Population-level effectiveness of a national HIV preexposure prophylaxis programme in MSM
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to evaluate Scotland's national HIV preexposure prophylaxis (PrEP) programme in relation to PrEP uptake and associated population-level impact on HIV incidence among MSM. DESIGN: A retrospective cohort study within real-world implementation. METHODS: Comparison of HIV diagnoses from national surveillance data and HIV incidence within a retrospective cohort of HIV-negative MSM attending sexual health clinics from the National Sexual Health information system between the 2-year periods pre(July 2015-June 2017) and post(July 2017-June 2019) introduction of PrEP. RESULTS: Of 16 723 MSM attending sexual health services in the PrEP period, 3256 (19.5%) were prescribed PrEP. Between pre-PrEP and PrEP periods, new HIV diagnoses among MSM declined from 229 to 184, respectively [relative risk reduction (RRR): 19.7%, 95% confidence interval (95% CI) 2.5-33.8]; diagnosed recently acquired infections declined from an estimated 73 to 47, respectively (35.6%, 95% CI 7.1-55.4). Among MSM attending sexual health clinics, HIV incidence per 1000 person-years declined from 5.13 (95% CI 3.90-6.64) pre-PrEP to 3.25 (95% CI 2.30-4.47) in the PrEP period (adjusted IRR 0.57, 95% CI 0.37-0.87). Compared with the pre-PrEP period, incidence of HIV was lower in the PrEP period for those prescribed PrEP (aIRR 0.25, 95% CI 0.09-0.70) and for those not prescribed PrEP (aIRR 0.68, 95% CI 0.43-1.05). CONCLUSION: We demonstrate national population-level impact of PrEP for the first time in a real-world setting. HIV incidence reduced in MSM who had been prescribed PrEP and, to a lesser extent, in those who had not. Promotion of the benefits of PrEP needs to extend to MSM who do not access sexual health clinics.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".