Title |
Discovering Multi-Scale Co-Occurrence Patterns of Asthma and Influenza with Oak Ridge Bio-Surveillance Toolkit
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Published in |
Frontiers in Public Health, August 2015
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DOI | 10.3389/fpubh.2015.00182 |
Pubmed ID | |
Authors |
Arvind Ramanathan, Laura L. Pullum, Tanner C. Hobson, Christopher G. Stahl, Chad A. Steed, Shannon P. Quinn, Chakra S. Chennubhotla, Silvia Valkova |
Abstract |
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 2009 to 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. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 3 | 60% |
Unknown | 2 | 40% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 2 | 40% |
Scientists | 1 | 20% |
Science communicators (journalists, bloggers, editors) | 1 | 20% |
Practitioners (doctors, other healthcare professionals) | 1 | 20% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 13 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Researcher | 3 | 23% |
Student > Ph. D. Student | 2 | 15% |
Student > Master | 2 | 15% |
Student > Doctoral Student | 1 | 8% |
Student > Bachelor | 1 | 8% |
Other | 2 | 15% |
Unknown | 2 | 15% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 3 | 23% |
Agricultural and Biological Sciences | 2 | 15% |
Pharmacology, Toxicology and Pharmaceutical Science | 1 | 8% |
Biochemistry, Genetics and Molecular Biology | 1 | 8% |
Nursing and Health Professions | 1 | 8% |
Other | 2 | 15% |
Unknown | 3 | 23% |