Biostatistics, Biomathematics, Pharmacoepidemiology and Infectious Diseases (B2PHI), Inserm, UVSQ, Institut Pasteur, Université Paris-Saclay, Paris, France.
Stat Med. 2018 Oct 30;37(24):3437-3454. doi: 10.1002/sim.7818. Epub 2018 Jun 25.
Burden analysis in public health often involves the estimation of exposure-attributable fractions from observed time series. When the entire population is exposed, the association between the exposure and outcome must be carefully modelled before the attributable fractions can be estimated. This article derives asymptotic convergences for the estimation of attributable fractions for commonly used time series models (ARMAX, Poisson, negative binomial, and Serfling), using for the most part the delta method. For the Poisson regression, the estimation of the attributable fraction is achieved by a Monte Carlo algorithm, taking into account both an estimation and a prediction error. A simulation study compares these estimations in the case of an epidemic exposure and highlights the importance of thorough analysis of the data: When the outcome is generated under an additive model, the additive models are satisfactory, and the multiplicative models are poor, and vice versa. However, the Serfling model performs poorly in all cases. Of note, a misspecification in the form or delay of the association between the exposure and the outcome leads to mediocre estimation of the attributable fraction. An application to the fraction of French outpatient antibiotic use attributable to influenza between 2003 and 2010 illustrates the asymptotic convergences. This study suggests that the Serfling model should be avoided when estimating attributable fractions while the model of choice should be selected after careful investigation of the association between the exposure and outcome.
公共卫生中的负担分析通常涉及从观察到的时间序列中估计暴露归因分数。当整个人群都暴露时，在估计归因分数之前，必须仔细建模暴露与结局之间的关联。本文使用差分法推导出了常用时间序列模型（ARMAX、泊松、负二项和 Serfling）归因分数估计的渐近收敛性。对于泊松回归，归因分数的估计是通过蒙特卡罗算法实现的，同时考虑了估计误差和预测误差。一项模拟研究比较了在传染病暴露情况下的这些估计，并强调了对数据进行彻底分析的重要性：当结局是在加性模型下生成时，加性模型是令人满意的，而乘法模型则较差，反之亦然。然而，Serfling 模型在所有情况下表现都不佳。值得注意的是，暴露与结局之间关联的形式或延迟的错误指定会导致归因分数的估计不佳。2003 年至 2010 年期间法国门诊抗生素使用归因于流感的比例的应用说明了渐近收敛性。本研究表明，在估计归因分数时应避免使用 Serfling 模型，而应在仔细研究暴露与结局之间的关联后选择合适的模型。
