School of Public Health, Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh 160012, India.
School of Public Health, Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh 160012, India.
Environ Int. 2019 Nov;132:104987. doi: 10.1016/j.envint.2019.104987. Epub 2019 Aug 6.
Advances in statistical analysis in the last few decades in the area of linear models enhanced the capability of researchers to study environmental procedures. In relation to general linear models; generalized linear models (GLM) provide greater flexibility in analyzing data related to non-normal distributions. Considering this, the current review explains various applications of the generalized additive model (GAM) to link air pollution, climatic variability with adverse health outcomes. The review examines the application of GAM within the varied field, focusing on the environment and meteorological data. Further, advantages and complications of applying GAM to environmental data are also discussed. Application of GAM allowed for specification for the error pattern and found to be an appropriate fit for the data sets having non-normal distributions; this results in lower and more reliable p-values. Since most environmental data is non-normal, GAM provides a more effective analytical method than traditional linear models. This review highlights on ambient air pollutants, climate change, and health by evaluating studies related to GAM. Additionally, an insight into the application of GAM in R software is provided, which is open source software with the extensive application for any type of dataset.
在过去几十年中，线性模型领域的统计分析进展提高了研究人员研究环境过程的能力。与一般线性模型相比，广义线性模型 (GLM) 在分析与非正态分布相关的数据时提供了更大的灵活性。有鉴于此，本综述解释了广义可加模型 (GAM) 在将空气污染、气候变异性与不良健康结果联系起来方面的各种应用。该综述考察了 GAM 在不同领域的应用，重点关注环境和气象数据。此外，还讨论了将 GAM 应用于环境数据的优点和复杂性。应用 GAM 允许指定误差模式，并发现非常适合具有非正态分布的数据集；这导致更低和更可靠的 p 值。由于大多数环境数据是非正态的，因此 GAM 提供了比传统线性模型更有效的分析方法。本综述通过评估与 GAM 相关的研究，强调了环境空气污染物、气候变化和健康之间的关系。此外，还介绍了在 R 软件中应用 GAM 的情况，R 软件是一个开源软件，可广泛应用于任何类型的数据集。
