Syndromic Surveillance

doi:10.1017/CBO9780511902482.013

11 - Syndromic Surveillance

from PART II - OPERATIONAL ISSUES

Published online by Cambridge University Press: 05 August 2011

Gary A. Roselle

Edited by

Kristi L. Koenig and

Carl H. Schultz

Show author details

Kristi L. Koenig: Affiliation:
University of California, Irvine
Carl H. Schultz: Affiliation:
University of California, Irvine

Book contents

Get access

Summary

OVERVIEW

Syndromic surveillance has been defined by the U.S. Centers for Disease Control and Prevention (CDC) as “the collection and analysis of health-related data that precede diagnoses or laboratory confirmation and signal with sufficient probability a case or an outbreak for further public health response.” Based on its original definition, the purpose of syndromic surveillance would be to prevent morbidity and mortality by early identification of case clusters in which mitigation would affect the outcome of the disease's natural course. This original definition was designed for early event detection and became prominent in the public domain after the September 11, 2001 terrorist attacks in the United States and the subsequent anthrax illnesses and deaths.

With a heightened sense of urgency related to the so-called “war on terror,” many systems were put into place within the United States for the protection of the public health. These included such diverse programs as vaccine initiatives (BioShield), static detectors located throughout large cities to identify specific organisms of interest in the air (BioWatch), and the beginning of a national syndromic surveillance system for early detection of outbreaks (BioSense). These three initiatives were designed for the following reasons, respectively: 1) prevention of disease if a terrorist attack occurred; 2) early identification of airborne pathogens during the asymptomatic phase of such disease; and 3) early identification of illness prior to definitive diagnosis that would be confirmed either by culture or laboratory tests.

Keywords

data integration mathematical data analysis nonhuman data U.S. surveillance systems public health authorities syndromic surveillance system

Information

Type: Chapter
Information: Koenig and Schultz's Disaster Medicine
Comprehensive Principles and Practices
, pp. 165 - 173
DOI: https://doi.org/10.1017/CBO9780511902482.013 [Opens in a new window]
Publisher: Cambridge University Press
Print publication year: 2009

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Book purchase

Temporarily unavailable

References

http://www.eurosurveillance.org/ViewArticle.aspx?ArticleId=667. Accessed November 29, 2008.

http://www.css.drdc-rddc.gc.ca/symposium/symposium/2008/06-0234TA-eng.asp. Accessed November 29, 2008.

http://www.chi.unsw.edu.au/CHIweb.nsf/pageprintfriendly/Syndromic%20Surveillance?opendocument. Accessed November 29, 2008.

http://www.invs.sante.fr/agenda/syndromic_surveillance_eu/information.htm. Accessed November 29, 2008.

http://www.eurosurveillance.org/ViewArticle.aspx?ArticleId=668. Accessed November 29, 2008.

http://www.ncbi.nlm.nih.gov/pubmed/16796513. Accessed November 29, 2008.

http://www.cdc.gov/mmwr/pdf/wk/mm54su01.pdf, pg 47. Accessed November 29, 2008.

http://www.biomedcentral.com/1471-2458/8/18. Accessed November 29, 2008.

Bravata, D, McDonald, K, Smith, W, et al. Systematic review: surveillance systems for early detection of bioterrorism-related diseases. Ann Intern Med. 2004;140(11):910–922.Google Scholar

Green, M, Kaufman, Z. Surveillance for early detection and monitoring of infectious disease outbreaks associated with bioterrorism. Isr Med Assoc J. 2002:4(7):503–506.Google Scholar

Irvin, C, Nouhan, P, Rice, K. Syndromic analysis of computerized emergency department patients' chief complaints: an opportunity for bioterrorism and influenza surveillance. Ann Emerg Med. 2003;41(4):447–452.Google Scholar

Begier, E, Sockwell, D, Branch, L, et al. The National Capitol Region's Emergency Department Syndromic Surveillance System: do chief complaint and discharge diagnosis yield different results? Emerg Infect Dis. 2003;9(3):393–396.Google Scholar

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(2Suppl 1):i25–i31.Google Scholar

Lober, W, 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.Google Scholar

Buehler, J, Berkelman, R, Hartley, D, Peters, C. Syndromic surveillance and bioterrorism-related epidemics. Emerg Infect Dis. 2003;9(10):1197–1204.Google Scholar

,Centers for Disease Control and Prevention. What is syndromic surveillance? MMWR. 2004;53(Suppl):7–11.Google Scholar

,Centers for Disease Control and Prevention. New York City syndromic surveillance systems. MMWR. 2004;53(Suppl):25–27.Google Scholar

