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Record W4307844553 · doi:10.2196/31237

The Use of HIV Pre- and Postexposure Prophylaxis Among a Web-Based Sample of HIV-Negative and Unknown Status Cisgender and Transgender Sexual Minority Men: Cross-sectional Study

2022· article· en· W4307844553 on OpenAlexvenueno aff
Steven A. John, K. Marie Sizemore, Ruben Jimenez, Stephen S. Jones, Andrew E. Petroll, H. Jonathon Rendina

Bibliographic record

VenueJMIR Public Health and Surveillance · 2022
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsnot available
FundersNational Institute of Allergy and Infectious DiseasesEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute on Drug AbuseNational Institute of Mental HealthNational Institutes of HealthNational Institute of Child Health and Human DevelopmentCity University of New York
KeywordsPre-exposure prophylaxisMen who have sex with menMedicineSocioeconomic statusDemographyTransgenderCross-sectional studyLogistic regressionSexual minorityHuman immunodeficiency virus (HIV)Sexual orientationFamily medicinePsychologyEnvironmental healthInternal medicinePopulationSocial psychologySyphilis

Abstract

fetched live from OpenAlex

BACKGROUND: HIV disproportionately affects sexual minority men (SMM) in the United States. OBJECTIVE: We sought to determine past HIV postexposure prophylaxis (PEP) use and current and prior pre-exposure prophylaxis (PrEP) use among a web-based sample of cisgender and transgender men who have sex with men. METHODS: In 2019, HIV-negative and unknown status SMM (n=63,015) were recruited via geosocial networking apps, social media, and other web-based venues to participate in a brief eligibility screening survey. Individuals were asked about past PEP use and current and prior PrEP use. We examined associations of demographics, socioeconomic indicators, and recent club drug use with PEP and PrEP use, as well as the association between past PEP use and current and prior PrEP use using generalized linear models and multinomial logistic regression. Statistical significance was considered at P<.001, given the large sample size; 99.9% CIs are reported. RESULTS: Prior PEP use was reported by 11.28% (7108/63,015) of the participants, with current or prior PrEP use reported by 21.95% (13,832/63,015) and 8.12% (5118/63,015), respectively. Nearly half (3268/7108, 46%) of the past PEP users were current PrEP users, and another 39.9% (2836/7108) of the participants who reported past PEP use also reported prior PrEP use. In multivariable analysis, past PEP use was associated with current (relative risk ratio [RRR] 23.53, 99.9% CI 14.03-39.46) and prior PrEP use (RRR 52.14, 99.9% CI 29.39-92.50). Compared with White men, Black men had higher prevalence of past PEP use and current PrEP use, Latino men had higher prevalence of PEP use but no significant difference in PrEP use, and those identifying as another race or ethnicity reported higher prevalence of past PEP use and lower current PrEP use. Past PEP use and current PrEP use were highest in the Northeast, with participants in the Midwest and South reporting significantly lower PEP and PrEP use. A significant interaction of Black race by past PEP use with current PrEP use was found (RRR 0.57, 99.9% CI 0.37-0.87), indicating that Black men who previously used PEP were less likely to report current PrEP use. Participants who reported recent club drug use were significantly more likely to report past PEP use and current or prior PrEP use than those without recent club drug use. CONCLUSIONS: PrEP use continues to be the predominant HIV prevention strategy for SMM compared with PEP use. Higher rates of past PEP use and current PrEP use among Black SMM are noteworthy, given the disproportionate burden of HIV. Nonetheless, understanding why Black men who previously used PEP are less likely to report current PrEP use is an important avenue for future research.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.063
GPT teacher head0.356
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2022
Admission routes1
Has abstractyes

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