Am J Epidemiol 2004; 159:217-224.
Copyright © 2004 by the Johns
Hopkins Bloomberg School of Public Health
ORIGINAL CONTRIBUTIONS |
A Generalized Linear Mixed Models Approach for Detecting Incident Clusters of Disease in Small Areas, with an Application to Biological Terrorism
1 Harvard Medical School, Boston, MA.
2 Harvard Pilgrim Health Care, Boston, MA.
3 Harvard Vanguard Medical Associates, Boston, MA.
4 Eastern Massachusetts Prevention Epicenter, Centers for Disease Control and Prevention, Boston, MA.
5 Center for Education and Research in Therapeutics, HMO Research Network, Boston, MA.
6 Brigham and Women’s Hospital, Boston, MA.
7 University of Sydney School of Public Health, Sydney, Australia.
Since the intentional dissemination of anthrax through the US postal system in the fall of 2001, there has been increased interest in surveillance for detection of biological terrorism. More generally, this could be described as the detection of incident disease clusters. In addition, the advent of affordable and quick geocoding allows for surveillance on a finer spatial scale than has been possible in the past. Surveillance for incident clusters of disease in both time and space is a relatively undeveloped arena of statistical methodology. Surveillance for bioterrorism detection, in particular, raises unique issues with methodological relevance. For example, the bioterrorism agents of greatest concern cause initial symptoms that may be difficult to distinguish from those of naturally occurring disease. In this paper, the authors propose a general approach to evaluating whether observed counts in relatively small areas are larger than would be expected on the basis of a history of naturally occurring disease. They implement the approach using generalized linear mixed models. The approach is illustrated using data on health-care visits (1996–1999) from a large Massachusetts managed care organization/multispecialty practice group in the context of syndromic surveillance for anthrax. The authors argue that there is great value in using the geographic data.
bioterrorism; communicable diseases; epidemiologic methods; generalized linear mixed model; population surveillance; spatial analysis
Abbreviations: Abbreviations: GLMM, generalized linear mixed models; ICD-9-CM, International Classification of Disease, Ninth Revision, Clinical Modification; NT, number of tests.
CiteULike 
Connotea 
Del.icio.us What's this?
Related articles in Am. J. Epidemiol.:
- Invited Commentary: Surveilling Surveillance—Some Statistical Comments
- Lance A. Waller
Am. J. Epidemiol. 2004 159: 225-227.[Extract] [FREE Full Text] - Kleinman et al. Respond to "Surveilling Surveillance"
- Ken Kleinman, Ross Lazarus, and Richard Platt
Am. J. Epidemiol. 2004 159: 228.[Extract] [FREE Full Text]
This article has been cited by other articles:
|
|
 |
|
  C. L V. Rodeiro and A. B Lawson Online updating of space-time disease surveillance models via particle filters Statistical Methods in Medical Research, October 1, 2006; 15(5): 423 - 444. [Abstract] [PDF]
|
|
|
|
|
|
K. P Kleinman and A. M Abrams Assessing surveillance using sensitivity, specificity and timeliness Statistical Methods in Medical Research, October 1, 2006; 15(5): 445 - 464. [Abstract] [PDF]
|
|
|
|
 |
|
 L. Held, M. Hofmann, M. Hohle, and V. Schmid A two-component model for counts of infectious diseases Biostat., July 1, 2006; 7(3): 422 - 437. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 J. S. Brownstein, K. P. Kleinman, and K. D. Mandl Identifying Pediatric Age Groups for Influenza Vaccination Using a Real-Time Regional Surveillance System Am. J. Epidemiol., October 1, 2005; 162(7): 686 - 693. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
L. A. Waller Invited Commentary: Surveilling Surveillance--Some Statistical Comments Am. J. Epidemiol., February 1, 2004; 159(3): 225 - 227. [Full Text] [PDF]
|
|
|
|
|
|
K. Kleinman, R. Lazarus, and R. Platt Kleinman et al. Respond to "Surveilling Surveillance" Am. J. Epidemiol., February 1, 2004; 159(3): 228 - 228. [Full Text] [PDF]
|
|