Skip to main content
Journal of Urban Health : Bulletin of the New York Academy of Medicine logoLink to Journal of Urban Health : Bulletin of the New York Academy of Medicine
. 2003 Mar;80(Suppl 1):i89–i96. doi: 10.1007/PL00022319

The bioterrorism preparedness and response Early Aberration Reporting System (EARS)

Lori Hutwagner 1,, William Thompson 2, G Matthew Seeman 1, Tracee Treadwell 1
PMCID: PMC3456557  PMID: 12791783

Abstract

Data from public health surveillance systems can provide meaningful measures of population risks for disease, disability, and death. Analysis and evaluation of these surveillance data help public health practitioners react to important health events in a timely manner both locally and nationally. Aberration detection methods allow the rapid assessment of changes in frequencies and rates of different health outcomes and the characterization of unusual trends or clusters.

The Early Aberration Reporting System (EARS) of the Centers for Disease Control and Prevention allows the analysis of public health surveillance data using available aberration detection methods. The primary purpose of EARS is to provide national, state, and local health departments with several alternative, aberration detection methods. EARS helps assist local and state health officials to focus limited resources on appropriate activities during epidemiological investigations of important public health events. Finally, EARS allows end users to select validated aberration detection methods and modify sensitivity and specificity thresholds to values considered to be of public health importance by local and state health departments.

Keywords: Aberration detection, Centers for Disease Control and Prevention, CUSUM

Full Text

The Full Text of this article is available as a PDF (104.4 KB).

References

  • 1.Langmuir AD. The surveillance of communicable diseases, of national importance. N Engl J Med. 1963;268:182–192. doi: 10.1056/NEJM196301242680405. [DOI] [PubMed] [Google Scholar]
  • 2.Thacker SB, Berkelman RL. Public health surveillance in the United States. Epidemiol Rev. 1988;10:164–190. doi: 10.1093/oxfordjournals.epirev.a036021. [DOI] [PubMed] [Google Scholar]
  • 3.Thacker SB, Berkelman RL, Stroup DF. The science of public health surveillance. J Public Health Policy. 1989;10:187–203. [PubMed] [Google Scholar]
  • 4.Teutsch SM, Churchill RE, editors. Principles and Practice of Public Health Surveillance. New York, NY: Oxford University Press; 2000. [Google Scholar]
  • 5.Centers for Disease Control and Prevention Guidelines for investigating clusters of health events. MMWR Morb Mortal Wkly Rep. 1990;39(RR-11):1–16. [PubMed] [Google Scholar]
  • 6.Centers for Disease Control and Prevention Guidelines for investigating clusters of health events. Appendix, Summary of methods for statistically assessing cluster of health events. MMWR Morb Mortal Wkly Rep. 1990;39(RR-11):17–23. [PubMed] [Google Scholar]
  • 7.Hutwagner LC, Maloney EK, Bean NH, Slutsker L, Martin SM. Using laboratory-based surveillance data for prevention: an algorithm for detectingSalmonella outbreaks. Emerg Infect Dis. 1997;3:395–400. doi: 10.3201/eid0303.970322. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Stroup DF, Williamson GD, Herndon JL, Karon J. Detection of aberrations in the occurrence of notifiable diseases surveillance data. Stat Med. 1989;8:323–329. doi: 10.1002/sim.4780080312. [DOI] [PubMed] [Google Scholar]
  • 9.Banks J. Principles of Quality Control. New York, NY: John Wiley and Sons; 1985. [Google Scholar]
  • 10.Farrington CP, Andrews NJ, Beale AD, Catchpole MA. A statistical algorithm for the early detection of outbreaks of infectious disease. J Roy Stat Soc A. 1996;159:547–563. [Google Scholar]
  • 11.Simonsen L, Clarke JM, Stroup DF, Williamson GD, Arden NH, Cox NJ. A method for timely assessment of influenza-associated mortality in the United States. Epidemiology. 1997;8:390–395. doi: 10.1097/00001648-199707000-00007. [DOI] [PubMed] [Google Scholar]
  • 12.Stern L, Lightfoot D. Automated outbreak detection: a quantitative retrospective analysis. Epidemiol Infect. 1999;122:103–110. doi: 10.1017/S0950268898001939. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Hutwagner L, Thompson W, Groseclose S, Williamson GD. An evaluation of alternative methods for detecting aberrations in public health surveillance data.American Statistical Association 2000 Proceedings of the Biometrics Section. 82–85.
  • 14.Centers for Disease Control and Prevention Proposed changes in format for presentation of notifiable disease report data. MMWR Morb Mortal Wkly Rep. 1989;38:805–809. [PubMed] [Google Scholar]
  • 15.Centers for Disease Control and Prevention Update: graphic method for presentation of notifiable disease data—United States 1990. MMWR Morb Mortal Wkly Rep. 1991;40:124–125. [PubMed] [Google Scholar]
  • 16.Rao CR. Statistic and Truth: Putting Chance to Work. Fairland, MD: International Co-Operative Publishing House; 1989. [Google Scholar]
  • 17.Stoumbos ZG, Reynolds MR, Ryan TP, Woodall WH. The state of statistical process control as we proceed into the 21st century. J. Am Stat Assoc. 2000;95:992–998. doi: 10.2307/2669484. [DOI] [Google Scholar]
  • 18.Centers for Disease Control and Prevention Syndromic surveillance for bioterrorism following the attacks on the World Trade Center, New York City, 2001. MMWR Morb Mortal Wkly Rep. 2002;51:13–15. [PubMed] [Google Scholar]

Articles from Journal of Urban Health : Bulletin of the New York Academy of Medicine are provided here courtesy of New York Academy of Medicine

RESOURCES