Surveillance for Early Event Detection with BioSense (Visualization Context #3)

Transcription

1 Surveillance for Early Event Detection with BioSense (Visualization Context #3) Henry Rolka National Academy of Sciences Workshop Toward Improved Visualization of Uncertain Information Washington, D.C. Mar. 4, 2005

2 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

3 Public Health Information Network Early Event Detection BioSense Outbreak Management Outbreak Management System Surveillance NEDSS Secure Communications Epi-X Analysis & Interpretation BioIntelligence analytic technology Information Dissemination & KM CDC Website Health alerting Federal Health Architecture, NHII & Consolidated Health Informatics PH Response Countermeasure administration Lab, vaccine, prophylaxis

4 Early Detection Activities Data collection, preprocessing Data View Reporting or recording anomaly Application of statistical algorithms Something unusual noted in data etc. Epidemiological decisions Data processing error Statistical aberration due to natural variability True increase in disease Naturally occurring outbreak Requires information from other data sources Deliberate exposure event

5 Early Outbreak Detection: General Time Frame Grocery sales Medications Absenteeism Information seeking Nurse triage calls Orders Lab tests Exposure Symptom Health Behavior Healthcare Encounter Medical Evaluation Initial Findings Final Diagnosis BT Attack Physician office visits EMS activity Emergency room visits Hospitalization Preliminary diagnosis Hours Days Weeks

6 EARLY DETECTION Biosurveillance Data Space LATER DETECTION GOLD INTELLIGENCE BIOSENSORS NON TRADITIONAL MEDICAL STANDARDS ANIMALS Vets Zoos Agribusiness Environmental Pollen counts Humidity Temperature Wind Speed/ direct. Allergy Index Pollution HUMAN BEHAVIORS OTC Pharm Absenteeism Utilities Coughs Web Queries Traffic Survey Public Transport Cafeteria Video Surv Newsgroup NON TRADITIONAL USES Tests ordered Complaints Poison Centers 911 Calls EMS Runs Nurse Calls ER Visits Prescriptions Radiograph Reports CLINICAL DATA Test Results Diagnosis Influenza isolates Medical Examiner Test Results Sentinel MD Investigations

7 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

8 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 BioIntelligence Center to support early detection analysis at local, state and national levels

9 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 VA - 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 VA 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

10 Current BioSense System Architecture Data Sources ELR LRN DOD VA Message Receiver BioRetriver Staging Area File server Message Transform Engine Tagging And Parsing SRT BioSense Message Repository SQL Server ETL SQL Server AVR Mart Analysis, Visualization, and SAS Server AVR APP Server GIS APP Server GIS Data Analytical Data Mart Reporting (AVR) Web Server SAS Enterprise Engine Public Health View CDC Analyst View

11 System Status Views for all states and all BioWatchcities state and local health department user accounts 49 states have BioSenseadministrators In use in CDC BioIntelligenceCenter Have set up custom views for high profile events eg G8 meeting Detection algorithms CuSum, Smart Scores implemented, SatScan-pending Working to augment data sources from data providers

12 User Interface for Analytics and Data Visualization BioSense Home Analytic Summary Report BioSense Health Indicator Pages

13 BioSense Home Page Region Selection Data Transmission Information Sentinel Infection Alerts (Demonstration data) Syndrome Punch Cards Analytical Results Records Received Table

14 BioSense Home Page Syndrome Specific SMART Score Results (Demonstration data) SMART Score Results For Specified Syndrome

15 CuSum Results BioSense Home Page Drill Down Syndrome Specific Full Analytical Results (Demonstration data) SMART Score Results Data Detail Table with counts producing elevated analytic results highlighted

16 BioSense Home Page (Demonstration data) Link to Data Visualization Pages

17 BioSense Health Indicators Page Data Visualization User Options Menus (Demonstration data) Links to Syndrome-Specific Display Pages Syndrome-Specific Consolidated Graphs

18 BioSense Health Indicators Page Syndrome-Specific Maps Zoom-In/Out And Map Navigation Tool Zip Code Mouse Over Display Data Source Specific Maps (Demonstration data)

19 BioSense User-Defined Options Data Selection Options

20 Early Detection at the Federal Level Support coverage for the nation. Maintain responsibility for providing public health guidance (legal authority). Coordinate broadly across jurisdictions.

21 Expanded Geographic Scope Wider variation in heterogeneity. System implications grow in complexity. Focus here on data issues in this regard.

22 Analytic Components of Early Detection and BioSense Developmental Process Data Management Statistical Analysis Public Health Decision Making Information System Development

23 Empirical Process for Analysis No sampling design Confounding: Reporting volume Event intensity Learn from experience Inductive and deductive analytic procedures

24 Important Data Characteristics Spontaneously generated Opportunistic Noisy Messy Duplicate/repeat records Geographic indicators vary Multiple sources Geographic distribution Lag time

25 Data Lag Time Really Timely surveillance Variable percentages of data available for most recent days Little attention to this in algorithm evaluations

26 Surveillance System Components Population of interest which generates events Public Health Response Measurement and Recording Transactional Data Interpretation for associations, Trends, unusual patterns, signals Analytical Applications Data Management Quality checks Editing Data preprocessing for a specific purpose ( views, data marts )

27 Data Lag Time Segments Event time Store time Store Mart time Access time Data Provisioning Criterion Application Architecture and Data Preprocessing

