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 gu

SUBMITTER: Ma Y 

PROVIDER: S-EPMC12669373 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature