A Strategy to Identify Event Specific Hospitalizations in Large Health Claims Databases Article Swipe
Joshua Lambert
,
Harpal S. Sandhu
,
Emily Kean
,
Teenu Xavier
,
Aviv Brokman
,
Zachary Steckler
,
Lee Park
,
Arnold J. Stromberg
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-1061834/v2
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-1061834/v2
Background : Health insurance claims data offer a unique opportunity to study disease distribution on a large scale. Challenges arise in the process of accurately analyzing these raw data. One important challenge to overcome is the accurate classification of study outcomes. For example, using claims data, there is no clear way of classifying hospitalizations due to a specific event. This is because of the inherent disjointedness and lack of context that typically come with raw claims data. Methods : In this paper, we propose a framework for classifying hospitalizations due to a specific event. Results : We then test this framework in a health insurance claims database with approximately 4 million US adults who tested positive with COVID-19 between March and December 2020. Our claims specific COVID-19 related hospitalizations proportion is then compared to nationally reported rates from the Centers for Disease Control by age and sex. Conclusions: The proposed methodology is a rigorous way to define event specific hospitalizations in claims data. This methodology can be extended to many different types of events and used on a variety of different types of claims databases.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-1061834/v2
- OA Status
- green
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4221055085
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4221055085Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-1061834/v2Digital Object Identifier
- Title
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A Strategy to Identify Event Specific Hospitalizations in Large Health Claims DatabasesWork title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-03-08Full publication date if available
- Authors
-
Joshua Lambert, Harpal S. Sandhu, Emily Kean, Teenu Xavier, Aviv Brokman, Zachary Steckler, Lee Park, Arnold J. StrombergList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-1061834/v2Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://doi.org/10.21203/rs.3.rs-1061834/v2Direct OA link when available
- Concepts
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Event (particle physics), Context (archaeology), Raw data, Computer science, Variety (cybernetics), Scale (ratio), Event data, Actuarial science, Database, Data science, Data mining, Business, Geography, Artificial intelligence, Data modeling, Cartography, Programming language, Archaeology, Physics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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27Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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