A generalized linear mixed models approach for detecting incident clusters of disease in small areas, with an application to biological terrorism
A generalized linear mixed models approach for detecting incident clusters of disease in small areas, with an application to biological terrorism
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DOI：
10.1093/aje/kwh029
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发表时间：
2004-02-01
影响因子：
5
通讯作者：
Platt, R
中科院分区：
文献类型：
--
作者：
Kleinman, K;Lazarus, R;Platt, R
自从2001年秋季通过美国邮政系统故意传播炭疽以来，人们对监测生物恐怖主义的兴趣越来越大。更一般地说，这可以被描述为对突发疾病群的检测。此外，由于出现了负担得起的快速地理编码，可以在比过去更精细的空间尺度上进行监测。在时间和空间两方面对突发性疾病的监测是统计方法学中相对不发达的竞技场。特别是，为侦测生物恐怖主义而进行的监测提出了方法上的独特问题。例如，最令人关切的生物恐怖主义制剂引起的最初症状可能难以与自然发生的疾病区分开来。在本文中，作者提出了一种通用的方法来评估在相对较小的地区观察到的计数是否大于自然发生疾病的历史的基础上预期的。他们使用广义线性混合模型实现该方法。该方法说明使用的数据从一个大的马萨诸塞州管理的保健组织/多专业实践组的背景下，炭疽综合征监测的卫生保健访问（1996-1999年）。作者认为，利用地理数据有很大的价值。
Since the intentional dissemination of anthrax through the US postal system in the fall of 2001, there has been increased interest in surveillance for detection of biological terrorism. More generally, this could be described as the detection of incident disease clusters. In addition, the advent of affordable and quick geocoding allows for surveillance on a finer spatial scale than has been possible in the past. Surveillance for incident clusters of disease in both time and space is a relatively undeveloped arena of statistical methodology. Surveillance for bioterrorism detection, in particular, raises unique issues with methodological relevance. For example, the bioterrorism agents of greatest concern cause initial symptoms that may be difficult to distinguish from those of naturally occurring disease. In this paper, the authors propose a general approach to evaluating whether observed counts in relatively small areas are larger than would be expected on the basis of a history of naturally occurring disease. They implement the approach using generalized linear mixed models. The approach is illustrated using data on health-care visits (1996-1999) from a large Massachusetts managed care organization/multispecialty practice group in the context of syndromic surveillance for anthrax. The authors argue that there is great value in using the geographic data.