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Tracking Patterns of Enteric Illnesses in Populations and Communities

BACKGROUND: Enteric illness arising from contaminated water and food is a major health concern worldwide, and tracking the incidences and severity of outbreaks is still a challenging task. Most developed and developing countries have administrative databases for medical visits and services maintaine...

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Detalles Bibliográficos
Autores principales: Peace, Terry, Mazumder, Asit
Formato: Texto
Lenguaje:English
Publicado: National Institute of Environmental Health Sciences 2007
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1797834/
https://www.ncbi.nlm.nih.gov/pubmed/17366820
http://dx.doi.org/10.1289/ehp.9199
Descripción
Sumario:BACKGROUND: Enteric illness arising from contaminated water and food is a major health concern worldwide, and tracking the incidences and severity of outbreaks is still a challenging task. Most developed and developing countries have administrative databases for medical visits and services maintained by the government and/or health insurance authorities. Although these databases could be extremely valuable resources to track patterns of environmental and other health issues, test hypotheses, and develop epidemiologic models and predictions, very little research has been done to develop methods to ensure the robustness of such databases and to demonstrate their utility as a research tool. OBJECTIVES: We used the Medical Services Plan (MSP) database of British Columbia, Canada, to develop innovative ways to use medical billing and fee-for-services data to track long-term patterns of enteric illness at the level of populations and communities. RESULTS: To illustrate the power and robustness of the method, we provided several examples covering 8 years of data from each of four communities covering a large range of population size. Not only could this method generalize to other diseases for which specific fee item markers can be found, but also it gives results consistent with a known outbreak and yields data patterns, which could not be revealed by the currently used methods. Because diagnostic code and fee item data for medical services are collected by most medical insurance agencies, our method can have global applications for tracking enteric and other illnesses at the level of populations and communities.