Use, costs, and predictors of psychiatric healthcare services following an autism spectrum diagnosis: Population-based cohort study
Bibliographic record
Abstract
A number of cross-sectional studies report extensive use of psychiatric services and high healthcare costs in autistic youths. However, little is known about how the use of these services evolves from the time of diagnosis, as children grow up. Our objectives were to investigate the use, costs, and predictors of psychiatric services following autism spectrum diagnosis. We built a cohort of 1227 newly diagnosed autism spectrum individuals identified in the Quebec (Canada) <i>Régie de l’assurance maladie du Québec</i> administrative database (January 1998 to December 2010). Mean number and cost per individual of psychiatric healthcare use (hospitalizations, medical visits, psychoactive drug use) were calculated yearly for 5 years following autism spectrum diagnosis. Mean number of psychiatric visits decreased over time by more than threefold (7.5 vs 2.1 visits) from year 1 to year 5, whereas psychoactive drug use increased from 16.0 to 25.2 claims. Psychiatric hospitalizations decreased during follow-up, but still represented the greatest costs per individual (CAD9820 for year 1; CAD4628 for year 5). Antipsychotics represented over 50% of drug costs. Mixed-effect model with repeated measures showed that previous psychoactive drug use was the strongest predictor of greater psychiatric healthcare cost during follow-up (odds ratio: 9.96; 95% confidence interval: 7.58–13.10). These trends contrast with guidelines advocating cautious prescribing of antipsychotics with periodical re-assessment of their benefit.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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 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".