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Estimation of Late Reporting Corrections for Health Indicator Surveillance. Howard Burkom 1 , PhD Yevgeniy Elbert 2 , MSc LTC Julie Pavlin 2 , MD MPH Christina Polyak 2 , MPH 1 The Johns Hopkins University Applied Physics Laboratory

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Estimation of Late Reporting Corrections for Health Indicator Surveillance

Howard Burkom1, PhD

Yevgeniy Elbert2, MSc

LTC Julie Pavlin2, MD MPH

Christina Polyak2, MPH

1TheJohns Hopkins University Applied Physics Laboratory

2 Walter Reed Army Institute for Research Global Emerging Infections Surveillance & Response System

San Francisco, CANovember 17, 2003

American Public Health Assoc. 131st Annual Meeting

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
essence biosurveillance systems
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
using lagged data counts for biosurveillance
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?
reporting of civilian office visits
Reporting of Civilian Office Visits

Daily Regional Civilian Diagnosis Counts

Respiratory Syndrome Group

office visit reporting promptness by data source
Office Visit Reporting Promptness by Data Source

Use of Kaplan-Meier “Failure Function” Curves

to Represent Reporting Promptness

using lagged data for biosurveillance
Using Lagged Data for Biosurveillance


  • 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
reporting of er outpatient visits
Reporting 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

using provider counts to adjust for lagged reporting
Using 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
counts of clinic mtf pairs military outpatient visit data
Counts of Clinic/MTF PairsMilitary Outpatient Visit Data

City-Wide Respiratory Diagnosis Counts

Number of Clinics Reporting

“Explains away” unexpected data dropoffs

effect of provider count regression
Effect of Provider Count Regression

Visit Counts and Residuals

Day-of-Week Effect Attenuation

Rise due to outbreak?