Low Rates of Influenza Immunization in Young Children Under Ontario’s Universal Influenza Immunization Program
Bibliographic record
Abstract
OBJECTIVES: To determine physician-administered influenza vaccine coverage for children aged 6 to 23 months in a jurisdiction with a universal influenza immunization program during 2002-2009 and to describe predictors of vaccination. METHODS: By using hospital records, we identified all infants born alive in Ontario hospitals from April 2002 through March 2008. Immunization status was ascertained by linkage to physician billing data. Children were categorized as fully, partially, or not immunized depending on the number and timing of vaccines administered. Generalized linear mixed models determined the association between immunization status and infant, physician, and maternal characteristics. RESULTS: Influenza immunization was low for the first influenza season of the study period (1% fully immunized during the 2002-2003 season), increased for the following 3 seasons (7% to 9%), but then declined (4% to 6% fully immunized during the 2006-2007 to 2008-2009 seasons). Children with chronic conditions or low birth weight were more likely to be immunized. Maternal influenza immunization (adjusted odds ratio 4.31; 95% confidence interval 4.21-4.40), having a pediatrician as the primary care practitioner (adjusted odds ratio 1.85; 95% confidence interval 1.68-2.04), high visit rates, and better continuity of care were all significantly associated with full immunization, whereas measures of social disadvantage were associated with nonimmunization. Low birth weight infants discharged from neonatal care in the winter were more likely to be immunized. CONCLUSIONS: Influenza vaccine coverage among children aged 6 to 23 months in Ontario is low, despite a universal vaccination program and high primary care visit rates. Interventions to improve coverage should target both physicians and families.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".