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Socioeconomic bias in influenza surveillance

Individuals in low socioeconomic brackets are considered at-risk for developing influenza-related complications and often exhibit higher than average influenza-related hospitalization rates. This disparity has been attributed to various factors, including restricted access to preventative and therap...

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Autores principales: Scarpino, Samuel V., Scott, James G., Eggo, Rosalind M., Clements, Bruce, Dimitrov, Nedialko B., Meyers, Lauren Ancel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7347107/
https://www.ncbi.nlm.nih.gov/pubmed/32644990
http://dx.doi.org/10.1371/journal.pcbi.1007941
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author Scarpino, Samuel V.
Scott, James G.
Eggo, Rosalind M.
Clements, Bruce
Dimitrov, Nedialko B.
Meyers, Lauren Ancel
author_facet Scarpino, Samuel V.
Scott, James G.
Eggo, Rosalind M.
Clements, Bruce
Dimitrov, Nedialko B.
Meyers, Lauren Ancel
author_sort Scarpino, Samuel V.
collection PubMed
description Individuals in low socioeconomic brackets are considered at-risk for developing influenza-related complications and often exhibit higher than average influenza-related hospitalization rates. This disparity has been attributed to various factors, including restricted access to preventative and therapeutic health care, limited sick leave, and household structure. Adequate influenza surveillance in these at-risk populations is a critical precursor to accurate risk assessments and effective intervention. However, the United States of America’s primary national influenza surveillance system (ILINet) monitors outpatient healthcare providers, which may be largely inaccessible to lower socioeconomic populations. Recent initiatives to incorporate Internet-source and hospital electronic medical records data into surveillance systems seek to improve the timeliness, coverage, and accuracy of outbreak detection and situational awareness. Here, we use a flexible statistical framework for integrating multiple surveillance data sources to evaluate the adequacy of traditional (ILINet) and next generation (BioSense 2.0 and Google Flu Trends) data for situational awareness of influenza across poverty levels. We find that ZIP Codes in the highest poverty quartile are a critical vulnerability for ILINet that the integration of next generation data fails to ameliorate.
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spelling pubmed-73471072020-07-17 Socioeconomic bias in influenza surveillance Scarpino, Samuel V. Scott, James G. Eggo, Rosalind M. Clements, Bruce Dimitrov, Nedialko B. Meyers, Lauren Ancel PLoS Comput Biol Research Article Individuals in low socioeconomic brackets are considered at-risk for developing influenza-related complications and often exhibit higher than average influenza-related hospitalization rates. This disparity has been attributed to various factors, including restricted access to preventative and therapeutic health care, limited sick leave, and household structure. Adequate influenza surveillance in these at-risk populations is a critical precursor to accurate risk assessments and effective intervention. However, the United States of America’s primary national influenza surveillance system (ILINet) monitors outpatient healthcare providers, which may be largely inaccessible to lower socioeconomic populations. Recent initiatives to incorporate Internet-source and hospital electronic medical records data into surveillance systems seek to improve the timeliness, coverage, and accuracy of outbreak detection and situational awareness. Here, we use a flexible statistical framework for integrating multiple surveillance data sources to evaluate the adequacy of traditional (ILINet) and next generation (BioSense 2.0 and Google Flu Trends) data for situational awareness of influenza across poverty levels. We find that ZIP Codes in the highest poverty quartile are a critical vulnerability for ILINet that the integration of next generation data fails to ameliorate. Public Library of Science 2020-07-09 /pmc/articles/PMC7347107/ /pubmed/32644990 http://dx.doi.org/10.1371/journal.pcbi.1007941 Text en © 2020 Scarpino et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Scarpino, Samuel V.
Scott, James G.
Eggo, Rosalind M.
Clements, Bruce
Dimitrov, Nedialko B.
Meyers, Lauren Ancel
Socioeconomic bias in influenza surveillance
title Socioeconomic bias in influenza surveillance
title_full Socioeconomic bias in influenza surveillance
title_fullStr Socioeconomic bias in influenza surveillance
title_full_unstemmed Socioeconomic bias in influenza surveillance
title_short Socioeconomic bias in influenza surveillance
title_sort socioeconomic bias in influenza surveillance
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7347107/
https://www.ncbi.nlm.nih.gov/pubmed/32644990
http://dx.doi.org/10.1371/journal.pcbi.1007941
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