HIV elimination in Québec: tracking progress and evaluating HIV prevention interventions among key populations
Bibliographic record
Abstract
Montréal was Canada’s first UNAIDS Fast-Track City, aiming to end HIV/AIDS by 2030. The initiative launched with goals for 2020: zero new HIV acquisitions, zero discrimination and stigma, and the 90-90-90 UNAIDS care cascade targets (90% of people living with HIV [PLHIV] diagnosed; of those, 90% on antiretroviral treatment [ART]; and, of those, 90% virally suppressed; advancing to 95-95-95 by 2025). Meeting these requires understanding current epidemics and reinforcing prevention for the key populations most vulnerable to HIV acquisition and transmission. My thesis informs HIV elimination by evaluating prevention use in men who have sex with men (MSM) and strengthening epidemic monitoring in MSM and people who inject drugs (PWID) in Québec.I first identified prevention patterns in Montréal MSM and described their associated factors. Applying latent class analysis to 2017-18 survey data, stratified by HIV serostatus, I uncovered classes of similar prevention users. In each, usage of different types was limited. While condoms stayed common practice, antiretroviral prevention arose. Treatment-as-prevention appeared fundamental to all classes of PLHIV and PrEP was central to a small biomedical use class. With multinomial logistic regression, I compared classes of less use to those defined by prevention types (condoms, seroadaptive behaviour, and biomedical). I found that the prevention classes had more anal sex partners. In those whose HIV-status was negative/unknown, they were also more likely to be recently diagnosed with a sexually transmitted infection. Another result was that belonging to the PrEP and other biomedical use class was associated with higher education.Secondly, I used an agent-based mathematical model to evaluate the population-level effectiveness of PrEP on sexual HIV transmission in Montréal MSM over 2013-2021. I simulated PrEP intervention and counterfactual scenarios, estimating the annual and cumulative fractions of HIV acquisitions averted. With low PrEP coverage until 2015, few acquisitions were initially averted, but the number started increasing in 2017. In 2019 coverage peaked at 10% and 36% of acquisitions were averted (90% credible interval [CrI]: 22%-48%). Afterward, this level of impact persisted despite use being affected by the COVID-19 pandemic. Cumulatively, excluding 2013-2014, PrEP prevented 20% (90%CrI: 11%-30%) of HIV acquisitions.Lastly, to benchmark and monitor elimination I developed a mathematical model synthesizing surveillance data to estimate HIV incidence in Québec MSM and PWID. It is an age-stratified, multi-state, back-calculation Bayesian model estimating incidence, prevalence and the care cascade by geography, key population, and age. My results showed drastic incidence declines and progress in diagnosis and care in MSM and PWID (<10% undiagnosed, <2 years to diagnosis, and high ART coverage in 2020). However, the 2020 zero acquisitions goal was not met. That year, there were an estimated 266 (95%CrI: 103-508) MSM and 6 (95%CrI:1-26) PWID acquisitions in Québec, of which 97 (95%CrI: 33-227) and 2 (95%CrI: 0-14) were acquired in Montréal. Additionally, slightly higher fractions of PLHIV were undiagnosed provincially, as well as in young MSM.Québec has made strides in addressing HIV. Nonetheless, my thesis showed that unmet prevention needs remain, especially for MSM. The reach of PrEP could especially be expanded –there are MSM eligible but not accessing it, and this limited its benefits. Also, while I showed that earlier adopters had higher education, identifying disparities in and barriers to PrEP access is critical. Diagnosis coverage differences also need to be to overcome by further prioritizing testing in young MSM and ensuring adequate access to such services outside urban centres. As the prevention landscape and epidemic drivers evolve, monitoring the epidemic will remain critical, and the models developed in my thesis provide the epidemic intelligence to do so
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".