Examining the secondary impacts of the COVID-19 pandemic on syndemic production and PrEP use among gay, bisexual and other men who have sex with men (GBM) in Vancouver, Canada
Bibliographic record
Abstract
BACKGROUND: The secondary impacts of the COVID-19 pandemic may disproportionately affect gay, bisexual, and other men who have sex with men (GBM), particularly related to HIV prevention and treatment outcomes. We applied syndemic theory to examine PrEP disruptions during the during the height of the COVID-19 pandemic in Vancouver, Canada. METHODS: Sexually-active GBM, aged 16 + years, were enrolled through respondent-driven sampling (RDS) from February 2017 to August 2019. Participants completed a Computer-Assisted Self-Interview every six months and data were linked to the BC PrEP Program (program responsible for publicly funded PrEP in the province) to directly measure PrEP disruptions. The analysis period for this study was from March 2018-April 2021. We used univariable generalized linear mixed models to examine (1) six-month trends for syndemic conditions: the prevalence of moderate/severe depressive or anxiety symptoms, polysubstance use, harmful alcohol consumption, intimate partner violence, and (2) six-month trends for PrEP interruptions among HIV-negative/unknown GBM. We also applied 3-level mixed-effects logistic regression with RDS clustering to examine whether syndemic factors were associated with PrEP interruptions. RESULTS: Our study included 766 participants, with 593 participants who had at least one follow-up visit. The proportion of respondents with abnormal depressive symptoms increased over the study period (OR = 1.35; 95%CI = 1.17, 1.56), but we found decreased prevalence for polysubstance use (OR = 0.89; 95%CI = 0.82, 0.97) and binge drinking (OR = 0.74; 95%CI = 0.67, 0.81). We also found an increase in PrEP interruptions (OR = 2.33; 95%CI = 1.85, 2.94). GBM with moderate/severe depressive symptoms had higher odds (aOR = 4.80; 95%CI = 1.43, 16.16) of PrEP interruptions, while GBM with experiences of IPV had lower odds (aOR = 0.38; 95%CI = 0.15, 0.95) of PrEP interruptions. GBM who met clinical eligibility for PrEP had lower odds of experiencing PrEP interruptions (aOR = 0.25; 95%CI = 0.11, 0.60). CONCLUSION: There were increasing PrEP interruptions since March 2020. However, those most at risk for HIV were less likely to have interruptions. Additional mental health services and targeted follow-up for PrEP continuation may help to mitigate the impacts of the COVID-19 pandemic on GBM.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 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".