Patterns and determinants of statin prescribing and discontinuation in individuals aged 80 and older: A 10-year population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Considering the limited knowledge regarding the benefits and risks of statins in people ≥80 years, population use patterns can provide valuable insights. OBJECTIVE: To describe prescribing and discontinuation patterns for statins, along with their determinants, in people ≥80 years in Québec, Canada. METHODS: Using the Quebec Integrated Chronic Disease Surveillance System, we built a population-based cohort of community-dwelling adults aged ≥80 years on January 4, 2018. We assessed statin use in the 5 years before and after cohort entry to calculate the proportion of prevalent, incident, and discontinued statin users. We used multivariable Cox models to identify factors associated with initiation and discontinuation, using hazard ratios (HRs) and 95% confidence intervals. RESULTS: A total of 317,027 individuals of mean age 85.2 years were included. At cohort entry, 51% were prevalent statin users. Within 5 years, 11% of nonusers initiated a statin. Among prevalent users, 22% discontinued their therapy during follow-up. Strongest factors associated with initiation included ages 80-84 (HRs ranging from 0.74 [95% confidence interval: 0.71-0.76] for 85-89 years to 0.26 [0.22-0.30] for 95+), previous statin use (1.99 [1.93-2.07]), and male sex (1.44 [1.39-1.49]). Older age (HRs ranging from 1.54 [1.51-1.58] for 85-89 years to 3.65 [3.42-3.89] for 95+), and Alzheimer's disease (1.77 [1.71-1.82]) were the most common factors for discontinuation. Previous cardiovascular disease was associated with both initiation (1.26 [1.07-1.49]) and reduced likelihood of discontinuation (0.69 [0.64-0.74]). CONCLUSION: Statin use remains prevalent among the ≥80 years. A thorough evaluation of the risk/benefit balance of such use is needed.
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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 | 0.001 | 0.002 |
| 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".