Title: Progress Toward Earlier Detection: Alerting Algorithms
1Estimation of Late Reporting Corrections for
Health Indicator Surveillance
Howard Burkom1, PhD Yevgeniy Elbert2, MSc LTC
Julie Pavlin2, MD MPH Christina Polyak2,
MPH 1The Johns Hopkins University Applied
Physics Laboratory 2 Walter Reed Army Institute
for Research Global
Emerging Infections Surveillance Response
System San Francisco, CA November 17,
2003 American Public Health Assoc. 131st Annual
Meeting
2ESSENCE An Electronic Surveillance System for
the Early Notification of Community-based
Epidemics
- Earlier detection of aberrant clinical patterns
at the community level to jump-start response - Rapid epidemiology-based targeting of limited
response assets (e.g., personnel and drugs) - Communication to reduce the spread of panic and
civil unrest
3ESSENCE Biosurveillance Systems
- Monitoring health care data from 800 mil.
treatment facilities since Sept. 2001 - System receives 100,000 patient encounters per
day - Adding, evaluating new sources
- Civilian physician visits
- OTC pharmacy sales
- Prescription data
- Expanding to nurse hotline, absenteeism data,
animal health, - Developing implementing alerting algorithms
4Using Lagged Data Counts for Biosurveillance
- ESSENCE II data gt hypothesis that earlier stages
of an outbreak may be more detectable in office
visit (OV) data than in emergency department data
- Depends on existence, duration of typical
prodrome for underlying disease - How to exploit this for earlier alerting?
- BUT, our electronic OV data is reported variably
late, depending on individual providers - QUESTION How can a timely source of data with a
reporting lag be used for biosurveillance?
5Reporting of Civilian Office Visits
Daily Regional Civilian Diagnosis Counts
Respiratory Syndrome Group
6Office Visit Reporting Promptness by Data Source
Use of Kaplan-Meier Failure Function Curves to
Represent Reporting Promptness
7Using Lagged Data for Biosurveillance
- Approaches
- Two steps estimate actual counts, apply
algorithm - use recent promptness functions by day-of-week,
other covariates - apply lateness factors to recent counts
- Brookmeyer R, Gail MH, AIDS Epidemiology A
Quantitative Approach. New York Oxford
University Press 1994 Chapter 7 - Use historically early reporting providers as
sentinels - Combined approach use regression on counts with
date and lag as predictors to determine whether
recent reported data are anomalous - Zeger, SL, See, L-C, Diggle, PJ, Statistical
Methods for Monitoring the AIDS Epidemic,
Statistics in Medicine 8 (1999) - Linear regression using number of providers
reporting each day
8Reporting of ER/Outpatient Visits
Outpatient 80 reported by day 3
ER 50 reported by day 3
Apparent difference in reporting promptness
between ER and other clinics
9Reporting of Civilian Office Visits21-day
adjustment Week 1
10Using Provider Counts to Adjust for Lagged
Reporting
- Concept (applied in recent DARPA eval.)
- tabulate doctors or clinics reporting each day
- use residuals of linear regression of daily data
counts on providers - accounts for known unknown dropoffs by
computing actual counts vs expected, given daily
providers - can include additional predictor variables
- Can apply process control alerting algorithms to
residuals - Significantly attenuates day-of-week effect
11Counts of Clinic/MTF PairsMilitary Outpatient
Visit Data
City-Wide Respiratory Diagnosis Counts
Number of Clinics Reporting Explains away
unexpected data dropoffs
12Effect of Provider Count Regression
Visit Counts and Residuals Day-of-Week Effect
Attenuation
Rise due to outbreak?
13Effectiveness in DARPA Outbreak Evaluation
Challenge