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Published in: BMC Proceedings 3/2008

Open Access 01-12-2008 | Proceedings

Statistical analyses in disease surveillance systems

Authors: Andres G Lescano, Ria Purwita Larasati, Endang R Sedyaningsih, Khanthong Bounlu, Roger V Araujo-Castillo, Cesar V Munayco-Escate, Giselle Soto, C Cecilia Mundaca, David L Blazes

Published in: BMC Proceedings | Special Issue 3/2008

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Abstract

The performance of disease surveillance systems is evaluated and monitored using a diverse set of statistical analyses throughout each stage of surveillance implementation. An overview of their main elements is presented, with a specific emphasis on syndromic surveillance directed to outbreak detection in resource-limited settings. Statistical analyses are proposed for three implementation stages: planning, early implementation, and consolidation. Data sources and collection procedures are described for each analysis.
During the planning and pilot stages, we propose to estimate the average data collection, data entry and data distribution time. This information can be collected by surveillance systems themselves or through specially designed surveys. During the initial implementation stage, epidemiologists should study the completeness and timeliness of the reporting, and describe thoroughly the population surveyed and the epidemiology of the health events recorded. Additional data collection processes or external data streams are often necessary to assess reporting completeness and other indicators. Once data collection processes are operating in a timely and stable manner, analyses of surveillance data should expand to establish baseline rates and detect aberrations. External investigations can be used to evaluate whether abnormally increased case frequency corresponds to a true outbreak, and thereby establish the sensitivity and specificity of aberration detection algorithms.
Statistical methods for disease surveillance have focused mainly on the performance of outbreak detection algorithms without sufficient attention to the data quality and representativeness, two factors that are especially important in developing countries. It is important to assess data quality at each state of implementation using a diverse mix of data sources and analytical methods. Careful, close monitoring of selected indicators is needed to evaluate whether systems are reaching their proposed goals at each stage.
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Metadata
Title
Statistical analyses in disease surveillance systems
Authors
Andres G Lescano
Ria Purwita Larasati
Endang R Sedyaningsih
Khanthong Bounlu
Roger V Araujo-Castillo
Cesar V Munayco-Escate
Giselle Soto
C Cecilia Mundaca
David L Blazes
Publication date
01-12-2008
Publisher
BioMed Central
Published in
BMC Proceedings / Issue Special Issue 3/2008
Electronic ISSN: 1753-6561
DOI
https://doi.org/10.1186/1753-6561-2-s3-s7

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