,Centers for Disease Control and Prevention. Progress in understanding and using over-the-counter pharmaceuticals for syndromic surveillance. MMWR. 2004;53(Suppl):117–122.Google Scholar

,Centers for Disease Control and Prevention. Use of Medicaid prescription data for syndromic surveillance – New York. MMWR. 2005;54(Suppl):31–4.Google Scholar

,Centers for Disease Control and Prevention. Poison control center-based syndromic surveillance for foodborne illness. MMWR. 2005;54(Suppl):35–40.Google Scholar

,Centers for Disease Control and Prevention. Monitoring over-the-counter medication sales for early detection of disease outbreaks – New York City. MMWR. 2005;54(Suppl):41–46.Google Scholar

,Centers for Disease Control and Prevention. Experimental surveillance using data on sales of over-the-counter medications – Japan, November 2003–April 2004. MMWR. 2005;54(Suppl):47–52.

,Centers for Disease Control and Prevention. Increased antiviral medication sales before the 2005–06 influenza season – New York City. MMWR. 2006;55(10):277–279.Google Scholar

Vergu, E, Grais, R, Sarter, H, et al. Medication sales and syndromic surveillance, France. Emerg Infect Dis. 2006;12(3):416–421.Google Scholar

Hope, K, Durrheim, DN, d'Espaignet, ET, Dalton, C. 2006. Syndromic surveillance: is it a useful tool for local outbreak detection? J Epidemiol Community Health. 60:374–375.Google Scholar

Chapman, W, Christensen, L, Wagner, M, et al. Classifying free-text triage chief complaints into syndromic categories with natural language processing. Artif Intell Med. 2005;33(1):1–10.Google Scholar

,Centers for Disease Control and Prevention. Taming variability in free text: application to health surveillance. MMWR. 2004;53(Suppl):95–100.Google Scholar

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.Google Scholar

,Centers for Disease Control and Prevention. ESSENCE II and the framework for evaluating syndromic surveillance systems. MMWR. 2004;53(Suppl):159–165.Google Scholar

Vourc'h, G, Bridges, V, Gibbens, J, et al. Detecting emerging diseases in farm animals through clinical observations. Emerg Infect Dis. 2006;12(2):204–210.Google Scholar

,Centers for Disease Control and Prevention. Information system architectures for syndromic surveillance. MMWR. 2004;53(Suppl):203–208.Google Scholar

Forslund, D, Joyce, E, Burr, T, et al. Setting standards for improved syndromic surveillance. IEEE Eng Med Biol Mag. 2004;23(1):65–70.Google Scholar

Mandl, K, Overhage, JM, Wagner, M, et al. Implementing syndromic surveillance: a practical guide informed by the early experience. J Am Med Inform Assoc. 2004;11(2):141–150.Google Scholar

Reis, B, Mandl, K. Time series modeling for syndromic surveillance. BMC Med Inform Decis Making. 2003;3:2.Google Scholar

Kleinman, K, Lazarus, R, Platt, R. A generalized linear mixed models approach for detecting incident clusters of disease in small areas, with an application to biological terrorism. Am J Epidemiol. 2004;159(3):217–224.Google Scholar

Reis, B, Mandl, K. Syndromic Surveillance: The effects of syndrome grouping on model accuracy and outbreak detection. Ann Emerg Med. 2004;44(3):235–241.Google Scholar

Feinberg, S, Shmueli, G. Statistical issues and challenges associated with rapid detection of bio-terrorist attacks. Statist Med. 2005;24:513–529.Google Scholar

Hutwagner, L, Thompson, W, Seeman, G, Treadwell, T. A simulation model for assessing aberration detection methods used in public health surveillance for systems with limited baselines. Statist Med. 2005;24:543–550.Google Scholar

,Centers for Disease Control and Prevention. Bivariate method for spatio-temporal syndromic surveillance. MMWR. 2004;53(Suppl);61–66.Google Scholar

,Centers for Disease Control and Prevention. Role of data aggregation in biosurveillance detection strategies with applications from ESSENCE. MMWR. 2004;53(Suppl):67–73.Google Scholar

,Centers for Disease Control and Prevention. Scan statistics for temporal surveillance for biologic terrorism. MMWR. 2004;53(Suppl):74–78.Google Scholar

,Centers for Disease Control and Prevention. Approaches to syndromic surveillance when data consist of small regional counts. MMWR. 2004;53(Suppl):79–85.Google Scholar

,Centers for Disease Control and Prevention. Measuring outbreak-detection performance by using controlled feature set simulations. MMWR. 2004;53(Suppl):130–136.Google Scholar

,Centers for Disease Control and Prevention. Benchmark data and power calculations for evaluating disease outbreak detection methods. MMWR. 2004;53(Suppl):144–151.Google Scholar

