The Space-Time Check Measurement for Different Information Streams

1610 days ago, 490 views
PowerPoint PPT Presentation
Harvard Pilgrim Health Care HMO individuals looked after by Harvard Vanguard Medical Associates ... National Center for Infectious Diseases, Centers for Disease Control ...

Presentation Transcript

Slide 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

Slide 2

Different Data Sources For instance: OTC Drug Sales, from drug store chains Nurses Hotline Calls, from Optum Regular Physician Visits, from HMOs/VA Emergency Department Visits, from doctor's facilities Ambulance Dispatches, from 911 call focuses Lab Test Results, from labs

Slide 3

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

Slide 4

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

Slide 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 unusual about late occasions? Diary of Urban Health, 80i:66-75, 2003.

Slide 6

Why Multivariate Detection Methods? We don't know whether an episode will make a flag in at least one information streams. The enlightening substance is diverse in various information streams.

Slide 7

Outline Method: Space-Time Permutation Scan Statistic Example: Gastrointestinal phone calls, pressing consideration visits and normal doctor visits in Boston

Slide 8

The Spatial Scan Statistic Create a general or unpredictable matrix of centroids covering the entire study area. Make a limitless number of circles around every centroid, with the range anywhere in the range of zero up to a greatest so that at most 50 percent of the populace is incorporated.

Slide 9

A little example of the circles utilized

Slide 10

Space-Time Scan Statistic Use a round and hollow window, with the roundabout base speaking to space and the tallness speaking to time. We will just consider chambers that achieve the present time.

Slide 11

Space-Time Permutation Scan Statistic 1. For every barrel, figure the normal number of cases molding on the marginals μ st = C s C t/C where C s = # cases in area s C t = # cases in time interim t C = add up to number of cases

Slide 12

Space-Time Permutation Scan Statistic Let c st = # cases in the chamber covering area s and time interim t.

Slide 13

Space-Time Permutation Scan Statistic 2. For every chamber, ascertain the Poisson probability T st = [c st/μ st ] c st x [(C-c st )/(C-μ st )] C-c st if c st/μ st > 1, T st = 1 generally 3. Test measurement T = max st log [ T st ]

Slide 14

Statistical Inference 4. Create arbitrary imitations of the information set molded on the marginals, by permuting the sets of spatial areas and times. 5. Think about test measurement in genuine and arbitrary information sets utilizing Monte Carlo speculation testing (Dwass, 1957): p = rank(T genuine )/(1+#replicas)

Slide 15

Multiple Data Streams For every chamber, include the Poisson log probabilities: T st = log[ T [1] st ] +log[ T [2] st ] +log[ T [3] st ] Test measurement T = max st T st

Slide 16

Syndromic Surveillance in Boston: Upper and Lower GI Harvard Pilgrim Health Care HMO individuals watched over by Harvard Vanguard Medical Associates Historical Data from Jan 1 to Dec 31, 2002 Mimicking Surveillance from Sept 1 to Dec 31, 2002

Slide 17

Three Data Streams Telephone Calls ( ~ 20/day) Urgent Care Visits ( ~ 9/day) Regular Physician Visits ( ~ 22/day) Multiple contacts by a similar individual evacuated.

Slide 18

Strongest Signal: October 18 p= Recurrence Int. Tele: 0.001 < 1/1000 days Urgent 0.91 ~ consistently Regular: 0.84 ~ consistently Multiple DS: 0.001 < 1/1000 days

Slide 19

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

Slide 20

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

Slide 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 (each of the 5) Same Family: Mother, Father, 3 Kids

Slide 22

2 nd Strongest Signal: December 20 p= Recurrence Int. Tele: 0.03 1/32 days Urgent 0.71 ~ consistently Regular: 0.003 1/333 days Multiple DS: 0.002 1/500 days

Slide 23

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

Slide 24

December 20 Signal Mostly various ambiguous GI analyze: Esophageal Reflux (3), Nausea (2), Abdominal Pain (2), Noninfectious GI (2), Acute pharyngitis, Mastodynia, Diarrhea, Anemia, Hypertension, Blood in stool, Holiday parties?

Slide 25

3 rd Strongest Signal: October 26 p= Recurrence Int. Tele: 0.07 1/14 days Urgent 0.85 ~ consistently Regular: 0.18 1/6 days Combined: 0.007 1/142 days

Slide 26

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

Slide 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

Slide 28

Free Software SaTScan v 5.1