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

• Main Authors: Arvind eRamanathan (Author), Laura L. Pullum (Author), Tanner C. Hobson (Author), Christopher G. Stahl (Author), Chad A. Steed (Author), Shannon P. Quinn (Author), Chakra S. Chennubhotla (Author)
• Format: Book
• Published: Frontiers Media S.A., 2015-08-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.

Summary

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.

Item Description

2296-2565
10.3389/fpubh.2015.00182