Characteristics of new HIV diagnoses over 1995–2019: A clinic-based study in Montréal, Canada
Bibliographic record
Abstract
BACKGROUND: Characterization of populations at risk of acquiring HIV is required to inform the public health response to HIV. To identify potential changing needs in HIV prevention and care cascade, we aim to describe how the demographic profiles and exposure categories of newly diagnosed HIV positive individuals attending a large sexual health clinic in Montréal (Canada) evolved since the beginning of the antiretroviral therapy era in the mid-1990s. METHODS: Using diagnosis data from participants of the Clinique médicale l'Actuel cohort of HIV-positive patients, we examined the distribution of exposure categories (sexual orientation, sexual behaviours, injection drug use, being born in an HIV-endemic country) by gender and year of diagnosis. Time trends in mean age and in the proportion of patients with late (CD4 <350 cells/μL) or advanced stage (CD4 <200 cells/μL) of HIV infection at diagnosis were assessed through meta-regressions. RESULTS: A total of 2,612 patients diagnosed with HIV between January 1st, 1995 and December 31st, 2019 were included. Overall, mean age was 35 years (standard deviation: 10 years) and remained stable over time. The proportion of patients with advanced stage of HIV infection decreased from 16% in 1995 to 4% in 2019. Although men who have sex with men (MSM) consistently accounted for the highest proportion of new diagnoses (77%, 2,022/2,612 overall), their proportion decreased since 2013. There was also a concomitant decrease in the proportion of people who inject drugs, with none of the newly diagnosed participants reporting injection drug use since 2017, and an important increase in the proportion of patients born in an HIV-endemic country (24%, 7/29 in 2019), especially among women. Compared to patients from non-endemic countries, those from HIV-endemic countries were characterized by higher proportions of heterosexuals (88% vs 17%) and of women (52% vs 7%), and were twice likely to get diagnosed at an advanced stage of HIV infection (32% vs 15%). CONCLUSIONS: In absolute numbers, MSM continue to account for the largest exposure category. However, patients from HIV-endemic countries, who tend to be diagnosed at later stages of HIV infection, constitute an increasing proportion of newly diagnosed individuals. These persons could face distinct barriers to rapid diagnosis. Tailoring HIV testing strategies and other prevention interventions to the specific unmet prevention needs of these individuals is warranted.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".