Prescribing trends of attention‐deficit hyperactivity disorder (ADHD) medications in UK primary care, 1995–2015
Bibliographic record
Abstract
AIM: The aim of the present study was to describe the prescription of medications for attention-deficit hyperactivity disorder (ADHD) in the UK between 1995 and 2015. METHODS: Using the Clinical Practice Research Datalink (CPRD), we defined a cohort of all patients aged 6-45 years, registered with a general practitioner between January 1995 and September 2015. All prescriptions of methylphenidate, dexamphetamine/lisdexamphetamine and atomoxetine were identified and annual prescription rates of ADHD were estimated using Poisson regression. RESULTS: Within a cohort of 7 432 735 patients, we identified 698 148 prescriptions of ADHD medications during 41 171 528 person-years of follow-up. Usage was relatively low until 2000, during which the prescription rate was 42.7 [95% confidence interval (CI) 20.9, 87.2] prescriptions per 10 000 persons, increasing to 394.4 (95% CI 296.7, 524.2) in 2015, corresponding to an almost 800% increase (rate ratio 8.87; 95% CI 7.10, 11.09). The increase was seen in all age groups and in both sexes but was steepest in boys aged 10-14 years. The prescription rate in males was almost five times that of females. Methylphenidate remained the most prescribed drug during the 20-year study period, representing 88.9% of all prescriptions in the 6-24-year-old group, and 63.5% of all prescriptions in adults (25-45 years of age). CONCLUSIONS: Prescription rates of ADHD medications have increased dramatically in the past two decades. This may be due, at least in part, to both an increase in the number of patients diagnosed with ADHD over time and a higher percentage of those patients treated with medication.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".