Measuring inequalities in COVID-19 vaccination uptake and intent: results from the Canadian Community Health Survey 2021
Bibliographic record
Abstract
BACKGROUND: By July 2021, Canada had received enough COVID-19 vaccines to fully vaccinate every eligible Canadian. However, despite the availability of vaccines, some eligible individuals remain unvaccinated. Differences in vaccination uptake can be driven by health inequalities which have been exacerbated and amplified by the pandemic. This study aims to assess inequalities in COVID-19 vaccination uptake and intent in adults 18 years or older across Canada by identifying sociodemographic factors associated with non-vaccination and low vaccination intent using data drawn from the June to August 2021 Canadian Community Health Survey (CCHS). METHODS: The CCHS is an annual cross-sectional and nationally representative survey conducted by Statistics Canada, which collects health-related information. Since September 2020, questions about the COVID-19 pandemic are asked. Adjusted logistic regression models were fitted to examine associations between vaccination uptake or intent and sociodemographic and health related variables. Region, age, gender, level of education, Indigenous status, visible minority status, perceived health status, and having a regular healthcare provider were considered as predictors, among other factors. RESULTS: The analysis included 9,509 respondents. The proportion of unvaccinated was 11%. Non-vaccination was associated with less than university education (aOR up to 3.5, 95% CI 2.1-6.1), living with children under 12 years old (aOR 1.6, 95% CI 1.1-2.4), not having a regular healthcare provider (aOR 1.6, 95% CI 1.1-2.2), and poor self-perceived health (aOR 1.8, 95% CI 1.3-2.4). Only 5% of the population had low intention to get vaccinated. Being unlikely to get vaccinated was associated with the Prairies region (aOR 2.2, 95% CI 1.2-4.1), younger age groups (aOR up to 4.0, 95% CI 1.3-12.3), less than university education (aOR up to 3.8, 95% CI 1.9-7.6), not being part of a visible minority group (aOR 3.0, 95% CI 1.4-6.4), living with children under 12 years old (aOR 1.8, 95% CI 1.1-2.9), unattached individuals (aOR 2.6, 95% CI 1.1-6.1), and poor self-perceived health (aOR 2.0, 95% CI 1.3-2.9). CONCLUSIONS: Disparities were observed in vaccination uptake and intent among various sociodemographic groups. Awareness of inequalities in COVID-19 vaccination uptake and intent is needed to determine the vaccination barriers to address in vaccination promotion strategies.
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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 | 0.035 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".