Web-scale pharmacovigilanceMaggie Mahan16 April 2013

Motivation Adverse drug events cause morbidity &

mortality Typically discovered after drug marketed

Increased internet searches of health information (~60% of American adults)

Mining web search history to identify unreported side effects of drugs or drug combinations

Logs are inexpensive to collect & mine

Drug safety surveillance

Background (1/2) Drug side effects reported but incomplete and biased

Leads to delayed reporting of adverse events Compounded with multiple drugs

Previous research on tracking seasonal influenza Search logs can be used for health monitoring Health-seeking activity captured in queries to web

search services mirrors trends gathered by traditional surveillance

Background (2/2) Present study used online health-seeking search activity to

identify adverse drug events associated with drug interactions Paroxetine: anti-depressant Pravastatin: cholesterol-lowering drug Interaction reported to create hyperglycemia

Hypothesis: patients taking these two drugs might experience symptoms of hyperglycemia and may have conducted internet searches on these symptoms and concerns related to hyperglycemia before the association was reported

Methods 12 months of search logs

Word used in user queries Pravastatin & brand names Paroxetine & brand names Hyperglycemia-associated words

Disproportionality analysis Assess increased chance of

search for hyperglycemia-related terms given search for both drugs

Reporting ratios based on observed versus expected

Results – user groups & prevalence

Searching both drugs = more likely to search hyperglycemia-associated terms

Difference between groups is consistent

Results – disproportionality analysis

Results - disproportionality analysis for known drug–drug

interactions

Conclusions Log analysis valuable for identifying drug pairs linked to hyperglycemia

Method generalizable, similar to a prediction task

Majority of TP identified provides validation for the set of terms used

Valuable signal even though search logs are unstructured, not necessarily related to health, and include any words entered by users

More in-depth analysis is needed

Patient search behavior directly can complement traditional sources of data for pharmacovigilance

References White RW, Tatonetti NP, Shah NH, Altman RB, Horvitz E (2013)

Web-scale pharmacovigilance: listening to signals from the crowd. J Am Med Infom Assoc. 20(3): 404-408.

http://scopeblog.stanford.edu/2013/03/06/researchers-mine-internet-search-data-to-identify-unreported-side-effects-of-drugs/