Challenges in defining the rates of ADHD diagnosis and treatment: trends over the last decade
Bibliographic record
Abstract
BACKGROUND: There is a global trend of large increases in the prevalence and incidence of Attention Deficit Hyperactivity Disorder (ADHD). This study aimed to address potential causes of these major changes. METHODS: The authors used a large cohort to analyze data employing patients' electronic medical records, with physicians' diagnosis of ADHD, including records of medication purchases. RESULTS: The prevalence of ADHD diagnoses rose twofold from 6.8% to 14.4% between 2005 and 2014 (p < 0.001), while the ratio of males to females with ADHD decreased from 2.94 in 2005 to 1.86 in 2014 (p < 0.001). The incidence increased, peaking in 2011 before declining in 2014. ADHD medication usage by children and adolescents was 3.57% in 2005 and 8.51% by 2014 (p < 0.001). CONCLUSIONS: We report a dramatic increase in the rate of ADHD diagnoses. One of the leading factors to which we attribute this increase is the physicians' and parents' changed attitude towards diagnosing attention/hyperactivity problems, with more parents appear to consider ADHD diagnosis and treatment as a means to improve their child's academic achievements, commonly with the aid of medications. This change in attitude may also be associated with the dramatic increase in female ADHD diagnosis prevalence.
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".