Common vaccines and the risk of dementia: a population‐based study
Bibliographic record
Abstract
Abstract Background The growing evidence for an infectious etiology of dementia supports the investigation of immunization as a preventative intervention. Prior observational studies suggesting that common vaccines are associated with an up to 60% decreased risk of dementia had several methodological limitations. Thus, to provide evidence for this question, we assessed in a large population whether common vaccines are associated with a decreased risk of dementia. Method We conducted a population‐based cohort study with a nested case‐control analysis using the Gold and Aurum datasets from the United Kingdom’s Clinical Practice Research Datalink (CPRD). The study cohort included all dementia‐free individuals aged ≥50 years enrolled in the CPRD between January 1988 and December 2018, and followed until March 2021. Each case of dementia identified during follow‐up was matched on sex, age, cohort entry date, duration of follow‐up, and CPRD dataset with up to four controls randomly selected via risk‐set sampling. Conditional logistic regression yielded confounder‐adjusted odds ratios (ORs) with 95% confidence intervals (CIs) of dementia associated with exposure to common vaccines (influenza, pneumonia, shingles, tetanus, diphtheria or pertussis) >2 years before the index date compared to no exposure to these vaccines during the study period. In sensitivity analyses, we applied a longer lag period (10 years) and used active comparators (participation in screening programs for breast and prostate cancer) to account for detection bias. Result Our study cohort included 13,383,431 dementia‐free individuals aged ≥50 years, including 212,562 who developed dementia over a mean 10‐year follow‐up (mean age 70 years; 62% female). Exposure to common vaccines was associated with an increased risk of dementia (OR, 1.38; 95% CI, 1.36‐1.40), compared with no exposure, driven mostly by immunization for influenza (OR, 1.39; 95% CI, 1.37‐1.41). Applying a longer lag period (OR, 1.22; 95% CI, 1.20‐1.24) and using active comparators (versus breast cancer screening: OR, 1.34; 95% CI, 1.26‐1.42 / versus prostate cancer screening: OR, 1.22; 95% CI, 1.14‐1.31) slightly attenuated the risk increase. Conclusion Our large population‐based study showed no decreased risk of dementia associated with exposure to common vaccines. The observed increase in the risk is likely a result of detection bias.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".