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

3.03.5.9~PPT

Reporting of Civilian Office Visits21-day adjustment: Week 1

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?

3.03.5.13~PPT

Effectiveness in DARPA Outbreak Evaluation Challenge