Online discussion of drug side effects and discontinuation among breast cancer survivors

Abstract

Purpose: While patients often use the internet as a medium to search for and exchange health-related information, little is known about the extent to which patients use social media to discuss side effects related to medications. We aim to understand the frequency and content of side effects and associated adherence behaviors discussed by breast cancer patients related to using aromatase inhibitors (AIs), with particular emphasis on AI-related arthralgia.

Methods: We performed a mixed methods study to examine content related to AI associated side effects posted by individuals on 12 message boards between 2002 and 2010. We quantitatively defined the frequency and association between side effects and AIs and identified common themes using content analysis. One thousand randomly selected messages related to arthralgia were coded by two independent raters.

Results: Among 25 256 posts related to AIs, 4589 (18.2%) mentioned at least one side effect. Top-cited side effects on message boards related to AIs were joint/musculoskeletal pain (N = 5093), hot flashes (1498), osteoporosis (719), and weight gain (429). Among the authors posting messages who self-reported AI use, 12.8% mentioned discontinuing AIs, while another 28.1% mentioned switching AIs. Although patients often cited severe joint pain as the reason for discontinuing AIs, many also offered support and advice for coping with AI-associated arthralgia.

Conclusion: Online discussion of AI-related side effects was common and often related to drug switching and discontinuation. Physicians should be aware of these discussions and guide patients to effectively manage side effects of drugs and promote optimal adherence.

Figure 1. Self-reported Paths of AI Switching in Breast Cancer Message Board Users

Node color refers to the particular drug they claimed to be on (Anastrozole: Blue, Exemestane: Red, Letrozole: Yellow, Not on Aromatase Inhibitor: White), and the thickness of the edge reflects the proportion of the original population that moved from that particular node to the next node. Only users whose transitions between drugs were explicitly stated and had mentioned being on at least one AI were used to generate this tree (n=917). Edges with less than three users switching between these nodes are not displayed. The numbers in bold refer to the number of users that mentioned beginning on that particular AI.