Coverage of the influenza and pneumococcal vaccinations among immigrant and non-immigrant older adults in Canada: a cross-sectional analysis of data from the Canadian Longitudinal Study on Aging (CLSA)
Bibliographic record
Abstract
BACKGROUND: Influenza and pneumococcal vaccination coverage in older adults fall below the target of 80%. Being an immigrant may be associated with lower coverage of both vaccinations, but limited efforts have been made in the Canadian context to explore such disparities. Therefore, we examined the association between immigrant status and coverage of influenza and pneumococcal vaccinations among older adults as well as the relative importance of immigrant status in predicting coverage of both vaccinations. METHODS: We conducted a cross-sectional secondary analysis of the Canadian Longitudinal Study on Aging data. We descriptively analyzed coverage of both vaccinations by immigrant status and used Poisson regression models with robust standard errors to estimate the associations of immigrant status and other key equity stratifiers with vaccination. Importance of various determinants, including immigrant status, in predicting both vaccinations were assessed using random forest algorithms. RESULTS: Immigrant participants reported lower coverage of influenza vaccination in the past 12 months (63.8% [95% CI: 60.9-66.7%] vs. 66.9% [95% CI: 65.5-68.3%]) and pneumococcal vaccination ever (48.7% [95% CI: 45.6-51.8%] vs. 55.8% [95% CI: 54.3-57.3%]). Prevalence of influenza and pneumococcal vaccinations were both lower among immigrant participants compared to non-immigrant participants. Immigrant status was among the 10 most important predictors of pneumococcal vaccination, but among the less important predictors of influenza vaccination. CONCLUSIONS: Overall, we found disparities in influenza and pneumococcal vaccination by immigrant status among older adults in Canada. Further studies on vaccination coverage and decision-making among marginalized communities, including immigrants, are warranted to equitably improve vaccine uptake.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".