ArticleAIDS and behavior2026
Identifying Hidden Barriers to PrEP Adherence Among Young Men Who Have Sex with Men: Application of Natural Language Processing.
Article in AIDS and behavior, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Natural Language Processing Analysis of WhatsApp Messages to Identify Unrecognized HIV Prevention Needs Among Young Women in Kenya.Journal of the International AIDS Society · 2026Article
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Authors and funding
8 authors.
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Abstract
In the United States, the incidence rate of new HIV diagnoses continues to increase among young men who have sex with men (YMSM). Despite the availability of effective preventive strategies such as pre-exposure prophylaxis (PrEP), the rate of PrEP adherence remains markedly low especially among YMSM. Previous studies have primarily relied on structured surveys with predefined responses to identify barriers to PrEP adherence which may not fully capture the complexities behind the barriers. This is a secondary data analysis of data from a prospective cohort study focusing on YMSM vulnerable to HIV. A total of 581 participants provided free-text responses regarding reasons for discontinuing PrEP, which served as the primary outcome for the analysis. Natural language processing was conducted to identify potential barriers to PreP adherence and to uncover any previously unidentified barriers in this population. A total of nine categories were identified, with the most prevalent being lack of sexual activity (n = 128), followed by issues related to monogamy/partnership/long-term relationship (n = 73), specific insurance or coverage issues (n = 52), medication-related concerns (n = 41), side effects/health concerns (n = 39), forgetfulness/inconvenience associated with the medication regimen (n = 33), limited healthcare access (n = 26), personal reasons (n = 9), and financial insecurity (n = 8). The NLP analysis demonstrated moderate performance via support vector machine, random forest, gradient boost, and random forest (F-score = 0.75). Our study provides critical insights into specific barriers faced by high-risk YMSM, emphasizing the need for development of targeted interventions aimed at these barriers to improve PrEP access and utilization.
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