De-identified data was directly collected from a survey with unpaid caregivers, of patients with SMA < 18 years old, who were routinely involved in the management of a patient’s disease. The survey was administered electronically through an online platform and was fielded in February 2019.
Through partnership with a large patient organization, a survey was administered to 101 respondents (56 adult patients and 45 unpaid caregivers of a patient < 18 years of age). Any patients diagnosed with type 1–4 SMA who were ≥ 18 years and unpaid (non-professional) adult caregivers who were routinely involved in the care and management of a patient with SMA (< 18 years of age) were eligible for the study. This paper summarizes the findings reported exclusively by caregivers of patients < 18 years of age as related to their HRQoL and their daily activities. Other findings from this study are reported elsewhere.
To recruit participants, a survey e-link was distributed through an email listserv and posted on the patient organization’s website between January to February 2019. The survey contained 45 structured questions, divided into multiple sections collating patient and caregiver demographics, patient health history, treatment and care, and their experience with treatment. A section was dedicated to collecting information on caregiver time to infer the impact of the disease and disease management on caregivers’ daily activities. Attributes such as treatment, the amount of the time that came from paid work, unpaid work, and social activities (e.g., household chores, childcare, hobbies, or lifestyle activities) was included to understand the proportion of time spent on disease management.
In addition, the EQ-5D-5L (EuroQoL-5 Dimension-5 Level) and visual analogue scale (VAS) were administered to caregivers . The EQ-5D, a simple validated instrument developed by a multi-disciplinary group of researchers, is commonly used to assess HRQoL in both the general population and a population with a disease . The EQ-5D assesses five dimensions of health: mobility, self-care, usual activities (e.g., work, study, housework, or leisure activities), pain/discomfort, and anxiety/depression. It is also a common tool used in health economic evaluations to calculate utility values, which capture the change of a patient or caregivers’ HRQoL, as related to a treatment. In the EQ-5D-5L, each dimension has 5 levels: no problems, slight problems, moderate problems, severe problems, and extreme problems . The respondent results are scored ranging from 0 to 1, where 0 corresponds to death and 1 corresponds to perfect health (negative value may be possible in certain instances). In addition, an EQ VAS records the patient’s self-rated health on a vertical scale, with the ends labelled ‘The best health you can imagine’ and ‘The worst health you can imagine’. The EQ VAS can be used as a quantitative measure of health outcomes that reflects the respondent’s own judgement. A central Institutional Review Board reviewed and approved the study.
Quality Assurance and Data Analysis
The data quality assurance and quality control included multiple steps. From a programing standpoint, skip logic was implemented to ensure caregivers were only asked relevant questions. Similarly, range checks were included to minimize erroneous responses which were outside the valid range (e.g., an age of “136” instead of “36”) and were inconsistent with previous responses. The survey was tested by multiple researchers using test links to ensure an accurate program. Further, a preliminary check of the program was conducted after approximately 10% of the sample had been recruited to ensure accuracy.
The caregiver sample was described with respect to demographics, and other characteristics of caregivers, as well as the demographics and clinical characterizes of the SMA patients less than 18 years old for whom they care. Descriptive statistics were used to characterize the study data. Frequencies and percentages were reported for categorical variables as well as means, medians, and standard deviations for continuous variables. The number of subgroup analyses performed was minimized due to the limited sample size. The two main sub-analyses performed were by type, (type 1 vs. type 2/3), and by current motor function status (minimal motor function, sitting, and standing/walking). All analyses remain descriptive; there was no attempt to conduct a statistical testing on these data. All results were reported for the total sample. The analysis was conducted in SAS version 9.3 and R version 3.3.