Treatment Patterns, Resource Use, and Economic Outcomes Associated with Atypical Antipsychotic Prescriptions in Children and Adolescents with Attention-Deficit Hyperactivity Disorder in Quebec
Bibliographic record
Abstract
OBJECTIVE: To assess treatment patterns, health care resource utilization (HRU), and costs among previously stimulant-treated children and adolescents with attention-deficit hyperactivity disorder (ADHD) receiving atypical antipsychotic (AAP) prescriptions in Quebec. METHODS: Health care claims data extracted from Quebec's provincial health plan database between March 2007 and February 2012 were analyzed. Children and adolescents (6 to 17 years) with ADHD who were taking a stimulant and either switched to, or augmented with, an AAP (with the first AAP defined as the index AAP) without a documented diagnosis for which AAPs are Health Canada-approved were included. Discontinuation, augmentation, and switching of the index AAP during the 12-month, follow-up period were estimated using Kaplan-Meier survival analysis. HRU and costs for the 6 months before (baseline period) and after initiation of the index AAP were compared. RESULTS: A total of 453 children and adolescents with ADHD, mostly male (74.6%) and aged 6 to 12 years (73.7%), met the inclusion criteria. The 12-month discontinuation, augmentation, and switching rates were 45.5%, 68.2%, and 80.7%, respectively. Patients had, on average, more all-cause prescription fills (22.2, compared with 13.3) and incurred more all-cause pharmacy ($889, compared with $710), total medical ($1096, compared with $644), and total health care ($1985, compared with $1354) costs during the 6-month study period than during the 6-month baseline period (all P < 0.05). Similarly, ADHD-related total health care costs were higher during the study period ($1269, compared with $835; P < 0.05); all-cause and ADHD-related total health care costs increased by 46.6% and 52.0%, respectively. CONCLUSION: Use of an AAP among stimulant-treated children and adolescents with ADHD in Quebec was associated with high rates of therapy changes and increased HRU and costs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".