Kleinman, K, Abrams, A, Kulldorff, M, Platt, R. A model-adjusted space-time scan statistic with an application to syndromic surveillance. Epidemiol Infect. 2005;133(3):409–419.Google Scholar

,Centers for Disease Control and Prevention. Use of multiple data streams to conduct Bayesian biologic surveillance. MMWR. 2005;54(Suppl):63–69.Google Scholar

,Centers for Disease Control and Prevention. Deciphering data anomalies in BioSense. MMWR. 2005;54(Suppl):133–139.Google Scholar

Najmi, A-H, Magruder, S. An adaptive prediction and detection algorithm for multistream syndromic surveillance. BMC Med Inform Decis Making. 2005;5:33.Google Scholar

,Centers for Disease Control and Prevention. Syndromic surveillance for bioterrorism following the attacks on the World Trade Center – New York City, 2001. MMWR. 2002;51(SI):13–15.Google Scholar

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.Google Scholar

Gesteland, P, Gardner, R, Tsui, F-C, et al. Automated syndromic surveillance for the 2002 Winter Olympics. J Am Med Inform Assoc. 2003;10(6):547–554.Google Scholar

,Centers for Disease Control and Prevention. Surveillance for early detection of disease outbreaks at an outdoor mass gathering – Virginia, 2005. MMWR. 2006;55(3):71–74.Google Scholar

Muscatello, D, Churches, T, Kaldor, J, et al. An automated, broad-based, near real-time public health surveillance system using presentations to hospital emergency departments in New South Wales, Australia. BMC Public Health. 2005;5:141.Google Scholar

Marx, M, Rodriguez, C, Greenko, J, et al. Diarrheal illness detected through syndromic surveillance after a massive power outage: New York City, August 2003. Am J Pub Health. 2006;96(3):547–553.Google Scholar

,Centers for Disease Control and Prevention. Syndromic surveillance at hospital emergency departments – southeastern Virginia. MMWR. 2004;53(Suppl):56–58.Google Scholar

,Centers for Disease Control and Prevention. Hospital admissions syndromic surveillance – Connecticut, October 2001-June 2004. MMWR. 2005;54(Suppl):169–173.Google Scholar

,Centers for Disease Control and Prevention. Framework for evaluating public health surveillance systems for early detection of outbreaks. MMWR. 2004;53(No. RR-5):1–11.Google Scholar

Mostashari, F, Hartman, J. Syndromic surveillance: a local perspective. J Urban Health. 2003;80(2 Suppl 1):i1–i7.Google Scholar

,Centers for Disease Control and Prevention. High-fidelity injection detectability experiments: a tool for evaluating syndromic surveillance systems. MMWR. 2005;54(Suppl):85–91.Google Scholar

,Centers for Disease Control and Prevention. Initial evaluation of the early aberration reporting system – Florida. MMWR. 2005;54(Suppl):123–130.Google Scholar

,Centers for Disease Control and Prevention. Evaluation of syndromic surveillance based on National Health Service direct derived data – England and Wales. MMWR. 2005;54(Suppl):117–122.Google Scholar

,Centers for Disease Control and Prevention. An evaluation model for syndromic surveillance: assessing the performance of a temporal algorithm. MMWR. 2005;54(Suppl):109–115.Google Scholar

,Centers for Disease Control and Prevention. Simulation for assessing statistical methods of biologic terrorism surveillance. MMWR. 2005;54(Suppl):101–108.Google Scholar

Stoto, M, Schonlau, M, Mariano, L. Syndromic surveillance: is it worth the effort? CHANCE. 2004;17(1):19–24.Google Scholar

Chapman, WW, Dowling, JN, Wagner, MM. Classification of emergency department chief complaints into 7 syndromes: a retrospective analysis of 527, 288 patients. Ann Emerg Med. 2005;46(5):445–455.Google Scholar

Shih, F-Y, Yen, M-Y, Wu, J-S, et al. Challenges faced by hospital healthcare workers in using a syndrome-based surveillance system during the 2003 outbreak of severe acute respiratory syndrome in Taiwan. Infect Control Hosp Epidemiol. 2007;28(3):354–357.Google Scholar

Turner, K, Shaw, K, Coleman, D, Misrachi, A. Augmentation of influenza surveillance with rapid antigen detection at the point-of-care: results of a pilot study in Tasmania, 2004. Commun Dis Intell. 2006;30(2):201–204.Google Scholar

Accessibility standard: Unknown

Why this information is here

This section outlines the accessibility features of this content - including support for screen readers, full keyboard navigation and high-contrast display options. This may not be relevant for you.

Accessibility Information

Accessibility compliance for the PDF of this chapter is currently unknown and may be updated in the future.