28 BioSense Data Lag Time Calculated for July 1, 2004 to January 31, 2005 Cumulative % of Messages Stored By Days From Event Time To Access Time since January 2004

29 Data Load Report Percentage of Records Received (from eventual estimate total) Percent Yesterday >=175 Fri Sat Sun Mon Tues Wed Thu Fri Sat Sun >=150 2/4 2/5 2/6 2/7 2/8 2/9 2/10 2/11 2/12 2/13 >=125 OTC StoreReports >= 75 DOD 108% 92% 88% 94% 94% 89% 79% 63% 45% 14% Encounters >= 50 VA 107% 106% 101% 100% 97% 94% 83% 7% 42% 0% Encounters >= 25 LAB 74% 132% 129% 111% 110% 114% 116% 113% 113% 36% > 0

30 Data Load Report Percentage of Records Received (from eventual estimate total) Percent Yesterday >=175 Sun Mon Tues Wed Thu Fri Sat Sun Mon Tue >=150 2/6 2/7 2/8 2/9 2/10 2/11 2/12 2/13 2/14 2/15 >=125 OTC StoreReports >= 75 DOD 90% 95% 96% 93% 85% 75% 59% 44% 20% 0% Encounters >= 50 VA 102% 101% 98% 96% 85% 14% 78% 85% 37% 10% Encounters >= 25 LAB 129% 111% 110% 114% 117% 115% 119% 89% 60% 0% > 0

31 BioSenseData Lag Time Average Days From Event Time To Access Time by Event Date since January 1, 2004

32 BioSense (DoD) Data Lag Time Mean Lag Days < 5 Number of States > 8 20 From Event Date to Access Date since Jan. 1, 2004

33 BioSense Average Weekly Volume DoD Ambulatory Care Clinics (based on January 1, January 31, 2005) Average Weekly Visits by Clinic Zip Code 500 or less 501-1,500 1,501-5,000 5,001-10,000 10,000-16,328

34 BioSense Average Weekly Volume VA Ambulatory Care Clinic (based on January 1, January 31, 2005) Average Weekly Visits by Clinic Zip Code 1,622-5,000 5,001-10,000 10,001-15,000 15,001-20,000 20,000-31,362

35 BioSense Average Weekly Volume VA Ambulatory Care Patients (based on January 1, January 31, 2005) Average Weekly Visits by Patient Zip Code 25 or less ,380

36 BioSense Average Weekly Volume LabCorp Lab Visits (based on January 1, January 31, 2005) Average Weekly Visits by AVR Zip Code* * AVR Zip Code uses Patient Zip Code. If unavailable, Lab Facility Zip Code, then Provider Zip Code is used

37 Example

38 1st week Oct 2003 Source:

39 Influenza Related Diagnoses From BioSenseVA and DoD Ambulatory Care Sites 1st week Oct Week of Flu Season For US Report created on February 21, 2005 (Based on MMWR Week)

40 Source - Report prepared on February 24, 2005

41 Source - Report prepared on February 3, 2005

42 Influenza-related Diagnoses from Ambulatory Care Sites Current Flu Season Current season Time Point July 4, 2004 January 22, 2005 Based on Data Received through 1/25/2005 Enid, OK (109 per 100,000) Ft. Leonard Wood, MO (140 per 100,000) Watertown, NY (111 per 100,000) Wichita Falls, TX (126 per 100,000) Juneau, AK (169 per 100,000) Ketchikan, AK (243 per 100,000) Influenza-related diagnoses per 100,000 visits Up to More than 100

43 Influenza-related Diagnoses from Ambulatory Care Sites Current Flu Season 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) Elizabeth, NJ (424 per 100,000) Influenza-related diagnoses per 100,000 visits Up to More than 100 Previous flu-related activity

44 Comparative View with Overall Data Reporting Volume BioSense Average Weekly Volume DoD Ambulatory Care Clinics (based on January 1, January 31, 2005) Influenza-related Diagnoses from Ambulatory Care Sites Current Flu Season Current season Time Point July 4, 2004 January 22, 2005 Based on Data Received through 1/25/2005 Enid, OK (109 per 100,000) Ft. Leonard Wood, MO (140 per 100,000) Watertown, NY (111 per 100,000) Wichita Falls, TX (126 per 100,000) Average Weekly Visits by Clinic Zip Code 500 or less 501-1,500 1,501-5,000 5,001-10,000 10,000-16,328 Juneau, AK (169 per 100,000) Ketchikan, AK (243 per 100,000) Influenza-related diagnoses per 100,000 visits Up to More than 100

45 Comparative View with Overall Data Reporting Volume BioSense Average Weekly Volume VA Ambulatory Care Patients (based on January 1, January 31, 2005) Influenza-related Diagnoses from Ambulatory Care Sites Current Flu Season Current season Time Point July 4, 2004 January 22, 2005 Based on Data Received through 1/25/2005 Enid, OK (109 per 100,000) Ft. Leonard Wood, MO (140 per 100,000) Watertown, NY (111 per 100,000) Wichita Falls, TX (126 per 100,000) Average Weekly Visits by Patient Zip Code Influenza-related diagnoses per 100,000 visits 25 or less ,380 Juneau, AK (169 per 100,000) Ketchikan, AK (243 per 100,000) Up to More than 100

46 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

47 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

48 Contact Information BioSense Questions and Answers: