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Am J Epidemiol 2004; 159:225-227.
Copyright © 2004 by the Johns Hopkins Bloomberg School of Public Health

ORIGINAL CONTRIBUTIONS

Invited Commentary: Surveilling Surveillance—Some Statistical Comments

Lance A. Waller 

From the Department of Biostatistics, Rollins School of Public Health, Emory University, Atlanta, GA.

Received for publication August 11, 2003; accepted for publication September 15, 2003.

The first 150 words of the full text of this article appear below.


   INTRODUCTION
 
I congratulate Kleinman et al. (1) on their thoughtful application of generalized linear mixed models (GLMM) to disease surveillance in space and time. In this commentary, I amplify some appealing features of the approach, provide an overview of data issues that may affect the field performance of a surveillance system based on such a method, and discuss several technical issues.

Attractive features of the approach
The authors’ approach offers a movement from statistical testing to statistical modeling for disease surveillance. Traditionally, statistical methods for surveillance tend to evolve from a hypothesis-testing framework, wherein one "detects" an outbreak (anomaly, cluster, etc.) as a "statistically significant" departure from a null hypothesis defined as the lack of an outbreak (e.g., constant age-specific incidence proportions or monthly seasonal incidence proportions based on historical data). The current approach uses GLMM to provide predictions of the expected number of cases under the model in the absence of an outbreak . . . [Full Text of this Article]

Data features affecting field performance
A few statistical details
Conclusion

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A Generalized Linear Mixed Models Approach for Detecting Incident Clusters of Disease in Small Areas, with an Application to Biological Terrorism
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Kleinman et al. Respond to "Surveilling Surveillance"
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Home page
 Am J EpidemiolHome page
K. Kleinman, R. Lazarus, and R. Platt
Kleinman et al. Respond to "Surveilling Surveillance"
Am. J. Epidemiol., February 1, 2004; 159(3): 228 - 228.
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