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Record W4399951668 · doi:10.1136/sextrans-2023-055961

HIV pre-exposure prophylaxis and opportunities for vaccination against hepatitis A virus, hepatitis B virus and human papillomavirus: an analysis of the Ontario PrEP cohort study

2024· article· en· W4399951668 on OpenAlexafffundabout
Matthew W McGarrity, Ryan Lisk, Paul MacPherson, David Knox, Kevin S Woodward, Jeffrey Reinhart, John Macleod, Isaac I. Bogoch, Deanna Clatworthy, Mia J. Biondi, Sean T Sullivan, Alan T W Li, Garfield Durrant, Andrew Schonbe, Fanta Ongoïba, Janet Raboud, Ann N. Burchell, Darrell H. S. Tan

Bibliographic record

VenueSexually Transmitted Infections · 2024
Typearticle
Languageen
FieldMedicine
TopicHepatitis B Virus Studies
Canadian institutionsPublic Health OntarioUniversity of TorontoBlack Coalition for AIDS PreventionYork UniversityOttawa HospitalToronto Metropolitan UniversityMcMaster UniversityToronto General HospitalSTART ClinicAIDS Committee of TorontoUniversity of OttawaMaple Leaf Medical ClinicSt. Michael's Hospital
FundersCanadian Institutes of Health ResearchOntario HIV Treatment NetworkUniversity of OttawaCanadian Foundation for AIDS Research
KeywordsMedicineVaccinationHepatitis B virusImmunityHepatitis A vaccineCohortImmunologyHepatitis BHepatitis ACohort studyLogistic regressionInternal medicineVirologyVirusImmune systemHepatitis

Abstract

fetched live from OpenAlex

OBJECTIVES: Populations who seek HIV pre-exposure prophylaxis (PrEP) are disproportionately affected by hepatitis A virus (HAV), hepatitis B virus (HBV) and human papillomavirus (HPV). We examined immunity/vaccination against these infections among participants in the Ontario PrEP cohort study (ON-PrEP). METHODS: ON-PrEP is a prospective cohort of HIV-negative PrEP users from 10 Ontario clinics. We descriptively analysed baseline immunity/vaccination against HAV (IgG reactive), HBV (hepatitis B surface antibody >10) and HPV (self-reported three-dose vaccination). We further performed multivariable logistic regression to identify characteristics associated with baseline immunity/vaccination. We used cumulative incidence functions to describe vaccine uptake among participants non-immune at baseline. RESULTS: Of 633 eligible participants, 59.1% were white, 85.8% were male and 79.6% were gay. We found baseline evidence of immunity/vaccination against HAV, HBV and HPV in 69.2%, 81.2% and 16.8% of PrEP-experienced participants and 58.9%, 70.3% and 10.4% of PrEP-naïve participants, respectively. Characteristics associated with baseline HAV immunity were greater PrEP duration (adjusted OR (aOR) 1.41/year, 95% CI 1.09 to 1.84), frequent sexually transmitted and bloodborne infection (STBBI) testing (aOR 2.38, 95% CI 1.15 to 4.92) and HBV immunity (aOR 3.53, 95% CI 2.09 to 5.98). Characteristics associated with baseline HBV immunity were living in Toronto (aOR 3.54, 95% CI 1.87 to 6.70) or Ottawa (aOR 2.76, 95% CI 1.41 to 5.40), self-identifying as racialised (aOR 2.23, 95% CI 1.19 to 4.18), greater PrEP duration (aOR 1.39/year, 95% CI 1.02 to 1.90) and HAV immunity (aOR 3.75, 95% CI 2.19 to 6.41). Characteristics associated with baseline HPV vaccination were being aged ≤26 years (aOR 9.28, 95% CI 2.11 to 40.77), annual income between CAD$60 000 and CAD$119 000 (aOR 3.42, 95% CI 1.40 to 8.34), frequent STBBI testing (aOR 7.00, 95% CI 1.38 to 35.46) and HAV immunity (aOR 6.96, 95% CI 2.00 to 24.25). Among those non-immune at baseline, overall cumulative probability of immunity/vaccination was 0.70, 0.60 and 0.53 among PrEP-experienced participants and 0.93, 0.80 and 0.70 among PrEP-naïve participants for HAV, HBV and HPV, respectively. CONCLUSIONS: Baseline immunity to HAV/HBV was common, and a sizeable proportion of non-immune participants were vaccinated during follow-up. However, HPV vaccination was uncommon. Continued efforts should be made to remove barriers to HPV vaccination such as cost, inclusion in clinical guidelines and provider recommendation.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.656
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.289
Teacher spread0.265 · 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 teacher head, not a consensus.

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

Citations2
Published2024
Admission routes3
Has abstractyes

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