Time series regression studies in environmental epidemiology

Time series regression studies in environmental epidemiology

24 April 2013 | Krishnan Bhaskaran, Antonio Gasparini, Shakoor Hajat, Liam Smeeth and Ben Armstrong
This article provides a comprehensive guide to conducting time series regression studies in environmental epidemiology, focusing on the analysis of short-term associations between environmental exposures and health outcomes. The authors, from the London School of Hygiene and Tropical Medicine, outline the general features of time series data and the analytical process, emphasizing the unique challenges and considerations in this field. Key topics include: 1. **Data Features and Introduction**: The article begins by introducing the concept of time series data, which consists of data points recorded at regular intervals, such as daily pollution levels and daily mortality counts. It highlights the importance of understanding the data through simple plots and tables to identify patterns and trends. 2. **Descriptive Analysis**: Initial descriptive analyses involve plotting the exposure and outcome variables over time to identify seasonal and long-term patterns. This step is crucial for understanding the underlying structure of the data and setting the stage for more advanced analyses. 3. **Time Series Regression**: The main focus is on developing regression models to investigate the short-term associations between the exposure and the outcome. The article discusses the use of Poisson regression for count data and addresses the challenges of autocorrelation and overdispersion. It introduces several methods to control for seasonality and long-term trends, including time stratified models, periodic functions (Fourier terms), and flexible spline functions. 4. **Controlling for Confounding Factors**: The article emphasizes the importance of controlling for potential confounding factors, such as temperature, which can influence both the exposure and the outcome. It provides examples of how to incorporate these factors into the regression models to adjust for their effects. 5. **Modeling Delayed Associations**: The concept of delayed or lagged effects is introduced, where the exposure on one day may influence the outcome on subsequent days. The article explains how to model these delayed associations using distributed lag models, which can help identify the timing and magnitude of these effects. 6. **Model Checking and Sensitivity Analysis**: The authors stress the importance of model checking and sensitivity analysis to ensure the robustness of the findings. This includes examining diagnostic plots, checking for autocorrelation, and performing multiple sensitivity analyses to validate the conclusions. 7. **Extensions and Further Considerations**: The article concludes with a discussion of extensions to the basic model, such as non-linear associations, investigation of effect modifiers, and analysis of data from multiple locations. It also provides practical advice on precision and power considerations in time series regression studies. Overall, the article serves as a valuable resource for epidemiologists and researchers interested in conducting time series regression studies in environmental epidemiology, providing a detailed and practical guide to the key steps and methodologies involved.This article provides a comprehensive guide to conducting time series regression studies in environmental epidemiology, focusing on the analysis of short-term associations between environmental exposures and health outcomes. The authors, from the London School of Hygiene and Tropical Medicine, outline the general features of time series data and the analytical process, emphasizing the unique challenges and considerations in this field. Key topics include: 1. **Data Features and Introduction**: The article begins by introducing the concept of time series data, which consists of data points recorded at regular intervals, such as daily pollution levels and daily mortality counts. It highlights the importance of understanding the data through simple plots and tables to identify patterns and trends. 2. **Descriptive Analysis**: Initial descriptive analyses involve plotting the exposure and outcome variables over time to identify seasonal and long-term patterns. This step is crucial for understanding the underlying structure of the data and setting the stage for more advanced analyses. 3. **Time Series Regression**: The main focus is on developing regression models to investigate the short-term associations between the exposure and the outcome. The article discusses the use of Poisson regression for count data and addresses the challenges of autocorrelation and overdispersion. It introduces several methods to control for seasonality and long-term trends, including time stratified models, periodic functions (Fourier terms), and flexible spline functions. 4. **Controlling for Confounding Factors**: The article emphasizes the importance of controlling for potential confounding factors, such as temperature, which can influence both the exposure and the outcome. It provides examples of how to incorporate these factors into the regression models to adjust for their effects. 5. **Modeling Delayed Associations**: The concept of delayed or lagged effects is introduced, where the exposure on one day may influence the outcome on subsequent days. The article explains how to model these delayed associations using distributed lag models, which can help identify the timing and magnitude of these effects. 6. **Model Checking and Sensitivity Analysis**: The authors stress the importance of model checking and sensitivity analysis to ensure the robustness of the findings. This includes examining diagnostic plots, checking for autocorrelation, and performing multiple sensitivity analyses to validate the conclusions. 7. **Extensions and Further Considerations**: The article concludes with a discussion of extensions to the basic model, such as non-linear associations, investigation of effect modifiers, and analysis of data from multiple locations. It also provides practical advice on precision and power considerations in time series regression studies. Overall, the article serves as a valuable resource for epidemiologists and researchers interested in conducting time series regression studies in environmental epidemiology, providing a detailed and practical guide to the key steps and methodologies involved.
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