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错误分类对莱姆病监测的影响。

Impacts of misclassification on Lyme disease surveillance.

作者信息

Rutz Heather, Hogan Brenna, Hook Sarah, Hinckley Alison, Feldman Katherine

机构信息

Emerging Infections Program, Maryland Department of Health, Baltimore, Maryland.

Division of Vector-Borne Diseases, Centers for Disease Control and Prevention, Fort Collins, Colorado.

出版信息

Zoonoses Public Health. 2019 Feb;66(1):174-178. doi: 10.1111/zph.12525. Epub 2018 Sep 21.

Abstract

In Maryland, Lyme disease (LD) is the most widely reported tickborne disease. All laboratories and healthcare providers are required to report LD cases to the local health department. Given the large volume of LD reports, the nuances of diagnosing and reporting LD, and the effort required for investigations by local health department staff, surveillance for LD is burdensome and subject to underreporting. To determine the degree to which misclassification occurs in Maryland, we reviewed medical records for a sample of LD reports from 2009. We characterized what proportion of suspected and "not a case" reports could be reclassified as confirmed or probable once additional information was obtained from medical record review, explored the reasons for misclassification, and determined multipliers for a more accurate number of LD cases. We reviewed medical records for reports originally classified as suspected (n = 44) and "not a case" (n = 92). Of these 136 records, 31 (23%) suspected cases and "not a case" reports were reclassified. We calculated multipliers and applied them to the case counts from 2009, and estimate an additional 269 confirmed and probable cases, a 13.3% increase. Reasons for misclassification fell into three general categories: lack of clinical or diagnostic information from the provider; surveillance process errors; and incomplete information provided on laboratory reports. These multipliers can be used to calculate a better approximation of the true number of LD cases in Maryland, but these multipliers only account for underreporting due to misclassification, and do not account for cases that are not reported at all (e.g., LD diagnoses based on erythema migrans alone that are not reported) or for cases that are not investigated. Knowing that misclassification of cases occurs during the existing LD surveillance process underscores the complexities of LD surveillance, which further reinforces the need to find alternative approaches to LD surveillance.

摘要

在马里兰州,莱姆病(LD)是报告最为广泛的蜱传疾病。所有实验室和医疗服务提供者都必须向当地卫生部门报告莱姆病病例。鉴于莱姆病报告数量众多、诊断和报告莱姆病的细微差别以及当地卫生部门工作人员进行调查所需的精力,莱姆病监测工作负担沉重且存在报告不足的情况。为了确定马里兰州错误分类的程度,我们查阅了2009年莱姆病报告样本的病历。我们确定了一旦从病历审查中获得更多信息,有多大比例的疑似病例和“非病例”报告可重新分类为确诊或可能病例,探究了错误分类的原因,并确定了用于更准确计算莱姆病病例数的乘数。我们查阅了最初分类为疑似(n = 44)和“非病例”(n = 92)的报告的病历。在这136份记录中,31份(23%)疑似病例和“非病例”报告被重新分类。我们计算了乘数并将其应用于2009年的病例数,估计另外有269例确诊和可能病例,增加了13.3%。错误分类的原因大致可分为三类:提供者缺乏临床或诊断信息;监测过程错误;实验室报告提供的信息不完整。这些乘数可用于更准确地计算马里兰州莱姆病病例的实际数量,但这些乘数仅考虑了因错误分类导致的报告不足,并未考虑完全未报告的病例(例如,仅基于游走性红斑做出的莱姆病诊断但未报告)或未进行调查的病例。认识到在现有的莱姆病监测过程中会出现病例错误分类,凸显了莱姆病监测的复杂性,这进一步强化了寻找莱姆病监测替代方法的必要性。

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