Centers for Disease Control Early Aberration Reporting System. Lori Hutwagner Matthew Seeman William Thompson Tracee Treadwell

Transcription

1 Centers for Disease Control Early Aberration Reporting System Lori Hutwagner Matthew Seeman William Thompson Tracee Treadwell

2 Introduction Describe development / purpose of EARS Provide Case Definition for Aberration Review Aberration Detection Methods used in EARS Provide Recommendations for Sites Provide Examples from EARS

3 EARS Background Developed by EPO and NCID Several methods developed previous to EARS EPO provided initial support for EARS NCID took over the support of EARS Aberration Detection Methods Suite of available aberration detection methods Developed by both CDC and Non-CDC collaborators Primary Purpose Providing aberration detection methods to local health departments that have been validated using several alternative data sources

4 EARS Background Currently used by many health department agencies for bioterrorism surveillance States Counties Cities Also used at several public events Democratic Convention 2 Super Bowl 2 World Series 2

5 Define Aberration Detection Case Definition for Aberration Change in the distribution or frequency of health events when compared to historical data. This May or May not be an outbreak This May or May not be of public health interest Principles and Practice of Public Health Surveillance (22)

6 Why is this case definition important? Validation of models requires a fair comparison across methods and data sources Data entry errors Would not be considered a false positive according to our definition Source of aberration is identified and understood Important issue in evaluating sensitivity and specificity Other similar events should be modeled and understood as well

7 Important characteristics of aberration detection methods High Sensitivity Necessary communication among staff Need to notify appropriate authorities When do you follow-up on aberrations???

8 Review of Literature Aberration Detection Method Case Definition Methods Pattern Recognition Methods Infectious Diseases Methods Chronic Diseases Methods

9 Infectious Disease Methods Long Term Implementation Extended baseline methods (3-5 years) Limited baseline methods (7 days - 3 years) Short Term Implementation Implementation expected for less then 3 days such as political conventions No initial baseline available (-6 days)

10 Long Term Implementation Extended Baseline Methods Historical Limits Method (Stroup et.al. 989) Seasonaly Adjusted (Hutwagner et.al. 997) Log Linear Regression (Farrington et.al. 996) Compound Smoothing (Stern et.al. 999) Cyclical Regression (Simonsen et.al. 997)

11 Long Term Implementation Summary Validation of Methods Selected 2 Methods for Implementation Historical Limits Method Method Methods complement each other CDC has 5+ experience problem solving with these methods EARS will implement additional methods Needs to further validate methods

12 Long Term Implementation Limited Baseline Methods C-MILD C2-MEDIUM C3-ULTRA 2 > - + Current Count (Baseline Mean + Baseline Std Dev) Baseline Std Dev

13 Timeline for Implementation Day-9 Day-8 Day-7 Day-6 Day-5 Day-4 Day-3 Day-2 Day- Day Baseline for C-MILD (- to -7 day) Current Event Baseline C2-MEDIUM (-3 to -9days) Baseline for C3-ULTRA (-3 to -9 days)

14 Short Term Implementation No Available Baseline P Chart 2X2 Tables (chi square) Moving Average Chart

15 Summary of Methods Available in EARS Historical Methods Historical Limits Seasonality adjusted Methods C-MILD C2-MEDIUM C3-ULTRA Drop In Surveillance Methods P Chart 2x2 Tables (Chi Square) Moving Average Chart

16 Why Does Industry Continue To Use Quality Control Methods? First developed P-Charts in 92 s Stoumbos et al. The State of Statistical Process Control as We Proceed into the 2 st Century, Journal of the American Statistical Association in 2 and P-Chart methods continue to be among the most important and widely used quality control tools in statistics Applied in manufacturing, engineering, environmental science, biology, genetics, epidemiology, medicine, finance, law enforcement and athletics

17 Time Event Day Day 2-6 Day 7+ 3 Years + Implementation Of Methods Based on Time Implementation of Method Implement emergency surveillance P-Chart P-Chart, C-MILD, C2-MEDIUM, C3-ULTRA Multiple methods depending on data source

18 EARS Drop In Surveillance Real Time Applications Foodborne outbreaks Respiratory illness Data entry errors Bioterrorism??

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20 Aberration Detection All USA Flagged Events for MMWR Weeks 48 through 5 Event Week Anthrax 48 Count (YTD) Mean STD () () () () () (6) Detection Flags.2.2 Hist Limits Botulism (Foodborne) 5 (24) (27) (4) (22) (8) (27).2.2 Botulism (Infant) 49 (68) 2 (68) (57) (84) (79) 2 (82).6.89 Botulism (Other/Wound) 5 (2) (9) (27) (36) (8) (2).2.45 Brucellosis 5 (94) 2 (74) (55) 2 (7) 2 (62) 3 (93).6.55 Cholera 48 (3) (6) (3) (5) (8) (4).2.2 Cyclosporiasis 48 () 3 (85) (54) (56) (5) 3 (24) () 2 (87) (55) (56) (5) 3 (27).6.89 EhrlichiosisHum Granu (HGE) 48 () (75) 2 (4) 2 (8) 2 (99) 5 (26) EhrlichiosisHum Mono (HME) 48 () (24) (9) 3 (77) 2 (98) 3 (93)..4 5 () (24) (9) (84) () (99).2.45 Enceph (St.Louis) 5 5 () (2) (24) (4).2.2 (3) (3).2.2

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22 Influenza Mortality Daily State Level //998 6/5/998 6/29/998 7/3/998 7/27/998 8//998 8/24/998 9/7/998 9/2/998 /5/998 /9/998 /2/998 /6/998 /3/998 2/4/998 2/28/998 //999 /25/999 2/8/999 2/22/999 3/8/999 3/22/999 4/5/999 4/9/999 5/3/999 5/7/999 5/3/999 Series Series7 Series2 Series4 Series5 Series6

23 FAQ What Happened with Anthrax in NYC? Our methods did not detect anthrax in NYC Why did this happen? We monitored Hospital EDs Case definition for Inhaled Anthrax 6 of 7 patients went to private physicians Reported cutaneous anthrax No one was monitoring this outcome at the time We believe our methods will detect these outcomes if they occur New case definitions have been added

24 Summary of EARS Aberration Detection Methods Suite of available aberration detection methods Developed by both CDC and Non-CDC colleagues Primary Purpose Providing aberration detection methods to local health departments that have been validated using several alternative data sources We have brought EARS CDs to distribute