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Ambient air pollution and hospital admissions for acute aortic dissection in Shantou, China: A time-series analysis

ATMOSPHERIC ENVIRONMENT 2024年 318卷

作者： Ji, Yanhu Yuan, Zhongjia Huang, Zepeng Xiong, Jianping Li, Liping Shantou Univ Sch Publ Hlth Shantou 515063 Peoples R China Shantou Univ Injury Prevent Res Ctr Med Coll Shantou 515041 Peoples R China Shantou Cent Hosp Shantou 515031 Peoples R China Shantou Univ Dept Cardiol Affiliated Hosp 2 Med Coll Shantou 515041 Peoples R China Shantou Univ Affiliated Hosp 1 Med Coll Shantou 515041 Peoples R China

Acute aortic dissection (AAD) is an extremely serious cardiovascular emergency. This study aimed to explore the acute association between short-term exposure to air pollution and hospitalization for AAD and to quantify the burden of disease due to air pollution exposure in the coastal city of Shantou in southeast China. Data on daily AAD hospitalizations and air pollutants were collected from January 1, 2015, to December 31, 2019. The overdispersed generalized additive model was adopted to estimate the relationship between air pollution and AAD hospitalizations. Stratified analysis was then conducted by age and sex. The burden of disease was expressed in terms of attributable fractions and numbers. A total of 2111 AAD hospitalizations were identified. For a 10 mu g/m(3) increase in the concentration of fine particulate matter (PM2.5), inhalable particulate matter (PM10), carbon monoxide (CO), sulfur dioxide (SO2) and ozone (O-3) at lag 01, the corresponding relative risks (RRs) and 95% confidence intervals (CIs) were 1.0917 (1.0449-1.1407), 1.0595 (1.0275-1.0925), 1.0043 (1.0013-1.0073), 1.1994 (1.0369-1.3872) and 1.0257 (1.0050-1.0468), respectively. However, no significant effects of nitrogen dioxide (NO2) on AAD hospitalizations were found. Elderly individuals (>= 60 years) and males were more susceptible to pollutants than their corresponding groups. In the two-pollutant model, the estimates of PM2.5 and PM10 were relatively robust. Taking the World Health Organization (WHO) air quality standard as a reference, 13.0% of AAD hospitalizations were attributable to PM2.5 exposure, followed by PM10 (6.1%) and O-3 (3.3%) exposure. Our results showed that short-term exposure to air pollutants, especially PM2.5 and PM10, was significantly associated with AAD and contributed to a significant disease burden. This study provides a reference for the future implementation of targeted air pollution control interventions.

关键词： Time-series study Air pollution Acute aortic dissection Disease burden generalized additive model

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A unified framework of analyzing missing data and variable selection using regularized likelihood

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2024年 194卷

作者： Bian, Yuan Yi, Grace Y. He, Wenqing Univ Western Ontario Dept Stat & Actuarial Sci 1151 Richmond St North London ON N6A 5B7 Canada Univ Western Ontario Dept Comp Sci 1151 Richmond St North London ON N6A 5B7 Canada

Missing data arise commonly in applications, and research on this topic has received extensive attention in the past few decades. Various inference methods have been developed under different missing data mechanisms, including missing at random and missing not at random. The assessment of a feasible missing data mechanism is, however, difficult due to the lack of validation data. The problem is further complicated by the presence of spurious variables in covariates. Focusing on missingness in the response variable, a unified modeling scheme is proposed by utilizing the parametric generalized additive model to characterize various types of missing data processes. Taking the generalized linear model to facilitate the dependence of the response on the associated covariates, the concurrent estimation and variable selection procedures are developed using regularized likelihood, and the asymptotic properties for the resultant estimators are rigorously established. The proposed methods are appealing in their flexibility and generality;they circumvent the need of assuming a particular missing data mechanism that is required by most available methods. Empirical studies demonstrate that the proposed methods result in satisfactory performance in finite sample settings. Extensions to accommodating missingness in both the response and covariates are also discussed.

