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Lessons Learned from the National Syndromic Surveillance Conference Sponsored by the Centers for Disease Control and Prevention NYC Department of Health and Mental Hygiene New York Academy of Medicine September 23-24, 2002 New York City
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What is Syndromic Surveillance? “Passive” Systems Minimal burden Designed to detect and monitor large # usual/mild illnesses “Active” Systems- Educational Outreach Tool Designed to detect and report small # unusual/severe syndromes
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Public Health Practice Local health officers shall exercise due diligence in ascertaining the existence of outbreaks of illness or the unusual prevalence of diseases, and shall immediately investigate the causes of same New York State Sanitary Code, 10 NYCRR Chapter 1, Section 2.16(a) Research & Development Non-traditional data sources Academia (training) & contractors Authorized agents of public health departments Legal Mandate: Who Should be Doing This?
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Privacy and Confidentiality Health departments have strong tradition of maintaining security of confidentiality information Public health provisions in HIPAA Data collected under auspices of bioterrorism surveillance de-linked from any identifiers for non-BT surveillance
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Goals Early detection of large outbreaks Characterization of size, spread, and tempo of outbreaks once detected Monitoring of disease trends
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Day 1- feels fine Day 2- headaches, fever- buys Tylenol Day 3- develops cough- calls nurse hotline Day 4- Sees private doctor: “flu” Day 5- Worsens- calls ambulance seen in ED Day 6- Admitted- “pneumonia” Day 7- Critically ill- ICU Day 8- Expires- “respiratory failure” Potential Syndromic Surveillance Data Sources
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Day 1- feels fine Day 2- headaches, fever- buys Tylenol Day 3- develops cough- calls nurse hotline Day 4- Sees private doctor: “flu” Day 5- Worsens- calls ambulance seen in ED Day 6- Admitted- “pneumonia” Day 7- Critically ill- ICU Day 8- Expires- “respiratory failure” Pharmaceutical Sales Nurse’s Hotline Managed Care Org Ambulance Dispatch (EMS) ED Logs Absenteeism Potential Syndromic Surveillance Data Sources Traditional Surveillance
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Data Transfer Emergency DepartmentEMSPharmacy
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Data requirements Core variables Hospital name Date of visit Time of visit Age Sex Chief complaint (free text) Home zip code +/- Unique identifier Discharge diagnosis not generally available in timely manner Need to consider response protocols – patient identification, logistics
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Electronic coding of chief complaints into clinical syndromes Performed in SAS Text-string recognition Mutually exclusive vs. overlapping Hierarchy of coding Iterative refinement of syndrome definition Entire dataset can be recoded easily – allows for changes in definition and addition of new syndromes
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Electronic ED logs AGE SEX TIME CHIEF COMPLAINT ZIP 15 M 01:04 ASSAULTED YESTERDAY, RT EYE REDDENED.11691 1 M 01:17 FEVER 104 AS PER MOTHER. 11455 42 F 03:20 11220 4 F 01:45 FEVER, COUGH, LABORED BREATHING. 11507 62 F 22:51 ASTHMA ATTACK. 10013 48 M 13:04 SOB AT HOME. 10027 26 M 06:02 C/O DIFFICULTY BREATHING. 66 M 17:01 PT. MOTTLED AND CYANOTIC. 10031 Text Recognition with SAS IFindex(cc,"FEV")>0 orindex(cc,"HIGH TEMP")>0 orindex(cc,"NIGHT SWEAT")>0 or(index(cc,"CHILL")>0 and index(cc,"ACHILLES")=0) orindex(cc,"780.6") etc. then FEVER=1;
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Data Summary EDPharmacyEMS Syndromic Grouping Call-TypeChief Complaint Drug Class Geographic Grouping Pickup ZipHome Zip Hospital Store Zip Other Information Age Gender Follow-up Possible Yes
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Data Summary EDPharmacyEMS Daily Volume~ 3,000 calls ~6,500 visits ~6,000 Rx ~26,000 OTC Coverage>95%65-70%~30% Prospective Data Collection March 1998October 2001 August 2002 Analytic Methods Cyclical Regression Scan Statistic CUSUM Scan Statistic In development
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Data Summary EDEMS Syndromes“ILI”Respiratory Febrile GI Detection Limit (city-wide) ~50 calls~100 visits Detection Limit (localized) ~10 calls10-20 visits
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Denominator Surveillance is Less Sensitive than Syndromic Total Visits Fever/Respiratory GI/ Vomiting
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Selected Antibiotic and Antiviral Prescriptions 1997-2002
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ED Respiratory Visits, Nov-May
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EMS calls Subway worker- “flu” ED respiratory visits Pharmacy Antiviral Rx
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West Nile Virus Activity Through September 2001
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Tabletop Drills REDEX (2001) Test of 911-EMS System SANDBOX (2002) Test of ED System
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Nov 12 9.17 am Flight AA 587 Crashes in Rockaways Respiratory Zip Code Signal (7 zips) 27 Observed / 10 Expected p<0.001 Hospital Signal 31 Observed/ 16 Expected p<0.05
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Investigation Key Questions True increase or natural variability? Bioterrorism or self-limited illness? Available Methods “Drill down” Query clinicians/ laboratories Chart reviews Patient followup Increased diagnostic testing
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Investigation Checked same-day logs at 2 hospitals Increase not sustained Chart review in one hospital (9 cases) Smoke Inhalation (1 case) Atypical Chest Pain/ Anxious (2 cases) Shortness of Breath- “Psych” (1 case) Asthma Exacerbation (3 cases) URI/LRI (2 cases)
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Future Directions Research Agenda More evaluations- Simulation models and “spiked” validation datasets Better cluster detection software Signal Integration Optimizing response protocols- Inexpensive (and accurate) rapid diagnostics Emergency Department Surveillance Chief Complaint and/or Discharge Diagnosis HL7 Standards Need standard cc->syndrome coder (SAS)
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Is It Worth the Effort? Costs Implementation costs can be modest Operational costs=time of public health staff, investigations Benefits Possibility of huge benefit if early detection Characterization Strengthening traditional surveillance Dual Use
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“Dual Use” Opportunity to use new syndromic surveillance infrastructure other public health activities as well as for bioterror events Can enhance all public health efforts Sets higher standard for all surveillance (e.g., laboratory)
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Cipro and Doxycycline Prescriptions
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Drug Overdose Epidemiology of drug overdoses Detection of outbreaks Sat Fri Day of Month Day of Week
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New Year’s
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Suicidal Ideation/Attempts Nov. 2001 to Sept. 19, 2002
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Asthma ED Visits and EMS Calls
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Improvement in Asthma Treatment
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So What? Strengthened surveillance systems in place Potential to better monitor all public health situations Even if there are no more bioterror attacks, preparation can strengthen our public health infrastructure and ability to respond “Syndromic” surveillance vs. better surveillance
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Acknowledgements NYCDOH Syndromic Surveillance Team: Joel Ackelsberg Sharon Balter Katie Bornschlegel Bryan Cherry Hyunok Choi Debjani Das Jessica Hartman Rick Heffernan Adam Karpati Marci Layton Jennifer Leng Karen Levin Mike Phillips Sudha Reddy Rich Rosselli Polly Thomas Don Weiss Field teams MIS staff
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Spatial Scan Statistic Developed by Martin Kulldorff Flexible windows in time and space Probability through Monte Carlo simulations Controls for multiple comparisons Modified for infectious disease surveillance
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