Gaps in immunization coverage at school entry and after two years of school attendance among immigrant and refugee children in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Foreign-born children may face greater barriers to accessing routine immunizations in Canada or their country of birth, but provincial surveillance data on immigration status are lacking. Using our provincial immunization repository linked to administrative data, we assessed immunization coverage among immigrant and refugee children in Ontario, Canada, compared with Ontario-born children and identified factors associated with being up-to-date (UTD). METHODS: We conducted a retrospective cohort study of children entering school during the 2012/13-2014/15 school years. We calculated UTD coverage for measles (2 doses), diphtheria (4 doses), and polio (3 doses) vaccines at school entry and two years after school attendance. We compared UTD coverage between immigrant/refugee children and Ontario-born children using standardized differences (SD). RESULTS: In a cohort of 363,662 children, 15,114 (4.2%) were immigrants/refugees (82.1% immigrants, 17.9% refugees). UTD coverage for all antigens combined was 59.2% among immigrant/refugee children compared with 87.9% among Ontario-born children at school entry (SD = 0.69), increasing to 84.9% and 94.3%, respectively, two years after school entry (SD = 0.31). Coverage was lower with greater disparities between immigrant/refugee and Ontario-born children for measles (87.9% vs. 94.8%, SD = 0.25) and diphtheria (94.6% vs. 97.4%, SD = 0.15) after two years than polio (97.1% vs. 98.4%, SD = 0.09). Among immigrant/refugee children, coverage was lowest in refugees (vs. immigrants), recent immigrants, and those born in certain regions. CONCLUSIONS: Immunization coverage among foreign-born children lagged behind their Ontario-born peers, even after two years of school attendance. Findings varied by vaccine, immigration category, time spent in Ontario, and country of birth.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".