Departamento de Epidemiologia, Instituto Nacional de Saúde Dr. Ricardo Jorge, Av. Padre Cruz, Lisbon, Portugal.
Stat Methods Med Res. 2011 Aug;20(4):331-45. doi: 10.1177/0962280209340201. Epub 2010 Mar 8.
The occurrence of influenza epidemics during winters, in the northern hemisphere countries, is known to be associated with observed excess mortality for all causes. A large variety of methods have been developed in order to estimate, from weekly or monthly mortality time series, the number of influenza-associated deaths in each season. The present work focus on the group of methods characterised by fitting statistical models to interrupted mortality time series. The study objective is to find a common ground between these methods in order to describe and compare them. They are unified in a single class, being categorised according to three main parameters: the model used to fit the interrupted time series and obtain a baseline, the a priori chosen type of periods used to estimate the influenza epidemic periods and the procedure used to fit the model to the time series (iterative or non-iterative). This generalisation led quite naturally to the construction of a set of user friendly R-routines, package flubase, implementing all these models. These routines were applied to data on about 20 years of weekly Portuguese number of deaths by pneumonia and influenza showing that, in this case, the parameter that had the highest impact on influenza-associated deaths estimates was the a priori chosen type of period used.
在北半球国家,冬季流感的爆发与所有原因导致的超额死亡率有关,这是众所周知的。为了从每周或每月的死亡率时间序列中估计每个季节与流感相关的死亡人数,已经开发了多种方法。目前的工作重点是拟合中断死亡率时间序列的统计模型的方法组。本研究的目的是在这些方法之间找到共同点,以便对其进行描述和比较。它们被统一到一个单独的类别中,根据三个主要参数进行分类:用于拟合中断时间序列并获得基线的模型、用于估计流感流行期的预先选择的时间段类型以及用于将模型拟合到时间序列的过程(迭代或非迭代)。这种概括自然导致了一组用户友好的 R 例程的构建,flubase 包实现了所有这些模型。这些例程应用于大约 20 年的葡萄牙每周肺炎和流感死亡人数的数据,结果表明,在这种情况下,对流感相关死亡估计影响最大的参数是预先选择的时间段类型。