Real-World Utilization Patterns of Long-Acting Injectable Antipsychotics in Canada: A Retrospective Study
Bibliographic record
Abstract
Objective: The objective of this study was to analyze the real-world prevalence of long-acting injectable (LAI) antipsychotic use and determine when LAIs are being used in sequencing of antipsychotic medications among Canadian patients with schizophrenia. Methods: This was a retrospective, longitudinal cohort study using Canadian pharmacy prescription data between August 2005 and June 2017. Patients with inferred schizophrenia spectrum disorder were indexed on the date of their first antipsychotic prescription and analyzed for minimum 12 months to track lines of antipsychotic therapy and LAI utilization. Results: A total of 16,300 patients were identified for analysis. 48.2% and 46.0% of index antipsychotic prescriptions were prescribed by a general practitioner/family medicine doctor and psychiatrist, respectively. 1,062 (6.5%) patients used an LAI during the study period. Of those patients, 789 used an LAI within two years of index (74.3% of LAI users; 4.8% of all patients). The majority of LAI use (62.0%) occurred in the third line of therapy or later. 65.0% of patients had tried at least two therapy lines, and most patients reported gaps of six months to one year between treatment lines. Conclusion: Despite their potential to reduce relapse in schizophrenia by improving treatment adherence, this study shows LAIs continue to be under-utilized in Canada. When used, LAIs are positioned late in sequencing of antipsychotic medications, often not initiated until years after diagnosis. Continued preference for oral APs with poor adherence may be negatively impacting prognosis and exacerbating burden of schizophrenia. Efforts should be invested to understand barriers to LAI uptake and advocate for earlier, widespread use of LAIs.
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 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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".