Monitoring change in spatial patterns of disease: comparing univariate and multivariate cumulative sum approaches
Monitoring change in spatial patterns of disease: comparing univariate and multivariate cumulative sum approaches
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DOI：
10.1002/sim.1806
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发表时间：
2004-07
影响因子：
2
通讯作者：
P. Rogerson;Ikuho Yamada
中科院分区：
文献类型：
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作者：
P. Rogerson;Ikuho Yamada
前瞻性疾病监测越来越受到关注，特别是考虑到最近对快速发现生物恐怖事件的关注。监测卫生事件有可能发现这类事件，但监测的好处更广泛地延伸到快速发现公共卫生的变化。本文比较了单变量和多变量累积和方法在疾病监测中的应用。虽然单变量方法已被用于卫生监督的背景下，多变量方法没有。单变量方法包括同时和独立地监测每个地区的发病率;多变量方法明确说明了地区之间的任何协变。单变量的方法是有限的，他们缺乏能力来解释区域数据的空间自相关性;多变量的方法是有限的，在准确地指定多区域协方差结构的困难。使用模拟数据和美国东北部乳腺癌的县级数据来说明这些方法。当空间自相关程度较低时，单变量方法通常更好地检测发生在少数区域中的速率变化;当变化发生在大量区域中时，多变量方法更好。版权所有© 2004年约翰威利父子有限公司。
Prospective disease surveillance has gained increasing attention, particularly in light of recent concern for quick detection of bioterrorist events. Monitoring of health events has the potential for the detection of such events, but the benefits of surveillance extend much more broadly to the quick detection of change in public health. In this paper, univariate and multivariate cumulative sum methods for disease surveillance are compared. Although the univariate method has been previously used in the context of health surveillance, the multivariate method has not. The univariate approach consists of simultaneously and independently monitoring the disease rate in each region; the multivariate approach accounts explicitly for any covariation between regions. The univariate approaches are limited by their lack of ability to account for the spatial autocorrelation of regional data; the multivariate methods are limited by the difficulty in accurately specifying the multiregional covariance structure. The methods are illustrated using both simulated data and county‐level data on breast cancer in the northeastern United States. When the degree of spatial autocorrelation is low, the univariate method is generally better at detecting changes in rates that occur in a small number of regions; the multivariate is better when change occurs in a large number of regions. Copyright © 2004 John Wiley & Sons, Ltd.