Data Blindspots: High-Tech Disease Surveillance Misses the Poor

Samuel V. Scarpino, James G. Scott, Rosalind Eggo, Nedialko B. Dimitrov, Lauren A. Meyers


Influenza hospitalizations are positively associated with poverty. Therefore, individuals in lower socioeconomic brackets are considered to be members of at-risk populations. With the goal of improving situational awareness, we developed a framework for combining multiple data sources to predict at-risk hospitalizations. The data sources considered were: emergency departments, primary health care providers, and Google Flu Trends. We demonstrate that out-of-sample performance was lowest in the most at-risk zip codes, which identifies a key data blindspot, highlights the importance of understanding the dynamics of influenza in at-risk populations, and reveals the far-reaching public health consequences of restricted access to health care.

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Online Journal of Public Health Informatics * ISSN 1947-2579 *