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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.

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Presentation on theme: "Lessons Learned from the National Syndromic Surveillance Conference Sponsored by the Centers for Disease Control and Prevention NYC Department of Health."— Presentation transcript:

1 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

2 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

3  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?

4 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

5 Goals  Early detection of large outbreaks  Characterization of size, spread, and tempo of outbreaks once detected  Monitoring of disease trends

6  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

7  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

8 Data Transfer Emergency DepartmentEMSPharmacy

9 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

10 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

11 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;

12 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

13 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

14 Data Summary EDEMS Syndromes“ILI”Respiratory Febrile GI Detection Limit (city-wide) ~50 calls~100 visits Detection Limit (localized) ~10 calls10-20 visits

15 Denominator Surveillance is Less Sensitive than Syndromic Total Visits Fever/Respiratory GI/ Vomiting

16

17 Selected Antibiotic and Antiviral Prescriptions 1997-2002

18 ED Respiratory Visits, Nov-May

19 EMS calls Subway worker- “flu” ED respiratory visits Pharmacy Antiviral Rx

20 West Nile Virus Activity Through September 2001

21 Tabletop Drills REDEX (2001) Test of 911-EMS System SANDBOX (2002) Test of ED System

22 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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24 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

25 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)

26 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)

27 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

28 “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)

29 Cipro and Doxycycline Prescriptions

30 Drug Overdose  Epidemiology of drug overdoses  Detection of outbreaks Sat Fri Day of Month Day of Week

31 New Year’s

32 Suicidal Ideation/Attempts Nov. 2001 to Sept. 19, 2002

33 Asthma ED Visits and EMS Calls

34 Improvement in Asthma Treatment

35

36 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

37 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

38 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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