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 Web searches are often linked with behavior, said Svetha Venkatesh,
one of the study’s co-authors.
For example, a trip to the gym may be predicted by a search for the
gym’s hours, said Venkatesh, who directs the Center for Pattern
Recognition and Data Analytics at Deakin University in Geelong,
Australia.
Or an order for food delivery might be predicted by a search for a
food delivery company, Venkatesh added.
“A diagnosis or (being) suspicious of heart problems is associated
with searching for symptoms, side effects and so on,” Venkatesh said
in an email to Reuters Health.
Venkatesh and her coauthors used Google Trends to identify search
terms for a one-year period and compared them to U.S. Centers for
Disease Control (CDC) data on state-based prevalences of risk
factors that can predict non-communicable disease, like exercise
frequency, tobacco use, diagnosed high blood pressure,
cardiovascular disease or diabetes.
Search trends from the previous year, divided by state, were
strongly tied to the CDC’s measured estimates of disease risk from
population data, the authors wrote March 24 online in the Journal of
Epidemiology and Community Health.
In 2011, the web search model predicted that 11.2% of people in
Alabama, 9.4% in New Jersey and 8.1% in Nevada had diabetes.
Measured values from the CDC for the same year were 11.8%, 8.8% and
10.3%, respectively.
“These may provide a means to understand the response to changes in
policy or other interventions in close to real time,” Venkatesh
said.
Getting data from search trends could allow for faster changes in
healthcare policies and other interventions, instead of waiting for
traditional data collection and processing, which can take up to
three years, she said.
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The web search technique won’t replace hard data measurements, but
does help support them at low additional cost, she said.
A similar approach has been used to predict and track influenza on
other occasions, she said.
“I’m not surprised that they found an association between search
behavior and chronic disease prevalence and it might have some value
in predicting prevalence,” said Diane T. Finegood, who leads the
Chronic Disease Systems Modeling Lab at Simon Fraser University in
Burnaby, Canada. But, she believes, it may not yet have direct
policy making applications.
The prevalence of conditions like diabetes changes slowly, and any
policy intervention would only have a notable impact far downstream,
she said in an email to Reuters Health.
Search behavior may be useful as a proxy for behavior change when it
closely correlates with CDC measures, Finegood said.
SOURCE: http://bmj.co/1xvBjiG
J Epidemiol Community Health 2015.
[© 2015 Thomson Reuters. All rights
reserved.] Copyright 2015 Reuters. All rights reserved. This material may not be published,
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