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
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,063 | 0,054 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,003 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,004 | 0,004 |
| Intégrité de la recherche | 0,004 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,030 | 0,009 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».