MétaCan
Menu
Back to cohort
Record W4401845076 · doi:10.1016/j.lanwpc.2024.101175

Syphilis testing, incidence, and reinfection among gay and bisexual men in Australia over a decade spanning HIV PrEP implementation: an analysis of surveillance data from 2012 to 2022

2024· article· en· W4401845076 on OpenAlexaff
Michael W. Traeger, Rebecca Guy, Caroline Taunton, Eric P. F. Chow, Jason Asselin, Allison Carter, Htein Linn Aung, Mark Bloch, Christopher K. Fairley, Anna McNulty, Vincent J. Cornelisse, Phillip Read, Louise Owen, Nathan Ryder, David J. Templeton, Darryl O’Donnell, Basil Donovan, Margaret Hellard, Mark Stoové

Bibliographic record

VenueThe Lancet Regional Health - Western Pacific · 2024
Typearticle
Languageen
FieldMedicine
TopicSyphilis Diagnosis and Treatment
Canadian institutionsSimon Fraser University
FundersNational Health and Medical Research CouncilMedical Research CouncilNSW Ministry of HealthACT GovernmentBurnet Institute
KeywordsSyphilisIncidence (geometry)Treatment as preventionMen who have sex with menHuman immunodeficiency virus (HIV)MedicineTransmission (telecommunications)PopulationDemographyImmunologyEnvironmental healthAntiretroviral therapyViral loadSociology

Abstract

fetched live from OpenAlex

Background: Gay and bisexual men (GBM) remain overrepresented among syphilis diagnoses in Australia and globally. The extent to which changes in sexual networks associated with HIV pre-exposure prophylaxis (PrEP) and treatment as prevention (TasP) may have influenced syphilis transmission among GBM at the population-level is poorly understood. We describe trends in syphilis testing and incidence among GBM in Australia over eleven years spanning widespread uptake of HIV PrEP and TasP. Methods: We analysed linked clinical data from GBM aged 16 years or older across a sentinel surveillance network in Australia from January 1, 2012, to December 31, 2022. Individuals with at least two clinic visits and with at least two syphilis tests during the observations period were included in testing and incidence analyses, respectively. Annual rates of testing and infectious syphilis incidence from 2012 to 2022 were disaggregated by HIV status and PrEP use (record of PrEP prescription; retrospectively categorised as ever or never-PrEP user). Cox regression explored associations between demographics, PrEP use and history of bacterial sexually transmissible infections (STIs) and infectious syphilis diagnosis. Findings: Among 129,278 GBM (mean age, 34.6 years [SD, 12.2]) included in testing rate analyses, 7.4% were living with HIV at entry and 31.1% were prescribed PrEP at least once during the study period. Overall syphilis testing rate was 114.0/100 person-years (py) and highest among GBM with HIV (168.4/100 py). Syphilis testing increased from 72.8/100 py to 151.8/100 py; driven largely by increases among ever-PrEP users. Among 94,710 GBM included in incidence analyses, there were 14,710 syphilis infections diagnosed over 451,560 person-years (incidence rate = 3.3/100 py). Syphilis incidence was highest among GBM with HIV (6.5/100 py), followed by ever-PrEP users (3.5/100 py) and never-PrEP users (1.4/100 py). From 2012 to 2022, syphilis incidence increased among ever-PrEP users from 1.3/100 py to 5.1/100 py, and fluctuated between 5.4/100 py and 6.6/100 py among GBM with HIV. In multivariable Cox regression, previous syphilis diagnosis (adjusted hazard ratio [aHR] = 1.98, 95% CI = 1.83-2.14), living with HIV (aHR = 1.83, 95% CI = 1.12-1.25) and recent (past 12 m) prescription of PrEP (aHR = 1.78, 95% CI = 1.61-1.97) were associated with syphilis diagnosis. Interpretation: Syphilis trends between GBM with HIV and GBM with evidence of PrEP use have converged over the past decade in Australia. Our findings recommend targeting emergent syphilis control strategies (e.g. doxycycline post-exposure prophylaxis) to GBM with prior syphilis diagnoses, using HIV PrEP or who are living with HIV. Funding: Australian Department of Health and Aged Care, National Health and Medical Research Council.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.209
GPT teacher head0.459
Teacher spread0.250 · 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.

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

Citations4
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueThe Lancet Regional Health - Western PacificSame topicSyphilis Diagnosis and TreatmentFrench-language works237,207