OBJECTIVES: Rare diseases contribute substantially to population morbidity and mortality. Understanding health related quality of life (HRQoL) across rare diseases is essential for evaluating new genomic technologies used in rare diseases (e.g., genomic sequencing), but most HRQoL studies focus on a single or small group of diseases. Our study aimed to identify patient- and disease-specific characteristics associated with differences in HRQoL across rare diseases.

METHODS: We conducted an online Facebook survey with rare disease patients and their caregivers utilizing a systematic sample of rare diseases with varying prevalence from the Orphanet database. Patients/caregivers completed the EQ-5D-5L or proxy version and measures of sociodemographic and disease characteristics. We calculated utility scores for US participants using population norms and conducted multivariate linear regression to examine predictors of EQ-5D-5L Visual Analogue Scale (VAS), Utility scores (US only), and each of the five EQ-5D dimensions.

RESULTS: In total 1,053 individuals with 103 different rare diseases participated, including 660 patients and 393 caregivers. Median VAS score was 71 (IQR 52-85), Utility was 0.786 (IQR 0.630-0.832) and domains ranged from 1 (IQR 1-2) for Mobility to 3 (IQR 1-3) for Anxiety. Having a disability was significantly associated with poorer scores across all outcomes, and lower disease prevalence was associated with poorer VAS, Utility, Mobility, Activities and Self-Care (p<0.001). Utility scores were also positively correlated with income (p=0.048). Lower VAS scores were associated with lack of a genetic diagnosis (p=0.025), younger age (p=0.034), unemployment (p=0.016) and urban location (p<0.001). Lack of a genetic diagnosis also was associated with poorer scores for Pain (p=0.001) but better scores for Self-Care (p<0.001).

CONCLUSIONS: Our results suggest that certain sociodemographic and disease characteristics may be predictive of HRQoL. Our findings fill an essential gap in knowledge regarding HRQoL across rare diseases and may be used for evaluating diagnostic and therapeutic interventions in the future.