关键词： generalized additive model generalized linear model Maximum likelihood Missing data Penalized likelihood Selection consistency Variable selection

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Seasonal Lead Release into Drinking Water and the Effect of Aluminum

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ACS ES&T WATER 2022年第5期2卷 710-720页

作者： Trueman, Benjamin F. Bleasdale-Pollowy, Aaron Locsin, Javier A. Bennett, Jessica L. Krkosek, Wendy H. Gagnon, Graham A. Dalhousie Univ Ctr Water Resources Studies Dept Civil & Resource Engn Halifax NS B3H 4R2 Canada Halifax Water Halifax NS B3K 5M1 Canada

Monitoring lead in drinking water is important for public health, but seasonality in lead concentrations can bias monitoring programs if it is not understood and accounted for. Here, we describe an apparent seasonal pattern in lead release into orthophosphate-treated drinking water, identified through point-of-use sampling at sites in Halifax, Canada, with various sources of lead. Using a generalized additive model, we extracted the seasonally varying components of time series representing a suite of water quality parameters and we identified aluminum as a correlate of lead. To investigate aluminum's role in lead release, we modeled the effect of variscite (AlPO4 center dot 2H(2)O) precipitation on lead solubility, and we evaluated the effects of aluminum, temperature, and orthophosphate concentration on lead release from new lead coupons. At environmentally relevant aluminum and orthophosphate concentrations, variscite precipitation increased predicted lead solubility by decreasing available orthophosphate. Increasing the aluminum concentration from 20 to 500 mu g L-1 increased lead release from coupons by 41% and modified the effect of orthophosphate, rendering it less effective. We attributed this to a decrease in the concentration of soluble (<0.45 mu m) phosphorus with increasing aluminum and an accompanying increase in particulate lead and phosphorus (>0.45 mu m).

关键词： Variscite PHREEQC generalized additive model orthophosphate mgcv coagulation

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pureGAM: Learning an Inherently Pure additive model 22

pureGAM: Learning an Inherently Pure Additive Model

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28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KKD)

作者： Sun, Xingzhi Wang, Ziyu Ding, Rui Han, Shi Zhang, Dongmei Yale Univ New Haven CT USA Peking Univ Beijing Peoples R China Microsoft Res Beijing Peoples R China

ISBN: (纸本)9781450393850

Including pairwise or higher-order interactions among predictors of a generalized additive model (GAM) is gaining increasing attention in the literature. However, existing models face an identifiability challenge. In this paper, we propose pureGAM, an inherently pure additive model of both main effects and higher-order interactions. By imposing the pureness condition to constrain each component function, pureGAM is proved to be identifiable without compromising accuracy. Furthermore, the pureness condition introduces additional interpretability in terms of simplicity. Practically, pureGAM is a unified model to support both numerical and categorical features with a novel learning procedure to achieve optimal performance. Evaluations show that pureGAM outperforms other GAMs and has very competitive performance even compared with opaque models, and its interpretability remarkably outperforms competitors in terms of pureness. We also share a successful adoption of pureGAM in one real-world application.

关键词： generalized additive model Pureness Identifiability Pure Coding Interpretable Machine Learning

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Machine Learning Analysis for Data Incompleteness (MADI): Analyzing the Data Completeness of Patient Records Using a Random Variable Approach to Predict the Incompleteness of Electronic Health Records

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IEEE ACCESS 2021年 9卷 95994-96001页

作者： Gurupur, Varadraj P. Shelleh, Muhammed Univ Cent Florida Dept Hlth Management & Informat Orlando FL 32826 USA Univ Cent Florida Dept Comp Sci Orlando FL 32826 USA

The purpose of this article is to propose a methodology involving various methods that can be used to predict the data incompleteness of a dataset. Here the investigators have presented data incompleteness as both continuous and discrete random variables. In addition the investigators used transfer entropy for the purpose of advancing the science associated with the analysis of data incompleteness of electronic health records. The underlying methodology has been coined as "Machine Learning Analysis for Data Incompleteness" (MADI) with the intention of developing a possible solution to data incompleteness in electronic health records. MADI advances the analysis of data incompleteness with the use of Kolomogorov Smirnov goodness of fit, mielke distribution, and beta distributions for a holistic analysis. Alongside the methodology presented, the investigators explored stochastic gradient descent, generalized additive models, and support vector machines for comparison. Overall, the investigators have presented a complete set of methods and algorithms to help predict data incompleteness in a medical setting and provided suggestions for practical applications into the prediction of data incompleteness.

