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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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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. |
format | Online Article Text |
id | pubmed-7347107 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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