Influenza vaccination coverage across ethnic groups in Canada
Bibliographic record
Abstract
BACKGROUND: The success of influenza vaccination campaigns may be suboptimal if subgroups of the population face unique barriers or have misconceptions about vaccination. We conducted a national study to estimate influenza vaccine coverage across 12 ethnic groups in Canada to assess the presence of ethnic disparities. METHODS: We pooled responses to the Canadian Community Health Survey between 2003 and 2009 (n = 437 488). We estimated ethnicity-specific self-reported influenza vaccine coverage for the overall population, for people aged 65 years and older, and for people aged 12-64 years with and without chronic conditions. We used weighted logistic regression models to examine the association between ethnicity and influenza vaccination, adjusting for sociodemographic factors and health status. RESULTS: Influenza vaccination coverage ranged from 25% to 41% across ethnic groups. After adjusting for sociodemographic factors and health status for people aged 12 years and older, all ethnic groups were more likely to have received a vaccination against influenza than people who self-identified as white, with the exception of those who self-identified as black (odds ratio [OR] 1.01, 95% confidence interval [CI] 0.88-1.15). Compared with white Canadians, Canadians of Filipino (OR 2.00, 95% CI 1.67-2.40) and Southeast Asian (OR 1.66, 95% CI 1.36-2.03) descent had the greatest likelihood of having received vaccination against influenza. INTERPRETATION: Influenza vaccine coverage in Canada varies by ethnicity. Black and white Canadians have the lowest uptake of influenza vaccine of the ethnic groups represented in our study. Further research is needed to understand the facilitators, barriers and misconceptions relating to vaccination that exist across ethnic groups, and to identify promotional strategies that may improve uptake among black and white Canadians.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".