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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".