COVID-19 pandemic impact on clinical condition capture in real-world data: an assessment framework.

IF 5 2区 医学 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
Yehua Wang, Yanning Wang, Earl J Morris, Nicole E Smolinski, Thuy N Thai, Almut G Winterstein
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引用次数: 0

Abstract

Background The COVID-19 pandemic has impacted healthcare utilization and, consequently, real-world data. In this study, we used analytical and data visualization approaches to untangle effects on condition measurement and true shifts in the patient population seeking healthcare. Methods We used MerativeTM MarketScan® 2018-2020 commercial claims data to develop 24 monthly cohorts of patients aged ≥18 years with 12 months baseline enrollment and an encounter for diabetes, cancer, hypertension, depression, myocardial infarction (MI), atrial fibrillation (Afib), or urinary tract infections (UTI) as the index condition in a given month. We compared monthly prevalence of each condition in 2020 vs. 2019. We then imposed 3, 6, and 12-month look-back periods (LBP) to capture comorbidities grouped by Clinical Classifications Software Refined (CCSR) or summarized in the Charleson Comorbidity Index (CCI) and conducted similar 2020 versus 2019 prevalence comparisons. Results Changes in condition prevalence varied across conditions with strongest declines for cancer in April 2020 (-57.4%) and strongest increases for depression in December 2020 (+11.8%). The mean CCI was higher for most conditions during the spring of 2020 and this difference was accentuated by applying a longer LBP. Similar trends were found regarding the number of CCSR categories. Conclusion Pandemic-related changes in condition capture were complex, involving both increases and decreases in encounters for specific conditions and in comorbidities, along with variations in comorbidity capture dependent on look-back periods. We provided a practical approach to untangle these phenomena along with open-source algorithms and visualization tools to assess these changes and inform study design and analysis.

COVID-19大流行对真实世界数据中临床状况捕获的影响:评估框架。
COVID-19大流行影响了医疗保健利用,从而影响了现实世界的数据。在这项研究中,我们使用了分析和数据可视化的方法来解开对状况测量和寻求医疗保健的患者群体的真实变化的影响。方法:我们使用MerativeTM MarketScan®2018-2020年商业索赔数据,对年龄≥18岁、基线入组12个月的患者进行24个月队列研究,这些患者在给定月份的指标条件为糖尿病、癌症、高血压、抑郁症、心肌梗死(MI)、心房颤动(Afib)或尿路感染(UTI)。我们比较了2020年和2019年每种疾病的月患病率。然后,我们施加3个月、6个月和12个月的回顾期(LBP)来捕获按临床分类软件精细化(CCSR)分组或按Charleson合并症指数(CCI)总结的合并症,并进行类似的2020年和2019年的患病率比较。结果不同疾病的患病率变化不同,2020年4月癌症发病率下降幅度最大(-57.4%),2020年12月抑郁症发病率上升幅度最大(+11.8%)。在2020年春季的大多数情况下,平均CCI更高,并且通过使用更长的LBP,这种差异更加突出。在CCSR类别的数量上也发现了类似的趋势。结论疾病捕获的大流行相关变化是复杂的,涉及特定疾病和合并症的增加和减少,以及合并症捕获的变化依赖于回顾期。我们提供了一种实用的方法来解开这些现象,以及开源算法和可视化工具来评估这些变化,并为研究设计和分析提供信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
American journal of epidemiology
American journal of epidemiology 医学-公共卫生、环境卫生与职业卫生
CiteScore
7.40
自引率
4.00%
发文量
221
审稿时长
3-6 weeks
期刊介绍: The American Journal of Epidemiology is the oldest and one of the premier epidemiologic journals devoted to the publication of empirical research findings, opinion pieces, and methodological developments in the field of epidemiologic research. It is a peer-reviewed journal aimed at both fellow epidemiologists and those who use epidemiologic data, including public health workers and clinicians.
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