Google Uses Searches to Track Flu’s Spread

How does this work?

We've found that certain search terms are good indicators of flu activity. Google Flu Trends uses aggregated Google search data to estimate flu activity in your state up to two weeks faster than traditional flu surveillance systems.

Each week, millions of users around the world search for online health information. As you might expect, there are more flu-related searches during flu season, more allergy-related searches during allergy season, and more sunburn-related searches during the summer. You can explore all of these phenomena using Google Trends. But can search query trends provide an accurate, reliable model of real-world phenomena?

We have found a close relationship between how many people search for flu-related topics and how many people actually have flu symptoms. Of course, not every person who searches for "flu" is actually sick, but a pattern emerges when all the flu-related search queries from each state and region are added together. We compared our query counts with data from a surveillance system managed by the U.S. Centers for Disease Control and Prevention (CDC) and discovered that some search queries tend to be popular exactly when flu season is happening. By counting how often we see these search queries, we can estimate how much flu is circulating in various regions of the United States.

During the 2007-2008 flu season, an early version of Google Flu Trends was used to share results each week with the Epidemiology and Prevention Branch of the Influenza Division at CDC. Across each of the nine surveillance regions of the United States, we were able to accurately estimate current flu levels one to two weeks faster than published CDC reports.

This graph shows five years of query-based flu estimates for the Mid-Atlantic region of the United States, compared against influenza surveillance data provided by CDC's U.S.

Influenza Sentinel Provider Surveillance Network. As you can see, estimates based on Google search queries about flu are very closely matched to a flu activity indicator used by CDC. Of course, past performance is no guarantee of future results. Our system is still very experimental, so anything is possible, but we're hoping to see similar correlations in the coming year.

CDC uses a variety of methods to track influenza across the United States each year. One method relies on a network of more than 1500 doctors who see 16 million patients each year. 

The doctors keep track of the percentage of their patients who have an influenza-like illness, also known as an "ILI percentage". CDC and state health departments collect and aggregate this data each week, providing a good indicator of overall flu activity across the United States.

So why bother with estimates from aggregated search queries? It turns out that traditional flu surveillance systems take 1-2 weeks to collect and release surveillance data, but Google search queries can be automatically counted very quickly. By making our flu estimates available each day, Google Flu Trends may provide an early-warning system for outbreaks of influenza.

For epidemiologists, this is an exciting development, because early detection of a disease outbreak can reduce the number of people affected. If a new strain of influenza virus emerges under certain conditions, a pandemic could emerge and cause millions of deaths (as happened, for example, in 1918). Our up-to-date influenza estimates may enable public health officials and health professionals to better respond to seasonal epidemics and — though we hope never to find out — pandemics.

Additional details about this research can be found in an early version of our manuscript. A later version has been accepted in principle for publication in Nature. To further explore the data behind Google Flu Trends you can download CSV spreadsheets containing Flu Trends estimates going back to 2003.

Protecting User Privacy

At Google, we are keenly aware of the trust our users place in us, and of our responsibility to protect their privacy. Google Flu Trends can never be used to identify individual users because we rely on anonymized, aggregated counts of how often certain search queries occur each week. We rely on millions of search queries issued to Google over time, and the patterns we observe in the data are only meaningful across large populations of Google search users. You can learn more about how this data is used and how Google protects users' privacy at our Privacy Center.

Mas información: http://www.google.org/about/flutrends/how.html

Coffee Intake Linked to Lower Risk of HCV-Related Liver Disease Progression

Moderate coffee consumption may help slow the progression of liver disease related to hepatitis C, according to an observational study.

Among patients with established liver disease, those who drank at least three cups of coffee daily had a 50% lower risk of progression over 3.5 years, Neal D. Freedman, Ph.D., of the National Cancer Institute reported at the American Association for the Study of Liver Diseases meeting here.

The findings added to those from previous studies linking coffee intake to lower concentrations of liver enzymes and a reduced risk of cirrhosis, chronic liver disease, and hepatocellular carcinoma.

"The results showed an inverse association between coffee intake and liver disease progression," Dr. Freedman said in an interview. "This was an observational study, so we couldn't show a cause-and-effect relationship. It's possible that coffee intake could be a marker for another exposure."

The findings came from a study of 795 adults who had detectable hepatitis C viral RNA, had not achieved a sustained virologic response with peginterferon/ribavirin therapy, and Ishak fibrosis stage ≥3. All participants completed a food frequency questionnaire at baseline and 13 months.

Investigators evaluated multiple outcomes and their association with coffee or tea intake:

• Ascites
• Child-Turcotte-Pugh disease score ≥7 on two consecutive visits
• Death
• Hepatic encephalopathy
• Spontaneous bacterial peritonitis
• Variceal hemorrhage
• ≥2 increase in Ishak score on year 1.5 or 3.5 biopsy for patients with bridging fibrosis at baseline

Multivariate proportional hazards analysis (adjusted for baseline age, sex, body mass index, education, race/ethnicity, diabetes, Ishak fibrosis score at baseline, lifetime alcohol intake, usual tea intake, and total calorie intake) resulted in hazard ratios (95% confidence intervals) for coffee drinking relative to non-drinking of 1.21 (0.81 to 1.79) for < value =" 0.0005).

Tea consumption had no influence on liver progression (HR 1.03, 95% CI 0.90 to 1.18).

The inverse association between coffee drinking and liver disease progression did not vary by treatment, cirrhosis at baseline, general health at baseline, alpha-fetoprotein levels, albumin, AST/ALT ratio, bilirubin concentration, esophageal varices, or hepatic steatosis grade.

 

 

Hepatology 2008; 48(4):1101A. Abstract 1778