Patterns of the Health and Economic Burden of 33 Rare Diseases in China: Nationwide Web-Based Study
Bibliographic record
Abstract
BACKGROUND: Rare diseases (RDs) affect millions of individuals collectively worldwide, contributing to significant burdens on patients and families in various aspects. However, there is a lack of evidence on the underlying patterns of burdens among diverse RDs for informing targeted social and health policies to address the unmet needs of this vulnerable population. OBJECTIVE: This study aimed to examine the underlying patterns of the health and economic burden of 33 different RDs in China and identify the potential determinants. METHODS: A nationwide internet-based cross-sectional survey was conducted in China between 2019 and 2020. Physical and mental health burden was measured by health-related quality of life. Economic burden was evaluated based on the proportions of direct medical, direct nonmedical, and indirect costs relative to household income. We used cluster analysis to identify patterns of health and economic burdens and conducted multinomial logistic regression to explore potential predictors of cluster membership. RESULTS: The study included 8454 adults and 8491 children affected by 33 RDs. The following 3 clusters were identified: "extremely high burden" (representing 92/8454, 1.1% and 19/8491, 0.2% of adult and pediatric patients, respectively), "overall high burden" (5933/8454, 70.2% and 4864/8491, 57.3%, respectively), and "overall low burden" (2429/8454, 28.7% and 3608/8491, 42.5%, respectively). Wilson disease, Marfan syndrome, and Langerhans cell histiocytosis more likely resulted in an "extremely high burden" than others. Poverty was significantly associated with being in this extremely high burden group. Diseases causing neuromuscular symptoms and requiring long-term treatment (eg, amyotrophic lateral sclerosis, spinocerebellar ataxia, and Dravet syndrome) were prevalent in the "overall high burden" group. Key predictors of this group included older age, lower socioeconomic status, diagnostic delay, and comorbidity. CONCLUSIONS: This study provides novel and valuable evidence on the burden of RDs in developing regions like China. The findings reveal significant disparities in the impact of RDs, emphasizing the need for targeted health care interventions and policies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| 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) | 0.002 | 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".