Effect of the Choosing Wisely Canada campaign on prescription of nonsteroidal anti-inflammatory drugs in people with hypertension, heart failure and chronic kidney disease in Canada
Bibliographic record
Abstract
ABSTRACT The Choosing Wisely Canada (CWC) campaign focuses on helping physicians and patients engage in conversations about unnecessary tests and treatments. In 2014, a CWC recommendation advised against prescribing nonsteroidal anti-inflammatory drugs (NSAIDs) in individuals with hypertension, heart failure or chronic kidney disease (CKD). We evaluated the impact of this recommendation on prescription of NSAIDs in British Columbia (BC), Canada. We identified all persons continuously registered with BC’s Medical Service Plan at any point between October 1, 2011 and December 31, 2017 with hypertension, heart failure or CKD. Using prescription claims data and an interrupted time series analysis, we estimated the quarterly number of people initiating NSAIDs and the proportion of days covered (PDC). We also conducted sub-group analyses to study the impact of the recommendation by patients’ demographics (sex and age). We analyzed 1,479,704 NSAID claims from 903,732 patients with a diagnosis of hypertension, heart failure and/or CKD. Overall, we found no statistically significant change either immediately or over time in initiation and PDC of NSAIDs. However, we did observe a decrease in both initiation and PDC of NSAIDs over time among male patients and patients aged ≤65 years. We found that the CWC recommendation had a mixed impact, with initiation and PDC of NSAIDs significantly declining only among male and younger patients. Highlights Reducing low-value care can help to counter increasing healthcare spending. CWC can contribute to reducing low-value care, however, evidence on its effectiveness remains elusive. CWC led to NSAIDs prescription decline for patients ≤65 years and male patients. Overall, NSAID prescribing has been declining over time irrespective of CWC.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".