Secular trends in diagnostic code density in electronic healthcare data from health care systems in the Vaccine Safety Datalink Project
Secular trends in diagnostic code density in electronic healthcare data from health care systems in the Vaccine Safety Datalink Project
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DOI：
10.1016/j.vaccine.2012.12.030
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发表时间：
2013-02-01
期刊：
影响因子：
5.5
通讯作者：
Jacobsen, Steven J.
中科院分区：
文献类型：
--
作者：
Hechter, Rulin C.;Qian, Lei;Jacobsen, Steven J.
大型观察性疫苗安全性研究通常使用从医疗保健数据库中提取的自动诊断来识别预先指定的免疫接种后潜在不良事件 (AEFI)。我们根据 2001-2009 年疫苗安全数据链项目的八个大型医疗保健系统的医疗保健环境、年龄和预先指定的条件，评估了每次诊断数量的长期趋势和变化，无论免疫状态如何（称为诊断代码密度）。在所有医疗机构和年龄组中都观察到诊断代码密度呈增加趋势，但各站点之间存在差异。当编码政策或数据包含标准发生变化时，在某些站点观察到诊断代码密度突然增加。当疫苗安全性研究使用历史比较器时，随着时间的推移，诊断代码密度的增加可能会产生较低的预期率（基于历史数据）和较高的观察率（基于当前数据），这表明疫苗与 AEFI 之间存在假阳性关联。对诊断代码密度的持续监测可以为研究设计和选择适当的对照组提供指导。它还可用于确保主动安全监视系统中的数据质量并及时纠正错误。 (C) 2012 Elsevier Ltd. 保留所有权利。
Large observational vaccine safety studies often use automated diagnoses extracted from medical care databases to identify pre-specified potential adverse events following immunization (AEFI). We assessed the secular trends and variability in the number of diagnoses per encounter regardless of immunization status referred as diagnostic code density, by healthcare setting, age, and pre-specified condition in eight large health care systems of the Vaccine Safety Datalink project during 2001-2009. An increasing trend in diagnostic code density was observed in all healthcare settings and age groups, with variations across the sites. Sudden increases in diagnostic code density were observed at certain sites when changes in coding policies or data inclusion criteria took place. When vaccine safety studies use an historical comparator, the increased diagnostic code density over time may generate low expected rates (based on historical data) and high observed rates (based on current data), suggesting a false positive association between a vaccine and AEFI. The ongoing monitoring of the diagnostic code density can provide guidance on study design and choice of appropriate comparison groups. It can also be used to ensure data quality and allow timely correction of errors in an active safety surveillance system. (C) 2012 Elsevier Ltd. All rights reserved.