Infant Vaccine Scheduling Intensity and Autism Incidence: A Preliminary Cross-National Analysis to Guide Public Health Policy
Bibliographic record
Abstract
This study explores the association between early-life vaccine scheduling intensity and autism prevalence across 12 high-income countries, aiming to inform evidence-based public health policy. We examined whether the number and timing of vaccine doses administered to infants under one year correlate with standardized autism incidence rates. Using cross-national data from countries with comparable healthcare systems and diagnostic standards, we applied descriptive statistics, partial correlations (controlling for overall vaccination coverage), and multivariate regression models. Nations with higher autism prevalence (USA, Canada, Australia, Japan, South Korea, Singapore) averaged 15 vaccine types and 20 doses for infants ≤1 year, whereas lower-prevalence countries (Norway, Denmark, Finland, Italy, Sweden, UK) averaged 8 vaccines and 9 doses. Partial correlations revealed strong positive associations between autism prevalence and both vaccine types (r = 0.87, p < 0.001) and doses (r = 0.79, p < 0.01). Regression analysis indicated that a 1% increase in vaccine types corresponded to a 0.47% increase in autism prevalence (p = 0.001), explaining 81% of variance. While these findings do not establish causality, they identify patterns warranting further investigation. For practitioners and policymakers, these results underscore the importance of evaluating vaccine scheduling strategies alongside developmental outcomes. More gradual schedules, as observed in countries with lower autism prevalence, may merit consideration in future research and policy discussions to optimize neurodevelopment while maintaining high immunization coverage. This study provides actionable insights for disease prevention and health promotion professionals seeking to balance immunization goals with long-term child health outcomes.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".