Progress in the application of generalized additive model in epidemiologic studies on air pollution
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Abstract
Establishing an exposure-response relationship between air pollution and public health is a difficult problem in the epidemiology of air pollution, and solving this problem has become an important challenge for environmental health researchers. The relationships between acute exposure to atmospheric pollutants and diseases related to respiratory, cardiovascular, and central nervous systems have been evaluated by a large number of studies both at home and abroad. The time-series model is the most commonly used method to study the acute health effects of air pollution, owing to the repeated observation of the same research population and therefore the controllable confounding effects of time-related variables (such as seasonal and long-term trends). The generalized additive model (GAM) has been the most widely used in the time-series model family to simultaneously evaluate the linear and non-linear relationships between air pollution and population health allowing adjustment for various confounding factors. This paper summarized the establishment and application of GAM in air pollution and public health studies, aiming to provide methodological support for further evaluations. In future research, it is necessary to combine various research methods, such as combining spatial analysis with temporal analysis, and develop multi-level spatial and temporal series models after taking into consideration of various confounding factors for health outcomes, so as to accurately assess the population-level health risks caused by air pollution, and ultimately provide scientific evidence for the formulation of environmental health policies.
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