Healthcare costs for patients with rare diseases: Evidence from China
Bibliographic record
Abstract
OBJECTIVE: Rare diseases cause a huge financial burden to countless patients and families. It is an important public health issue that requires widespread attention. This study analyzes medical expenses composition and the change in trends of out-of-pocket (OOP) expenses for patients with Amyotrophic lateral sclerosis (ALS) and explores the factors influencing these changes. METHODS: Data were obtained from the Chinese Medical Insurance Department database from 2018 to 2020, including 857 patients with ALS in 60 cities across 30 provinces. We used descriptive methods to analyse the baseline characteristics and medical expenses of outpatients and inpatients with ALS. And we used quantile regression to analyse the differences in patient OOP ratio and the factors influencing them. RESULTS: In China, 80.3% of ALS patients chose tertiary hospitals, with an annual direct medical cost of 11,339.7 RMB per patient and an OOP ratio of 41.6%. The annual medical cost for outpatients was 345.1 RMB per patient, with an OOP ratio of 36.7%. The annual medical cost for inpatients was 28,139.8 RMB per patient, with an OOP ratio of 41.7%. Compared to outpatients, inpatients had higher medical costs but lower actual reimbursement rates. The OOP ratio of ALS patients decreased, then increased over time. And the OOP ratio was influenced by medical institution, medical insurance, and age (p < 0.05). Patients who chose tertiary hospitals, those who were covered by the urban resident basic medical insurance and younger patients had relatively higher OOP ratio. CONCLUSION: In recent years, although China has begun to pay attention to the rights and interests of patients with rare diseases, the government has provided some healthcare security to patients with rare diseases. However, the level of medical insurance coverage was still low, the equity of protection was still insufficient and the financial burden on patients was high. Therefore, the government should further improve the healthcare system to provide full life-cycle and affordable healthcare services to patients with rare diseases.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".