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Methodologies for data collection

BACKGROUND: Electronic disease surveillance systems can be extremely valuable tools; however, a critical step in system implementation is collecting data. Without accurate and complete data, statistical anomalies that are detected hold little meaning. Many people who have established successful surv...

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Detalles Bibliográficos
Autores principales: Lewis, Sheryl Happel, Wojcik, Richard
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2008
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2587691/
https://www.ncbi.nlm.nih.gov/pubmed/19025682
Descripción
Sumario:BACKGROUND: Electronic disease surveillance systems can be extremely valuable tools; however, a critical step in system implementation is collecting data. Without accurate and complete data, statistical anomalies that are detected hold little meaning. Many people who have established successful surveillance systems acknowledge the initial data collection process to be one of the most challenging aspects of system implementation. METHODS: This discussion will describe the various methods for collecting data as well as describe some of the more common data feeds used in surveillance systems today. Given that every city/region/country looking to establish a surveillance capability has varying degrees of automated data, alternative data collection methods must be considered. RESULTS: While it would be ideal to collect automated electronic data in a real-time fashion without human intervention, data may also be effectively collected via telephone (both mobile and land lines), fax, and email. Another consideration is what type of data will be used in a surveillance system. If one data source is of high value to one locality, it should not be assumed that it will be as useful in another area. Determining what data sources work best for a particular area is a critical step in system implementation. CONCLUSION: Regardless of data type and how they are collected, surveillance systems can be successful if the implementers and end users understand the limitations of both the data and the collection methodology and incorporate that knowledge into their interpretation procedures.