Discovering Multi-scale Co-occurrence Patterns of Asthma and Influenza with the Oak Ridge Bio-surveillance Toolkit

• Main Authors: Arvind eRamanathan, Laura L. Pullum, Tanner C. Hobson, Christopher G. Stahl, Chad A. Steed, Shannon P. Quinn, Chakra S. Chennubhotla
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2015-08-01
• Series: Frontiers in Public Health
• Subjects: Asthma; Influenza, Human; non-negative matrix factorization; Public Health Surveillance; Disease co-occurrence; Electronic Healthcare reimbursement claims
• Online Access: http://journal.frontiersin.org/Journal/10.3389/fpubh.2015.00182/full

We describe a data-driven unsupervised machine learning approach to extract geo-temporal co-occurrence patterns of asthma and the flu from large-scale electronic healthcare reimbursement claims (eHRC) datasets. Specifically, we examine the eHRC data from the 2009-2010 pandemic H1N1 influenza season and analyze whether different geographic regions within the United States (US) showed an increase in co-occurrence patterns of the flu and asthma. Our analyses reveal that the temporal patterns extracted from the eHRC data show a distinct lag time between the peak incidence of the asthma and the flu. While the increased occurrence of asthma contributed to increased flu incidence during the pandemic, this co-occurrence is predominant for female patients. The geo-temporal patterns reveal that the co-occurrence of the flu and asthma are typically concentrated within the south-east US. Further, in agreement with previous studies, large urban areas (such as New York, Miami and Los Angeles) exhibit co-occurrence patterns that suggest a peak incidence of asthma and flu significantly early in the spring and winter seasons. Together, our data-analytic approach, integrated within the Oak Ridge Bio-surveillance Toolkit platform, demonstrates how eHRC data can provide novel insights into co-occurring disease patterns.