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Innovation

Biosurveillance program aims to stop disease outbreaks early

By shifting the top-down approach of disease outbreak awareness to a model that gets local health authorities medical data quickly, researchers in Illinois are hoping to prevent illness outbreaks early.
Written by Christina Hernandez Sherwood, Contributing Writer

By shifting the top-down approach of disease outbreak awareness to a model that gets local health authorities medical data quickly, researchers in Illinois are hoping to prevent illness outbreaks early.

I spoke last week with Ian Brooks of theNational Center for Supercomputing Applications at the University of Illinois, about how a new biosurveillance program could help authorities develop better strategies for combating outbreaks.

How are disease outbreaks currently tracked?

There are a number of systems that are in production right now. Most work in one of two ways. The [Centers for Disease Control and Prevention] system is a collection of data on diseases that have been identified by the CDC. They must be notified within a certain amount of time. There are some other systems that work on syndromic information. That's non-diagnosed cases: influenza-like illness, chest pain, things like that.

There are a few that work on both syndromic and reportable diseases. One of the criticisms we hear from the local health community is that information sent to a big central location tends to take too long to get back to them in terms of actionable information.

Talk about how your system, Indicator, works differently.

Indicator was designed working with local health care and public health officials. The data we're getting is multiple kinds of information -- from school attendance information to patient advisory nurse calls to emergency department data. All of those are very local. When we see something, we immediately notify the people that are sending us the data and the public health organization.

What's an example of something you'd see in the data that would cause you to notify the local health authority?

We use a number of different detection algorithms. Some things we see that are unusual are not of public health interest. For example, a couple of days ago we saw a statistically unusual number of people with kids that swallowed foreign objects. That may be statistically significant, but it's not of any public health interest. On the other hand, if we see a lot of nausea, vomiting, diarrhea, GI issues -- as we did the day after Thanksgiving two years ago -- we do notify public health.

Talk about how your trial in the Champaign-Urbana community is going and what you've found.

It's been going pretty well. [In 2008, we] started with patient advisory nurse data. Last year, we added emergency department data and school attendance data. The school attendance data was very useful for the local health authority during the H1N1 outbreak. They were seeing what happened in the schools compared with the rest of the community and using that to make decisions on the vaccination recommendations and strategy. There was a lot more in the schools than in some other parts of the community, so they decided to move the vaccination to the schools. [Then they decided] which schools to go to based on what we were seeing in terms of the absences.

Are you expanding the trial?

Over the next year, we will be expanding to also taking reportable disease information and expanding to a six-county region in central Illinois. We're also starting to add veterinary surveillance.

How do you see this technology continuing to evolve?

One of the questions in the community is whether syndromic information is good or not. With traditional disease reporting, you have a clinical diagnosis often backed up by lap tests. But that can be relatively slow. If you just look at syndromes -- people complaining about flu-like illness [for instance] -- then you can see something happening much quicker. But it doesn't have a clinical diagnosis to back it up.

One of the questions is: Is syndromic information actually worth it? I think the answer is pretty clearly yes. We and some of the other groups are trying to collect both kinds of information and model how an outbreak spreads. You might see something significant through a chain of different kinds of detections. You may see something in the veterinary surveillance and then in syndromic and then in reportable diseases, for example. With H1N1, we saw it in the school attendance data before we saw it in the patient advisory nurse calls before we saw it in the emergency department.

Image: Ian Brooks

This post was originally published on Smartplanet.com

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