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Henry Rolka National Academy of Sciences Workshop

Toward Improved Visualization of Uncertain InformationWashington, D.C.

Mar. 4, 2005

Surveillance for Early Event Detection with BioSense

(Visualization Context #3)

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Outline• Introduction, concepts and context • Overview of BioSense

⁻ Initiative⁻ System⁻ Interface

• General considerations of scope and complexity• Data acquisition issues as analytic factors

⁻ Data lag time⁻ Geographic distribution

• Example• Summary

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Early Event DetectionBioSense

Outbreak Management Outbreak

Management System

SurveillanceNEDSS

Secure CommunicationsEpi-X

Analysis & InterpretationBioIntelligence

analytic technology

Information Dissemination & KM

CDC WebsiteHealth alerting

PH ResponseCountermeasure administration

Lab, vaccine,prophylaxis

Federal Health Architecture, NHII & Consolidated

Health Informatics

Public Health Information Network

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Early Detection Activities

Data View

‘Something unusual’ noted

in data

Reporting or recording anomaly

Data processing error

True increase in disease

Naturally occurring outbreakDeliberate exposure event

Data collection, preprocessing

Application of statistical algorithms

Epidemiological decisions

Requires information from other data sources

Statistical aberration due to natural

variability

etc.

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Early Outbreak Detection: General Time Frame

Exposure Symptom HealthBehavior

HealthcareEncounter

MedicalEvaluation

InitialFindings

FinalDiagnosis

Grocery salesMedicationsAbsenteeism Information seekingNurse triage calls

Physician office visitsEMS activityEmergency room visitsHospitalization

OrdersLab tests

Preliminarydiagnosis

BTAttack

Hours Days Weeks

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Biosurveillance Data SpaceEARLY DETECTION LATER DETECTION

INTELLIGENCE BIOSENSORS MEDICALNON TRADITIONAL

ANIMALS CLINICAL DATANON TRADITIONAL USESHUMAN BEHA VIORS

Test Results

Investi-gations

Sentinel MDEnvironmental

Test Results

Influenzaisolates

Diagnosis

Medical Examiner

OTC Pharm

Utilities

Absenteeism

Web Queries

Cafeteria

Coughs

Traffic

Newsgroup

Survey

Video Surv

Public Transport

Tests ordered

Poison Centers

Complaints

EMS Runs

Radiograph Reports

911 Calls

Nurse Calls

ER Visits

Prescriptions

Pollen counts

Temperature

Humidity

Wind Speed/direct.

Allergy Index

Pollution

Vets

Agribusiness

Zoos

GOLDSTANDARDS

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BioSense: The Initiative

• Identify common state and local needs

• Promote the use of national standards

• Increase the sharing of approaches and technology

• Ensure integration with other public health systems

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BioSense: The System

• National “safety net” for early detection in major cities

• Infrastructure and data acquisition for near real-time reporting

• A platform for the implementation and evaluation of different analytic approaches

• Connection to the CDC BioIntelligenceCenter to support early detection analysis at local, state and national levels

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Current (Phase I) Data Sources• DoD – Ambulatory Care and ER Diagnoses - Up to four

diagnosis codes (IDC-9-CM) identifying the reason for every ambulatory care (including ER) visit

• DoD - Procedures - Procedure codes (CPT) ordered for every ambulatory care visit

• V A - Ambulatory Care and ER Diagnoses -Diagnosis codes (IDC-9-CM) for every ambulatory care visit (including ER) in 172 hospitals and 650 outpatient clinics nationwide

• V A – Procedures- Procedure codes (CPT) for every ambulatory care visit

• Clinical Laboratory Tests - Clinical lab tests ordered nationally through LabCorp

• BioWatch Results - Lab result for BioWatch environmental collectors

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Current BioSense System Architecture

ELR

LRN

DOD

VA

DataSources

MessageReceiver

BioRetriver File server

TaggingAnd

Parsing

SRT

SQL ServerAVR Mart

SQL Server

ETL

SASServer

GISAPP

Server

AVRAPP

Server

WebServer

PublicHealthView

GISData

AnalyticalData Mart

SASEnterprise

Engine

CDCAnalystView

StagingArea

MessageTransform

Engine

BioSenseMessage

Repository

Analysis, Visualization, and

Reporting (AVR)

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System Status• Views for all states and all BioWatch cities

• 340+ state and local health department user accounts

• 49 states have BioSense administrators

• In use in CDC BioIntelligence Center

• Have set up custom views for high profile events—egG8 meeting

• Detection algorithms – CuSum, “Smart Scores” –implemented, SatScan - pending

• Working to augment data sources from data providers

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User Interface for Analytics and Data Visualization

BioSense Home – Analytic Summary Report

BioSense Health Indicator Pages

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BioSense Home Page

Syndrome “Punch Cards”

