TY - JOUR AU - Scarpino, Samuel V. AU - Scott, James G. AU - Eggo, Rosalind AU - Dimitrov, Nedialko B. AU - Meyers, Lauren A. PY - 2016/03/24 Y2 - 2024/03/29 TI - Data Blindspots: High-Tech Disease Surveillance Misses the Poor JF - Online Journal of Public Health Informatics JA - OJPHI VL - 8 IS - 1 SE - Oral Presentations DO - 10.5210/ojphi.v8i1.6451 UR - https://ojphi.org/ojs/index.php/ojphi/article/view/6451 SP - AB - <p class="p1">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.</p> ER -