Improving national surveillance of Lyme neuroborreliosis in Denmark through electronic reporting of specific antibody index testing from 2010 to 2012
Improving national surveillance of Lyme neuroborreliosis in Denmark through electronic reporting of specific antibody index testing from 2010 to 2012
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DOI：
10.2807/1560-7917.es2015.20.28.21184
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发表时间：
2015-07-16
期刊：
影响因子：
19
通讯作者：
Voldstedlund, M.
中科院分区：
文献类型：
--
作者：
Dessau, R. B.;Espenhain, L.;Voldstedlund, M.
我们的目的是评估丹麦使用国家微生物学数据库(MIBA)对莱姆氏脑疏螺旋体病(LNB)进行自动监测的结果，并在国家一级描述实验室确认的LNB的流行病学。基于MIBA的监测包括实验室结果的电子传输，而不是基于人工处理的通知的法定监测。抗体指数(AI)检测是丹麦推荐的支持LNB诊断的实验室测试。在2010年至2012年期间，向法定监测系统通报了217例LNB临床病例，而MIBA系统报告了533例禽流感阳性病例。已通报35例未确诊病例(29例AI阴性，6例未检测)，但未被MIBA捕获。使用MIBA，报告的病例数量几乎增加了2.5倍。此外，报告的时间更及时(中位数滞后时间：6vs58天)。在丹麦，经人工智能确认的LNB的年平均发病率为3.2/10万人口，按市政当局分层的发病率从零到超过10/10万不等。这是第一次使用客观实验室标准报告全国LNB发病率的研究。与基于人工处理的通知的监测相比，采用电子数据传输的实验室监测更加准确、完整和及时。我们建议将AI检测结果用于LNB监测，而不是临床报告。
Our aim was to evaluate the results of automated surveillance of Lyme neuroborreliosis (LNB) in Denmark using the national microbiology database (MiBa), and to describe the epidemiology of laboratory-confirmed LNB at a national level. MiBa-based surveillance includes electronic transfer of laboratory results, in contrast to the statutory surveillance based on manually processed notifications. Antibody index (AI) testing is the recommend laboratory test to support the diagnosis of LNB in Denmark. In the period from 2010 to 2012, 217 clinical cases of LNB were notified to the statutory surveillance system, while 533 cases were reported AI positive by the MiBa system. Thirty-five unconfirmed cases (29 AI-negative and 6 not tested) were notified, but not captured by MiBa. Using MiBa, the number of reported cases was increased almost 2.5 times. Furthermore, the reporting was timelier (median lag time: 6 vs 58 days). Average annual incidence of AI-confirmed LNB in Denmark was 3.2/100,000 population and incidences stratified by municipality ranged from none to above 10/100,000. This is the first study reporting nationwide incidence of LNB using objective laboratory criteria. Laboratory-based surveillance with electronic data-transfer was more accurate, complete and timely compared to the surveillance based on manually processed notifications. We propose using AI test results for LNB surveillance instead of clinical reporting.