Common Vaccines and the Risk of Incident Dementia: A Population-based Cohort Study
Bibliographic record
Abstract
BACKGROUND: Observational studies suggesting that immunizations strongly decrease the risk of dementia had several methodological limitations. We assessed whether common vaccines are associated with the risk of dementia. METHODS: We assembled a population-based cohort of dementia-free individuals aged ≥50 years in the United Kingdom's Clinical Practice Research Datalink between 1988 and 2018. Using a nested case-control approach, we matched each patient with dementia with 4 controls. Conditional logistic regression yielded confounder-adjusted odds ratios (ORs) with 95% confidence intervals (CIs) of dementia associated with common vaccines >2 years before the index date compared with no exposure during the study period. Moreover, we applied a 10-year lag period and used active comparators (participation in breast or prostate cancer screening) to account for detection bias. RESULTS: Common vaccines were associated with an increased risk of dementia (OR, 1.38 [95% CI, 1.36-1.40]), compared with no exposure. Applying a 10-year lag period (OR, 1.20 [95% CI, 1.18-1.23]) and comparing versus prostate cancer screening (1.19 [ 1.11-1.27]) but not breast cancer screening (1.37 [1.30-1.45]) attenuated the risk increase. CONCLUSIONS: Common vaccines were not associated with a decreased risk of dementia. Unmeasured confounding and detection bias likely accounted for the observed increased risk.
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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 | 0.003 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".