Abstract
The Electronic Surveillance System for the Early Notification of Community-Based Epidemics, or ESSENCE II, uses syndromic and nontraditional health information to provide very early warning of abnormal health conditions in the National Capital Area (NCA). ESSENCE II is being developed for the Department of Defense Global Emerging Infections System and is the only known system to combine both military and civilian health care information for daily outbreak surveillance. The National Capital Area has a complicated, multijurisdictional structure that makes data sharing and integrated regional surveillance challenging. However, the strong military presence in all jurisdictions facilitates the collection of health care information across the region. ESSENCE II integrates clinical and nonclinical human behavior indicators as a means of identifying the abnormality as close to the time of onset of symptoms as possible. Clinical data sets include emergency room syndromes, private practice billing codes grouped into syndromes, and veterinary syndromes. Nonclinical data include absenteeism, nurse hotline calls, prescription medications, and over-the-counter self-medications. Correctly using information marked by varying degrees of uncertainty is one of the more challenging as pects of this program. The data (without personal identifiers) are captured in an electronic format, encrypted, archived, and processed at a secure facility. Aggregated information is then provided to users on secure Web sites. When completed, the system will provide automated capture, archiving, processing, and notification of abnormalities to epidemiologists and analysts. Outbreak detection methods currently include temporal and spatial variations of odds ratios, autoregressive modeling, cumulative summation, matched filter, and scan statistics. Integration of nonuniform data is needed to increase sensitivity and thus enable the earliest notification possible. The performance of various detection techniques was compared using results obtained from the ESSENCE II system.
Keywords: Evaluation, Nontraditional, Surveillance, Syndromes, Test bed
Full Text
The Full Text of this article is available as a PDF (339.5 KB).
References
- 1.Pavlin JA. Rapid detection of disease outbreaks.Army AL&T. November–December 2001;47–48.
- 2.Eidson M, Komar N, Sorhage F, et al. Crow deaths as a sentinel surveillance system for West Nile Virus in the northeastern United States, 1999. Emerg Infect Dis. 2001;7:615–620. doi: 10.3201/eid0704.010402. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Sweeney L. Guaranteeing anonymity when sharing medical data, the datafly system. Proceedings, American Medical Informatics Association. 1997;4:51–55. [PMC free article] [PubMed] [Google Scholar]
- 4.Franz DR, Jahrling PB, Friedlander AM, et al. Clinical recognition and management of patients exposed to biological warfare agents. JAMA. 1997;278:399–411. doi: 10.1001/jama.278.5.399. [DOI] [PubMed] [Google Scholar]
- 5.Magruder SF. Evaluation of potential data sources for surveillance. Poster presentation at: National Syndromic Surveillance Conference, New York Academy of Medicine; September 23–24, 2002; New York, NY.
- 6.Sari JW. Lagged Correlations Between Weather, Product Sales, Emergency Room Visits, and Claim Syndrome Groups in the Baltimore-Washington areas for 1999–2001. Laurel, MD: The Johns Hopkins University Applied Physics Laboratory; 2002. [Google Scholar]
- 7.Wojcik RA. Automated Data Ingestion. Laurel, MD: The Johns Hopkins University Applied Physics Laboratory; 2002. [Google Scholar]
- 8.Sniegoski CA. Free Text Chief Complaint Text Processing. Laurel, MD: The Johns Hopkins University Applied Physics Laboratory; 2002. [Google Scholar]
- 9.Elbert E, Burkom HS. Temporal alerting algorithm methodology for ESSENCE syndromic surveillance data. Poster presentation at: National Syndromic Surveillance Conference, New York Academy of Medicine; September 23–24, 2002; New York, NY.
- 10.Gorgis L. Epidemiology. 2nd ed. Philadelphia, PA: W. B. Saunders Co.; 2000. [Google Scholar]
- 11.Gallant AR, Goebel JJ. Nonlinear regression with autocorrelation errors. J Am Stat Assoc. 1976;71:961–967. doi: 10.2307/2286869. [DOI] [Google Scholar]
- 12.Tillett HE, Spencer IL. Influenza surveillance in England and Wales using routine statistics. J Hyg Camb. 1982;88:83–94. doi: 10.1017/s0022172400069928. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Burkom HS, Lombardo JS, Newhall BK, et al. Automated Alerting for Bioterrorism Using Autonomous Agents. Laurel, MD: The Johns Hopkins University Applied Physics Laboratory; 2001. [Google Scholar]
- 14.Lawson B, Fitzhugh E, Hall S, Garcia M, Hutwagner L, Seeman GM. Implementing the CDC Early Aberration Reporting System (EARS): a front-line perspective from the Knox County (TN) Health Department. Poster presentation at: National Syndromic Surveillance Conference, New York Academy of Medicine; September 23–24, 2002; New York, NY.
- 15.Kulldorff M, Nagarwalla N. Spatial disease clusters: detection and inference. Stat Med. 1995;14:799–810. doi: 10.1002/sim.4780140809. [DOI] [PubMed] [Google Scholar]
- 16.Kulldorff M. A spatial scan statistic. Commun Stat Theory Meth. 1997;26:1481–1496. [Google Scholar]
- 17.Burkom HS, Elbert E. Biosurveillance applying scan statistics with multiple, disparate data sources. Poster presentation at: National Syndromic Surveillance Conference, New York Academy of Medicine; September 23–24, 2002; New York, NY. [DOI] [PMC free article] [PubMed]
- 18.Loschen WA. User Interfaces for ESSENCE II. Laurel, MD: The Johns Hopkins University Applied Physics Laboratory; 2002. [Google Scholar]
- 19.Happel Lewis SL, Cutchis PN, Babin SM. Simulation of Pneumonic Plague in Montgomery County, Maryland. Laurel, MD: The Johns Hopkins University Applied Physics Laboratory; 2000. [Google Scholar]
- 20.Sartwell PE. The distribution of incubation periods of infectious disease. Am J Epidemiol. 1995;141:386–394. doi: 10.1093/oxfordjournals.aje.a117440. [DOI] [PubMed] [Google Scholar]