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BioSense Program: Scientific Collaboration

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BioSense is an all-hazards surveillance program for achieving near real-time national public health situation awareness and early detection. Prospective anomaly detection methods such as the Modified …

BioSense is an all-hazards surveillance program for achieving near real-time national public health situation awareness and early detection. Prospective anomaly detection methods such as the Modified EARS C2 are commonly adapted and used in BioSense and other public health syndromic surveillance systems. These methods however can produce an excessive false alert rate. Analyses results will be presented on the combined use of retrospective (e.g., Change Point Analysis (or CPA)) and prospective (e.g., C2) anomaly detection methods. This combined approach will help detect sudden aberrations in addition to subtle changes in local trends, help rule out alarm investigations, and assist with retrospective follow-ups. Examples on the utility of this combined approach in working collaboratively with the scientific community are applied to BioSense emergency departments' visits due to ILI. Methods, limitations, future work, and invitation to the scientific community to collaborate with us will be discussed at this talk.

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  • A total of 640 civilian hospitals [~12% of EDs in the US in 28 jurisdictions] currently represented in BioSense comprised 8 health department sending data from 482 hospitals, 165 facilities reporting ED data directly to CDC or a health department.
  • Public health surveillance is best characterized as cyclical (Buckeridge and Cadieux 2007), with steps ranging from identifying individual case to detecting population patterns to conveying information for action.
  • Identify days when disease indicator activity is higher than expectedAlert at the three-sigma levelEarly Aberration Reporting System (EARS)Modified Early Aberration Reporting System (EARS) C2 Algorithm*
  • Transcript

    • 1. BioSense Program: Scientific Collaboration
      The 2010 Joint Statistical Meetings (JSM)
      Defense and National Security: Disease Surveillance
      Monday August 2nd, 2010: 10:30 AM-12:20 PM – Room: CC-10 (East)
      Vancouver, British Columbia (Canada)
      Taha A. Kass-Hout, MD, MS
      Deputy Director for Information Science and BioSenseProgram Manager
      Soyoun Park, MS (PhD Candidate)
      Statistician
      Zhiheng (Roy) Xu, MS (PhD Candidate)
      Senior Research Scientist
      Paul C. McMurray, MDS
      Senior Statistician
      Division of Healthcare Information (DHI)
      Public Health Surveillance Program Office (PHSPO)
      Office of Surveillance, Epidemiology, and Laboratory Services (OSELS)
      Centers for Disease Control & Prevention (CDC)
      Any views or opinions expressed here do not necessarily represent the views of the CDC, HHS, or any other entity of the United States government. Furthermore, the use of any product names, trade names, images, or commercial sources is for identification purposes only, and does not imply endorsement or government sanction by the U.S. Department of Health and Human Services.
    • 2. BioSense Updated Vision
      … provide multi-purpose value in timely data for national public health situation awareness, routine public health practice, improving health outcomes and public health, and monitoring healthcare quality
    • 3. Data Sources
      Civilian Hospitals
      • ~640 facilities [~12% ED coverage in US, patchy geo coverage] [Chief complaints: median 24-hour latency, Diagnoses: median 6 days latency]
      • 4. 8 health department sending data from 482 hospitals
      • 5. 165 facilities reporting ED data directly to CDC or a health department
      Veterans Affairs and Department of Defense
      • ~1400 facilities in 50 states, District of Columbia, and Puerto Rico [final diagnosis ~2->5 days latency]
      National Labs [LabCorp and Quest]
      • 47 states, the District of Columbia, and Puerto Rico [24-hour latency]
      Hospital Labs
      • 49 hospital labs in 17 states/jurisdictions [24-hours latency]
      Pharmacies
      • 50,000 (27,000 Active) in 50 states [24-hour latency]
    • The Problem
      Early Event Detection
      Monitoring Health-Related Events and Maintaining Situation Awareness
      Biosurveillance: Methods and Case Studies, eds. Kass-Hout, T. and Zhang, X., CRC Press, Taylor & Francis LLC. September 2010.
    • 6. Complementary Analytic Methods
      The data
      Available data from most recent day(s) may be unstable due to incomplete reporting and delays
      Instability of daily data: 2-3 day trends not consistently born out by subsequent observations
      Reporting latency of 1-3+ days
      The analytic methods [complimentary approach]
      Detect major changes using the Modified Early Aberration Reporting System (EARS) C2 method
      Find abnormalities in daily data
      Detect more subtle changes using the Change Point Analysis (CPA) method
      Detect the series mean-shifts in historical data
      Alternatives to the mean-shift model are currently being explored with the community
      Fill up the incomplete data with forecasting
    • 7. Open-Access Scientific Collaboration
      https://sites.google.com/site/changepointanalysis
      58 Collaborators, > 100 users from 46 cities
    • 8. Change Point Analysis (CPA)
      Purpose
      CPA aims at detecting any change in the mean of a process (e.g., time series)
      Benefits
      Detect change in historical data
      Investigate what might have caused the change
      Real-time trend analysis
      Example
      Did a change in % Influenza-like illness (ILI) occur?
      Did more than one change occur?
      When did the changes occur?
      Since last change, is Influenza activity going up, down or stable?
      How confident are we that the change is a real one?
    • 9. Change Point Analysis
      A change point indicates the series means shifts from its previous mean to another. The green piece-wise constant lines represent mean shifts.
    • 10. Change Point Analysis
      Determine the Series Mean
      Accumulate Running Sum of differences between Mean and individual values [residuals]
      Plot the cumulative sum of the residuals [CUSUM] for the time series
      The point farthest from 0 denotes a Change-Point (CP)
      Break into two sections at CP:
      analyze each subseries for additional significant CPs, and repeat the process
      Bootstrapping provides us with a measure of the CP’s significance
    • 11. Level 1: Find a change point maximizing |S|
      Initial Time Series
      Level 2: Find a change point on each sub-series
      Level n: Final result
      Repeat the algorithm until no
      more change points are detected
      Apply CPA
      Apply CPA
    • 12. Complementary Methods
      Aberration detection methods are generally better at detecting isolated or grouped abnormalities [assumption: mean is stable], while CPA is better at detecting subtle changes which may not be detected by aberration methods (assumption: mean is unstable). We use both methods in a complementary fashion to get better results.
    • 13. Open Access Scientific Collaboration: Explore Alternative Methods & Address Limitations
      Alternative methods to mean-shift model
      Autocorrelation in biosurveillance data
      Bayesian CPA
      Weak prior
      Posterior distributions of the change points
      Example: R package bcp
      Structural change model
      Minimize the sum of squared residuals
      Advantage:
      Allows for auto-correlated time-series data
      Disadvantage:
      Assumes a stationary process
      Asymptotic distribution for change points
      Example: R package strucchange
    • 14. Bai, J. Estimation of a change point in multiple regression models. Review of Economics and Statistics, 79: 551-563, 1997.
      Bai, J. and Perron, P. Computation and analysis of multiple structural change models. Journal of Applied Economics, 18: 1-22, 2003.
      bcp: An R package for performing a Bayesian analysis of change point problems. Journal of Statistical Software, 23 (3): 1-13, 2007.
      Tokars, J., et.al. Enhancing Time-Series Detection Algorithms for Automated Biosurveillance. Emerging Infectious Diseases, 15 (4): 533-539.
      Wayne A. Taylor, Change-Point Analysis: A Powerful New Tool for Detecting Changes. Retrieved from http://www.variation.com/anonftp/pub/changepoint.pdf
      References
    • 15. Thank YOU!
      Follow BioSense on Twitter
      Join BioSense on Facebook
    • 16. Data Sources
      As of May 2010