Regional variation in the use of medications by older Canadians—a persistent and incompletely understood phenomena
Bibliographic record
Abstract
BACKGROUND: We have previously reported on regional variability in medication consumption by older Canadians. In this study, we used longitudinal data to determine whether regional differences in commonly consumed medications persisted and to explore potential explanatory factors for observed differences. METHODS: We utilized data from the second phase of the Canadian Study of Health and Aging to assess the number, types, and variability of medications used between regions. Linear and logistic regressions (LRs) were used to predict the number of medications and the use of specific agents where significant regional variability was found to exist. RESULTS: There were significant regional differences in the number of medications consumed and in the prevalence of use of acetaminophen (p < 0.002), benzodiazepines (p < 0.020), nitrates (p = 0.040), and complementary and alternative medicines (CAMs; p < 0.020). The proportion of subjects using acetaminophen was highest in British Columbia (44.6%) and lowest in Quebec (27.3%). Benzodiazepine and nitrate consumption was highest in Quebec (35.9 and 19%, respectively) and lowest in the Praires (18.2%) and Atlantic Canada (6.6%). CAM use was highest in British Columbia (47.1%) and lowest in the Atlantic region (26.8%). Similar inter-regional differences had been found 5 years previously. There were no significant regional differences in the prevalence of hypertension, myocardial infarction, diabetes, arthritis/rheumatism, or depression. Region remained a significant explanatory variable for the number of medications and nitrate, benzodiazepine, and CAM use in our multivariate models. CONCLUSIONS: Regional differences in medication use persisted over the course of this longitudinal study. Much of the variability remains unexplained. The reasons for regional differences in consumption of drugs and their clinical significance should be addressed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".