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在目标监测中利用流行病学信息进行人群推断。

Use of epidemiologic information in targeted surveillance for population inference.

作者信息

Wells Scott J, Ebel Eric D, Williams Michael S, Scott Aaron E, Wagner Bruce A, Marshall Katherine L

机构信息

Department of Veterinary Population Medicine, University of Minnesota, 136 ABLMS, 1354 Eckles Avenue, St. Paul, MN 55108, USA.

出版信息

Prev Vet Med. 2009 May 1;89(1-2):43-50. doi: 10.1016/j.prevetmed.2009.01.007. Epub 2009 Mar 9.

DOI:10.1016/j.prevetmed.2009.01.007
PMID:19269705
Abstract

Epidemiologic information, including animal characteristics (e.g., observable risk factors or clinical signs) predisposing to animal disease, is frequently used for design of targeted surveillance systems, but this information is infrequently used for population inference. In this study, we report the evaluation of use of epidemiologic information for population inference in targeted surveillance in three animal disease scenarios. We adapted sampling theory using Monte Carlo methods to determine target population sample size to detect disease with 95% confidence, using information from the epidemiologic parameters risk ratio and fraction of the population with the characteristic. These parameters and their uncertainties were derived from a reference population. The next step was to use a second (sampled) population to evaluate effects of sampling the targeted population. The focus of the study was on estimation of prevalence. Our results showed that if one is less certain of the epidemiologic parameters, a rational decision is to model the input parameter distributions reflecting this uncertainty, thereby increasing the sample size above the minimum needed for the detection of the disease with a known confidence. Targeted surveillance is appropriate for prevalence estimation when one has representative and justifiable estimates of key epidemiologic parameters.

摘要

流行病学信息,包括易引发动物疾病的动物特征(如可观察到的风险因素或临床症状),经常用于设计针对性监测系统,但这类信息很少用于总体推断。在本研究中,我们报告了在三种动物疾病情形下,对针对性监测中用于总体推断的流行病学信息的评估。我们采用蒙特卡罗方法调整抽样理论,以确定目标总体样本量,从而在95%置信度下利用流行病学参数风险比和具有该特征的总体比例信息来检测疾病。这些参数及其不确定性源自一个参考总体。下一步是使用第二个(抽样)总体来评估对目标总体进行抽样的效果。该研究的重点是患病率估计。我们的结果表明,如果对流行病学参数的确定性较低,合理的决策是对反映这种不确定性的输入参数分布进行建模,从而使样本量增加到高于在已知置信度下检测疾病所需的最小样本量。当对关键流行病学参数有代表性且合理的估计时,针对性监测适用于患病率估计。

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