use in eight Canadian provinces, 2014–2018
Bibliographic record
Abstract
INTRODUCTION: Canada has endorsed the Joint United National Programme on HIV and AIDS global targets to end the acquired immunodeficiency syndrome (AIDS) epidemic, including reducing new human immunodeficiency virus (HIV) infections to zero, by 2030. Given the effectiveness of pre-exposure prophylaxis (PrEP) to prevent new infections, it is important to measure and report on PrEP utilization to help inform planning for HIV prevention programs and policies. METHODS: Annual estimates of persons using PrEP in Canada were generated for 2014-2018 from IQVIA's geographical prescription monitor dataset. An algorithm was used to distinguish users of tenofovir disoproxil fumarate/emtricitabine (TDF/FTC) for PrEP versus treatment or post-exposure prophylaxis. We provide the estimated number of people using PrEP in eight Canadian provinces by sex, age group, prescriber specialty and payment type. RESULTS: The estimated number of PrEP users increased dramatically over the five-year study period, showing a 21-fold increase from 460 in 2014 to 9,657 in 2018. Estimated PrEP prevalence was 416 users per million persons across the eight provinces in 2018. Almost all PrEP users were male. Use increased in both sexes, but increase was greater for males (23-fold) than females (five-fold). Use increased across all provinces, although there were jurisdictional differences in the prevalence of use, age distribution and prescriber types. CONCLUSION: The PrEP use in Canada increased from 2014 to 2018, demonstrating increased awareness and uptake of its use for preventing HIV transmission. However, there was uneven uptake by age, sex and geography. Since new HIV infections continue to occur in Canada, it will be important to further refine the use of PrEP, as populations at higher risk of HIV infection need to be offered PrEP as part of comprehensive sexual healthcare.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".