Association between benzodiazepines and dementia: A case-control study from Canadian health surveys and medico-administrative databases
Bibliographic record
Abstract
BACKGROUND: Benzodiazepines (BZDs) are widely prescribed for insomnia and anxiety, but long-term use may accelerate cognitive decline. Evidence is inconsistent because prodromal dementia symptoms can prompt BZD prescriptions, creating reverse-causality bias. OBJECTIVE: Examine whether BZD exposure, duration and elimination half-life are independently associated with incident dementia, and explore confounding by the prodromal period. METHOD: We performed a case-control study in the Torsade Cohort, derived from the Canadian Community Health Survey linked to health-administrative databases. BZD exposure, duration and half-life were analyzed with multivariate conditional logistic regression. Model 1 adjusted for dementia risk factors; Model 2 additionally adjusted for potential BZD indications (insomnia, anxiety, depression). To test prodromal effects, the index date was moved 1-10 years before diagnosis. Cases were adults ≥50 years with dementia; controls were matched on sex, age, follow-up and education. RESULTS: Among 1082 cases and 4262 controls, Model 1 showed that any BZD use was associated with dementia (OR 1.65, 95 % CI 1.42-1.93). Risk was higher with long half-life molecules (OR 2.81) than medium half-life (OR 1.57). In Model 2, chronic use (>180 days) was linked to dementia only within four years before diagnosis. CONCLUSIONS: BZD exposure is associated with increased dementia risk, strongest for long half-life agents. The restriction of chronic-use associations to the four-year prodrome suggests confounding by indication or reverse causality. These findings stress cautious, time-limited BZD prescribing in older adults.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 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".