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美国行政索赔数据中检测急性和播散性莱姆病算法的验证

Validation of Algorithms to Detect Acute and Disseminated Lyme Disease in U.S. Administrative Claims Data.

作者信息

Kluberg Sheryl A, Cocoros Noelle M, O'Neill June, Boyce Thomas G, Sundaram Maria E, Schotthoefer Anna, Greenlee Robert T, Djibo Djeneba Audrey, McMahill-Walraven Cheryl N, Aucott John, Moïsi Jennifer C, Jodar Luis, Willis Sarah J, Stark James H

机构信息

Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts, USA.

Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston, Massachusetts, USA.

出版信息

Open Forum Infect Dis. 2025 Feb 27;12(4):ofaf109. doi: 10.1093/ofid/ofaf109. eCollection 2025 Apr.

Abstract

BACKGROUND

Lyme disease (LD) is the most common vector-borne disease in the United States, though traditional LD surveillance underestimates the burden of disease. We validated algorithms for early localized and disseminated LD, with and without LD-specific diagnosis codes, in states with high incidence and their neighboring states with low LD incidence.

METHODS

We identified cohorts of potential incident LD cases in administrative insurance claims data, October 2015-October 2023, in 1 national and 1 regional insurer. Three algorithms were studied: a primary algorithm of an LD-specific diagnosis code and indicated antibiotic and 2 secondary algorithms for disseminated LD requiring a non-LD-specific musculoskeletal or neurologic diagnosis code, an antibiotic, and an LD diagnostic test. We included individuals from high LD-incidence states and neighboring low LD-incidence states. We validated the algorithms using medical records for a sample of potential cases, classifying them according to modified surveillance case definitions. We calculated positive predictive values (PPVs) for each algorithm.

RESULTS

Overall, we identified 9483 potential LD cases in claims data and reviewed 841 medical records. The PPVs for the primary algorithm were 90.7% and 81.3% in high-incidence and neighboring states, respectively, when suspect, probable, and confirmed cases were included; they were 76.6% and 28.0% when only confirmed and probable were included. For confirmed and possible cases, the secondary musculoskeletal algorithm PPVs were 12.9% and 4.1%, and the secondary neurologic algorithm PPVs were 6.2% and 1.8% in high-incidence and neighboring states, respectively.

CONCLUSIONS

This study found that claims-based algorithms requiring diagnosis codes for LD or for related symptoms, in addition to other criteria, can identify cohorts of true LD cases. These algorithms, adjusted for PPV, can be used to estimate LD incidence in the United States.

摘要

背景

莱姆病(LD)是美国最常见的媒介传播疾病,不过传统的莱姆病监测低估了疾病负担。我们在高发病率州及其低莱姆病发病率的相邻州验证了用于早期局限性和播散性莱姆病的算法,包括有无莱姆病特异性诊断编码的情况。

方法

我们在2015年10月至2023年10月期间从1家全国性和1家区域性保险公司的行政保险理赔数据中识别出潜在的莱姆病新发病例队列。研究了三种算法:一种以莱姆病特异性诊断编码和指定抗生素为主要算法,另外两种辅助算法用于播散性莱姆病,需要非莱姆病特异性的肌肉骨骼或神经诊断编码、一种抗生素以及一项莱姆病诊断检测。我们纳入了来自高莱姆病发病率州和相邻低莱姆病发病率州的个体。我们使用潜在病例样本的病历对算法进行验证,并根据修改后的监测病例定义对其进行分类。我们计算了每种算法的阳性预测值(PPV)。

结果

总体而言,我们在理赔数据中识别出9483例潜在莱姆病病例,并审查了841份病历。当纳入疑似、可能和确诊病例时,主要算法在高发病率州和相邻州的PPV分别为90.7%和81.3%;当仅纳入确诊和可能病例时,PPV分别为76.6%和28.0%。对于确诊和可能病例,辅助肌肉骨骼算法在高发病率州和相邻州的PPV分别为12.9%和4.1%,辅助神经算法的PPV分别为6.2%和1.8%。

结论

本研究发现,除其他标准外,基于理赔且需要莱姆病或相关症状诊断编码的算法能够识别出真正的莱姆病病例队列。这些根据PPV进行调整的算法可用于估计美国的莱姆病发病率。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8375/11953000/09a8169bef5b/ofaf109f1.jpg

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