Antipsychotic use and effectiveness in a population-based cohort: a focus on long-acting injectable antipsychotics
Bibliographic record
Abstract
Background: Antipsychotics play a crucial role in the treatment of psychotic disorders. Long-acting injectable antipsychotics (LAIAs) have some important advantages over oral antipsychotics, including improvements in adherence. Second-generation long-acting injectable antipsychotics entered the Canadian market in the mid-2000s, but little is known about their uptake. Further study is needed to establish their place in therapy. Methods: Using the Manitoba Population Research Data Repository, a series of population-based studies were conducted in antipsychotic users from 1995 to 2020. Antipsychotic users were defined as having at least one dispensation for a medication with Anatomical Therapeutic Classification code beginning with N05A, excluding lithium (ATC N05AN01). LAIAs were identified by route of administration and generic name. A medical records review was conducted to validate the definition of LAIA exposure from claims data. Prescription claims data were linked to medical services, hospital abstracts, population registry, vital statistics and justice data to determine characteristics of users and outcomes of antipsychotic use. Outcomes studied were treatment failure, a composite of psychiatric hospitalization, incarceration, completed suicide and treatment discontinuation, and risk of being accused or victim/witness of a crime. Results: There were 3,380 prevalent and 2,375 incident LAIA users in Manitoba over the study period. Use of second-generation LAIAs increased, while first-generation LAIAs decreased over the study period; the proportion of antipsychotic users with a dispensation of LAIA decreased from 8.4% in 1995 to 3.0% in 2004, then increased to 3.6% in 2015. Overall, second-generation LAIAs did not reduce risk of treatment failure compared to other antipsychotics adjusted hazard ratio [aHR] 1.07, 95% confidence interval [CI] 0.98-1.15), except in incident new-users (aHR 0.57, 95% CI 0.47-0.70). Cohort members were less likely to be accused of a crime during exposure to antipsychotics (aHR 0.72, 95% CI 0.61, 0.86) and LAIAs (aHR 0.66, 95% CI 0.51, 0.86) versus non-exposure. Conclusions: LAIA use increased after the market entry of second-generation products. Early use of LAIAs appears to reduce the risk of treatment failure in first-time antipsychotic users. LAIAs were associated with reduced risk of being accused of a crime.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| 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".