Socioeconomic bias in influenza surveillance

TL;DR

This paper evaluates the effectiveness of traditional and next-generation influenza surveillance systems in capturing data across socioeconomic groups, revealing significant gaps in coverage for high-poverty areas.

Contribution

It introduces a flexible statistical framework to assess multiple data sources and highlights limitations of current surveillance systems in low-income communities.

Findings

ILINet underrepresents high-poverty zip codes

Next-generation data sources do not fully address surveillance gaps

Integrated data sources improve situational awareness overall

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