Daisuke Yoneoka1,2, Takayuki Kawashima3,4, Yuta Tanoue5, Shuhei Nomura3,6, Akifumi Eguchi3,7. 1. Infectious Disease Surveillance Center, National Institute of Infectious Diseases, Tokyo, Japan. blue.sky.sea.dy@gmail.com. 2. Graduate School of Medicine, The University of Tokyo, Tokyo, Japan. blue.sky.sea.dy@gmail.com. 3. Graduate School of Medicine, The University of Tokyo, Tokyo, Japan. 4. Department of Mathematical and Computing Science, Tokyo Institute of Technology, Tokyo, Japan. 5. Institute for Business and Finance, Waseda University, Tokyo, Japan. 6. School of Medicine, Keio University, Tokyo, Japan. 7. Center for Preventive Medical Sciences, Chiba University, Chiba, Japan.
Abstract
BACKGROUND: Interrupted time series (ITS) analysis has become a popular design to evaluate the effects of health interventions. However, the most common formulation for ITS, the linear segmented regression, is not always adequate, especially when the timing of the intervention is unclear. In this study, we propose a new model to overcome this limitation. METHODS: We propose a new ITS model, ARIMAITS-DL, that combines (1) the Autoregressive Integrated Moving Average (ARIMA) model and (2) distributed lag functional terms. The ARIMA technique allows us to model autocorrelation, which is frequently observed in time series data, and the decaying cumulative effect of the intervention. By contrast, the distributed lag functional terms represent the idea that the intervention effect does not start at a fixed time point but is distributed over a certain interval (thus, the intervention timing seems unclear). We discuss how to select the distribution of the effect, the model construction process, diagnosing the model fitting, and interpreting the results. Further, our model is implemented as an example of a statement of emergency (SoE) during the coronavirus disease 2019 pandemic in Japan. RESULTS: We illustrate the ARIMAITS-DL model with some practical distributed lag terms to examine the effect of the SoE on human mobility in Japan. We confirm that the SoE was successful in reducing the movement of people (15.0-16.0% reduction in Tokyo), at least between February 20 and May 19, 2020. We also provide the R code for other researchers to easily replicate our method. CONCLUSIONS: Our model, ARIMAITS-DL, is a useful tool as it can account for the unclear intervention timing and distributed lag effect with autocorrelation and allows for flexible modeling of different types of impacts such as uniformly or normally distributed impact over time.
BACKGROUND: Interrupted time series (ITS) analysis has become a popular design to evaluate the effects of health interventions. However, the most common formulation for ITS, the linear segmented regression, is not always adequate, especially when the timing of the intervention is unclear. In this study, we propose a new model to overcome this limitation. METHODS: We propose a new ITS model, ARIMAITS-DL, that combines (1) the Autoregressive Integrated Moving Average (ARIMA) model and (2) distributed lag functional terms. The ARIMA technique allows us to model autocorrelation, which is frequently observed in time series data, and the decaying cumulative effect of the intervention. By contrast, the distributed lag functional terms represent the idea that the intervention effect does not start at a fixed time point but is distributed over a certain interval (thus, the intervention timing seems unclear). We discuss how to select the distribution of the effect, the model construction process, diagnosing the model fitting, and interpreting the results. Further, our model is implemented as an example of a statement of emergency (SoE) during the coronavirus disease 2019 pandemic in Japan. RESULTS: We illustrate the ARIMAITS-DL model with some practical distributed lag terms to examine the effect of the SoE on human mobility in Japan. We confirm that the SoE was successful in reducing the movement of people (15.0-16.0% reduction in Tokyo), at least between February 20 and May 19, 2020. We also provide the R code for other researchers to easily replicate our method. CONCLUSIONS: Our model, ARIMAITS-DL, is a useful tool as it can account for the unclear intervention timing and distributed lag effect with autocorrelation and allows for flexible modeling of different types of impacts such as uniformly or normally distributed impact over time.
Authors: Racquel Jandoc; Andrea M Burden; Muhammad Mamdani; Linda E Lévesque; Suzanne M Cadarette Journal: J Clin Epidemiol Date: 2015-03-11 Impact factor: 6.437
Authors: Atle Fretheim; Fang Zhang; Dennis Ross-Degnan; Andrew D Oxman; Helen Cheyne; Robbie Foy; Steve Goodacre; Jeph Herrin; Ngaire Kerse; R James McKinlay; Adam Wright; Stephen B Soumerai Journal: J Clin Epidemiol Date: 2014-12-11 Impact factor: 6.437