Measures of stimulant medications: A population-based study in Alberta, Canada
Bibliographic record
Abstract
• Stimulants are being used by a high percentage of the general population for a wide range of reasons. • A clear measure of stimulant use is needed to determine potential interventions to combat future stimulant-related mortalities. • The literature showed broad definitions on stimulant use, primarily on the proportion of stimulants being prescribed within a population and inappropriate use of stimulants. • The college of physicians and surgeons in alberta's approach to measuring stimulant use operationalized stimulant use at both the individual and population-level, stratified by age and sex. • In the absence of a standardized measurement, clinicians and prescribers should consider both the advantages and disadvantages of each approach to characterize stimulant users. Stimulants are a class of drugs approved for the treatment of attention-deficit hyperactivity disorder (ADHD) and narcolepsy. However, they are also often used “off-label” as adjunct therapies for the treatment of obesity and depression. The objective of this study is to summarize how stimulant use is globally measured in the literature and to explore rates of stimulant use in Alberta, Canada. A traditional narrative literature review was conducted to summarize global methods of stimulant assessment. Then using definitions guided by the literature and current regulatory bodies in Alberta, we conducted a series of descriptive analyses to assess how frequent stimulant use was in Alberta patients from 2019 to 2021: 1) number of dispenses by year; 2) average days of drug supply; 3) proportion of days covered (PDC); and 4) defined daily dose (DDDs). In the literature review, the most frequently used measures of stimulant drug use were trends over time (prevalence), types of drug use, and dispensations of prescriptions. In all, there is a global trend of increased use of stimulants among both adults and children. In Alberta, 173,789 patients were prescribed stimulant medication in 2019–2021, representing approximately 4 % of the entire Alberta population. Overall, 61.1 % were between the ages of 10–34 and 46.8 % were female. The number of dispensations rose from 713,896 in 2019 to 973,930 in 2021 – with up to 43 % being lisdexamfetamine stimulant dispenses. Although stimulant use in AB was measured using similar trend estimates as the literature, there is a lack of research to support whether these measures are accurate and effective at the population-level. Future steps to standardize both medical and nonmedical use of prescription stimulants are warranted in efforts to fully quantify both benefits and risks associated with stimulant use.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.012 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".