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ISDS 2012 Conference Abstracts

Using Search Volume for Surveillance of Medication
Prescribing
Jacob E. Simmering*, Linnea A. Polgreen and Philip M. Polgreen

University of Iowa, Iowa City, IA, USA

Objective
To validate search volume estimation for outpatient medication

prescribing.

Introduction
Investigators have used the volume of internet search queries to

model disease incidence, especially influenza and general consumer
behavior [1]. Our group has used search volume to model interest in
FDA safety alerts and adverse drug event incidence. We found evi-
dence of changes in search behavior following warnings and the ex-
pected relationship between search volume and adverse drug event
incidence. Thus, search volume may help provide near real time sur-
veillance of drug use patterns to help monitor and mitigate risk to the
population from adverse drug events. However, the use of search
query volume as a proxy for drug use has yet to be validated. 

We attempt to validate search volume estimation of drug utilization
in three ways: 1) explore seasonal variations in search volume and
outpatient utilization, 2) monitor change between substitute drugs fol-
lowing patent expirations and 3) use search volume estimation meth-
ods to estimate TB incidence.

Methods
Google Insights normalized search share was used to characterize

interest in a drug. The estimates of drug utilization were derived from
the Medical Expenditure Panel Survey (MEPS), a nationally repre-
sentative sample of the US population. Substitute drugs and notable
patent expirations between 2004 and 2011 were obtained via phar-
macist review. TB incidence was derived from the MMWR yearly
Summary of Notifiable Diseases. 

To validate the assumption that search volume relates to drug uti-
lization, we estimated weekly utilization for 9 drugs (amoxicillin,
azelastine, azithromycin, benzomatate, cefdinir, ciprofloxacin, lev-
ofloxacin, moxifloxacin and olopatadine) using MEPS for 2004-
2009. The weekly utilization volume was cross-correlated with the
Google Insights series with lags ranging from -6 to +6 months. 

To compare the rate of substitution between name brand and
generic drugs following the expiration of a patent, we treated the
generic drug search volume as the independent variable and the name
brand as the dependent variable. Using OLS, we calculated the mar-
ginal rate of substitution between the name brand and generic search
queries. Preliminary work has focused on substitution of generic sim-
vastatin for branded Zocor.

As TB treatment regimens usually include a fixed set of medica-
tions (isoniazid, rifampin, pyrazinamide, ethambutol), the utilization
of these drugs should correspond with TB incidence. We modeled na-
tional TB incidence using OLS with search volume and an indicator
for the month of December. The number of reported cases in De-

cember is inconsistent with the seasonality of TB in the US and is a
significant departure from the expected value given the rest of the se-
ries. We suspect this is due to a reporting artifact and include the in-
dicator variable in our model to mitigate the effects of this
inconsistency.

Results
The seasonality of drug use is reflected in search volume. Only 3

of the 9 drugs (33%, amoxicillin, azithromycin and cefdinir) had
enough volume in the MEPS to create a reasonable time series. All 3
drugs had statistically significant positive correlations at lags near 0
and significant negative correlations at lags of +/- 6 months. Amox-
icillin, for example, had a significant correlation at lags around 0 of
0.55-0.60 and correlations at a lag of -5 or +5 months of -0.4. The
magnitude of this correlation coefficient would suggest that the two
series track closely.

Patent expirations (and the resulting emergence of generic med-
ications with new names) are apparent in search volume as well. We
find a strong negative relationship between search volume for sim-
vastatin and Zocor. Specifically, a one unit increase in search volume
for ‘simvastatin’ is associated with a 0.96 (p < 0.0001) unit decrease
in the search volume for ‘Zocor.’ 

The simple model for TB incidence demonstrates the utility of
using drugs as queries for disease. Search volume was a significant
(p = 0.006) and positive predictor of TB incidence controlling for the
December aberrations.

Conclusions
The Google Insights search volume for a set of highly seasonal

drugs is highly correlated with community utilization as measured
by seasonal variance in utilization, change in search and prescribing
patterns and expected prescribing following TB. The ability to esti-
mate use of drugs from search volume presents a new method for
keyword selection in search based incidence models and a method to
monitor changes in the pharmaceutical market.

Keywords
pharmacovigilance; web search; prescribing behavior

References

1. Goel, S. et al. (2010) Predicting Consumer Behavior with Web Search.
PNAS 104:17486-17490

*Jacob E. Simmering
E-mail: jacob-simmering@uiowa.edu

Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 5(1):e119, 2013