Association between socioeconomic status and adverse events following immunization at 2, 4, 6 and 12 months
Bibliographic record
Abstract
Using a population-based self-controlled case series design, we examined data on children born between the years 2002 and 2009 in the province of Ontario, Canada. We specifically examined how socioeconomic status (SES) influences rates of adverse events following immunization (AEFI), defined as emergency room visits and / or hospital admissions. For vaccination at 2, 4 and 6 mo combined, the relative incidence of AEFI (95% CI) in the first 72 h after vaccination was 0.69 (0.67 to 0.71). For all three vaccinations combined, we observed no relationship between the relative incidence of an event and quintile of socioeconomic status (p = 0.1433). For the 12-mo vaccination alone, the relative incidence of events (95% CI) on days 4 to 12 following immunization was 1.35 (1.31 to 1.38). We observed a significant relationship between socioeconomic status and vaccination at 12 mo, with lower SES being associated with a higher relative incidence of events (p = 0.0075). When the lowest 2 quintiles of income combined were compared with the highest 3 quintiles, the relative incidence ratio (95% CI) was 0.94 (0.89 to 0.99, p = 0.02). These results translate to 150 additional adverse events in the lower SES quintiles as compared with the higher SES quintiles for every 100,000 children vaccinated, or 1 additional event for every 666 individuals vaccinated. Future studies should explore potential explanations for this observation.
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 | 0.001 |
| 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) | 0.001 | 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".