Endometriosis Online Communities: A Quantitative Analysis

Abstract

AbstractBackgroundEndometriosis is a chronic condition that affects 10% of people with a uterus. Due to the complex social and psychological impacts caused by the condition, people with endometriosis often turn to online health communities (OHCs) for support.ObjectivePrior work identifies a lack of large-scale analyses of endometriosis patient experiences and of OHCs. Our study fills this gap by investigating aspects of the condition and aggregate user needs that emerge from two endometriosis OHCs,r/Endoandr/endometriosis.MethodsWe leverage topic modeling and supervised machine learning to identify associations between a post’s subject matter (“topics”), the people and relationships (“personas”) mentioned, and the type of support the post seeks (“intent”).ResultsThe most discussed topics in posts aremedical stories, medical appointments, sharing symptoms, menstruation, andempathy. In addition, when discussingmedical appointments, users are more likely to mention theendometriosis OHCsthanmedical professionals. Furthermore,medical professionalis the least likely of any persona to be associated withempathy. Posts that mentionpartnerorfamilyare likely to discuss topics from thelife issuescategory, in particularfertility. Lastly, we find that while users seek experiential knowledge regarding treatments and healthcare processes, they also wish to vent and to establish emotional connections about the life-altering aspects of the condition.ConclusionsEndometriosis OHCs provide members a space where they can discuss care pathways, learn to manage symptoms, and receive validation. Our results emphasize the need for greater empathy within clinical settings, easier access to appointments, more information on care pathways, and further support for patient loved ones. In addition, this study demonstrates the value of quantitative analyses of OHCs: they can support and extend findings from small-scale studies about patient experiences and provide insight into hard-to-reach groups. Lastly, analyses of OHCs can help design interventions to improve care, as argued in previous studies.