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Body Mass Index and Mortality in Blunt Trauma: The Right BMI can be Protective

AMERICAN JOURNAL OF SURGERY 2020年第6期220卷 1475-1479页

作者： Choi, James Smiley, Abbas Lati, Rifat Gogna, Shekhar Prabhakaran, Kartik Con, Jorge Anderson, Patrice Policastro, Anthony Beydoun, Malk Rhee, Peter New York Med Coll Westchester Med Ctr 100 Woods Rd Valhalla NY 10595 USA

Background: There are limited studies examining the role of BMI on mortality in the trauma population. The aim of this study was to analyze whether the "obesity paradox" exists in non-elderly patients with blunt trauma. Methods: A retrospective study was performed on the Trauma Quality Improvement Program (TQIP) database for 2016. All non-elderly patients aged 18-64, with blunt traumatic injuries were identified. A generalized additive model (GAM) was built to assess the association of mortality and BMI adjusted for age, gender, race, and injury severity score (ISS). Results: 28,475 patients (mean age = 42.5, SD = 14.3) were identified. 20,328 (71.4%) were male. Age (p < 0.0001), gender (p < 0.0001), and ISS (p < 0.0001) had significant associations with mortality. After GAM, BMI showed a significant U-shaped association with mortality (EDF = 3.2, p = 0.003). A BMI range of 31.5 +/- 0.9 kg/m(2) was associated with the lowest mortality. Conclusion: High BMI can be a protective factor in mortality within non-elderly patients with blunt trauma. However, underweight or morbid obesity suggest a higher risk of mortality. (c) 2020 Elsevier Inc. All rights reserved.

关键词： generalized additive model Blunt trauma Obesity Retrospective Obesity paradox

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Tracking Sustainable Restoration in Agro-Pastoral Ecotone of Northwest China

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REMOTE SENSING 2021年第24期13卷 5031-5031页

作者： Yang, Lixiao Horion, Stephanie He, Chansheng Fensholt, Rasmus Lanzhou Univ Coll Earth & Environm Sci Minist Educ Key Lab Western Chinas Environm Syst Lanzhou 730000 Peoples R China Univ Copenhagen Dept Geosci & Nat Resource Management DK-1350 Copenhagen Denmark Western Michigan Univ Dept Geog Kalamazoo MI 49008 USA

Large-scale ecological restoration (ER) projects have been implemented in northwest China in recent decades as a means to prevent desertification and improve ecosystem services. However, previous studies have demonstrated adverse impacts in the form of widespread soil water deficit caused by intensive ER activities. Understanding the role of climate change and ER efforts in vegetation dynamics and soil moisture consumption is essential for sustainable ecosystem management. Here, we used the break for additive season and trend (BFAST) method to analyse spatial patterns in the normalized difference vegetation index (NDVI) variation over the agro-pastoral ecotone of northwest China (APENC) for 2000-2015. From the combined use of generalized additive modelling (GAM) and residual-trend analysis (RESTREND), we distinguished and quantified the effects of climate and human management on vegetation and soil water dynamics. Approximately 78% of the area showed vegetation variations representing a significant change in NDVI, of which more than 68% were categorized as abrupt changes. Large areas of the abrupt change type, interrupted increase and monotonic increase in NDVI were observed before 2006, and small areas of the change type of negative reversals were observed after 2012. Anthropogenic activity was found to be the major driving factor of variation in vegetation (contribution rate of 56%) and soil moisture (contribution rate of 78%). The vegetation expansion, which was mainly related to the large number of ER programs that started in 2000, was found to increase soil moisture depletion. By comparing areas where anthropogenic activities had a high contribution rate to vegetation increase and areas where soil moisture consumption was severely increased, we identify and discuss hotspot areas of soil moisture consumption caused by the ER programs. The current methodological workflow and results represent a novel foundation to inform and support water resource management and

关键词： vegetation dynamics break for additive season and trend (BFAST) soil water ecological restoration temperature vegetation dryness index generalized additive model residual-trend analysis

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Effects of short-term ambient PM_2.5 exposure on the blood cell count and hemoglobin concentration among 82,431 people in eastern China

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SCIENCE OF THE TOTAL ENVIRONMENT 2021年 776卷 146046-146046页

作者： Li, Zhongqi Li, Xiaona Song, Huan Tao, Bilin Qiu, Beibei Tian, Dan Zhan, Mengyao Wu, Zhuchao Wu, Jizhou Zhang, Qun Wang, Jianming Nanjing Med Univ Ctr Global Hlth Sch Publ Hlth Dept Epidemiol 101 Longmian Ave Nanjing 211166 Peoples R China Nanjing Med Univ Sch Publ Hlth Ctr Global Hlth Dept Hlth Management 101 Longmian Ave Nanjing 211166 Peoples R China Nanjing Med Univ Affiliated Hosp 1 Hlth Management Ctr Nanjing 210029 Peoples R China

