Syndromic Surveillance in New York State Loretta A. Santilli, MPH James R. Miller, MPH, MD Hwa-Gan Chang, PhD New York State Department of Health Division.

Slides:



Advertisements
Similar presentations
Copyright 2004 Northrop Grumman Corporation 0 HIT Summit Leveraging HIT for Public Health Surveillance HIT Summit Leveraging HIT for Public Health Surveillance.
Advertisements

Consolidation Communicable Diseases User Stories: Meeting Agenda 1.News from other domains 2.Recap of a previous meeting 3.Consolidation of three more.
National Notifiable Disease Surveillance CSTE/CDC collaboration Reporting mandated at state level Reportable diseases vary by state Health care providers,
Adverse Event Reporting: Getting started Lynn Bahta, R.N., B.S.N Minnesota Department of Health August 2008.
Reeder et al. Perceived usefulness of a distributed community-based syndromic surveillance system: a pilot qualitative evaluation study. BMC Research Notes.
Comparability of Electronic and Manual Influenza-like Illness (ILI) Surveillance Methods Robin M. Williams, Nebraska Department of Health & Human Services/University.
Local Health Department Perspective Electronic Medical Record Software and Health Information Exchanges Kathleen Cook Information & Fiscal Manager, Lincoln-Lancaster.
NYS Department of Health Bureau of Healthcom Network Systems Management.
Disease surveillance is an epidemiological practice by which.
Northwest Center for Public Health Practice University of Washington School of Public Health and Community Medicine 1 Preparing for and Responding to Bioterrorism:
Public Health Preparedness Arizona’s Near Real Time School-based Syndromic Surveillance Program Lea Trujillo PhD, Yue Qiu, MPH, Kenneth Komatsu, MPH, Laura.
Overview of Uses for Public Health Surveillance Daniel M. Sosin, M.D., M.P.H. Division of Public Health Surveillance and Informatics Epidemiology Program.
From Data to Signals to Screenshots: Recent Developments in NYCDOHMH ED Syndromic Surveillance. Marc Paladini New York City Department of Health and Mental.
EPIDEMIOLOGY AND SURVEILLANCE Southwest Florida Disaster Healthcare Coalition June 13, 2014 Jennifer Roth, MSPH Director of Epidemiology Florida Department.
Outbreak Investigation: The First 48 Rachel Radcliffe, DVM, MPH Career Epidemiology Field Officer Division of Infectious Disease Epidemiology West Virginia.
UNCLASSIFIED Building Biosurveillance Systems for Early Detection of Public Health Events Central Asia Regional Health Security Conference April.
Implementing a Syndromic Surveillance System in Miami-Dade County Fermin Leguen, MD, MPH Chief Physician Director, Office of Epidemiology & Disease Control.
Syndromic Surveillance in Georgia: A Grassroots Approach February 22, 2006 Erin L. Murray Karl Soetebier Georgia Division of Public Health.
Staffing RODS in Ohio February 23 rd, 2006 Biosurveillance Information Exchange Working Group Rutgers University Piscataway, NJ Loren Shaffer, MPH
Overview of ‘Syndromic Surveillance’ presented as background to Multiple Data Source Issue for DIMACS Working Group on Adverse Event/Disease Reporting,
“To Ignore or Not to Ignore?” Follow-up to Statistically Significant Signals" Biosurveillance Information Exchange Working Group Reflections from San Diego.
Aaron Fleischauer, PhD, MPH
The following are instructions for public health officials and hospital users to conduct syndromic surveillance on influenza-like illnesses using ESSENCE.
Public Health Surveillance
Bryon Backenson New York State Department of Health.
Epidemiology Tools and Methods Session 2, Part 1.
Disease Surveillance in NYS Jim Miller, MD, MPH Director, Bioterrorism Epidemiology New York State Department of Health
Public Health Surveillance
Additional Data For Harmonized Use Case for Biosurveillance HINF 5430 Final Project By Maria Metty, Priyaranjan Tokachichu &Resty Namata December 13, 2007.
Surveillance Overview Julia Gunn Boston Public Health Commission.
United States Department of Agriculture Food Safety and Inspection Service FSIS Foodborne Illness Investigations: Current Thinking Scott A. Seys, MPH Chief,
Page 1 Texas Department of State Health Services NEDSS Project Office Reengineering the BioSense Data Feed to Support State Notifiable Conditions Reporting.
