Long-term exposure to ambient fine particulate matter chemical composition and in-hospital case fatality among patients with stroke in China

Cai, Miao; Lin, Xiaojun; Wang, Xiaojie; Zhang, Shiyu; Wang, Chongjian; Zhang, Zilong; Pan, Jay; Lin, Hualiang

doi:10.1016/j.lanwpc.2022.100679

Cited by 13 publications

(14 citation statements)

References 49 publications

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“…This study used an administrative inpatient discharge dataset collected by the Health Commission of Shanxi Province, China from January 1, 2014 to December 31, 2017 ( 4 – 9 ). This database routinely collects data on individual's demographic information (age, sex, and ethnicity), socioeconomic status (marital status and occupation), disease severity, one main diagnosis code and up to 10 secondary diagnosis codes based on International Classification of Diseases, Tenth Revision (ICD-10).…”

Section: Methodsmentioning

confidence: 99%

Predicting preterm birth using auto-ML frameworks: a large observational study using electronic inpatient discharge data

Kong,

Tao,

Xiao

et al. 2024

Front. Pediatr.

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BackgroundTo develop and compare different AutoML frameworks and machine learning models to predict premature birth.MethodsThe study used a large electronic medical record database to include 715,962 participants who had the principal diagnosis code of childbirth. Three Automatic Machine Learning (AutoML) were used to construct machine learning models including tree-based models, ensembled models, and deep neural networks on the training sample (N = 536,971). The area under the curve (AUC) and training times were used to assess the performance of the prediction models, and feature importance was computed via permutation-shuffling.ResultsThe H2O AutoML framework had the highest median AUC of 0.846, followed by AutoGluon (median AUC: 0.840) and Auto-sklearn (median AUC: 0.820), and the median training time was the lowest for H2O AutoML (0.14 min), followed by AutoGluon (0.16 min) and Auto-sklearn (4.33 min). Among different types of machine learning models, the Gradient Boosting Machines (GBM) or Extreme Gradient Boosting (XGBoost), stacked ensemble, and random forrest models had better predictive performance, with median AUC scores being 0.846, 0.846, and 0.842, respectively. Important features related to preterm birth included premature rupture of membrane (PROM), incompetent cervix, occupation, and preeclampsia.ConclusionsOur study highlights the potential of machine learning models in predicting the risk of preterm birth using readily available electronic medical record data, which have significant implications for improving prenatal care and outcomes.

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Section: Methodsmentioning

confidence: 99%

Predicting preterm birth using auto-ML frameworks: a large observational study using electronic inpatient discharge data

Kong,

Tao,

Xiao

et al. 2024

Front. Pediatr.

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“…This algorithm enhanced the spatial resolution of environmental variables at a specific location by calculating a weighted average of the nearest 4 grids. The closer air pollutant and meteorological variable grids were to the location of the cases, the larger the associated weights were for the grids ( Cai et al, 2023a , Cai et al, 2023b ).…”

Section: Methodsmentioning

confidence: 99%

Air pollution and stroke hospitalization in the Beibu Gulf Region of China: A case-crossover analysis

Edgell

Wei

et al. 2023

Ecotoxicology and Environmental Safety

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“…In the first stage, we geocoded the latitudes and longitudes of residential addresses for each case using the application programming interface provided by amap (also known as Gaode map) [36], a leading mapping, navigation, and location-based service provider in China. In the second stage, we used a bilinear interpolation algorithm to estimate the exposure to air pollutants and meteorological variables [37]. This algorithm enhanced the spatial resolution of environmental variables at a specific location by calculating a weighted average of the nearest four grids.…”

Section: Exposure Assessmentmentioning

confidence: 99%

Short-term air pollution exposure associated with death from kidney diseases: a nationwide time-stratified case-crossover study in China from 2015 to 2019

Cai

Wei

Zhang

et al. 2023

BMC Med

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Background Long-term exposure to air pollution has been associated with the onset and progression of kidney diseases, but the association between short-term exposure to air pollution and mortality of kidney diseases has not yet been reported. Methods A nationally representative sample of 101,919 deaths from kidney diseases was collected from the Chinese Center for Disease Control and Prevention from 2015 to 2019. A time-stratified case-crossover study was applied to determine the associations. Satellite-based estimates of air pollution were assigned to each case and control day using a bilinear interpolation approach and geo-coded residential addresses. Conditional logistic regression models were constructed to estimate the associations adjusting for nonlinear splines of temperature and relative humidity. Results Each 10 µg/m3 increment in lag 0–1 mean concentrations of air pollutants was associated with a percent increase in death from kidney disease: 1.33% (95% confidence interval [CI]: 0.57% to 2.1%) for PM1, 0.49% (95% CI: 0.10% to 0.88%) for PM2.5, 0.32% (95% CI: 0.08% to 0.57%) for PM10, 1.26% (95% CI: 0.29% to 2.24%) for NO2, and 2.9% (95% CI: 1.68% to 4.15%) for SO2. Conclusions Our study suggests that short-term exposure to ambient PM1, PM2.5, PM10, NO2, and SO2 might be important environmental risk factors for death due to kidney diseases in China.

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Long-term exposure to ambient fine particulate matter chemical composition and in-hospital case fatality among patients with stroke in China

Cited by 13 publications

References 49 publications

Predicting preterm birth using auto-ML frameworks: a large observational study using electronic inpatient discharge data

Predicting preterm birth using auto-ML frameworks: a large observational study using electronic inpatient discharge data

Air pollution and stroke hospitalization in the Beibu Gulf Region of China: A case-crossover analysis

Short-term air pollution exposure associated with death from kidney diseases: a nationwide time-stratified case-crossover study in China from 2015 to 2019

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