Background: Air pollution has posed a considerable threat to human health. To explore the effects of short-term PM2.5 exposure on peripheral blood cell count and hemoglobin, we performed a correlation study on a large sample of Chinese people who took part in routine physical examinations in eastern China. Methods: We enrolled 82,431 participants from the Health Management Center of the First Affiliated Hospital of Nanjing Medical University in 2018. Routine blood test data, including the red blood cell (RBC), white blood cell (WBC) and platelet counts and hemoglobin concentration, were collected. We also extracted data on daily meteorological factors and air pollutants from December 21, 2017, to December 31, 2018, from the National Meteorological Data Sharing Center or the China Air Quality Online Monitoring and Analysis Platform. The generalized additive model was applied to estimate the associations between short-term ambient PM2.5 exposure and the blood cell count and hemoglobin concentration. Results: For a 10 ?g/m3 increase in PM2.5, the red blood cell count decreased by 0.005 ? 1012/L [95% confidence interval (CI): ?0.006, ?0.00 3], the hemoglobin concentration decreased by 0.123 g/L (95% CI: ?0.165, ?0.081), the lymphocyte count increased by 0.126% (95% CI: 0.021, 0.231), and the monocyte count decreased by 0.434% (95% CI: ?0.569, ?0.298). After excluding people with abnormal blood test results, the association remained significant. Additionally, the effect could be modified by sex, age, body mass index, and smoking status. Conclusions: Short-term ambient PM2.5 exposure is negatively correlated with the RBC count, hemoglo-bin concentration, and monocyte count and positively correlated with the lymphocyte count, suggesting that PM2.5 has multiple effects on health. (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Air pollution Health indicators generalized additive model Time-series study

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Linking livestock snow disastermortality and environmental stressors in the Qinghai-Tibetan Plateau: Quantification based on generalized additive models

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SCIENCE OF THE TOTAL ENVIRONMENT 2018年 625卷 87-95页

作者： Li, Yijia Ye, Tao Liu, Weihang Gao, Yu Beijing Normal Univ Fac Geog Sci State Key Lab Earth Surface Proc & Resource Ecol Key Lab Environm Change & Nat DisasterMinist Edu Beijing 100875 Peoples R China East China Normal Univ Sch Geog Sci Shanghai 200241 Peoples R China

Livestock snowdisaster occurswidely in Central-to-Eastern Asian temperate and alpine grasslands. The effects of snowdisaster on livestock involve a complex interaction between precipitation, vegetation, livestock, and herder communities. Quantifying the relationship among livestock mortality, snow hazard intensity, and seasonal environmental stressors is of great importance for snow disaster early warning, risk assessments, and adaptation strategies. Using a wide-spatial extent, long-time series, and event-based livestock snow disaster dataset, this study quantified those relationships and established a quantitative model of livestock mortality for prediction purpose for the Qinghai-Tibet Plateau region. Estimations using generalized additive models (GAMs) were shown to accurately predict livestock mortality and mortality rate due to snow disaster, with adjusted-R-2 up to 0.794 and 0.666, respectively. These results showed that a longer snow disaster duration, lower temperatures during the disaster, and a drier summer with less vegetation all contribute significantly and non-linearly to higher mortality ( rate), after controlling for elevation and socioeconomic conditions. These results can be readily applied to risk assessment and risk-based adaptation actions. (c) 2017 Elsevier B.V. All rights reserved.

关键词： Livestock snow disaster Snow hazard intensity Environmental stressors generalized additive model Qinghai-Tibet Plateau

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A graphical method for assessing risk factor threshold values using the generalized additive model: the multi-ethnic study of atherosclerosis

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HEALTH SERVICES AND OUTCOMES RESEARCH METHODOLOGY 2012年第1期12卷 62-79页

作者： Setodji, Claude Messan Scheuner, Maren Pankow, James S. Blumenthal, Roger S. Chen, Haiying Keeler, Emmett RAND Pittsburgh PA 15213 USA RAND Santa Monica CA 90401 USA Univ Minnesota Minneapolis MN 55455 USA Johns Hopkins Univ Baltimore MA 21218 USA Wake Forest Univ Winston Salem NC 27157 USA

Continuous variable dichotomization is a popular technique used in the estimation of the effect of risk factors on health outcomes in multivariate regression settings. Researchers follow this practice in order to simplify data analysis, which it unquestionably does. However thresholds used to dichotomize those variables are usually ad-hoc, based on expert opinions, or mean, median or quantile splits and can add bias to the effect of the risk factors on specific outcomes and underestimate such effect. In this paper, we suggest the use of a semi-parametric method and visualization for improvement of the threshold selection in variable dichotomization while accounting for mixture distributions in the outcome of interest and adjusting for covariates. For clinicians, these empirically based thresholds of risk factors, if they exist, could be informative in terms of the highest or lowest point of a risk factor beyond which no additional impact on the outcome should be expected.

