Sage Journals: Discover world-class research

Abstract

Oral manifestations of diseases caused by bioterrorist agents could be a potential data source for biosurveillance. This study had the objectives of determining the oral manifestations of diseases caused by bioterrorist agents, measuring the prevalence of these manifestations in emergency department reports, and constructing and evaluating a detection algorithm based on them. We developed a software application to detect oral manifestations in free text and identified positive reports over three years of data. The normal frequency in reports for oral manifestations related to anthrax (including buccal ulcers-sore throat) was 7.46%. The frequency for tularemia was 6.91%. For botulism and smallpox, the frequencies were 0.55% and 0.23%. We simulated outbreaks for these bioterrorism diseases and evaluated the performance of our system. The detection algorithm performed better for smallpox and botulism than for anthrax and tularemia. We found that oral manifestations can be a valuable tool for biosurveillance.

Get full access to this article

View all access options for this article.

References

Buehler JW, Hopkins RS, Overhage JM, Sosin DM, Tong V, CDC Working Group (2004). Framework for evaluating public health surveillance systems for early detection of outbreaks: recommendations from the CDC Working Group. MMWR Recomm Rep53:1–11.

Centers for Disease Control and Prevention (2008). Syndromic surveillance: an applied approach to outbreak detection. http://www.cdc.gov/ncphi/disss/nndss/syndromic.htm (accessed Sept 24, 2008)

Chapman WW, Bridewell W, Hanbury P, Cooper GF, Buchanan BG (2001). A simple algorithm for identifying negated findings and diseases in discharge summaries. J Biomed Inform34:301–310.

Chapman WW, Dowling JN, Wagner MM (2004a). Fever detection from free-text clinical records for biosurveillance. J Biomed Inform37:120–127.

Chapman WW, Fiszman M, Dowling JN, Chapman BE, Rindflesch TC (2004b). Identifying respiratory findings in emergency department reports for biosurveillance using MetaMap. Stud Health Technol Inform107(Pt 1):487–491.

Duchin JS (2003). Epidemiological response to syndromic surveillance signals. J Urban Health80(2 Suppl 1):i115–i116.

Fawcett T, Provost F (1999). Activity monitoring: noticing interesting changes in behavior. Proceedings of the Fifth International Conference on Knowledge Discovery and Data Mining. http://doi.acm.org/10.1145/312129.312195 (accessed Sept 24, 2008)

Flores S, Mills SE, Shackelford L (2003). Dentistry and bioterrorism. Dent Clin North Am47:733–744.

Goldenberg A, Shmueli G, Caruana RA, Fienberg SE (2002). Early statistical detection of anthrax outbreaks by tracking over-the-counter medication sales. Proc Natl Acad Sci USA99:5237–5240.

10.

Gupta D, Saul M, Gilbertson J (2004). Evaluation of a deidentification (De-Id) software engine to share pathology reports and clinical documents for research. Am J Clin Pathol121:176–186.

11.

Jernigan JA, Stephens DS, Ashford DA, Omenaca C, Topiel MS, Galbraith M, et al. (2001). Bioterrorism-related inhalational anthrax: the first 10 cases reported in the United States. Emerg Infect Dis7:933–944.

12.

Meltzer MI, Damon I, LeDuc JW, Millar JD (2001). Modeling potential responses to smallpox as a bioterrorist weapon. Emerg Infect Dis7:959–969.

13.

Pennsylvania Department of Health (2007). Data from the Annual Hospital Questionnaire, Reporting Period July 1, 2001—June 30, 2002. Pennsylvania Department of Health, Bureau of Health Statistics and Research. http://www.dsf.health.state.pa.us/health/lib/health/facilities/hosamb/2001-2002/H0204.PDF. (accessed Sept 24, 2008)

14.

Reis BY, Pagano M, Mandl KD (2003). Using temporal context to improve biosurveillance. Proc Natl Acad Sci USA100:1961–1965.

15.

Samaranayake LP, Peiris M (2004). Severe acute respiratory syndrome and dentistry: a retrospective view. J Am Dent Assoc135:1292–1302.

16.

Wagner MM (2006). Introduction. In: Handbook of biosurveillance. Wagner MM, Moore AW, Aryel RM, editors. Burlington: Elsevier Academic Press, pp. 3–12.

17.

Wagner MM, Tsui FC, Espino JU, Dato VM, Sittig DF, Caruana RA, et al. (2001a). The emerging science of very early detection of disease outbreaks. J Public Health Manag Pract7:51–59.

18.

Wagner M, Aryel R, Dato VM, Krenzelok E, Fapohunda A, Sharma R (2001b). Availability and comparative value of data elements required for an effective detection system. Contract No. 290-00-0009. Rockville, MD: Agency for Healthcare Research and Quality.

19.

Wagner MM, Dato V, Dowling JN, Allswede M (2003). Representative threats for research in public health surveillance. J Biomed Inform36:177–188.

20.

Wallstrom GL, Wagner M, Hogan W (2005). High-fidelity injection detectability experiments: a tool for evaluating syndromic surveillance systems. MMWR Morb Mortal Wkly Rep54(Suppl):85–91.

21.

Wong WK, Moore AW (2006). Classical time-series methods for biosurveillance. In: Handbook of biosurveillance. Wagner MM, Moore AW, Aryel R, editors. Burlington: Academic Press, pp. 217–234.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

2.04 MB

Detection of Disease Outbreaks by the Use of Oral Manifestations

Abstract

Get full access to this article

References

Supplementary Material