1. The Space-Time Scan Statistic for Multiple Data Streams
Martin Kulldorff, Katherine Yih, Ken Kleinman, Richard Platt, Harvard Medical School and Harvard Pilgrim Health Care
Farzad Mostashari, New York City Department of Health and Mental Hygiene
Luiz Duczmal, Univ Fed Minas Gerais, Brazil

2. Different Data Sources
For example:
OTC Drug Sales, from pharmacy chains
Nurses Hotline Calls, from Optum
Regular Physician Visits, from HMOs/VA
Emergency Department Visits, from hospitals
Ambulance Dispatches, from 911 call centers
Lab Test Results, from laboratories

3. Different Types of Data from the Same Data Source
For example, HMO data concerning:
Telephone Calls to Physicians
Regular Physician Visits
Emergency Department Visits
Lab Test Requests
Lab Test Results
Drug Prescriptions

4. Different Groupings in the Same Type of Data
Children, Young Adults, Adults age 65+
Male, Female
Diarrhea, Vomiting

5. Early Work
Burkom HS, Biosurveillance Applying Scan Statistics with Multiple, Disparates Data Sources, Journal of Urban Health, 80i:57-65, 2003
Wong WK, Moore A, Cooper G, Wagner M. WSARE: What’s strange about recent events? Journal of Urban Health, 80i:66-75, 2003.

6. Why Multivariate Detection Methods?
We do not know whether an outbreak will create a signal in one or more data streams.
The informational content is different in different data streams.

7. Outline Method: Space-Time Permutation Scan Statistic
Example: Gastrointestinal telephone calls, urgent care visits and regular physician visits in Boston

8. The Spatial Scan Statistic
Create a regular or irregular grid of centroids covering the whole study region.
Create an infinite number of circles around each centroid, with the radius anywhere from zero up to a maximum so that at most 50 percent of the population is included.

9. A small sample of the circles used

10. Space-Time Scan Statistic
Use a cylindrical window, with the
circular base representing space and the height representing time.
We will only consider cylinders that reach the present time.

11. Space-Time Permutation Scan Statistic
1. For each cylinder, calculate the expected
number of cases conditioning on the marginals
μst = Cs Ct / C
where Cs = # cases in location s
Ct = # cases in time interval t
C = total number of cases

12. Space-Time Permutation Scan Statistic
Let cst = # cases in the cylinder covering
location s and time interval t.

13. Space-Time Permutation Scan Statistic
2. For each cylinder, calculate the Poisson likelihood Tst =
[cst / μst ]cst x [(C-cst)/(C- μst)] C-cst
if cst / μst > 1, Tst = 1 otherwise
3. Test statistic T = maxst log [ Tst ]

14. Statistical Inference
4. Generate random replicas of the data set conditioned on the marginals, by permuting the pairs of spatial locations and times.
5. Compare test statistic in real and random data sets using Monte Carlo hypothesis testing (Dwass, 1957):
p = rank(Treal) / (1+#replicas)

15. log[ T[1]st ] +log[ T[2]st ] +log[ T[3]st ]
Multiple Data Streams
For each cylinder, add the Poisson log likelihoods: Tst =
log[ T[1]st ] +log[ T[2]st ] +log[ T[3]st ]
Test statistic T = maxst Tst

16. Syndromic Surveillance in Boston: Upper and Lower GI
Harvard Pilgrim Health Care HMO members cared for by Harvard Vanguard Medical Associates
Historical Data from Jan 1 to Dec 31, 2002
Mimicking Surveillance from Sept 1 to Dec 31, 2002

17. Three Data Streams Telephone Calls ( ~ 20 / day)
Urgent Care Visits ( ~ 9 / day)
Regular Physician Visits ( ~ 22 / day)
Multiple contacts by the same person removed.

18. Strongest Signal: October 18
p= Recurrence Int.
Tele: < 1 / 1000 days
Urgent ~ every day
Regular: ~ every day
Multiple DS: < 1 / 1000 days

19. October 18 Signal Friday Number of Cases: 5 Expected Cases: 0.04
Location: Zip Code 01740
Time Length: One Day

20. October 18 Signal Friday Number of Cases: 5 Expected Cases: 0.04
Location: Zip Code 01740
Time Length: One Day
Diagnosis: Pinworm Infestation (all 5)

21. October 18 Signal Friday Number of Cases: 5 (all tele)
Expected Cases: 0.04
Location: Zip Code 01740
Time Length: One Day
Diagnosis: Pinworm Infestation (all 5)
Same Family: Mother, Father, 3 Kids

22. 2nd Strongest Signal: December 20
p= Recurrence Int.
Tele: / 32 days
Urgent ~ every day
Regular: / 333 days
Multiple DS: / 500 days

23. December 20 Signal Number of Cases: 16 (7 tele, 7 regular, 2 urgent)
Expected Cases: 3.5
Location: Zips 01810,26,45,50,52,76
Time Length: Two Days (Thu, Fri)
Strong signals on the two following days.

24. December 20 Signal Mostly diverse vague GI diagnoses:
Esophageal Reflux (3), Nausea (2),
Abdominal Pain (2), Noninfectious GI (2),
Acute pharyngitis, Mastodynia, Diarrhea,
Anemia, Hypertension, Blood in stool,
Holiday parties?

25. 3rd Strongest Signal: October 26
p= Recurrence Int.
Tele: / 14 days
Urgent ~ every day
Regular: / 6 days
Combined: / 142 days

26. October 26 Signal Saturday Number of Cases: 8 ( 5 tele, 3 regular)
Expected Cases: 0.9
Location: Zip Codes 01902,07,15,45,70
Time Length: Two Days (Fri, Sat)
Various specific diagnoses.

27. Research Funded By Methods: Alfred P Sloan Foundation
Data, National Bioterrorism Syndromic Surveillance Demonstration Program:
National Center for Infectious Diseases, Centers for Disease Control and Prevention

28. SaTScan v 5.1 www.satscan.org
Free Software
SaTScan v 5.1