Influenza-like Illness Surveillance at the National Level
1 ESSENCE: Biosurveillance in Support of the DoD Health Mission.
Successful Alerts and Responses: Real-time Monitoring of ED Chief Complaints and Investigation of Anomalies CDC Public Health Preparedness Conference February.
Queen’s University Public Health Informatics (QPHI) Team Occupational Health Surveillance Tara Donovan QPHI Surveillance Meeting Exploring.
Infectious Disease Epidemiology Surveillance. 9/22/00ANN JOLLY 2 Definition n “Ongoing systematic collection, analysis, and interpretation of health data.
NY State Health Commerce System Integrated Information Systems for Public Health Preparedness, Response and Recovery.
Aaron Kite-Powell, MS Florida Department of Health Bureau of Epidemiology International Society of Disease Surveillance Webinar May 2009 Emergency Department-
INFLUENZA SURVEILLANCE Julie L Freshwater, MPH PhD Influenza Surveillance Coordinator.
TM Emerging Health Threats and Health Information Systems: Getting Public Health and Clinical Medicine to Real Time Response John W. Loonsk, M.D. Associate.
Using Informatics to Promote Community/Population Health
Harmonized Biosurveillance Use Case By Resty Namata, Maria Metty & Priyaranjan Tokachichu December 13, 2007.
Emergency Department Syndromic Surveillance (EDSS): A public health unit perspective alPHa Meeting Feb 1, 2007.
CIFOR Council to Improve Foodborne Outbreak Response CIFOR Guidelines and CIFOR Toolkit Donald J. Sharp, MD, DTM&H Food Safety Office National Center for.
Integrated Health Information Systems for Public Health Preparedness, Planning, Communications, Response & Recovery NY State Health COMMERCE System.
Public Health Laboratory Department of Public Health Ministry of Health National Early Warning Alert Response Surveillance (NEWARS) Sonam.
H1N1 Disease Surveillance Team Project Week 5 Presentation Melody Dungee Beena Joy David Medina Calvin Palmer.
Disease Outbreak Maria del Rosario, MD, MPH Infectious Disease Epidemiology Program WVDHHR/BPH/DSDC February
Severe Acute Respiratory Syndrome (SARS) and Preparedness for Biological Emergencies 27 April 2004 Jeffrey S. Duchin, M.D. Chief, Communicable Disease.
~PPT Howard Burkom 1, PhD Yevgeniy Elbert 2, MSc LTC Julie Pavlin 2, MD MPH Christina Polyak 2, MPH 1 The Johns Hopkins University Applied Physics.
Michigan Disease Surveillance System Syndromic Surveillance Project January 2005.
Severe Acute Respiratory Syndrome (SARS) and Preparedness for Biological Emergencies 27 April 2004 Jeffrey S. Duchin, M.D. Chief, Communicable Disease.
Epi Program Overview Disease Surveillance and Reporting.
A Strategy for Success Creating a regional approach to epidemiological practice & response Emily LlinásKaye Reynolds Kirstin Short.
Correlation of National Influenza Surveillance Data to the Local Experience Kate Goodin, MPH Florida Department of Health Bureau of Epidemiology 6 th Annual.
Infectious Disease Surveillance & National/Health Security Michael A. Stoto CNSTAT Workshop on Vital Data for National Needs April 30, 2008, Washington.
Lesson 3 Page 1 of 24 Lesson 3 Considerations in Planning Public Health Surveillance.
 Exists to serve the community’s interests by providing social conditions in which people maintain health  Describes epidemics and the spread of disease,
The BioSense Influenza Module Craig Hales MD MPH, Roseanne English BS, Paul McMurray MDS, Michelle Podgornik MPH, Jerome Tokars MD MPH Centers for Disease.
Syndromic Surveillance and The Health Alert Network Lex Gibson Epidemiologist Alleghany/Roanoke City Health Districts.
Roles and Responsibilities of VDH Epidemiologists
Flu Update and Overview of Flu Surveillance in RI
Partnerships for Pandemic & Bioterrorism Incidents
What is “Syndromic” Surveillance?
Maria del Rosario, MD, MPH Arianna DeBarr, RN, BSN
One Health Early Warning Alert
Influenza-like Illness Surveillance at the National Level
Public Health Surveillance
Automated Monitoring of Injuries Due to Falls Using the BioSense System Achintya N. Dey, MA1, Jerome I Tokars, MD MPH1, Peter Hicks, MPH2, Matthew Miller,
Presentation transcript:

