Comparison of crowd-sourced, electronic health records based, and traditional health-care based influenza-tracking systems at multiple spatial resolutions in the United States of America

Details

• Alternative Title:
  BMC Infect Dis
• Personal Author:
  Baltrusaitis, Kristin ; Brownstein, John S. ; Scarpino, Samuel V. ; Bakota, Eric ; Crawley, Adam W. ; Conidi, Giuseppe ; Gunn, Julia ; Gray, Josh ; Zink, Anna ; Santillana, Mauricio
• Description:
  Background
  
  Influenza causes an estimated 3000 to 50,000 deaths per year in the United States of America (US). Timely and representative data can help local, state, and national public health officials monitor and respond to outbreaks of seasonal influenza. Data from cloud-based electronic health records (EHR) and crowd-sourced influenza surveillance systems have the potential to provide complementary, near real-time estimates of influenza activity. The objectives of this paper are to compare two novel influenza-tracking systems with three traditional healthcare-based influenza surveillance systems at four spatial resolutions: national, regional, state, and city, and to determine the minimum number of participants in these systems required to produce influenza activity estimates that resemble the historical trends recorded by traditional surveillance systems.
  
  Methods
  
  We compared influenza activity estimates from five influenza surveillance systems: 1) patient visits for influenza-like illness (ILI) from the US Outpatient ILI Surveillance Network (ILINet), 2) virologic data from World Health Organization (WHO) Collaborating and National Respiratory and Enteric Virus Surveillance System (NREVSS) Laboratories, 3) Emergency Department (ED) syndromic surveillance from Boston, Massachusetts, 4) patient visits for ILI from EHR, and 5) reports of ILI from the crowd-sourced system, Flu Near You (FNY), by calculating correlations between these systems across four influenza seasons, 2012–16, at four different spatial resolutions in the US. For the crowd-sourced system, we also used a bootstrapping statistical approach to estimate the minimum number of reports necessary to produce a meaningful signal at a given spatial resolution.
  
  Results
  
  In general, as the spatial resolution increased, correlation values between all influenza surveillance systems decreased. Influenza-like Illness rates in geographic areas with more than 250 crowd-sourced participants or with more than 20,000 visit counts for EHR tracked government-lead estimates of influenza activity.
  
  Conclusions
  
  With a sufficient number of reports, data from novel influenza surveillance systems can complement traditional healthcare-based systems at multiple spatial resolutions.
  
  Electronic supplementary material
  
  The online version of this article (10.1186/s12879-018-3322-3) contains supplementary material, which is available to authorized users.
• Subjects:
  • Research Article
  • Community-based participatory research
  • Crowd-sourcing
  • Digital disease detection
  • Influenza
  • Public health informatics
  • Public health surveillance
• Source:
  BMC Infect Dis. 18.
• Pubmed ID:
  30111305
• Pubmed Central ID:
  PMC6094455
• Document Type:
  Journal Article
• Funding:
  T32 GM074905/GM/NIGMS NIH HHS/United States ; T32GM074905/National Institutes of Health/
• Place as Subject:
  United States
• Volume:
  18
• Download URL:
  https://stacks.cdc.gov/view/cdc/58427/cdc_58427_DS1.pdf
• File Type:
  [PDF - 1.02 MB]
• Collection(s):
  CDC Public Access
• Main Document Checksum:
  urn:sha256:65280953a9b908b4072834d45235a5742a5920c8bf0fa88034a7e7d7b506741e