Region Selection

Data Transmission Information

Sentinel Infection Alerts

Analytical Results

Records Received Table

(Demonstration data)

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BioSense Home PageSyndrome Specific SMART Score Results

SMART ScoreResults

For SpecifiedSyndrome

(Demonstration data)

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BioSense Home Page Drill DownSyndrome Specific Full Analytical Results

CuSumResults

SMART ScoreResults

Data Detail Tablewith counts producing

elevated analyticresults highlighted

(Demonstration data)

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BioSense Home Page

Link toData

VisualizationPages

(Demonstration data)

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BioSense Health Indicators PageData Visualization

User Options Menus

Links to Syndrome-Specific

Display Pages

Syndrome-Specific“Consolidated”

Graphs

(Demonstration data)

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BioSense Health Indicators PageSyndrome-Specific Maps

Data SourceSpecific

Maps

Zip Code“Mouse Over”

DisplayZoom-In/Out

And Map Navigation

Tool

(Demonstration data)

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BioSense User-Defined OptionsData Selection Options

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• Support coverage for the nation.• Maintain responsibility for providing

public health guidance (legal authority). • Coordinate broadly across jurisdictions.

Early Detection at the Federal Level

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• Wider variation in heterogeneity.

• System implications grow in complexity.

• Focus here on data issues in this regard.

Expanded Geographic Scope

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Analytic Components of Early Detectionand BioSense Developmental Process

InformationSystem

Development

Data Management

StatisticalAnalysis

Public HealthDecisionMaking

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Empirical Process for Analysis• No “sampling design”• Confounding:

⁻ Reporting volume⁻ Event intensity

• Learn from experience• Inductive and deductive analytic

procedures

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Important Data Characteristics• Spontaneously generated• Opportunistic• Noisy• Messy

⁻ ‘Duplicate/repeat’ records⁻ Geographic indicators vary

• Multiple sources• Geographic distribution• Lag time

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Data Lag Time

• “Really Timely” surveillance• Variable percentages of data

available for most recent days• Little attention to this in algorithm

evaluations

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Measurement and Recording

Transactional Data

Data Management•Quality checks•Editing

Data preprocessing for a specific purpose

(‘views’, ‘data marts’)

Analytical Applications

Interpretation for associations,Trends, unusual patterns, signals

Public Health ResponsePopulation of interest which generates events

Surveillance System Components

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Data Lag Time Segments

Eventtime

Storetime

StoreMarttime Access

time

Application Architectureand

Data Preprocessing

DataProvisioning

Criterion

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BioSense Data Lag Time

Cumulative % of Messages Stored By Days From ‘Event Time’To ‘Access Time’ since January 2004

Calculated for July 1, 2004 to January 31, 2005

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Data Load Report

> 036%113%113%116%114%110%111%129%132%74%LAB

>= 25Encounters0%42%7%83%94%97%100%101%106%107%VA

>= 50Encounters14%45%63%79%89%94%94%88%92%108%DOD

>= 75StoreReportsOTC

>=1252/132/122/112/102/92/82/72/62/52/4

>=150SunSatFriThuWedTuesMonSunSatFri

>=175Yesterday

PercentPercentage of Records Received (from eventual estimate total)

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Data Load Report

> 00%60%89%119%115%117%114%110%111%129%LAB

>= 25Encounters10%37%85%78%14%85%96%98%101%102%VA

>= 50Encounters0%20%44%59%75%85%93%96%95%90%DOD

>= 75StoreReportsOTC

>=1252/152/142/132/122/112/102/92/82/72/6

>=150TueMonSunSatFriThuWedTuesMonSun

>=175Yesterday

PercentPercentage of Records Received (from eventual estimate total)

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Average Days From ‘Event Time’ To ‘Access Time’ by ‘Event Date’since January 1, 2004

BioSense Data Lag Time

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BioSense (DoD) Data Lag Time

20> 8

225 – 8

4< 5

Mean Lag Days Number of States

From ‘Event Date’ to ‘Access Date’ since Jan. 1, 2004

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BioSense Average Weekly VolumeDoD Ambulatory Care Clinics

(based on January 1, 2004 - January 31, 2005)

Average Weekly Visitsby Clinic Zip Code

500 or less

501 - 1,5001,501 - 5,000

5,001 - 10,000

10,000 - 16,328

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BioSense Average Weekly VolumeVA Ambulatory Care Clinic

(based on January 1, 2004 - January 31, 2005)

Average Weekly Visitsby Clinic Zip Code

1,622 - 5,0005,001 - 10,000

10,001 - 15,000

15,001 - 20,000

20,000 - 31,362

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BioSense Average Weekly VolumeVA Ambulatory Care Patients

(based on January 1, 2004 - January 31, 2005)

Average Weekly Visitsby Patient Zip Code

25 or less

26 - 50

51 - 150

151 - 500

501 - 1,380

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BioSense Average Weekly VolumeLabCorp Lab Visits

