Measurement of Health-Related Quality of Life in Individuals With Rare Diseases in China: Nation-Wide Online Survey
Bibliographic record
Abstract
BACKGROUND: Rare diseases (RDs) affect millions of people worldwide, and these diseases can severely impact the health-related quality of life (HRQoL) of those affected. Despite this, there is a lack of research measuring HRQoL using the EQ-5D-5L, which is one of the most widely used generic preference-based instruments to measure HRQoL in populations living with RDs. OBJECTIVE: This study aimed to measure HRQoL using the EQ-5D-5L in a large number of patients with various types of RDs in China, and to examine the relationship between respondents' socioeconomic characteristics and preference-based health utility scores. METHODS: The data used in this study were obtained from a web-based survey conducted in China. The survey aimed to explore and understand the health and socioeconomic status of patients with RDs in China. We recruited registered and eligible members, including patients or their primary caregivers, from 33 RD patient associations to complete the questionnaires via their internal social networks. HRQoL was measured using the EQ-5D-5L utility score, which was calculated based on an established Chinese value set. Utility scores have been presented based on demographics and disease-related information. Univariate linear regression analysis was used to assess the differences in the EQ-5D-5L utility scores between subgroups. RESULTS: A total of 12,502 respondents completed the questionnaire and provided valid responses, including 6919 self-completed respondents and 5583 proxy-completed respondents. Data from 10,102 participants over the age of 12 years were elicited for analysis. Among patients with RDs, 65.3% (6599/10,102), 47.5% (4799/10,102), 47.0% (4746/10,102), 24.8% (2506/10,102), and 18.4% (1855/10,102) reported no problems for "self-care," "usual activities," "mobility," "pain/discomfort," and "anxiety/depression," respectively. A full health state was reported by 6.0% (413/6902) and 9.2% (295/3200) of self- and proxy-completed patients, respectively. Among self-completed patients, 69.9% (4826/6902) and 50.4% (3478/6902) reported no problems for "self-care" and "usual activities," respectively, whereas only 17.7% (1223/6902) reported problems for "anxiety/depression." Proxy-completed respondents showed a higher proportion of reporting extreme problems than self-completed respondents in all 5 dimensions. The mean utility scores reported by self- and proxy-completed respondents were 0.691 and 0.590, respectively. Different types of caregivers reported different utility scores, and among them, proxy-completed (mother) respondents reported the highest mean utility score. CONCLUSIONS: The establishment of a normative profile for RD patients can facilitate patients' adaptation and assess the effectiveness of interventions to improve the HRQoL and well-being of this population. Differences between self- and proxy-completed HRQoL assessed by the EQ-5D-5L have been identified in this study. This finding highlights the importance of incorporating perspectives from both patients and their proxies in clinical practice. Further development of the patient cohort is necessary to assess long-term changes in HRQoL in the RD population.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".