Analyzing social media conversations to gain insights into the experiences of patients with hereditary angioedema.

Abstract

Background: Hereditary angioedema (HAE) is a rare genetic disease characterized by recurrent, unpredictable, painful, and potentially life-threatening angioedema attacks, which substantially impair patients' quality of life. Gathering patients' perspectives is important to understand lived experiences, the patient journey, and preferences to determine optimal HAE management. Social media platforms have become important mediums through which patient and carer groups share experiences and advice. Objective: We conducted the first social media listening (SML) study in an HAE setting to identify and characterize the experiences of patients with HAE to better understand disease and treatment burden and their unmet needs. Methods: This was a retrospective analysis of social media content that relates to the experience of individuals with HAE from January 2018 to October 2023. We used keyword-based searches of social media platforms to identify posts that relate to burdens, unmet needs, and other themes shared by patients, caregivers, or health-care professionals. After anonymization and removal of duplicates, content was extracted and categorized by subject matter experts for qualitative and quantitative analysis according to themes. Results: Of 6,012 posts collected, 892 were identified as relevant patient-mentioned posts. Of these, 459 posts related to burden; the most common topic was disease burden and discussed symptoms such as persistent swelling, painful attacks, and frequent attacks. Other burden-related topics included health service, medication, and injections. Unmet needs were discussed in 270 posts; the most common were treatment-related needs such as insurance denials, inadequate medication availability, and treatment costs. Other unmet needs related to education, health service, and diagnosis. Conclusion: Evaluation of the lived experience of patients with HAE and their caregivers via SML can be a valuable tool to aid optimizing HAE management. Our findings add to existing evidence that the disease burden is still a considerable issue for patients with HAE, who continue to have unmet treatment needs.