Comparison of Medicare claims-based Clostridioides difficile infection epidemiologic case classification algorithms to medical record review by the Emerging Infections Program using a linked cohort, 2016-2021.

IF 3 4区 医学 Q2 INFECTIOUS DISEASES
Dustin W Currie, Chantal Lewis, Joseph D Lutgring, Sophia V Kazakova, James Baggs, Lauren Korhonen, Maria Correa, Dana Goodenough, Danyel M Olson, Jill Szydlowski, Ghinwa Dumyati, Scott K Fridkin, Christopher Wilson, Alice Y Guh, Sujan C Reddy, Kelly M Hatfield
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引用次数: 0

Abstract

Background: Medicare claims are frequently used to study Clostridioides difficile infection (CDI) epidemiology. However, they lack specimen collection and diagnosis dates to assign location of onset. Algorithms to classify CDI onset location using claims data have been published, but the degree of misclassification is unknown.

Methods: We linked patients with laboratory-confirmed CDI reported to four Emerging Infections Program (EIP) sites from 2016-2021 to Medicare beneficiaries with fee-for-service Part A/B coverage. We calculated sensitivity of ICD-10-CM codes in claims within ±28 days of EIP specimen collection. CDI was categorized as hospital, long-term care facility, or community-onset using three different Medicare claims-based algorithms based on claim type, ICD-10-CM code position, duration of hospitalization, and ICD-10-CM diagnosis code presence-on-admission indicators. We assessed concordance of EIP case classifications, based on chart review and specimen collection date, with claims case classifications using Cohen's kappa statistic.

Results: Of 12,671 CDI cases eligible for linkage, 9,032 (71%) were linked to a single, unique Medicare beneficiary. Compared to EIP, sensitivity of CDI ICD-10-CM codes was 81%; codes were more likely to be present for hospitalized patients (93.0%) than those who were not (56.2%). Concordance between EIP and Medicare claims algorithms ranged from 68% to 75%, depending on the algorithm used (κ = 0.56-0.66).

Conclusion: ICD-10-CM codes in Medicare claims data had high sensitivity compared to laboratory-confirmed CDI reported to EIP. Claims-based epidemiologic classification algorithms had moderate concordance with EIP classification of onset location. Misclassification of CDI onset location using Medicare algorithms may bias findings of claims-based CDI studies.

2016-2021年基于医疗保险索赔的艰难梭菌感染流行病学病例分类算法与新发感染项目使用相关队列的医疗记录审查的比较
背景:医疗保险索赔经常用于研究艰难梭菌感染(CDI)的流行病学。然而,他们缺乏标本收集和诊断日期来确定发病部位。使用索赔数据对CDI发病位置进行分类的算法已经发表,但分类错误的程度尚不清楚。方法:我们将2016-2021年在四个新发感染计划(EIP)站点报告的实验室确诊CDI患者与按服务收费的医疗保险A/B部分保险受益人联系起来。我们计算了在EIP标本采集后±28天内索赔中ICD-10-CM代码的灵敏度。使用基于索赔类型、ICD-10-CM代码位置、住院时间和ICD-10-CM诊断代码入院指标的三种不同的医疗保险索赔算法,将CDI分类为医院、长期护理机构或社区发病。我们评估了EIP病例分类的一致性,基于图表审查和标本收集日期,并使用Cohen的kappa统计来评估索赔病例分类。结果:在12671例符合关联条件的CDI病例中,9032例(71%)与单一、独特的医疗保险受益人关联。与EIP相比,CDI ICD-10-CM编码的灵敏度为81%;住院患者(93.0%)比未住院患者(56.2%)更容易出现编码。EIP和Medicare索赔算法之间的一致性从68%到75%不等,取决于所使用的算法(κ = 0.56-0.66)。结论:与EIP报告的实验室确认的CDI相比,医疗保险索赔数据中的ICD-10-CM代码具有较高的敏感性。基于索赔的流行病学分类算法与EIP发病部位分类有中等程度的一致性。使用医疗保险算法对CDI发病位置的错误分类可能会使基于索赔的CDI研究结果产生偏差。
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来源期刊
CiteScore
6.40
自引率
6.70%
发文量
289
审稿时长
3-8 weeks
期刊介绍: Infection Control and Hospital Epidemiology provides original, peer-reviewed scientific articles for anyone involved with an infection control or epidemiology program in a hospital or healthcare facility. Written by infection control practitioners and epidemiologists and guided by an editorial board composed of the nation''s leaders in the field, ICHE provides a critical forum for this vital information.
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