Travis-Lumer Yael, Goldberg Yair, Levine Stephen Z
Faculty of Industrial Engineering and Management, Israel Institute of Technology, 3200003, Haifa, Israel.
School of Public Health, University of Haifa, Haifa, Israel.
Emerg Themes Epidemiol. 2022 Nov 11;19(1):9. doi: 10.1186/s12982-022-00118-7.
Interrupted time series (ITS) analysis is a time series regression model that aims to evaluate the effect of an intervention on an outcome of interest. ITS analysis is a quasi-experimental study design instrumental in situations where natural experiments occur, gaining popularity, particularly due to the Covid-19 pandemic. However, challenges, including the lack of a control group, have impeded the quantification of the effect size in ITS. The current paper proposes a method and develops a user-friendly R package to quantify the effect size of an ITS regression model for continuous and count outcomes, with or without seasonal adjustment.
The effect size presented in this work, together with its corresponding 95% confidence interval (CI) and P-value, is based on the ITS model-based fitted values and the predicted counterfactual (the exposed period had the intervention not occurred) values. A user-friendly R package to fit an ITS and estimate the effect size was developed and accompanies this paper. To illustrate, we implemented a nation population-based ITS study from January 2001 to May 2021 covering the all-cause mortality of Israel (n = 9,350 thousand) to quantify the effect size of Covid-19 exposure on mortality rates. In the period unexposed to the Covid-19 pandemic, the mortality rate decreased over time and was expected to continue decreasing had Covid-19 not occurred. In contrast, the period exposed to the Covid-19 pandemic was associated with an increased all-cause mortality rate (relative risk = 1.11, 95% CI = 1.04, 1.18, P < 0.001).
For the first time, the effect size in ITS: was quantified, can be estimated by end-users with an R package we developed, and was demonstrated with data showing an increase in mortality following the Covid-19 pandemic. ITS effect size reporting can assist public health policy makers in assessing the magnitude of the entire intervention effect using a single, readily understood measure.
中断时间序列（ITS）分析是一种时间序列回归模型，旨在评估干预措施对感兴趣的结果的影响。ITS分析是一种准实验研究设计，在自然实验发生的情况下很有用，特别是由于新冠疫情，它越来越受欢迎。然而，包括缺乏对照组在内的挑战阻碍了ITS中效应大小的量化。本文提出了一种方法，并开发了一个用户友好的R包，用于量化具有或不具有季节性调整的连续和计数结果的ITS回归模型的效应大小。
本研究中呈现的效应大小及其相应的95%置信区间（CI）和P值，基于基于ITS模型的拟合值和预测的反事实（假设未发生干预的暴露期）值。开发了一个用户友好的R包来拟合ITS并估计效应大小，并随本文一同发布。为了说明，我们实施了一项基于全国人口的ITS研究，时间跨度为2001年1月至2021年5月，涵盖以色列的全因死亡率（n = 935万），以量化新冠暴露对死亡率的效应大小。在未暴露于新冠疫情的时期，死亡率随时间下降，并且如果没有新冠疫情预计会继续下降。相比之下，暴露于新冠疫情的时期与全因死亡率增加相关（相对风险 = 1.11，95% CI = 1.04，1.18，P < 0.001）。
首次对ITS中的效应大小进行了量化，终端用户可以使用我们开发的R包进行估计，并通过数据证明了新冠疫情后死亡率的增加。ITS效应大小报告可以帮助公共卫生政策制定者使用单一、易于理解的指标来评估整个干预效果的大小。
