COVID-19 vaccine coverage among immigrants and refugees in Alberta: A population-based cross-sectional study
Bibliographic record
Abstract
Background: Studies have shown that immigrants have lower vaccination rates than the Canadian-born population. We sought to assess COVID-19 vaccine coverage and factors associated with uptake among foreign-born immigrants relative to the non-immigrant population in Alberta, Canada. Methods: In this cross-sectional study, we analysed population-based linked administrative health data from Alberta to examine vaccine coverage for 3 931 698 Albertans, of which 731 217 were immigrants. We calculated COVID-19 vaccination coverage as the proportion of eligible Albertans with a record of receiving at least one dose of a COVID-19 vaccine as of November 29, 2021. We used multivariable logistic regression to examine the association of vaccine coverage with migration status (immigrants: four categories based on time since migration and non-immigrants) adjusting for socio-demographic variables. Results: Overall, COVID-19 vaccination coverage was 78.2% (95% confidence interval (CI) = 78.1%-78.3%) among immigrants and 76.0% (95% CI = 75.9%-76.0%) among non-immigrants. Coverage among immigrants differed by continent of origin, with North America, Oceania, and Europe having the lowest coverage. Although vaccine coverage was relatively uniform across neighbourhood income quintiles for immigrants, immigrants living in rural areas had lower vaccine coverage compared to non-immigrants living in rural areas. Multivariable logistic regression analysis showed a significant interaction between age category and migration status. While immigrants below 50 years of age generally had significantly higher vaccine coverage compared to non-immigrants, there was some variation based on time since migration. Immigrants above 50 years of age showed significantly lower coverage compared to non-immigrants of the same age. Conclusions: Public health interventions should focus on older immigrants, immigrants living in rural areas, and immigrants from specific continental backgrounds in order to improve COVID-19 vaccination coverage.
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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; 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".