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Results about Chief Complaints and OTC. Michael Wagner, MD PhD Real-time Outbreak and Disease Surveillance (RODS) Laboratory University of Pittsburgh. National Retail Data Monitor. OTC products have UPC bar codes Stores use optical scanners

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slide1
Results about Chief Complaints and OTC

Michael Wagner, MD PhDReal-time Outbreak and Disease Surveillance (RODS) LaboratoryUniversity of Pittsburgh

national retail data monitor
National Retail Data Monitor
  • OTC products have UPC bar codes
  • Stores use optical scanners
  • Nine national chains own >19,000 stores and they agreed to send daily sales data from their data warehouses
  • “Antidiarrheals” and 17 other surveillance categories
  • Health Department Use
  • 260+ accounts/39 States)
    • Raw data feeds: New York State, New York City, National Capital Area (MD, VA, DC), CDC, New Jersey, Georgia, Indiana…

Wagner et al, Design of a National Retail Data Monitor, JAMIA, Sept. 2003;10(5) 409-20

slide3
RODS

OPEN SOURCE DEVELOPERS!

Monday 3:30-5:30 PM

JEREMY ESPINO

openrods.sourceforge.net

  • Emergency room triage personnel enter chief complaints routinely
  • The registration computer transmits chief complaints as HL7 messages to HL7 message routers, which can transmit to a health dept
  • Bayesian classifier assigns the chief complaint to “respiratory” or other syndrome category

Health Department Use

-Pennsylvania ~50 hospitals

-Utah ~25 EDs and Urgent cares

-Ohio 3 hospitals

-Under development Michigan, Atlantic City

Tsui et al, Technical Description of RODS: A Real-time Public Health Surveillance System, JAMIA, Sept. 2003;10(5) 399-408

research question 1
Research Question 1
  • Does the Bayesian classifier have any ability to detect patients with respiratory (or other relevant bioterrorism syndrome)?
case detection accuracy of bayesian classifier vs gold standard doh manual ed log review
Case Detection Accuracy of Bayesian Classifier vs. gold standard DOH Manual ED log review

Answer

There is information

(Area under curve is not 0.5)

There is noise

For many syndromes of interest to bioterrorism, we can detect half the cases

Next question: can we use an array of these noisy detectors to achieve accurate detection of outbreaks?

Sensitivity, specificity and likelihood ratio positive (LR+) measurements for the CoCo

classifier using the Utah Department of Health emergency department gold standard.

CoCo Syndrome

UDOH Syndrome

Sensitivity

Specificity

LR+

Respiratory

Respiratory infection with fever*

0.52

0.89

5

Gastrointestinal

Gastroenteritis without blood

0.71

0.90

7

Encephalitic

Meningitis / encephalitis

0.47

0.93

7

Rash

Febrile illness with rash*

0.50

0.99

56

Botulinic

Botulism-like syndrome

0.17

0.998

104

*Required documentation of fever in the patient record.

Courtesy Per Gesteland, MD

research question 2
Research Question 2
  • Can we detect outbreaks by monitoring daily counts of “respiratory” (and other syndromes of interest to bioterrorism) produced by Bayesian classification of all ED visits in a region?
detecting respiratory outbreaks by monitoring free text chief complaints
Detecting Respiratory Outbreaks by Monitoring Free-text Chief Complaints

Hospital Pneumonia and Influenza Diagnoses

Respiratorychief complaints

SDs from Mean

7 Years

Ivanov and Gesteland

slide8
Detection from CCs precede that from admissions by 23 days

(95% CI 12-33)

ICD-9s for gastroenteritis and rotavirus

research question 3
Research Question 3
  • Can we detect outbreaks by monitoring daily sales of “diarrheal remedies”?

(…and other product categories related to the early symptomatic treatment of bioterrorism diseases)?

2001 crypto in north battleford saskatchewan
2001 Crypto in North Battleford, Saskatchewan

… Precautionary water advisory issued on 4/23

Detectable peak on 4/2 in sales of over-the-counter diarrheal remedies

cryptosporidium outbreak milwaukee
Cryptosporidium Outbreak: Milwaukee

3X increase in sales

March 1

Public health awareness April 5, 1993

Proctor et al. Surveillance data for waterborne illness detection: an assessment following a massive waterborne outbreak of Cryptosporidium infection. Epidemiol Infect. 1998;120(1):43-54.

***Proctor et al. Surveillance data for waterborne illness detection: an assessment following a massive waterborne outbreak of Cryptosporidium infection. Epidemiol Infect. 1998;120(1):43-54.

research question 4
Research Question 4
  • How small of an outbreak can we detect?
using realistic injects deriving shape of injects from real sales data during a real outbreak
Using Realistic Injects--Deriving Shape of Injects From Real Sales Data During a Real Outbreak

Fit curve using expectation-maximization algorithm

4.6 std dev increase

average detection performance for a 4 6 sd north battleford perturbation into 500 u s zip codes
False Alarm Rate=25%

10%

5%

2%

Probability of Detection

Detection Delay (days from start of inject)

Average Detection Performance for a 4.6 SD North Battleford Perturbation into 500 U.S. Zip Codes

X

summary of methods
Summary of Methods
  • Case detection
  • Outbreak detection
      • Retrospective studies of actual outbreaks
        • “Single disease” e.g., Influenza, Cryptosporidium
        • Seasonal (winter outbreaks)
      • Prospective
      • Injects

Maximum validity, but rare

Maximum validity, and answer question whether heretofore undetectable outbreaks can be detected, but expensive

Very important for exploring “detectability”

acknowledgements
Acknowledgements
  • Commonwealth of Pennsylvania
  • DARPA
  • AHRQ
  • Alfred P. Sloan Foundation
  • New York State Department of Health
  • NLM Fellowship support of Dr. Espino
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