HIV elimination in Québec: tracking progress and evaluating HIV prevention interventions among key populations
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,010 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,005 |
| Études des sciences et des technologies | 0,004 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,004 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».