在大型健康索赔数据库中识别事件特定住院的策略
A strategy to identify event specific hospitalizations in large health claims database
摘要：
健康保险索赔数据为大规模研究疾病分布提供了独特的机会。在准确分析这些原始数据的过程中出现了挑战。需要克服的一个重要挑战是研究结果的准确分类。例如，使用索赔数据，没有明确的方法对因特定事件而住院进行分类。这是因为原始索赔数据通常具有固有的脱节性和缺乏背景。在本文中，我们提出了一个框架，由于一个特定的事件住院分类。然后，我们在健康保险索赔数据库中测试了这一框架，其中约有400万美国成年人在2020年3月至12月期间对COVID-19检测呈阳性。我们的索赔特定COVID-19相关住院比例然后与疾病控制中心按年龄和性别报告的全国性比率进行比较。
英文摘要：
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. In this paper, we propose a framework for classifying hospitalizations due to a specific event. 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.
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