Impact of the Early COVID-19 Pandemic on the Number of HIV Preexposure Prophylaxis Uses and the Proportion of Preexposure Prophylaxis Users Receiving Sexually Transmitted Infection Testing Services
Bibliographic record
Abstract
BACKGROUND: With the potential impact of the COVID-19 pandemic on HIV preexposure prophylaxis (PrEP) care management, we assessed the number of PrEP users and sexually transmitted infection (STI) testing-eligible PrEP users, STI testing rates, and prevalence between prepandemic (January 1, 2018-March 31, 2020) and early-pandemic (April 1, 2020-September 30, 2020) periods. METHODS: In this retrospective cohort study, a PrEP user for a given quarter is defined as either a previous PrEP user or a PrEP initiator who has at least 1-day coverage of tenofovir/emtricitabine in the given quarter. The STI testing-eligible PrEP users for a given quarter were defined as those persons whose runout date (previous dispense date + days of tenofovir/emtricitabine supply) was in the given quarter. RESULTS: The quarterly number of PrEP users increased from the first quarter of 2018 to the first quarter of 2020 and then decreased in the second and third quarter of 2020. Among STI testing-eligible PrEP users who had ≤14 days between runout and next refill date, gonorrhea and chlamydia screening testing rates were 95.1% for prepandemic and 93.4% for early pandemic ( P = 0.1011). Among all STI testing-eligible PrEP users who were tested for gonorrhea and chlamydia, gonorrhea prevalence was 6.7% for prepandemic and 5.7% for early pandemic ( P = 0.3096), and chlamydia prevalence was 7.0% for prepandemic and 5.8% for early pandemic ( P = 0.2158). CONCLUSIONS: Although the early COVID-19 pandemic resulted in lower numbers of PrEP users and PrEP initiators, individuals who remained continuous users of PrEP maintained extremely high rates of bacterial STI screening. With high STI prevalence among PrEP users, assessments of PrEP care management are continuously needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".