Seasonal variation in rates of emergency room visits and acute admissions following recommended infant vaccinations in Ontario, Canada: A self-controlled case series analysis
Bibliographic record
Abstract
OBJECTIVES: To determine if birth month has an effect on the incidence of adverse events following the 2- and 12-month recommended vaccinations. STUDY DESIGN: Using health administrative databases, we conducted a population-based retrospective cohort study and employed a self-controlled case series analysis approach. We included children born in Ontario, Canada between April 1st 2002 and March 31st 2010 who received the diphtheria, tetanus, pertussis, inactivated poliovirus and Haemophilus influenzae type b (DTaP-IPV-Hib) vaccine recommended at 2 months and/or the measles, mumps, and rubella vaccine recommended at 12 months. We calculated the relative incidence (RI) of hospitalizations and emergency room visits within a pre-specified risk period compared to a control period following vaccination. We measured the effect of birth month using relative incidence ratios (RIRs) to compare the RI for infants born in each month to that for the month having the lowest RI. RESULTS: For the 2-month vaccination, we observed the lowest and highest RIs for infants born in October and April, respectively. The RIR (95% CI) for April compared to October was 2.06 (1.59-2.67, p<0.0001), consistent with a strong seasonal effect. For the 12-month vaccination, November births had the lowest RI, whereas August births had the highest. The RIR (95% CI) for August compared to November was 1.52 (1.30-1.77, p<0.0001). CONCLUSIONS: Our findings suggest a seasonal effect on susceptibility to adverse events following vaccination exists. Further study will be important to elucidate potential biological and/or behavioral explanations for the seasonal effect we observed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".