Post on 30-Dec-2015
description
3.03.5.1~PPT
Howard Burkom1, PhDYevgeniy Elbert2, MSc
LTC Julie Pavlin2, MD MPHChristina 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
Estimation of Late Reporting Corrections for Health Indicator
Surveillance
3.03.5.2~PPT
ESSENCE: 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
3.03.5.3~PPT
ESSENCE 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
3.03.5.4~PPT
Using Lagged Data Counts for Biosurveillance
• ESSENCE II data => 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?
3.03.5.5~PPT
Reporting of Civilian Office Visits
Daily Regional Civilian Diagnosis Counts Respiratory Syndrome Group
3.03.5.6~PPT
Office Visit Reporting Promptness by Data Source
Use of Kaplan-Meier “Failure Function” Curves to Represent Reporting Promptness
3.03.5.7~PPT
Using 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 countsBrookmeyer 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 anomalousZeger, 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
3.03.5.8~PPT
Reporting of ER/Outpatient Visits
Comparison of MTF Reporting of ER and Outpatient Visits, Aug-Dec2002
F
ract
ion
Re
po
rte
d
Days After Visit1 2 3 4 5 6 7 8 9 10 11 12 13 14
0.00
0.25
0.50
0.75
1.00outpatient
er
Apparent difference in reporting promptness between ER and other clinics
ER: 50% reported by day 3
Outpatient: 80% reported by day 3
3.03.5.10~PPT
• 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
Using Provider Counts to Adjust for Lagged Reporting
3.03.5.11~PPT
Counts of Clinic/MTF PairsMilitary Outpatient Visit Data
City-Wide Respiratory Diagnosis Counts
Number of Clinics Reporting“Explains away” unexpected data dropoffs
3.03.5.12~PPT
Effect of Provider Count Regression
Visit Counts and Residuals
Day-of-Week Effect Attenuation
Rise due to outbreak?