# Presentation: 102548

## Methods for Real-Time Detection and Assessment of Disease Outbreaks Using Information Technology

**Michael Wagner, M.D., Ph.D.**
- 	Director, Real-Time Outbreak and Disease Surveillance Laboratory
- 	Assistant Professor, Medicine and Intelligent Systems
- 	Center for Biomedical Informatics
- 	University of Pittsburgh
- 	Pittsburgh, PA

## What is the Mission of the RODS Lab?

_**First Hint of Trouble**_
- statistical analysis of data
- astute observer
- definitive diagnosis of new or “terrorism” organism

_**Analysis/Characterization**_
- Is it an emergency?
- Quarantine?
- Get more antibiotics?

_**Additional data collection**_
- ”shoeleather”
- microchip testing
- decision support at the point-of-care

**RESPONSES**

## National Retail Data Monitor 

- Every purchase already is scanned optically
- 12 big chains already merged thousands of stores and already receive daily batch feeds of sales data from those stores by midnight
- We “asked” for the data
- We worked with the industry to securely transmit the data every day to Pittsburgh (by 3 pm)
- We built the databases, created the analytic product categories, and the analytic tools

- National Retail Data Monitor

**Notes:**
1. What is the National Retail Data Monitor?  
The NRDM is a public health surveillance system that allows public health officials to track the sales of cough syrups antidiarrheals and other healthcare products in a region in near real time.  The NRDM went into operation in December and has quickly expanded to national coverage.   Public health officials can log into the system once per day to review information about sales of these products in their jurisdictions.  Founding sponsor was Commonwealth of PA but additional funding has recently come from the Alfred P. Sloan Foundation and New York State.

## Current Status 

- 18,000 stores (35% market share)

- 220 user accounts in 33 States, CDC

**Notes:**
2. What is the current status of the National Retail Data Monitor
18,000 stores nationwide are sending data at least once a day.  These stores represent 35% market share.  There are 200 user accounts in 30+ states...

## Future Plans

- Achieve 70% market share
- Decrease time latency
- Add monitoring of prescription antibiotics
- Deploy in second country
- More automation of detection analysis
- Transition

- NRDM development supported by PA Bioinformatics Grant #ME-01-737,
- The Alfred P. Sloan Foundation and New York State

**Notes:**
Expansion to achieve 70% market share, 
decrease time latency of data feeds by working with industry, 
include selected prescription drugs, especially antibiotics

expand to international, 
We are educating and grooming public health to use and assume ownership for the system long term. 

## For More Information

- _[www.health.pitt.edu](http://www.health.pitt.edu/rods)__[/rods](http://www.health.pitt.edu/rods)_
- Free account: _[nrdm-accounts@cbmi.pitt.edu](mailto:nrdm-accounts@cbmi.pitt.edu)_
- (public health officials only)
- Paper: Wagner, Robinson, Tsui, Espino, *Design of a National Retail Data Monitor*, JAMIA, 2003;10(5) 409-20

- NRDM development supported by PA Bioinformatics Grant #ME-01-737,
- The Alfred P. Sloan Foundation and New York State

## Real-time Surveillance of Hospital Data: The RODS System

**Notes:**
What is the RODS system?
The RODS system collects data from hospitals in real time and is operational in Utah, PA, (NJ, Ohio probably by the time of the call) ... It was used in the olympics in 2002.  The University of Pittsburgh made the software available for free in December and open sourced it in Sept..   Health departments can install the software, or if they want to avoid the expense and effort of running a 7x24 operation, they can contact with the laboratory to run it under an ASP model.

## HL7 Admission, Discharge, Transfer Message

**Notes:**
One of the key points about RODS is HL7, which is commonly used in hospitals for data exchange.  Here is a sample HL7 message collected from an emergency department for syndromic surveillance.  As you can tell, it’s extremely simple.  

The primary components in the received HL7 messages are patient home zip code, visit date and time,  and free-text chief complaint.

Free-text chief complaints, the fundamental component for syndromic surveillance, are collected during the ED registration process and the registration systems transmit these data in HL7 ADT messages to message routers of hospitals.  We found based on our work in Pennsylvania and Utah that virtually all hospitals follow this practice.  

Taken from our documentation that we provide

## Naïve Bayes Text Classifier

**Chief Complaint**

**Naïve Bayes****Classifier**

- Naïve Bayes Text Classifier

- P(Respiratory|NVD)= .05
- P(Botulinic|NVD)= .001
- P(Constitutional|NVD)= .01
**P(GI|NVD) = .9**
- P(Hemorrhagic|NVD)= .001
- P(Neurologic|NVD)= .001
- P(Rash|NVD)= .001
- P(None|NVD)= .036

**Notes:**
We use naïve bayes classifier to map a free-text chief complaint into one of seven prodromes and none of the above category.

Here is one simple example: the free-text cc “N/V/D” is passed into naive Bayes classifier.  
The naïve bayes classifier computes the probability for each syndrome category.  
It then outputs the prodrome with highest probability, which is GI prodrome, in this example.

I am about to show you the web interfaces of the Utah RODS system.

## Time Series

- Copyright University of Pittsburgh 2002

- All visits

- Respiratory

- GI

- Rash

- Botulinic

- Neurological

- Hemorrhagic

- Constitutional

**Notes:**
There were three main Web screens that were used by public health officials to review the data, which they did every six hours during the Games.  
The graph on the top left shows daily counts of visits to EDs and acute care facilities for the past 7 days for all reasons (including trauma).  
The count average is about 2800 visits per day.   
More importantly, the main screen also shows graphs for each of the 7 prodromes. 
The graph to the “all visits” plot shows visits for respiratory prodrome and the average is around 150 per day.

Public health officials can review this screen very quick because they are simply scanning the data for spikes of activity. 

The view you are looking at are aggregate counts for all 7 counties involved in the Olympics. 
The user could also examine the same plots for each county individually, which they did, using the pull-down menu at the bottom to select the county.

## Map

**Notes:**
The officials could also examine the spatial distribution of visits for each of the seven syndromes.  
The pull-down menu at the right allows users to select a syndrome.

On this slide, the irregular regions are zip code areas and their color represents the incidence of the prodrome.  
The white color represents zero case and the light yellow color represents low sample size (total visits < 20).  
Also darker color means higher incidence as you see from the legend at the left. 
The zip codes level are from the patient’s home address, not their work address, nor the hospital address.

The user, of course, is looking for black areas, especially if they were to line up in a linear downwind distribution.

## RODS Open Source Project

- Real-time biosurveillance depends (heavily) on software
- Good software doesn’t grow on trees
- University of Pittsburgh created RODS and then released it—for free
- Consultants, users, programmers also do not arise by spontaneous generation
- Commonwealth of Pennsylvania funding is being used to catalyze and transition RODS to become one of the world’s most used and most advanced syndromic surveillance systems

## For More Information

- www.health.pitt.edu/rods
- RODS Open Source Project:  http://openrods.sourceforge.net
- Papers: Tsui et al. *Technical Description of RODS*.  JAMIA, 2003;10(5) 409-20

- RODS development supported by PA Bioinformatics Grant #ME-01-737, AHRQ,
- National Library of Medicine Training Grant (Dr. Espino)

**Notes:**
How do I get more information about RODS
Same place as for NRDM.