Correction to: Prenatal exposure to ambient air pollution and adverse pregnancy outcomes in Ahvaz, Iran: a generalized additive model

Dastoorpoor, Maryam; Khanjani, Narges; Moradgholi, Asghar; Sarizadeh, Reihaneh; Cheraghi, Maria; Estebsari, Fatemeh

doi:10.1007/s00420-022-01899-9

Cited by 2 publications

(3 citation statements)

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“…The study area is Nanjing City, the capital of Jiangsu province, which is located on the eastern coast of mainland China (as shown in Figure 1). Nanjing City (118 traffic accidents, and medicine [40][41][42][43][44][45][46][47]. However, to the best of our knowledge, few previous studies have applied GAMs to investigate the causes of the variations in LST.…”

Section: Study Areamentioning

confidence: 99%

“…The Generalized Additive Models (GAMs) were developed by Hastie and Tibshirani in 1990 as an extension of the Generalized Linear Model, which could not only characterize the complex nonlinear relationship between response variables and explanatory variables but also could explain the importance of each explanatory variable and their interactions [39,40]. Due to their practical mechanisms and inherent effectiveness, GAMs have been widely utilized in numerous research fields, including the studies of air quality, traffic accidents, and medicine [40][41][42][43][44][45][46][47]. However, to the best of our knowledge, few previous studies have applied GAMs to investigate the causes of the variations in LST.…”

Section: Introductionmentioning

confidence: 99%

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Using GAMs to Explore the Influence Factors and Their Interactions on Land Surface Temperature: A Case Study in Nanjing

Zhang,

Yang,

Zhang

et al. 2024

Land

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The identification of influencing factors (IFs) of land surface temperature (LST) is crucial for developing effective strategies to mitigate global warming and conducting other relevant studies. However, most previous studies ignored the potential impact of interactions between IFs, which might lead to biased conclusions. Generalized additivity models (GAMs) can provide more explanatory results compared to traditional machine learning models. Therefore, this study employs GAMs to investigate the impact of IFs and their interactions on LST, aiming to accurately detect significant factors that drive the changes in LST. The results of this case study conducted in Nanjing, China, showed that the GAMs incorporating the interactions between factors could improve the fitness of LST and enhance the explanatory power of the model. The autumn model exhibited the most significant improvement in performance, with an increase of 0.19 in adjusted-R2 and a 17.9% increase in deviance explained. In the seasonal model without interaction, vegetation, impervious surface, water body, precipitation, sunshine hours, and relative humidity showed significant effects on LST. However, when considering the interaction, the previously observed significant influence of the water body in spring and impervious surface in summer on LST became insignificant. In addition, under the interaction of precipitation, relative humidity, and sunshine hours, as well as the cooling effect of NDVI, there was no statistically significant upward trend in the seasonal mean LST during 2000–2020. Our study suggests that taking into account the interactions between IFs can identify the driving factors that affect LST more accurately.

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Section: Study Areamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using GAMs to Explore the Influence Factors and Their Interactions on Land Surface Temperature: A Case Study in Nanjing

Zhang,

Yang,

Zhang

et al. 2024

Land

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“…It can express the linear and nonlinear relationship between explanatory variables and response variables in details, reducing the estimation bias caused by linear settings [30]. Therefore, the GAM has a good effect when applied to medical data [31,32]. The mathematical formula of GAM is shown as follows,…”

Section: ̂ ∑ ∑mentioning

confidence: 99%

Robust Analysis of the Influencing Factors for Hospitalization Costs of Senile Cataracts Patients in Chengdu Considering Different Types of Insurance

Tian,

Li,

2024

AJLS

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Chengdu is one of the earliest pilot cities for urban-rural basic medical insurance integration in China. This study aimed to analyze the influencing factors of hospitalization costs of senile cataract in a tertiary hospital in Chengdu by robust method, especially considering the influence of medical insurance type. A total of 1310 discharged patients from a tertiary hospital from January 2020 to June 2021 who were mainly diagnosed with senile cataracts were selected as the research subjects. Kruskal-Wallis H test and Spearman correlation analysis are used to conduct univariate statistical analysis. The robust multivariate linear regression model and a semi-parametric multivariate regression model are established to obtain the influencing factors for their hospitalization costs. The robust multivariate regression model results show that reimbursement ratio, number of surgeries, type of medical insurance, hospitalization days, number of additional diagnoses and material proportion have significant correlations with the response variable, i.e. total hospitalization costs of the senile cataract patients. In the robust multivariate regression analysis, the type of insurance is significantly associated with the hospitalization costs. Fixing other variables, the hospitalization costs of patients with UEBMI insurance were 7.6% higher than those with URRBMI insurance. Generalized additive model (GAM) can express the nonlinear relationship between explanatory variables and response variable. Because of the nonlinear part of the GAM, the interpretation and description of the model can provide more knowledge than the linear models. In the GAM model, the type of insurance is also significantly related to the total costs. According to the regression effects of reimbursement ratio, number of surgeries, type of medical insurance, hospitalization days, number of additional diagnoses and material proportion on total costs, the paper aims to provide some references for promoting the reform of the local medical system and improving the eye health status and quality of life of middle-aged and elderly groups.

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Correction to: Prenatal exposure to ambient air pollution and adverse pregnancy outcomes in Ahvaz, Iran: a generalized additive model

Cited by 2 publications

References 0 publications

Using GAMs to Explore the Influence Factors and Their Interactions on Land Surface Temperature: A Case Study in Nanjing

Using GAMs to Explore the Influence Factors and Their Interactions on Land Surface Temperature: A Case Study in Nanjing

Robust Analysis of the Influencing Factors for Hospitalization Costs of Senile Cataracts Patients in Chengdu Considering Different Types of Insurance

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