Journal of Urban Health

, Volume 80, Supplement 1, pp i1–i7 | Cite as

Syndromic surveillance: A local perspective

Editorial

Conclusions

The promise of syndromic surveillance extends beyond early warning for bioterrorist attacks. Even if bioterrorism is first detected by an astute clinician, syndromic surveillance can help delineate the size, location, and tempo of the epidemic or provide reassurance that a large outbreak is not occurring when a single case or a small, localized cluster of an unusual illness is detected. More broadly, however, as public health and medicine proceed in our information age, the use of existing electronic data for public health surveillance will not appear to be an untested experiment for long. The challenge is to allow these systems to flower without burdening them with unrealistic expectations, centralized control, and unbalanced funding. To help syndromic surveillance systems reach their full potential, we need data standards, guidance to the developers of clinical information systems that will ensure data flow and interoperability, evaluations of best practices, links to improved laboratory diagnostics, regulations that protect privacy and data security, and reliable sustained funding for public health infrastructure to ensure the capacity to respond when the alarm sounds.

Referneces

  1. 1.
    Das D, Weiss D, Mostashari F, et al., Enhanced drop-in syndromic surveillance in New York City following September 11, 2001.J Urban Health. 2003;80(2, suppl 1):i76–i88.PubMedGoogle Scholar
  2. 2.
    Newton E. Video and Click-Stream Data as Surveillance Tools. Presented at: The First National Syndromic Surveillance Conference, September 23–24, 2002; New York, NY.Google Scholar
  3. 3.
    Lawson A. Temporal-Spatial Methodologies and Data Sources. Presented at: The First National Syndromic Surveillance Conference, September 23–24, 2002; New York, NY.Google Scholar
  4. 4.
    Hutwagner L, Thompson W, Seeman GM, Treadwell T. The bioterrorism preparedness and response Early Aberration Reporting Systems (EARS).J Urban Health. 2003;80(2, suppl 1):i89–96.PubMedGoogle Scholar
  5. 5.
    Mostashari F, Fine A, Das D, Adams J, Layton M. Use of ambulance dispatch data as an early warning system for communitywide influenzalike illness, New York City.J Urban Health. 2003;80(2, suppl 1):i43–i49.PubMedGoogle Scholar
  6. 6.
    Wong W-K, Moore A, Cooper G, Wagner M. WSARE: what’s strange about recent events?J Urban Health. 2003;80(2, suppl 1):i66–i75.PubMedGoogle Scholar
  7. 7.
    Burkom HS. Biosurveillance applying scan statistics with multiple, disparate data sources.Journal of Urban Health. 2003;80(2, suppl 1):i57–i65.PubMedGoogle Scholar
  8. 8.
    Tsui FC, Wagner MM, Dato V, Chang CC. Value ofICD-9 coded chief complaints for detection of epidemics.Proc AMIA Symp. 2001;711–715.Google Scholar
  9. 9.
    Lazarus R, Kleinman K, Dashevsky I, et al., Use of automated ambulatory-care encounter records for detection of acute illness clusters, including potential bioterrorism events.Emerg Infect Dis. 2002;8:753–760.PubMedGoogle Scholar
  10. 10.
    Goldenberg A, Shmueli G, Caruana RA, Fienberg SE. Early statistical detection of anthrax outbreaks by tracking over-the-counter medication sales.Proc Natl Acad Sci USA. 2002;99:5237–5240.CrossRefPubMedGoogle Scholar
  11. 11.
    Lombardo J, Burkom H, Elbert E, et al. A systems overview of the Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE II).J Urban Health. 2003;80(2, suppl 1):i32–i42.PubMedGoogle Scholar
  12. 12.
    Carley K Yahja A, Kaminsky B. BIOWAR city-scale multi-agent network model of bioattacks. Available at: www.casos.ece.cmu.edu/projects/BioWar/BioWarPoster.ppt. Accessed January 31, 2003.Google Scholar
  13. 13.
    Sosin DM. Draft framework for evaluating syndromic surveillance systems.J Urban Health. 2003;80(2, suppl 1):i8–i13.PubMedGoogle Scholar
  14. 14.
    Greenko J, Mostashari F, Fine A, Layton M. Clinical evaluation of the emergency medical services (EMS) ambulance dispatch-based syndromic surveillance system, New York City.J Urban Health. 2003;80(2, suppl 1):i50–i56.PubMedGoogle Scholar
  15. 15.
    Fawcett T, Provost F. Activity monitoring: noticing interesting changes in behavior. Paper presented at: Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; August 15–18, 1999; San Diego, CA.Google Scholar
  16. 16.
    Weiss D. The Public Health Response to Syndromic Surveillance Signals. Presented at: The First National Syndromic Surveillance Conference; September 23–24, 2002; New York, NY.Google Scholar
  17. 17.
    Duchin JS. Epidemiological response to syndromic surveillance signals.J Urban Health. 2003;80(2, suppl 1):i115–i116.PubMedGoogle Scholar
  18. 18.
    Pavlin JA. Investigation of disease outbreaks detected by “syndromic” surveillance systems.J Urban Health. 2003;80(2, suppl 1):i107–i114.PubMedGoogle Scholar
  19. 19.
    Platt R, Bocchino C, Caldwell B, et al., Syndromic surveillance using minimum transfer of identifiable data: The example of the National Bioterrorism Syndromic Surveillance Demonstration Program.J Urban Health 2003;80(2, suppl 1):i25–i31.PubMedGoogle Scholar
  20. 20.
    Lober WB, Trigg L, Karras B, et al. Syndromic surveillance using automated collection of computerized discharge diagnoses.J Urban Health. 2003;80(2, suppl 1):i97–i106.PubMedGoogle Scholar
  21. 21.
    Broome CV, Horton HH, Tress D, Lucido SJ, Koo D. Statutory basis for public health reporting beyond specific diseases.J Urban Health. 2003;80(2, suppl 1):i14–i22.PubMedGoogle Scholar
  22. 22.
    Sidel VW, Gould RM, Cohen HW. Bioterrorism preparedness: cooptation of public health?Med Global Surviv. 2002;7:82–89.Google Scholar
  23. 23.
    Norovirus activity—United States, 2002.MMWR Morb Mortal Wkly Rep. 2003;52:41–44.Google Scholar

Copyright information

© The New York Academy of Medicine 2003

Authors and Affiliations

  1. 1.Bureau of Epidemiological ServicesNew York City Department of Health and Mental HygieneNew York
  2. 2.New York Academy of MedicineNew YorkUSA

Personalised recommendations