The epidemiology of HIV and other sexually transmitted infections in African, Caribbean and Black men in Toronto, Canada
Bibliographic record
Abstract
BACKGROUND: African, Caribbean, and Black (Black) men account for 16.5% of new HIV diagnoses among men in Ontario. There is substantial evidence that sexually transmitted infections (STIs) are associated with increased likelihood of HIV infection; however, little is known regarding the prevalence of HIV/STI co-infections among Black men in Toronto. Progress has been made in understanding factors contributing to racial/ethnic disparities in HIV between among men who have sex with men (MSM). In this study, we investigate within-racial group patterns of HIV/STI infection between Black MSM and Black men who only have sex with women (MSW). METHODS: A cross-sectional descriptive epidemiological study was conducted with a non-probability sample of Black men recruited from Toronto, Ontario. Audio Computer Assisted Self-Interviews (ACASI) surveys were used to collect demographic and behavioral data. Biological specimens were collected to screen for HIV and other STIs. Chi-Square tests were used to compare the prevalence of (1) HIV and current STIs between MSM and MSW and (2) current STIs between people living with HIV and people not living with HIV. Logistic regression models were constructed to assess whether or not history of STIs were associated with current HIV infection. RESULTS: The prevalence of HIV (9.2%), syphilis (7.2%), hepatitis B (2.7%), and high-risk anal HPV (8.4%) and penile HPV (21.3%) infections were high in Black men (N = 487) and were significantly increased in Black MSM compared with MSW; the prevalence of syphilis and high-risk HPV were also increased in men living with HIV. Men with a history of syphilis (OR = 6.48, 95% CI: 2.68,15.71), genital warts (OR = 4.32, 95% CI: 1.79,10.43) or genital ulcers (OR = 21.3, 95% CI: 1.89,239.51) had an increased odds of HIV infection. CONCLUSIONS: The HIV/STI prevalence was high among this sample of Black men, although the study design may have led to oversampling of men living with HIV. The associations between STIs and current HIV infection highlight the need for integrated of HIV/STI screening and treatment programs for Black men. Public health strategies are also needed to reduce disproportionate HIV/STI burden among Black MSM-including improving HPV vaccine coverage.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".