Utilization of Biologics in Saskatchewan
Bibliographic record
Abstract
Background Few details are available about the factors driving cost increases of biologic medications. Objectives To describe trends in utilization and cost of biologic agents using administrative databases in Saskatchewan, Canada. Methods Two analyses were conducted. First, aggregate utilization of biologics based on prescriptions dispensed was measured in each calendar year between 2001 and 2013. Second, a retrospective cohort of new biologic users was created to examine trends in spending between 2001 and 2013. During the first year of biologic therapy, biologic cost was quantified for each specific biologic agent as: (a) total spending; (b) total mil - ligrams dispensed; and (c) estimated unit cost (i.e., total cost in 2013 $CAD divided by total milligrams dispensed during the year). Data analyses were descriptive and all biologic costs were adjusted to 2013 dollars (CAD). Results In the first year of biologic availability in Saskatchewan (2001), 133 patients were dispensed at least one biologic agent for a total cost of $0.5 million. In 2013, 2,402 biologic recipients were identified for a total cost of $51.8 million. Almost all of these biologic costs (88.9%) were paid by the provincial government. In 2013, infliximab was the most frequently used agent, accounting for 46.5% of all spending on biolog - ics. Infliximab was also the most expensive agent in 2013 (mean cost $31,340 ± 15,307) and showed the highest increase in the mean yearly cost over time due to greater quantities dispensed. Conclusion Biologic utilization will require ongoing monitoring to optimize patient-level and societal-level benefits.
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 | 0.000 | 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) | 0.001 | 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 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".