We advised on a project aimed at developing a comprehensive smoke-ready plan for communities at increasing risk of wildfires due to climate change and changing land use patterns. The lead agencies for the project were the EPA and the U.S. Forest Service. The scope of the work was to identify the factors contributing to the […]
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We reviewed a project that evaluated the health impacts of wildfires on vulnerable populations. Led by the EPA, the project aimed to assess the spatial distribution of health effects and to develop targeted interventions for those at risk of cardiovascular and respiratory issues, especially following severe wildfire seasons. This review provided high-level interdisciplinary guidance. This
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We advised on a project that assessed the impacts of environmental conditions on the health effects of wildfire smoke and air pollution mixtures. The lead agency for this project was the EPA. The scope of work identifying the health risks associated with pollutant mixtures and changing environmental conditions aligns closely with the current proposal’s objective
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We evaluated the health impacts of air pollution exposure on vulnerable populations. The lead agency for this project was the EPA. This project’s scope and the current proposal involve spatial analysis and the evaluation of environmental health risks. The focus on protecting public health in areas affected by air pollution aligns closely with the current
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Cox Regression Cox regression, also known as the Cox proportional hazards model, is a statistical technique used to explore the relationship between the survival time of subjects and one or more predictor variables. It is used in medical research, particularly for time-to-event data, where the goal is to investigate how certain factors influence the time
Exploratory Factor Analysis Exploratory Factor Analysis (EFA) is a statistical technique to identify underlying relationships between measured variables. It aims to uncover the latent structure (factors) within a set of observed variables without imposing any preconceived structure on the outcome. EFA is often employed in the early stages of research to explore a dataset’s dimensionality
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Confirmatory Factor Analysis Confirmatory Factor Analysis (CFA) is a statistical technique used to test the hypothesis that a relationship exists between observed variables and their underlying latent constructs. Unlike Exploratory Factor Analysis (EFA), which explores the data to identify potential underlying structures without preconceived notions, CFA confirms whether the data fit a hypothesized measurement model.
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Hierarchical Linear Modeling Hierarchical Linear Modeling (HLM), also known as multilevel modeling or mixed-effects modeling, is a statistical technique used to analyze nested data. This approach is particularly useful when dealing with data where observations are grouped at multiple levels, such as students within classrooms, employees within companies, or patients within hospitals. HLM accounts for
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Time series analysis is a statistical technique used to analyze data points collected or recorded at specific intervals. Unlike other data types, time series data are chronological, meaning each data point depends on the preceding values. This characteristic makes time series analysis particularly useful for identifying trends, cycles, and seasonal patterns and forecasting future values
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Moderation analysis is a statistical technique used to examine whether the strength or direction of the relationship between an independent variable (X) and a dependent variable (Y) changes across levels of a third variable, known as the moderator (M). It explores the conditions under which specific effects occur, offering insights into the variability of the
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