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Record W3177574421 · doi:10.1136/sextrans-2021-sti.128

O14.1 Behaviour or Identity? Differences in HIV testing by sexual identity among MSM in high-income countries: an individual participant data meta-analysis

2021· article· en· W3177574421 on OpenAlexaffabout
Tyrone Curtis, Nigel Field, Lorraine McDonagh, Axel J. Schmidt, Martin Holt, Benjamin R. Bavinton, Peter Saxton, Nathan J. Lachowsky, Catherine H Mercer

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsMen who have sex with menMedicineDemographyHomosexualityAnal intercourseSexual identityHuman immunodeficiency virus (HIV)Human sexualityPsychologyFamily medicineSyphilisGender studies

Abstract

fetched live from OpenAlex

Background HIV testing guidelines recommend MSM test at least annually. However, heterosexual- and bisexual-identifying MSM (heterosexual-MSM; bisexual-MSM) may be less likely to test for HIV than gay-identifying MSM (gay-MSM). We hypothesised that differences in HIV testing may reflect differences in sexual behaviour and the extent to which MSM engage with gay communities. Methods We harmonised individual participant data (IPD) of 155,205 MSM not previously diagnosed with HIV (heterosexual-MSM: 900; bisexual-MSM: 20,409; gay-MSM: 133,896) from four cross-sectional behavioural surveys conducted in Western Europe (2010; n=94,294), Australia (2010–2017; n=46,965), New Zealand (2008, 2011, 2014; n=7,673), and Canada (2015; n=6,273). We conducted IPD meta-analysis using multilevel modified Poisson regression to calculate adjusted prevalence ratios (APRs) comparing heterosexual-MSM and bisexual-MSM with gay-MSM for reporting HIV testing (past year), stratified by reporting of behavioural indicators for more frequent testing (i.e., reporting condomless anal intercourse with non-steady partner(s) and/or >10 male partners in past year). We then examined the impact of gay community engagement. Results Overall, testing was more common among gay-MSM (52.4%) than bisexual-MSM (38.5%, APR=0.75 (95%-CI:0.70–0.80)) and heterosexual-MSM (29.6%, APR=0.60 (95%-CI:0.51–0.71)). More gay- and bisexual-MSM reported behavioural indicators for testing than heterosexual-MSM (45.8% and 43.3% vs 36.3%, respectively) but this did not explain testing disparities, with similar differences in testing observed when focusing on these ‘higher risk’ groups (gay-MSM: 61.7%; bisexual-MSM: 44.9%, APR=0.76 (95%CI:0.72–0.80); heterosexual-MSM: 35.9%, APR=0.66 (95%CI:0.55–0.78)). Gay community engagement was associated with an increased testing likelihood of up to 104% among heterosexual-MSM and 57% among bisexual-MSM. Conclusion Differences in sexual behaviour do not fully explain testing disparities by sexual identity among MSM. In contrast, greater engagement with gay communities is linked to increased testing likelihood regardless of sexual identity. Capitalising on this knowledge for MSM whose lives do not intersect these communities is likely to be challenging, however further investigation is warranted.

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)0.029
metaresearch head score (Gemma)0.047
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.989
Threshold uncertainty score0.152

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.047
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0110.059
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0030.002
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.330
GPT teacher head0.438
Teacher spread0.108 · 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.

Study designMeta-analysis
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

Citations0
Published2021
Admission routes2
Has abstractyes

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