关键词： Histograms Fitting Electronic medical records Data models Entropy Machine learning Random variables Health informatics big data models data completeness probability density Kolomogorov-Smirnov test support vector machine stochastic gradient descent generalized additive model electronic health records

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Association between particulate matter pollution and the incidence of mumps in 31 provinces from China

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2021年第37期28卷 51210-51216页

作者： Zhang, Mengru Zhu, Yongjian Univ Sci & Technol China Sch Management Hefei Peoples R China

Previous studies have found that particulate matter (PM) pollution is a risk factor for respiratory disease by affecting body's immunity and carrying microorganisms. This study aimed to explore the association between PM and the incidence of mumps in 31 provinces from China. Monthly mumps cases, air pollution concentration, and meteorological factors in each province were obtained between January 2014 to December 2017. We used a generalized additive model (GAM) to investigate the associations of PM2.5 and PM10 with monthly mumps cases. We also tested the statistical significance of the differences between effect estimates in the warm season (April to September) and cold season (October to March) to explore potential effect modification. We found that a 10-mu g/m3 increase (lag0) in PM2.5, and PM10 was associated with a 2.34% (95% CI: 1.32 to 3.36) and 1.90% (95% CI: 1.19 to 2.62) increase in the monthly counts of mumps cases, respectively. We also observed significant positive associations of PM2.5 and PM10 with mumps cases at lag0-1. These results were robust in our sensitivity analyses. No significant differences were found between the season-specific effects. Our results indicate that there is a positive relationship between PM and the incidence of mumps, which provides important implications for the prevention and control of mumps.

关键词： Particulate matter pollution Mumps China generalized additive model

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Fire-susceptibility mapping in the natural areas of Iran using new and ensemble data-mining models

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2021年第34期28卷 47395-47406页

作者： Eskandari, Saeedeh Pourghasemi, Hamid Reza Tiefenbacher, John P. Agr Res Educ & Extens Org AREEO Forest Res Div Res Inst Forests & Rangelands Tehran 13185116 Iran Shiraz Univ Coll Agr Dept Nat Resources & Environm Engn Shiraz Iran Texas State Univ Dept Geog San Marcos TX USA

Fires have increased in northeastern Iran as its semi-arid climate landscape is desiccated by human activities. To combat fire outbreaks in any region, fire susceptibility must be mapped using accurate and efficient models. This research mapped fire susceptibility in the forests and rangelands of Golestan Province in northeastern Iran using new data-mining models. Fire effective factors, including elevation, slope angle, annual mean rainfall, annual mean temperature, wind effect, topographic wetness index (TWI), plan curvature, distance to river, distance to road, and distance to village were obtained from several sources. The relative importance of each variable was determined using a random-forest algorithm. Fire-susceptibility maps were produced in R 3.0.2 software using GAM, MARS, SVM algorithms, and a new ensemble of the three models: GAM-MARS-SVM. The four fire-susceptibility maps were validated using the area under the curve. The results show that the distance to the village, annual mean rainfall, and elevation were of greatest importance in predicting fire susceptibility. The new GAM-MARS-SVM ensemble model achieved the highest precision of fire-susceptibility mapping. The fire-susceptibility map produced using the GAM-MARS-SVM ensemble model best detected the high fire risk areas in Golestan Province. The fire-susceptibility map produced by the ensemble model can be very useful for creating and enhancing management strategies for preventing fires, particularly in the higher-risk portions of Golestan Province.

关键词： Fire hazard mapping modeling generalized additive model Multivariate adaptive regression spline Support vector machine Random forest GIS

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Meteorological association for prevalence dynamics of Acute Encephalitis Syndrome: a modeling study

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modelING EARTH SYSTEMS AND ENVIRONMENT 2022年第2期8卷 2249-2259页

作者： Kumar, Praveen Parth Sarthi, Pradhan Bhakuni, Bharat Minist Earth Sci India Meteorol Dept Numer Weather Predict Div Lodhi Rd New Delhi 110003 India Cent Univ South Bihar Dept Environm Sci Gaya India Mahalnobis Natl Crop Forecast Ctr New Delhi India

