Measurement of Health-Related Quality of Life in Individuals With Rare Diseases in China: Nation-Wide Online Survey
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».