Treatment for ADHD: Is More Complex Treatment Cost‐Effective for More Complex Cases?
Bibliographic record
Abstract
OBJECTIVE: To determine the cost-effectiveness of three alternative high-quality treatments for attention deficit hyperactivity disorder (ADHD) relative to community care (CC) and to determine whether cost-effectiveness varies with the presence of comorbid disorders. DATA SOURCES/COLLECTION: The study included 579 children ages 7-9.9 with diagnosed ADHD at six sites. Data for the study were distilled from administrative data and from interviews with parents, including estimates of the child's functional impairment. These analyses focus on changes in functional impairment over 14 months. STUDY DESIGN: The study involved a large clinical trial that randomized participants to one of four arms: routine CC, intensive medication management (MedMgt), multicomponent behavioral treatment, and a combination of behavioral treatment and medication. PRINCIPAL FINDINGS: We assessed the cost-effectiveness of the alternatives using costs measured from a payer perspective. The preferred cost-effective treatment varies as a function of the child's comorbidity and of the policy maker's willingness to pay. For pure (no comorbidity) ADHD, high-quality MedMgt appears likely to be cost-effective at all levels of willingness to pay. In contrast, for some comorbid conditions, willingness to pay is critical: the policy maker with low willingness to pay likely will judge MedMgt most cost-effective. On the other hand, a policy maker willing to pay more now in expectation of future costs savings (involving, for example, juvenile justice), will recognize that the most cost-effective choice for comorbid conditions likely involves behavior therapy, with or without medication. CONCLUSIONS: Analyses of costs and effectiveness of treatment for ADHD must consider the role of comorbidities.
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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 | 0.007 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".