Socioeconomic bias in influenza surveillance
| dc.creator | Scarpino, Samuel V. | |
| dc.creator | Scott, James | |
| dc.creator | Eggo, Rosalind M. | |
| dc.creator | Clements, Bruce | |
| dc.creator | Dimitrov, Nedialko B. | |
| dc.creator | Meyers, Lauren Ancel | |
| dc.date.accessioned | 2024-07-29T20:41:37Z | |
| dc.date.available | 2024-07-29T20:41:37Z | |
| dc.date.issued | 2020-07 | |
| dc.description.abstract | 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. | |
| dc.description.department | Operations Research and Industrial Engineering | |
| dc.description.department | Integrative Biology | |
| dc.description.department | Statistics | |
| dc.description.sponsorship | The authors received funding from the National Institutes of Health (MIDAS U01GM087719 to LAM; https://www.nigms.nih.gov/Research/specificareas/MIDAS/), Postdoctoral Fellowship Omidyar Group (to SVS; https://www.omidyargroup.com/), Postdoctoral Fellowship Santa Fe Institute (https://santafe.edu/), Startup Funds University of Vermont (https://www.uvm.edu/), and Startup Funds Northeastern University (https://www.northeastern.edu/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. | |
| dc.identifier.doi | 10.1371/journal.pcbi.1007941 | |
| dc.identifier.uri | https://hdl.handle.net/2152/126256 | |
| dc.identifier.uri | https://doi.org/10.26153/tsw/52793 | |
| dc.publisher | PLOS Computational Biology | |
| dc.rights | Attribution 4.0 International | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.source.uri | https://doi.org/10.1371/journal.pcbi.1007941 | |
| dc.subject | influenza | |
| dc.subject | disease surveillance | |
| dc.subject | socioeconomic bias | |
| dc.title | Socioeconomic bias in influenza surveillance | |
| dc.type | JournalArticle |