Comparison of rates of nausea side effects for prescription medications from an online patient community versus medication labels: an exploratory analysis

Abstract

Background: While medication labels are considered the authoritative resource for medication information, emerging research suggests that patient-generated health data (PGHD) are a valuable tool to understand the ways in which patients experience medications in real world settings. However, the relationship between these two data sources has not been closely examined. Methods: To understand how rates of medication side effects compare between a source of PGHD and medication labels, the current study compares adverse drug reaction rates from FDA medication labels with those self-reported by patients from an online patient community, PatientsLikeMe (PLM). The linear association between medication label and PLM nausea rates was evaluated using Spearman correlation, with an associated 95% confidence interval calculated based on 10,000 bootstrap iterations. The reporting ratio of PLM nausea rates to medication label nausea rates was defined for all treatments with non-zero medication label nausea rates. Lognormality of the distribution of this reporting ratio was assessed based on a Kolmogorov-Smirnov test (α = 0.05). Results: Nausea rates for 163 medications were compared between the two data sources. Overall rates ranged from 0 to 60% for medication labels and 0 to 36% for PLM data with median rates of 6.4 and 3.7%, respectively. In general, nausea rates reported by patients in the online community were lower than those found in medication labels. This inconsistency was attributed to a variety of factors, including differences in data collection mechanisms and product use factors. Conclusions: Quantifiable and consistent differences exist between side effect rates reported on medication labels and those self-reported by patients based on real-world use. In general, self-reported rates of nausea associated with medication use were lower than those reported in medication labels. Although considered a definitive resource for medication information, this discrepancy demonstrates that medication labels may not comprehensively describe the patient experience. Results suggest that a combination of information from different sources may provide a more rounded and holistic view on medication safety and tolerability.

The value of social media analysis for adverse events detection and pharmacovigilance: a Scoping Review (Preprint)

“…Many of the publications state that similar patterns of adverse events were reported in social media as compared to other traditional pharmacovigilance data sources [35-43, 47, 48, 51-56, 60-62, 64-70, 74-78, 82, 83, 85-89, 92, 94, 98, 99, 102-105]. However, some studies [24,45,46,50,57,59,62,89,94,102,103] detected fewer numbers of adverse events on social media.…”

“…Interestingly, two studies [21] noted the number of new ADEs identified in social media was higher than with SIDER. However, fewer new ADEs are identified in social media if a comparison is made to more uptodate sources such as clinicaltrials.gov, FDA data and Pubmed or MEDLINEPlus 46 .…”

“…One study searched for posts of interest using 4 named adverse events and then extracted drugs mentioned in these posts. The majority did not restrict their search or analysis to any named adverse events (50 studies), whilst the other 10 studies named adverse events (such as fever or cutaneous adverse events) [44,46,56,65,68,70,84,[92][93][94]. The large numbers of drugs and adverse events included and the lack of detail in naming them meant that we could not conduct any additional analysis by type of drug or type of adverse event.…”

Online Health Communities: an alternative feasible data registry tool for developing countries

“…Other studies indicated that OHC increased patient empowerment which consequently improved health outcomes [ 39 ]. Additionally, the studies also address various themes and perspectives such as patient perception [ 40 ], patients reported outcomes (PROs) [ 41 ], crowdsourcing, patient-centeredness [ 42 , 43 ], pharmacovigilance and adverse drug reactions reporting [ 44 ], drug development process [ 45 ] and off-label prescribing [ 46 ].…”