Public drug insurance, moral hazard and children's use of mental health medication: Latent mental health risk‐specific responses to lower out‐of‐pocket treatment costs
Bibliographic record
Abstract
Studies have shown that reducing out-of-pocket costs can lead to higher medication initiation rates in childhood. Whether the cost of such initiatives is inflated by moral hazard issues remains a question of concern. This paper looks to the implementation of a public drug insurance program in Québec, Canada, to investigate potential low-benefit consumption in children. Using a nationally representative longitudinal sample, we harness machine learning techniques to predict a child's risk of developing a mental health disorder. Using difference-in-differences analyses, we then assess the impact of the drug program on children's mental health medication uptake across the distribution of predicted mental health risk. Beyond showing that eliminating out-of-pocket costs led to a 3 percentage point increase in mental health drug uptake, we show that demand responses are concentrated in the top two deciles of risk for developing mental health disorders. These higher-risk children increase take-up of mental health drugs by 7-8 percentage points. We find even stronger effects for stimulants (8-11 percentage point increases among the highest risk children). Our results suggest that reductions in out-of-pocket costs could achieve better uptake of mental health medications, without inducing substantial low-benefit care among lower-risk children.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".