History of Benzodiazepine Prescriptions and Risk of Dementia: Possible Bias Due to Prevalent Users and Covariate Measurement Timing in a Nested Case-Control Study
Bibliographic record
Abstract
Previous estimates of whether long-term exposure to benzodiazepines increases dementia risk are conflicting and are compromised by the difficulty of controlling for confounders and by reverse causation. We investigated how estimates for the association between benzodiazepine use and later dementia incidence varied based on study design choices, using a case-control study nested within the United Kingdom's Clinical Practice Research Datalink. A total of 40,770 dementia cases diagnosed between April 2006 and July 2015 were matched on age, sex, available data history, and deprivation to 283,933 control subjects. Benzodiazepines and Z-drug prescriptions were ascertained in a drug-exposure period 4-20 years before dementia diagnosis. Estimates varied with the inclusion of new or prevalent users, with the timing of covariate ascertainment, and with varying time between exposure and outcome. There was no association between any new prescription of benzodiazepines and dementia (adjusted odds ratio (OR) = 1.03, 95% confidence interval (CI): 1.00, 1.07), whereas an inverse association was observed among prevalent users (adjusted OR = 0.91, 95% CI: 0.87, 0.95), although this was likely induced by unintentional adjustment for colliders. By considering the choice of confounders and timing of exposure and covariate measurement, our findings overall are consistent with no causal effect of benzodiazepines or Z-drugs on dementia incidence.
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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 | 0.062 | 0.169 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".