Acute Encephalitis Syndrome (AES) has been treated as a 'mysterious disease' because of the unknown aetiology in most of the reported cases. AES is assumed to have a linkage with meteorological variables and other environmental parameters in and around the Gorakhpur district of Uttar Pradesh, India. Hence, the current research is aimed to frame a model for AES association with meteorological variables. The Land Use Land Cover (LULC) changes are also considered in the analysis since 78% (approximately) of the land of Gorakhpur is under agriculture practices. The data for AES cases and meteorological variables rainfall (RF, mm), relative humidity (RH, %), and temperature (degrees C) are considered from Jan 2012 to Dec 2017. The Bayesian generalized additive model (GAM) builds an association between AES and the meteorological variables and predicts AES cases over the study area. Various GAM models for different possible combinations of meteorological covariates are analysed. The least Deviance Information Criterion (DIC) (83.90) is found for the model with covariates-Relative Humidity (RH, %), Skin Temperature (SKT, degrees C), Two-meter Temperature (T2, degrees C), the Month of the Year (MoY), and lag-1 of AES cases. The impacts of covariates on AES cases are found non-linear and inconsistent. Results show the positive effect of the covariates in the following ranges;RH in the range of 55-63%, SKT greater than 30 degrees C, and 2-m temperature greater than 25 degrees C. Similarly, a positive impact of MoY is observed for July to September. The predictive ability of the model is assessed using an appropriate cross-validation technique. The absolute percentage error is found in the range of 0.5-47.6% monthly. The root mean square errors are also observed within the range of 0.05-2.0.

关键词： Acute Encephalitis Syndrome (AES) generalized additive model Meteorological variables Gorakhpur

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Reductions of migrant population reduces the number of COVID-19 epidemic: a case study in China

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2021年第41期28卷 58364-58374页

作者： Han, Lizhen Jia, Jinzhu Peking Univ Sch Publ Hlth Dept Biostat 38 Xueyuan Rd Beijing 100191 Peoples R China

The novel coronavirus disease (COVID-19) broke out worldwide in 2020. The purpose of this paper was to find out the impact of migrant population on the epidemic, aiming to provide data support and suggestions for control measures in various epidemic areas. generalized additive model was utilized to model the relationship between migrant population and the cumulative number of confirmed cases of COVID-19. The difference of spatial distribution was analyzed through spatial autocorrelation and hot spot analysis. generalized additive model demonstrated that the cumulative number of confirmed cases was positively correlated with migration index and population density. The predictive results showed that, if no travel restrictions are imposed on the migrant population as usual, this number of COVID-19 would have reached 27,483 (95% CI 16,074, 48,097;the actual number was 23,177). The increase in one city (Jian) would be 577.23% (95% CI 322.73%, 972.73%) compared with the real confirmed cases of COVID-19. The average increase in 73 cities was 85.53% (95% CI 19.53%, 189.81%). Among the migration destinations, the number of cases in cities of Hubei province, Chongqing, and Beijing was relatively high, and there were large-scale high-prevalence clusters in eastern Hubei province. Without restrictions on migration, the high prevalence areas in Hubei province and its surrounding areas will be further expanded. The reduced population mobility and population density can greatly slow down the spread of the epidemic. All epidemic areas should suspend the transportation between cities, strictly control the population travel, and decrease the population density, so as to reduce the spread of COVID-19.

关键词： Coronavirus COVID-19 generalized additive model Migration Spatial distribution

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Associations of acute exposure to airborne pollutants with COVID-19 infection: evidence from China

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2021年第36期28卷 50554-50564页

作者： Lu, Bingqing Wu, Na Jiang, Jiakui Li, Xiang Fudan Univ Dept Environm Sci & Engn Shanghai 200032 Peoples R China Shanghai Inst Pollut Control & Ecol Secur Shanghai 200092 Peoples R China

The outbreak of COVID-19, caused by SARS-CoV-2, has spread across many countries globally. Greatly, there are limited studies concerned with the effect of airborne pollutants on COVID-19 infection, while exposure to airborne pollutants may harm human health. This paper aimed to examine the associations of acute exposure to ambient atmospheric pollutants to daily newly COVID-19 confirmed cases in 41 Chinese cities. Using a generalized additive model with Poisson distribution controlling for temperature and relative humidity, we evaluated the association between pollutant concentrations and daily COVID-19 confirmation at single-city level and multicity levels. We observed a 10-mu g/m(3) rise in levels of PM2.5 (lag 0-14), O-3 (lag 0-1), SO2 (lag 0), and NO2 (lag 0-14) were positively associated with relative risks of 1.050 (95% CI: 1.028, 1.073), 1.011 (1.007, 1.015), 1.052 (1.022, 1.083), and 1.094 (1.028, 1.164) of daily newly confirmed cases, respectively. Further adjustment for other pollutants did not change the associations materially (excepting in the model for SO2). Our results indicated that COVID-19 incidence may be susceptible to airborne pollutants such as PM2.5, O-3, SO2, and NO2, and mitigation strategies of environmental factors are required to prevent spreading.

关键词： Air pollution COVID-19 Novel coronavirus pneumonia generalized additive model

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