The prevalence of pre-exposure prophylaxis use and the pre-exposure prophylaxis–to-need ratio in the fourth quarter of 2017, United States
Bibliographic record
Abstract
PURPOSE: The number of individuals who have started a regimen for HIV pre-exposure prophylaxis (PrEP) in the United States is not well characterized but has been on the rise since 2012. This analysis assesses the distribution of PrEP use nationally and among subgroups. METHODS: A validated algorithm quantifying tenofovir disoproxil fumarate/emtricitabine for PrEP in the United States was applied to a national prescription database to determine the quarterly prevalence of PrEP use. HIV diagnoses from 2016 were used as an epidemiological proxy for PrEP need. The PrEP-to-need ratio (PnR) was defined as the number of PrEP users divided by new HIV diagnoses. RESULTS: A total of 70,395 individuals used PrEP in the fourth quarter of 2017: 67,166 males and 3229 females. Nationally, prevalence of PrEP use was 26/100,000 (range across states per 100,000 [RAS/100k]: 4-73) and the PnR was 1.8 (RAS: 0.5-6.6). Prevalence of PrEP use among males and females, respectively, was 50/100,000 and 2/100,000 (RAS/100k: 7-143 and 0.3-7) and PnR was 2.1 and 0.4 (RAS: 0.6-7.1 and 0.1-4.0). Prevalence of PrEP use was lowest among individuals aged less than or equal to 24 and more than or equal to 55 years (15/100,000 and 6/100,000, RAS/100k: 1-45 and 0.4-14), with PnR 0.9 and 1.5 (RAS: 0.2-5.6 and 0.3-7.0). The Northeast had the highest PnR (3.3); the South had the lowest (1.0). States with Medicaid expansion had more than double the PnR than states without expansion. CONCLUSIONS: Available data suggest that females, individuals aged less than or equal to 24 years and residents of the South had lower levels of PrEP use relative to epidemic need. These results are ecological, and misclassification may attenuate results. PnR is useful for future assessments of HIV prevention strategy uptake.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".