Establish a PrEP (Pre-Exposure Prophylaxis) Epidemiology, Modeling, and Surveillance (PREMISE) System to Analyze Trends in PrEP Uptake and the Impact of PrEP Programs and Policies: Protocol for a Natural Experiment and Modeling Study in the United States
Bibliographic record
Abstract
BACKGROUND: Pre-exposure prophylaxis (PrEP) is highly effective in preventing HIV transmission; yet, many people who would benefit from PrEP are not currently using it. Numerous programs and policies, including those provided under the US Ending the HIV Epidemic effort, have been implemented to increase PrEP use. Programs vary enormously, ranging from telemedicine PrEP support to electronic medical record prompts to social marketing and messaging campaigns. However, limited evidence exists regarding their relative impact on PrEP uptake. OBJECTIVE: The aims of the PREMISE (PrEP Epidemiology, Modeling, and Surveillance) research program are to (1) provide context for PrEP scale-up in the United States, (2) assess the impact of different programs and policies on PrEP use, and (3) model the impact of PrEP-related programs and policies on population health. METHODS: The primary outcomes of PrEP use and PrEP-to-need ratios will be extracted from a national medical data aggregator database that represents a majority of PrEP users in the United States. These data will inform all proposed analyses of the project: the dataset will allow the exploration of longitudinal trends in PrEP use by modality as a cohort study, it will be the outcome data for assessing changes associated with particular PrEP programs and policies for quasi-experiments, and it will provide baseline information to inform modeling regarding future impacts of PrEP policies and programs. The implementation of policies will be assessed using legal coding at the state level, and the implementation of programs across health jurisdictions will be assessed using a jurisdiction survey conducted in collaboration with participating health departments. Guided by a legal implementation framework, we will use descriptive and regression analyses to contextualize PrEP scale-up and use quasi-experimental designs to inform causal assessments of the effect of programs and policies. Here, we provide, as preliminary data, our extraction of PrEP prescribing from the national dataset. RESULTS: This research was funded in August 2024. We obtained the national PrEP database and started data cleaning in March 2025. From 2016 to 2024, there were 20,394,619 claims for medications that were FDA-approved for PrEP, and we determined 13,644,979 claims to be PrEP prescriptions, representing over 1 million PrEP users. For medical benefit claims, there were 34,525 procedure claims for PrEP medications, and we determined 22,910 procedure claims to be for PrEP, representing over 6000 PrEP users. CONCLUSIONS: To optimally use HIV prevention resources, it is critical to understand the effects of different programs and policies. Over 1 million people have started PrEP, and tracking how this scale-up has occurred by PrEP modality and user groups will inform future HIV prevention efforts. By collaborating with health jurisdictions, we will provide systematic data regarding the panoply of programs and policies that have been enacted to support PrEP use. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/80911.
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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 | 0.063 | 0.054 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.030 | 0.009 |
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".