Uncovering SARS-COV-2 vaccine uptake and COVID-19 impacts among First Nations, Inuit and Métis Peoples living in Toronto and London, Ontario
Bibliographic record
Abstract
BACKGROUND: First Nations, Inuit and Métis Peoples across geographies are at higher risk of SARS-CoV-2 infection and COVID-19 because of high rates of chronic disease, inadequate housing and barriers to accessing health services. Most Indigenous Peoples in Canada live in cities, where SARS-CoV-2 infection is concentrated. To address gaps in SARS-CoV-2 information for these urban populations, we partnered with Indigenous agencies and sought to generate rates of SARS-CoV-2 testing and vaccination, and incidence of infection for First Nations, Inuit and Métis living in 2 Ontario cities. METHODS: = 364), Ontario, who were recruited using respondent-driven sampling. We linked to ICES SARS-CoV-2 databases and prospectively monitored rates of SARS-CoV-2 testing, diagnosis and vaccination for First Nations, Inuit and Métis, and comparator city and Ontario populations. RESULTS: We found that SARS-CoV-2 testing rates among First Nations, Inuit and Métis were higher in Toronto (54.7%, 95% confidence interval [CI] 48.1% to 61.3%) and similar in London (44.5%, 95% CI 36.0% to 53.1%) compared with local and provincial rates. We determined that cumulative incidence of SARS-CoV-2 infection was not significantly different among First Nations, Inuit and Métis in Toronto (7364/100 000, 95% CI 2882 to 11 847) or London (7707/100 000, 95% CI 2215 to 13 200) compared with city rates. We found that rates of vaccination among First Nations, Inuit and Métis in Toronto (58.2%, 95% CI 51.4% to 64.9%) and London (61.5%, 95% CI 52.9% to 70.0%) were lower than the rates for the 2 cities and Ontario. INTERPRETATION: Although Ontario government policies prioritized Indigenous populations for SARS-CoV-2 vaccination, vaccine uptake was lower than in the general population for First Nations, Inuit and Métis Peoples in Toronto and London. Ongoing access to culturally safe testing and vaccinations is urgently required to avoid disproportionate hospital admisson and mortality related to COVID-19 in these communities.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".