Substance use and other factors associated with COVID-19 vaccine uptake among people at risk for or living with HIV: Findings from the C3PNO consortium
Bibliographic record
Abstract
Objective: We describe the prevalence of COVID-19 vaccine uptake, substance use, and other factors associated with vaccine hesitancy among participants from nine North American cohort studies following a diverse group of individuals at risk for or living with HIV. Methods: Between May 2021 and January 2022, participants completed a survey related to COVID-19 vaccination. Participants included those with and without substance use. Those responding as 'no' or 'undecided' to the question "Do you plan on getting the COVID-19 vaccine?" were categorized as vaccine hesitant. Differences between groups were evaluated using chi-square methods and multivariable log-binomial models were used to calculate prevalence ratios (PR) of COVID-19 vaccine hesitancy with separate models for each substance. Results: Among 1,696 participants, COVID-19 vaccination was deferred or declined by 16%. Vaccine hesitant participants were younger, with a greater proportion unstably housed (14.8% vs. 10.0%; p = 0.02), and not living with HIV (48.% vs. 36.6%; p <.01). Vaccine hesitant participants were also more likely to report cannabis (50.0% vs. 42.4%; p = 0.03), methamphetamine (14.0% vs. 8.2%; p <.01), or fentanyl use (5.5% vs. 2.8%; p = 0.03). Based on multivariable analyses methamphetamine or fentanyl use remained associated with COVID-19 vaccine hesitancy (Adjusted PR = 1.4; 95% CI 1.1-1.9 and Adjusted PR = 1.6; 95% CI 1.0-2.6, respectively). Conclusion: As new COVID-19 vaccines and booster schedules become necessary, people who use drugs (PWUD) may remain vaccine hesitant. Strategies to engage hesitant populations such as PWUD will need to be tailored to include special types of outreach such as integration with substance use programs such as safe injection sites or recovery programs.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".