Prevalence, incidence, and medications of narcolepsy in Japan: a descriptive observational study using a health insurance claims database
Bibliographic record
Abstract
The objectives of this study were to describe prevalence, incidence, and medications among patients who were diagnosed with narcolepsy in Japan using a claims database. Patients diagnosed with narcolepsy were identified from January 2010 to December 2019 using an employment-based health insurance claims database compiled by JMDC Inc. The prevalence and incidence of narcolepsy were estimated annually in the overall population and by age and sex among employees and their dependents aged < 75 years. Medications, examined for each quarter in the overall population, were modafinil, methylphenidate, pemoline, tricyclic antidepressants, selective serotonin reuptake inhibitors, and serotonin-norepinephrine reuptake inhibitors. We identified 1539 patients with narcolepsy. The overall annual prevalence increased from 5.7 to 18.5/100,000 persons in 2010 and 2019, respectively. Large increases were found from 2010 to 2019 in patients aged 20-29 years and 10-19 years, with the highest prevalence in 2019 (9.7-37.5/100,000 persons and 5.0-27.1/100,000 persons). The overall incidence slightly increased from 3.6 to 4.3/100,000 person-year from 2010 to 2019, and the highest incidence was found in patients aged 20-29 years and 10-19 years (5.8-11.3/100,000 person-year, and 3.8-7.4/100,000 person-year from 2010 to 2019, respectively). Methylphenidate and modafinil were commonly prescribed in 2010 (27.3-38.9% and 17.5-45.5%, respectively). Methylphenidate prescriptions declined during the 10 years, whereas modafinil prescriptions increased (15.6-17.1% and 43.8-45.8% in 2019, respectively). The estimated prevalence and incidence of narcolepsy appeared to increase from 2010 to 2019, especially in teenagers and 20-year olds. Supplementary Information: The online version contains supplementary material available at 10.1007/s41105-022-00406-4.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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 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".