(based on January 1, 2004 - January 31, 2005)

* AVR Zip Code uses Patient Zip Code. If unavailable, Lab Facility Zip Code, then Provider Zip Code is used

Average Weekly Visitsby AVR Zip Code*

1 - 2526 - 5051 - 100

101 - 200201 - 1015

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Example

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Source: http://www.cdc.gov/flu/weekly/fluactivity.htm

1st week Oct 2003

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Influenza Related Diagnoses From BioSense V A and DoDAmbulatory Care Sites

Week of Flu Season For USReport created on February 21, 2005 (Based on MMWR Week)

1st week Oct

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Source - http://www.cdc.gov/flu/weekly/weeklyarchives2004-2005/weekly04.htmReport prepared on February 24, 2005

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Source - http://www.cdc.gov/flu/weekly/weeklyarchives2004-2005/weekly04.htmReport prepared on February 3, 2005

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Influenza-relateddiagnoses per 100,000 visits

Up to 11 - 1010.1 - 100More than 100

Ft. Leonard Wood, MO(140 per 100,000)

Wichita Falls, TX(126 per 100,000)

Juneau, AK(169 per 100,000)

Influenza-related Diagnoses from Ambulatory Care SitesCurrent Flu Season 2004-2005 – Current season Time Point

July 4, 2004 – January 22, 2005Based on Data Received through 1/25/2005

Enid, OK(109 per 100,000)

Watertown, NY(111 per 100,000)

Ketchikan, AK(243 per 100,000)

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Influenza-relateddiagnoses per 100,000 visits

Up to 11 - 1010.1 - 100More than 100Previous flu-related activity

Elizabeth, NJ(424 per 100,000)

Influenza-related Diagnoses from Ambulatory Care SitesCurrent Flu Season 2004-2005 – New Activity, Recent Two Weeks

(Event Date Range January 9 – 22, 2005)Based on Data Received through 1/25/2005

Miles City, MT(219 per 100,000)

Presque Isle, ME(199 per 100,000)

Sarasota Springs, NY(159 per 100,000)

Burlington, VT(160 per 100,000)

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Comparative View with OverallData Reporting Volume

Influenza-relateddiagnoses per 100,000 visits

Up to 11 - 1010.1 - 100More than 100

Ft. Leonard Wood, MO(140 per 100,000)

Wichita Falls, TX(126 per 100,000)

Juneau, AK(169 per 100,000)

Influenza-related Diagnoses from Ambulatory Care SitesCurrent Flu Season 2004-2005 – Current season Time Point

July 4, 2004 – January 22, 2005Based on Data Received through 1/25/2005

Enid, OK(109 per 100,000)

Watertown, NY(111 per 100,000)

Ketchikan, AK(243 per 100,000)

BioSense Average Weekly VolumeDoD Ambulatory Care Clinics

(based on January 1, 2004 - January 31, 2005)

Average Weekly Visitsby Clinic Zip Code

500 or less

501 - 1,500

1,501 - 5,000

5,001 - 10,000

10,000 - 16,328

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Comparative View with OverallData Reporting Volume

Influenza-relateddiagnoses per 100,000 visits

Up to 11 - 1010.1 - 100More than 100

Ft. Leonard Wood, MO(140 per 100,000)

Wichita Falls, TX(126 per 100,000)

Juneau, AK(169 per 100,000)

Influenza-related Diagnoses from Ambulatory Care SitesCurrent Flu Season 2004-2005 – Current season Time Point

July 4, 2004 – January 22, 2005Based on Data Received through 1/25/2005

Enid, OK(109 per 100,000)

Watertown, NY(111 per 100,000)

Ketchikan, AK(243 per 100,000)

BioSense Average Weekly VolumeVA Ambulatory Care Patients

(based on January 1, 2004 - January 31, 2005)

Average Weekly Visitsby Patient Zip Code

25 or less

26 - 5051 - 150

151 - 500

501 - 1,380

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Summary • Modern surveillance data availability is

dynamic• Consideration for realism in evaluation of

algorithm performance• Implications for evaluation of system

timeliness • Quick ‘data knowledge’ methodologies • Analytic refinement consistent with

operational requirements

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Acknowledgements

• John Loonsk, CDC• David Walker, CDC• Paul McMurray, SAIC• Steve Bloom, SAIC• Haobo Ma, CDC• Roseanne English-

Bullard, CDC • Jerry Tokars, CDC

• Kyumin Shim, CDC• Colleen Bradley, SAIC• Leslie Sokolow, IEM• Matthew Miller, IEM• David King, CDC• John Copeland, CDC• Nancy Grady, SAIC

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Contact Information• HRolka@CDC.Gov• DWalker@CDC.Gov• BioSense Questions and Answers:

⁻ http://www.cdc.gov/phin/Webinars/BioSense.htm