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Enhancing Disease Surveillance with Spatial-temporal Results Patricia Araki, MPH County of Los Angeles – Department of Public Health Acute Communicable Disease Control Program

Enhancing Disease Surveillance with Spatial-temporal Results

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Enhancing Disease Surveillance with Spatial-temporal Results. Patricia Araki, MPH County of Los Angeles – Department of Public Health Acute Communicable Disease Control Program. Basic types of surveillance: Active Passive Enhanced Syndromic Surveillance Origins of Syndromic Surveillance - PowerPoint PPT Presentation

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Page 1: Enhancing Disease Surveillance with Spatial-temporal Results

Enhancing Disease Surveillance with Spatial-temporal Results

Patricia Araki, MPHCounty of Los Angeles – Department of Public Health

Acute Communicable Disease Control Program

Page 2: Enhancing Disease Surveillance with Spatial-temporal Results

Introduction• Basic types of surveillance:

– Active– Passive– Enhanced

Syndromic Surveillance

• Origins of Syndromic Surveillance– 9/11– Primary purpose: identify outbreaks before

definitive diagnoses are made

Page 3: Enhancing Disease Surveillance with Spatial-temporal Results

• Type of surveillance that looks at trends within syndrome categories

• Daily Emergency Department (ED) admission data from participating hospitals

• Classify each ED visit by chief complaint into syndrome categories

• Tally and compare observed to expected values using statistical algorithm

What is Syndromic Surveillance and how is it conducted?

Page 4: Enhancing Disease Surveillance with Spatial-temporal Results

Mapping and cluster analysis:• Data within each syndrome category are

analyzed by patient zip code using SaTScan™– Likelihood ratio is calculated for each point/cluster– Most likely cluster is determined by selecting the

maximum likelihood ratio (scan statistic) over all possible clusters

– p-value for the most likely cluster is calculated via Monte Carlo hypothesis testing

• Results may then be visualized by using ESRI ArcGIS or SAS Graph

Page 5: Enhancing Disease Surveillance with Spatial-temporal Results

Graphing SaTScan™ Geospatial results

• Key info about the most likely cluster appears in red text above the map

• Zip codes in most likely cluster are listed below the map in black text

• Zip code colored red is the center of the most likely cluster

• Zip code(s) colored in grey are other zip codes in the most likely cluster

Page 6: Enhancing Disease Surveillance with Spatial-temporal Results

Spatial-temporal resultsDay 1 Day 2 Day 3

Day 4 Day 5 Day 6

Page 7: Enhancing Disease Surveillance with Spatial-temporal Results

Example: Potential school Meningitis outbreak

• On November 14, 2006, ACDC was alerted to a high school student who attended school while symptomatic for meningitis

• Public health officials offered prophylaxis at the school

• Two students eventually confirmed positive

• Data were assessed for both temporal trends and spatial SatScanTM clusters– Neurological syndrome visits – Potential meningitis CCs:

“Fever,” “headache,” “meningitis”

Page 8: Enhancing Disease Surveillance with Spatial-temporal Results

findings…RA STS PALPITATIONS,BLURRED VISION

PTS MOTHER STS MENINGITIS SYMPTOMS

FTH STS MENINGITIS BREAK OUT

FATHER ST EXP MENINGITIS,H/A NAUSEA FEVER

PT STS MENINGITIS BREAK OUT AT CV HIGH

PT STS MENINGITIS BREAK OUT AT CV HIGH

RA STS MENINGITIS,ALLERGIC REACTION

PT STS RT ARM NUMB,BLURRED VISION

VOMITING,HEADACHE,FEVER

HEADACHE,VOMITING,FEVER

NECK STIFFNESS/BACK PAIN/ R/O MENINGHITIS

FEVER SORETHROAT HEADACHE ABNORMAL VAG BLEED

FEVER/HEADACHE

HEADACHE FEVER R/O MENENGITIS

Page 9: Enhancing Disease Surveillance with Spatial-temporal Results

Application of Syndromic Surveillance and SaTScan™ to LA County data sources

• Emergency department logs

• NRDM-OTC (medication sales)

• KP-Nurse Call

• Coroners

• Poison Control

• EMS Fire/dispatch

• Hepatitis

Page 10: Enhancing Disease Surveillance with Spatial-temporal Results

Resources• SaTScan™: www.satscan.org

• Syndromic surveillance: www.syndromic.org