1 / 10

Enhancing Disease Surveillance with Spatial-temporal Results

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

lori
Télécharger la présentation

Enhancing Disease Surveillance with Spatial-temporal Results

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


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

  2. Basic types of surveillance: Active Passive Enhanced Syndromic Surveillance Origins of Syndromic Surveillance 9/11 Primary purpose: identify outbreaks before definitive diagnoses are made Introduction

  3. 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?

  4. 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 Mapping and cluster analysis:

  5. 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 Graphing SaTScan™ Geospatial results • 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

  6. Spatial-temporal results Day 1 Day 2 Day 3 Day 4 Day 5 Day 6

  7. 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” Example: Potential school Meningitis outbreak

  8. findings…

  9. Emergency department logs NRDM-OTC (medication sales) KP-Nurse Call Coroners Poison Control EMS Fire/dispatch Hepatitis Application of Syndromic Surveillance and SaTScan™ to LA County data sources

  10. SaTScan™: www.satscan.org Syndromic surveillance: www.syndromic.org Resources

More Related