Cargando…

Statistical analyses in disease surveillance systems

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 detect...

Descripción completa

Detalles Bibliográficos
Autores principales: Lescano, Andres G, Larasati, Ria Purwita, Sedyaningsih, Endang R, Bounlu, Khanthong, Araujo-Castillo, Roger V, Munayco-Escate, Cesar V, Soto, Giselle, Mundaca, C Cecilia, Blazes, David L
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2008
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2587693/
https://www.ncbi.nlm.nih.gov/pubmed/19025684
_version_ 1782160921764823040
author Lescano, Andres G
Larasati, Ria Purwita
Sedyaningsih, Endang R
Bounlu, Khanthong
Araujo-Castillo, Roger V
Munayco-Escate, Cesar V
Soto, Giselle
Mundaca, C Cecilia
Blazes, David L
author_facet Lescano, Andres G
Larasati, Ria Purwita
Sedyaningsih, Endang R
Bounlu, Khanthong
Araujo-Castillo, Roger V
Munayco-Escate, Cesar V
Soto, Giselle
Mundaca, C Cecilia
Blazes, David L
author_sort Lescano, Andres G
collection PubMed
description 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.
format Text
id pubmed-2587693
institution National Center for Biotechnology Information
language English
publishDate 2008
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-25876932008-11-26 Statistical analyses in disease surveillance systems Lescano, Andres G Larasati, Ria Purwita Sedyaningsih, Endang R Bounlu, Khanthong Araujo-Castillo, Roger V Munayco-Escate, Cesar V Soto, Giselle Mundaca, C Cecilia Blazes, David L BMC Proc Proceedings 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. BioMed Central 2008-11-14 /pmc/articles/PMC2587693/ /pubmed/19025684 Text en Copyright © 2008 Lescano et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Proceedings
Lescano, Andres G
Larasati, Ria Purwita
Sedyaningsih, Endang R
Bounlu, Khanthong
Araujo-Castillo, Roger V
Munayco-Escate, Cesar V
Soto, Giselle
Mundaca, C Cecilia
Blazes, David L
Statistical analyses in disease surveillance systems
title Statistical analyses in disease surveillance systems
title_full Statistical analyses in disease surveillance systems
title_fullStr Statistical analyses in disease surveillance systems
title_full_unstemmed Statistical analyses in disease surveillance systems
title_short Statistical analyses in disease surveillance systems
title_sort statistical analyses in disease surveillance systems
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2587693/
https://www.ncbi.nlm.nih.gov/pubmed/19025684
work_keys_str_mv AT lescanoandresg statisticalanalysesindiseasesurveillancesystems
AT larasatiriapurwita statisticalanalysesindiseasesurveillancesystems
AT sedyaningsihendangr statisticalanalysesindiseasesurveillancesystems
AT bounlukhanthong statisticalanalysesindiseasesurveillancesystems
AT araujocastillorogerv statisticalanalysesindiseasesurveillancesystems
AT munaycoescatecesarv statisticalanalysesindiseasesurveillancesystems
AT sotogiselle statisticalanalysesindiseasesurveillancesystems
AT mundacaccecilia statisticalanalysesindiseasesurveillancesystems
AT blazesdavidl statisticalanalysesindiseasesurveillancesystems