Syndromic Surveillance in New York State Loretta A. Santilli, MPH James R. Miller, MPH, MD Hwa-Gan Chang, PhD New York State Department of Health Division of Epidemiology Presented February 24, 2005 at the CDC Public Health Preparedness Conference - Atlanta, GA

2 Objectives  Familiarize participants with the goals and specific components of the NYS Syndromic Surveillance System.  Describe unique aspects of the system.  Discuss current limitations and future enhancements.

3 Definitions  Traditional notifiable disease surveillance  Relies on patient seeking medical care, laboratory test being ordered and laboratory/clinician reporting  Reporting lag is typically days to weeks  “Syndromic” surveillance  Tracking non-specific symptoms or health “events” (sale of diarrhea medication)  “Real time” (within hours)

4 Early Syndromic Surveillance, 1665

5 Syndromic Surveillance Goals  Recognize an outbreak due to a natural cause or a terrorist agent earlier than physician, laboratory or citizen reporting.  Monitor general community health – track level of disease.  Provide objective evidence that an outbreak is not occurring.  Help sustain a strong ongoing relationship between public health and clinical medicine.

6 Background  Syndromic surveillance can detect outbreaks.  Syndromic surveillance is a supplement to traditional disease reporting.  Statistically significant signals must be verified clinically to determine public health significance.  Inter-system comparisons may help in the interpretation of a positive signal.  Although a promising approach, there is insufficient experience to evaluate if syndromic surveillance improves public health response.

7 Syndromic Surveillance Systems Operated by NYSDOH (Emergency Department Data) SystemName Data Source/ Participants (No./Type) Collection Method/ Start Date/ Frequency Data Content Analysis Current Output/ Frequency ED Phone Calls Hospital ED & LHD staff 52 counties 157 hospitals Phone calls w/ HIN data entry Nov 2001 Daily Voluntary except during times of heightened concern Unusual events or clusters of illnesses Counts of unusual cases/ clusters with descriptive narrative Internal NYSDOH report Mon-Fri only, Sat-Sun compiled on Mon, Daily during times of heightened concern ED Syndromic Surveillance System Hospital ED 20 hospitals 9 counties Westchester County w/ 12 hospitals is on cusp Electronic batch files via ECLRS* Dec 2003 Daily ED chief complaints categorized into 6 syndromes Resp, GI, Fever, Asthma, Rash, Neuro Counts by syndrome/ hospital, CuSum analysis Counts and pt. lists by syndrome/hospital, statistical analysis & trends - on Commerce for participating counties & hospitals– Daily * Electronic Clinical Laboratory Reporting System

8 Syndromic Surveillance Systems Operated by NYSDOH (Pharmacy Data) SystemName Data Source/ Participants (No./Type) Collection Method/ Start Date/ Frequency Data Content Analysis Current Output/ Frequency NRDM/RODS (National Retail Data Monitor/ Real-time Outbreak and Disease Surveillance) OTC drug sales from 12 major retailers, 20,000 stores Nationwide as of 8/04 Electronic Batch file July 2003 Daily 6,500-8,000 OTC drug sales in NY 15 Categories Counts by category, CuSum analysis Internal NYSDOH report Mon-Fri Signals shared with LHD/RO as necessary Medicaid Over the Counter (OTC) and Prescription Medications Office of Medicaid Management (OMM) Data Warehouse: 22,000-26,000 medications Electronic Batch file March 2003 Daily Medicaid scripts filled in NYS 18 Categories Counts by category, CuSum analysis County and Regional counts by drug category on Commerce Daily Short and long term graphs for signals in past 3 days only

9 Additional Syndromic Surveillance Systems Operated by LHDs (examples)  NYCDOHMH ED syndromic system, 9-1-1, absenteeism, OTC medications  Westchester County ED & outpatient depts.  Suffolk County  HVRHON (Orange & Dutchess Co.), school absenteeism  Erie County EMT transport monitoring  Monroe County ED Census, ILI,  Albany County ED Census

10 National Syndromic Surveillance System: BioSense  Data Sources  Department of Defense Military Treatment Facilities  Veteran’s Administration facilities  National clinical testing labs and nurse hotline data (under development)  Data elements include health syndromes, diagnosis codes, facility name, patient zip code  Part of the multi-department Federal BioSurveillance initiative  BioSense = health surveillance data  BioWatch = air monitoring  BioShield = treatments (vaccines, new drugs, etc.)

11 NYSDOH Operating Principles - 1  LHD participation is essential  Hospital inclusion limited to counties where LHD is an active participant.  LHD has the lead in investigating syndromic signals from hospitals or medication sales within their county.  As appropriate, LHDs will provide additional data/reports regarding syndromic signals from their county to other LHDs or hospitals.

12 NYSDOH Operating Principles - 2  Hospitals submit data in standard format to NYS.  Existing LHD systems will be encouraged to submit data to NYS consistent with NYS standard format.  Daily reports sent back to LHDs and hospitals via Commerce.  Includes reports at county and region levels.

13

14

15

16 County = County Name

17 County = County Name

18

19 Practical Uses of Data: Examples  Pertussis  RNC surveillance  Influenza  Public reassurance

20 NYSDOH Next Steps  Invite additional LHDs and hospitals to participate in syndromic surveillance data submission  Active recruitment at local level by ss coordinator  Promote user access to Syndromic Surveillance Commerce website  Develop step-by-step user’s guide for system  Enhance communication  Ensure user access to all data sets via Commerce website.  Investigate opportunities to interface multiple systems  Develop recommendations for response protocol  Features to consider when determining significance of a signal  Possible actions when investigating a signal

21 NYSDOH Next Steps  Explore disease models associated with various medications  Consider availability of data analyses by zip code, gender, age groups  Conduct validation studies with filters  Compare NYSDOH filters to other systems  Fund expanded/enhanced syndromic surveillance activities

Questions