COVID-19 vaccine coverage and factors associated with vaccine uptake among individuals with a recent experience of homelessness: a population-based analysis from Ontario, Canada.
Bibliographic record
Abstract
ObjectivesTo describe COVID-19 vaccine coverage (i.e., the estimated percentage of people who have received a vaccine) and determinants of vaccine receipt among individuals with a recent experience of homelessness in Ontario, Canada. ApproachWe conducted a retrospective, population-based cohort study of 23,247 individuals (≥18 years) with a recent experience of homelessness as recorded in routinely collected healthcare databases. Participants were followed from December 14, 2020 to September 30, 2021 for the receipt of a COVID-19 vaccine. Using modified Poisson regression, we identified sociodemographic, healthcare usage, and clinical factors associated with the receipt of one or more doses of a COVID-19 vaccine. ResultsBy September 30, 14,271 (61.4%) of participants with a recent experience of homelessness had received a first dose of a COVID-19 vaccine and 11,082 (47.7%) had received two doses. Over the same period, 86.6% and 81.6% of the total adult population of Ontario had received a first dose and second dose, respectively. In multivariable analysis, factors associated with increased COVID-19 uptake included ≥1 visit to a general practitioner (adjusted Risk Ratio [aRR]:1.37[95% CI 1.31-1.42]), older age (vs. 18-29 years: 50-59 years, aRR:1.18[1.14-1.22]; 60+ years, aRR:1.27[1.22-1.31]), receipt of an influenza vaccine (aRR:1.25[1.23-1.28]), receipt of ≥1 SARS-CoV-2 test (aRR:1.23[1.20-1.26]) and the presence of chronic health conditions (vs. 0 conditions: 1 condition, aRR:1.05[1.03, 1.08]; 2+ conditions, aRR:1.11[1.08-1.14]). In contrast, living in a smaller metropolitan region (aRR:0.92[0.90-0.94]) or rural location (aRR:0.93[0.90-0.97]) compared to a large metropolitan region was associated with lower uptake. ConclusionsAs of September 30, 2021, COVID-19 vaccine coverage among individuals with a recent experience of homelessness in Ontario was substantially lower than the general adult population of Ontario for a first and second dose. Findings underscore the importance of leveraging organizations that are accessed and trusted by people who experience homelessness for targeted vaccine delivery.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".