BioSense Program: Scientific Collaboration

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)

Zhiheng (Roy) Xu, MS (PhD Candidate)Senior Research Scientist

Soyoun Park, MS (PhD Candidate)Statistician

Paul C. McMurray, MDSSenior Statistician

Taha A. Kass-Hout, MD, MSDeputy Director for Information Science and BioSense Program Manager

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.

The 2010 Joint Statistical Meetings (JSM)Defense and National Security: Disease SurveillanceMonday August 2nd, 2010: 10:30 AM-12:20 PM – Room: CC-10 (East)Vancouver, British Columbia (Canada)

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

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]

• 8 health department sending data from 482 hospitals

• 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

Level 1: Perception of Elements in Current Situation

Level 2: Comprehension of Current Situation

Level 3: Projection of Future Status

Decision

Performance of Actions

Situation Awareness

“Raw” Data

Clinical and ER FeedsOTC DataAnimal DataAbseenteeDataNews Feeds Resource Status(needCDC examples)

Interpretation

Detection Algorithms and alerts

Visualization

Collection sources (WHO, OIE)

SME Interpretation and Collaboraiton(Epi-X, ProMed, etc)

Planning and Simulation

Knowledge of Interventions

Disease outbreak Modeling

Historical Data Analysis

Capacity and Resource Planning

Effects from Actions

Current State

Monitoring

Detection

Outbreak Management

Remediaiton

Retrospective Analysis

Ou

tbreak C

ycle

Biosurveillance: Methods and Case Studies, eds. Kass-Hout, T. and Zhang, X., CRC Press, Taylor & Francis LLC. September 2010.

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 datao Alternatives to the mean-shift model are currently being explored with the

community

Fill up the incomplete data with forecasting

Open-Access Scientific Collaboration

https://sites.google.com/site/changepointanalysis

58 Collaborators, > 100 users from 46 cities

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?

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.

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

n

XXXX n

...21

00 S XXSS iii 1

1maxarg iSCP

Level 1: Find a change point maximizing |S|

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

Initial Time Series

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.

Open Access Scientific Collaboration: Explore Alternative Methods & Address

Limitations

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

Alternative methods to mean-shift model

Autocorrelation in biosurveillance data

References

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

Taha A. Kass-Hout, MD, MSDeputy Director for Information Science and BioSense Program ManagerDivision of Healthcare Information (DHI) Public Health Surveillance Program Office (PHSPO)Office of Surveillance, Epidemiology, & Laboratory Services (OSELS)Centers for Disease Control & Prevention (CDC)1600 Clifton Road, NE, MS E-51, Atlanta, GA 30329

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Data Sources

As of May 2010

Hospital Data N Direct Reporting Hospitals Health Departments

Civilian Hospitals 640 162 478

Outpatient reason for visit

93 91 2

Outpatient final diagnosis

70 68 2

ED chief complaint 640 162 478 ED final diagnosis 207 76 131 Inpatient reason for admit

120 118 2

Inpatient final diagnosis

79 77 2

Census 100 99 1 ED clinical 119 50 69 Laboratory results 66 64 2 Radiology results 33 31 2 Pharmacy orders 35 34 1

VA final diagnosis 887DoD final diagnosis 368