surveillance for early event detection with...
TRANSCRIPT
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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• [email protected]• [email protected]• BioSense Questions and Answers:
⁻ http://www.cdc.gov/phin/Webinars/BioSense.htm