Quantifying trends in disease impact to produce a consistent and reproducible definition of an emerging infectious disease.
Quantifying trends in disease impact to produce a consistent and reproducible definition of an emerging infectious disease.
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量化疾病影响的趋势，以产生新兴传染病的一致且可重复的定义。
DOI：
10.1371/journal.pone.0069951
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发表时间：
2013
期刊：
影响因子：
3.7
通讯作者：
Daszak P
中科院分区：
文献类型：
--
作者：
Funk S;Bogich TL;Jones KE;Kilpatrick AM;Daszak P
为研究和控制适当分配公共卫生资源，需要对疾病目前的负担及其影响趋势进行量化。已被标记为“新发传染病”（EID）的传染病已得到科学和公众的高度关注和资源。然而，“新兴”这个标签很少有定量分析的支持，而且经常被主观地使用。这可能导致为被错误地称为“新出现”的疾病分配过多的资源，而为有明显证据表明其影响日益严重或持续性很高的疾病分配的资源不足。我们提出了一个简单的定量方法，分段回归，来描述疾病的趋势和出现。分段回归识别时间序列中的一个或多个趋势，并确定时间序列中统计上最简约的分割（或接合点）。时间序列中的这些连接点表明趋势发生变化的时间点，并可以确定疾病影响变化的驱动因素的时期。我们通过分析12种疾病的发病率数据的时间模式来说明该方法。这种方法提供了一种方法，可以根据时间趋势将疾病分类为目前正在出现、重新出现、消退或稳定，以及确定这些趋势发生变化的时间。我们认为，定量方法来定义的基础上出现的疾病的影响趋势，在适当的情况下，可以用来优先考虑资源的研究和控制。实施这一更严格的EID定义将需要科学家、政策制定者、同行评审员和期刊编辑的支持和执行，但有可能改善全球卫生的资源分配。
The proper allocation of public health resources for research and control requires quantification of both a disease's current burden and the trend in its impact. Infectious diseases that have been labeled as “emerging infectious diseases” (EIDs) have received heightened scientific and public attention and resources. However, the label ‘emerging’ is rarely backed by quantitative analysis and is often used subjectively. This can lead to over-allocation of resources to diseases that are incorrectly labelled “emerging,” and insufficient allocation of resources to diseases for which evidence of an increasing or high sustained impact is strong. We suggest a simple quantitative approach, segmented regression, to characterize the trends and emergence of diseases. Segmented regression identifies one or more trends in a time series and determines the most statistically parsimonious split(s) (or joinpoints) in the time series. These joinpoints in the time series indicate time points when a change in trend occurred and may identify periods in which drivers of disease impact change. We illustrate the method by analyzing temporal patterns in incidence data for twelve diseases. This approach provides a way to classify a disease as currently emerging, re-emerging, receding, or stable based on temporal trends, as well as to pinpoint the time when the change in these trends happened. We argue that quantitative approaches to defining emergence based on the trend in impact of a disease can, with appropriate context, be used to prioritize resources for research and control. Implementing this more rigorous definition of an EID will require buy-in and enforcement from scientists, policy makers, peer reviewers and journal editors, but has the potential to improve resource allocation for global health.
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影响因子：
168.9
作者：
Kilpatrick, A. Marm;Randolph, Sarah E.
通讯作者：
Randolph, Sarah E.
影响因子：
4.5
作者：
Russell KL;Rubenstein J;Burke RL;Vest KG;Johns MC;Sanchez JL;Meyer W;Fukuda MM;Blazes DL
通讯作者：
Blazes DL
DOI：
10.1098/rstb.2010.0031
发表时间：
2010-08-27
期刊：
Philosophical transactions of the Royal Society of London. Series B, Biological sciences
影响因子：
--
作者：
Sharp, Paul M;Hahn, Beatrice H
通讯作者：
Hahn, Beatrice H
影响因子：
7.7
作者：
Krause, Gerard
通讯作者：
Krause, Gerard
影响因子：
11.8
作者：
Morse SS
通讯作者：
Morse SS