Socioeconomic bias in influenza surveillance.

Samuel V. Scarpino,Lauren Ancel Meyers,Nedialko B. Dimitrov,James G. Scott,Rosalind M. Eggo,Bruce Clements,Matthew (Matt) Ferrari

doi:10.1371/journal.pcbi.1007941

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.

Highlights

As part of a broader national security strategy, US President Obama created the first National Strategy for Biosurveillance, outlining the nation’s key strategic goals in disease surveillance [1]
Traditional influenza surveillance is based on primary healthcare provider reports, which may be biased towards serving populations with higher socioeconomic status because of the costs and accessibility of healthcare [5, 6]
We considered six different model variations, each using a distinct combination of data from BioSense 2.0, influenza surveillance system (ILINet), and Google Flu Trends (GFT)

Summary

Introduction

As part of a broader national security strategy, US President Obama created the first National Strategy for Biosurveillance, outlining the nation’s key strategic goals in disease surveillance [1]. Biosurveillance using advanced technologies may be most important in lower socioeconomic areas, where influenza burden tends to be highest [2,3,4]. This article assesses the capacity for traditional and novel data sources to provide real-time influenza risk assessments in under-served populations. Traditional influenza surveillance is based on primary healthcare provider reports, which may be biased towards serving populations with higher socioeconomic status because of the costs and accessibility of healthcare [5, 6]. A systematic evaluation of the current surveillance system is needed to evaluate where it falls short, and whether new data can fill gaps

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Conclusion