  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
blind-spot for ILINet that the integration of next generation data fails to
ameliorate.
