ArticleBMC health services research2022
A strategy to identify event specific hospitalizations in large health claims databases.
Article in BMC health services research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Mycophenolate mofetil associates with COVID-19 hospitalization in patients with systemic lupus erythematosus: a nationwide population-based study from China.Clinical rheumatology · 2026Article
- Analysis of Oral Anticoagulant Dosing and Adherence to Therapy Among Patients With Nonvalvular Atrial Fibrillation.JAMA network open · 2023Article
- Research note changes in the number of outpatient visits in Japan during the COVID-19 pandemic.BMC research notes · 2023Article
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Authors and funding
8 authors.
Funding
Abstract
backgroundHealth 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.
methodsIn this paper, we propose a framework for classifying hospitalizations due to a specific event. We then tested this framework in a private health insurance claims database (Symphony) 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.
resultsAcross all ages (18 +) the total percentage of Symphony patients who met our definition of hospitalized due to COVID-19 was 7.3% which was similar to the CDC's estimate of 7.5%. By age group, defined by the CDC, our estimates vs. the CDC's estimates were 18-49: 2.7% vs. 3%, 50-64: 8.2% vs. 9.2%, and 65 + : 14.6% vs. 28.1%.
conclusionsThe 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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.