Comparison of the burden of self-reported bacterial sexually transmitted infections among men having sex with men across 68 countries on four continents
Bibliographic record
Abstract
BACKGROUND: Men who have sex with men (MSM) are in general more vulnerable to sexually transmitted infections (STIs) than the heterosexual men population. However, surveillance data on STI diagnoses lack comparability across countries due to differential identification of MSM, diagnostic standards and methods, and screening guidelines for asymptomatic infections. METHODS: We compared self-reported overall diagnostic rates for syphilis, gonorrhea, and chlamydia infections, and diagnostic rates for infections that were classified to be symptomatic in the previous 12 months from two online surveys. They had a shared methodology, were conducted in 68 countries across four continents between October 2017 and May 2018 and had 202,013 participants. RESULTS: Using multivariable multilevel regression analysis, we identified age, settlement size, number of sexual partners, condom use for anal intercourse, testing frequency, sampling rectal mucosa for extragenital testing, HIV diagnosis, and pre-exposure prophylaxis use as individual-level explanatory variables. The national proportions of respondents screened and diagnosed who notified some or all of their sexual partners were used as country-level explanatory variables. Combined, these factors helped to explain differences in self-reported diagnosis rates between countries. The following differences were not explained by the above factors: self-reported syphilis diagnoses were higher in Latin America compared with Europe, Canada, Israel, Lebanon, and the Philippines (aORs 2.30 - 3.71 for symptomatic syphilis compared to Central-West Europe); self-reported gonorrhea diagnoses were lower in Eastern Europe and in Latin America compared with all other regions (aORs 0.17-0.55 and 0.34 - 0.62 for symptomatic gonorrhea compared to Central-West Europe); and self-reported chlamydia diagnoses were lower in Central East and Southeast Europe, South and Central America, and the Philippines (aORs 0.25 - 0.39 for symptomatic chlamydia for Latin American subregions compared to Central West Europe). CONCLUSIONS: Possible reasons for differences in self-reported STI diagnosis prevalence likely include different background prevalence for syphilis and syndromic management without proper diagnosis, and different diagnostic approaches for gonorrhea and chlamydia.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".