Burden of narcolepsy in Japan: A health claims database study evaluating direct medical costs and comorbidities
Bibliographic record
Abstract
OBJECTIVE: This study aimed to determine the burden of narcolepsy in terms of direct medical costs and comorbidities and compare it with the respective burden of schizophrenia, epilepsy, and ulcerative colitis as controls. METHODS: Patients diagnosed with narcolepsy (at least once based on the International Statistical Classification of Diseases and Related Health Problems, 10th Revision, code G47.4) between April 2017 and March 2022 were identified on the health insurance claims database compiled by JMDC Inc. Patients with schizophrenia (F20), epilepsy (G40), and ulcerative colitis (K51) were matched as controls. Direct medical costs (including inpatient, outpatient, and medication costs) and comorbidities were analyzed. RESULTS: We identified 4,594 patients with narcolepsy (≥18 years), 18,376 with schizophrenia, 18,376 with epilepsy, and 4,594 with ulcerative colitis. The total annual direct medical cost per person with narcolepsy was 349,188 JPY. The cost for narcolepsy was less than that for schizophrenia, epilepsy, and ulcerative colitis. Several comorbidities, such as sleep apnea, attention deficit hyperactivity disorder (ADHD), and obesity were more prevalent in the narcolepsy group. CONCLUSIONS: The total direct cost for narcolepsy was approximately three times higher than the national medical expense for people aged 15-44 years (122,000 JPY in 2020), but lower than the total cost for all control diseases. The patients with narcolepsy were also likely to have comorbidities that affected their burden. These findings can contribute to future discussions on medical expense assistance programs for patients with narcolepsy.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".