โก Quick Summary
This review explores the use of Interrupted Time Series (ITS) designs in environmental epidemiology, highlighting both traditional and innovative modeling approaches. The integration of machine learning and Bayesian frameworks enhances the analysis of complex temporal patterns, as demonstrated through a case study on respiratory hospitalizations during the 2018 wildfire smoke event in San Francisco.
๐ Key Details
- ๐ Focus: Interrupted Time Series (ITS) analysis in environmental health
- ๐งฉ Methods: Traditional ITS, ARIMA, machine learning models, Bayesian ITS
- ๐ Case Study: Respiratory hospitalizations during the 2018 wildfire smoke event in San Francisco
- ๐ Data Sharing: Annotated datasets and R scripts provided for reproducibility
๐ Key Takeaways
- ๐ ITS designs are increasingly utilized to assess the impact of environmental events and policies.
- ๐ก Machine learning and Bayesian methods offer enhanced flexibility in modeling complex data.
- ๐ฅ The study focused on acute exposures, particularly from wildfire smoke.
- ๐ The framework is applicable to various public health interventions beyond environmental applications.
- ๐ The review provides actionable guidance for researchers in causal inference.
- ๐ Datasets and scripts are shared to promote reproducibility in research.
- ๐ This work advances ITS methodology by integrating contemporary statistical innovations.
๐ Background
Environmental epidemiology plays a crucial role in understanding the health impacts of environmental factors, such as extreme weather events. Interrupted Time Series (ITS) designs are a powerful tool for evaluating these impacts, allowing researchers to analyze trends over time and assess the effectiveness of interventions. However, traditional methods often struggle with complex temporal patterns and heterogeneous effects, necessitating the exploration of novel approaches.
๐๏ธ Study
The authors conducted a comprehensive review of both traditional and contemporary ITS methodologies, focusing on their application in environmental health. They examined various modeling techniques, including ARIMA, machine learning algorithms, and Bayesian frameworks, to assess their effectiveness in capturing complex data patterns. A real-world case study was presented, estimating excess respiratory hospitalizations during the 2018 wildfire smoke event in San Francisco, showcasing the practical application of these methods.
๐ Results
The study highlighted the advantages of using machine learning and Bayesian approaches in ITS analysis, particularly in handling nonlinear trends and seasonality. The comparative analysis demonstrated that these modern techniques could provide more accurate estimates of treatment effects, thereby enhancing the reliability of findings in environmental epidemiology.
๐ Impact and Implications
The findings from this review have significant implications for public health research and policy. By advancing the methodology of ITS analysis, researchers can better understand the health impacts of environmental exposures, leading to more effective interventions. The shared datasets and R scripts promote reproducibility, encouraging further exploration and application of these innovative methods in various public health contexts.
๐ฎ Conclusion
This review underscores the importance of integrating contemporary statistical innovations into environmental epidemiology. The advancements in Interrupted Time Series analysis through machine learning and Bayesian frameworks offer exciting opportunities for more nuanced understanding of health impacts from environmental factors. Continued research in this area is essential for improving public health outcomes and informing policy decisions.
๐ฌ Your comments
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Interrupted Time Series Analysis in Environmental Epidemiology: A Review of Traditional and Novel Modeling Approaches.
Abstract
PURPOSE OF REVIEW: Interrupted time series (ITS) designs are increasingly used in environmental health to evaluate impacts of extreme weather events or policies. This paper aims to introduce traditional and contemporary ITS approaches, including machine learning algorithms and Bayesian frameworks, which enhance flexibility in modeling complex temporal patterns (e.g., seasonality and nonlinear trends) and spatially heterogeneous treatment effects. We present a comparative analysis of methods such as ARIMA, machine learning models, and Bayesian ITS, using a real-world case study: estimating excess respiratory hospitalizations during the 2018 wildfire smoke event in San Francisco.
RECENT FINDINGS: Our study demonstrates the practical application of these methods and provides a guide for selecting and implementing ITS designs in environmental epidemiology. To ensure reproducibility, we share annotated datasets and R scripts, allowing researchers to replicate analyses and adapt workflows. While focused on environmental applications, particularly acute exposures like wildfire smoke, the framework is broadly applicable to public health interventions. This work advances ITS methodology by integrating contemporary statistical innovations and emphasizing actionable guidance for causal inference in complex, real-world settings.
Author: [‘Ma Y’, ‘Benmarhnia T’]
Journal: Curr Environ Health Rep
Citation: Ma Y and Benmarhnia T. Interrupted Time Series Analysis in Environmental Epidemiology: A Review of Traditional and Novel Modeling Approaches. Interrupted Time Series Analysis in Environmental Epidemiology: A Review of Traditional and Novel Modeling Approaches. 2025; 12:50. doi: 10.1007/s40572-025-00517-3