Ten-Year Trends in Lithium Prescribing in Alberta, Canada
Bibliographic record
Abstract
AIMS: Despite lithium's clinical efficacy, it is commonly thought that its use is declining. The objective of this study is to describe the new and prevalent lithium users as well as rates of discontinuation of lithium use over a 10-year period. METHODS: This study used provincial administrative health data from Alberta, Canada between January 1, 2009 and December 31, 2018. Lithium prescriptions were identified within the Pharmaceutical Information Network database. Total and subgroup specific frequencies of new and prevalent lithium use were determined over the 10-year study period. Lithium discontinuation was also estimated through survival analysis. RESULTS: Between the calendar years of 2009 and 2018, 580,873 lithium prescriptions were dispensed in Alberta to 14,008 patients. The total number of new and prevalent lithium users appears to be decreasing over the 10-year timeframe, although the decline may have stopped or reversed in the latter years of the study period. Prevalent use of lithium was lowest among individuals between the ages of 18-24 years while the highest number of prevalent users were in the 50-64 age group, particularly among females. New lithium use was lowest amongst those 65 years and older. More than 60% (8,636) of patients prescribed lithium, discontinued use during the study timeframe. Lithium users between ages of 18-24 years were at the highest risk of discontinuations. CONCLUSIONS: Rather than a general decline in prescribing, trends in lithium use are dependent on age and sex. Further, the period soon after lithium initiation appears to be a key time period in which many lithium trials are abandoned. Detailed studies using primary data collection are needed to confirm and further explore these findings. These population-based results not only confirm a decline in lithium use, but also suggest that this may have stopped or even reversed. Population-based data on discontinuation pinpoint the period soon after initiation as the time when trials are most often discontinued.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".