关键词： generalized additive model Smearing estimates Threshold detection Recycled prediction

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Evaluation of performance of energy efficient hybrid ventilation system and analysis of occupants' behavior to control windows

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BUILDING AND ENVIRONMENT 2021年 188卷 107434-107434页

作者： Cho, Haein Cabrera, Daniel Sardy, Sylvain Kilchherr, Romain Yilmaz, Selin Patel, Martin K. Univ Geneva Fac Sci Energy Efficiency Inst Environm Sci ISE CH-1211 Geneva 4 Switzerland Univ Geneva Fac Sci Dept FA Forel Environm & Aquat Sci DEFSE CH-1211 Geneva 4 Switzerland Univ Geneva Sect Math CH-1211 Geneva 4 Switzerland Perenzia Ingn Sarl Lausanne Switzerland

Hybrid ventilation systems have become increasingly attractive, because they are considered to provide good indoor air quality (IAQ) with low energy consumption. In this study, we evaluate the performance of a hybrid ventilation system (operating on both mechanical and natural ventilation mode) by monitoring the IAQ (e.g. temperature, humidity, CO2 levels) and the energy consumption of a multifamily building. We then investigate occupants' window-opening behavior during the heating season by means of window sensors. The results show that the hybrid ventilation system maintains a good level of IAQ while being energy efficient. We also find that occupants' window-opening behavior is significantly affected by indoor temperature and CO2 concentration. We therefore recommend adopting them as input parameters when designing ventilation control strategies to enhance the performance.

关键词： Energy use Energy efficiency Hybrid ventilation Occupannt behavior modeling generalized additive model

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Short-term effects of ambient air pollution on the incidence of influenza in Wuhan, China: A time-series analysis

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ENVIRONMENTAL RESEARCH 2021年 192卷 110327-110327页

作者： Meng, Yongna Lu, Yuanan Xiang, Hao Liu, Suyang Wuhan Univ Sch Hlth Sci 115 Donghu Rd Wuhan 430071 Peoples R China Univ Hawaii Manoa Environm Hlth Lab Dept Publ Hlth Sci 1960 East West RdBiomed BldgD105 Honolulu HI 96822 USA

Background: Evidence suggests that air pollution is associated with many adverse health outcomes such as cardiovascular diseases (CVD), respiratory diseases, cancer, and birth defects. Yet few studies dig into the relationship between air pollution and airborne infectious diseases. Methods: Daily data on influenza incidence were obtained from Hubei Provincial Center for Disease Control and Prevention (Hubei CDC). Data on air pollutants including nitrogen dioxide (NO2), sulfur dioxide (SO2), ground-level ozone (O-3), particulate matter (PM) with aerodynamic diameter < 2.5 mu m (PM2.5), and PM with aerodynamic diameter < 10 mu m (PM10) were retrieved from ten national air sampling stations located at Wuhan. We applied generalized additive model (GAM) to estimate the associations between air pollution and the risk of influenza in Wuhan, China during 2015-2017. Results: In the single-day lag model, the largest effect estimates were observed at lag 0. An increased relative risk (RR) of influenza was significantly associated with a 10 mu g/m(3) increase in SO2 (RR: 1.099;95% confidence interval [CI]: 1.011-1.195), NO2 (RR: 1.039;95% CI: 1.013-1.065), and O-3 (RR: 1.005;95% CI: 0.994-1.016), respectively. In the multi-day lag model, concentrations of SO2, NO2, and O-3 were statistically significantly associated with the risk of influenza at lag 0-1. The seasonal analysis suggests that the influence of air pollution on influenza is greater in the cold season as compared in the warm season in the early lag days. The multi-pollutant model indicates that NO2 may be a potential confounder for co-pollutants. Conclusions: Our study shows that air pollution may be associated with the risk of influenza in a broad sense. Therefore, when formulating policies to deal with influenza outbreaks in the future, factors regarding air pollution should be taken into consideration.

关键词： Air pollution Influenza generalized additive model Time-series Infectious diseases

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modeling of Temporal Exposure to the Ambient Environment and Eczema Severity

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JID Innovations 2022年第1期2卷 100062-100062页

作者： Thomas, Bjorn R. Tan, Xiang L. Javadzadeh, Shagayegh Robinson, Elizabeth J. McDonald, Bryan S. Krupiczojc, Malvina A. Rahman, Syedia R. Rahman, Samiha Ahmed, Rehana A. Begum, Rubina Khanam, Habiba Kelsell, David P. Grigg, Jonathan Knell, Robert J. Centre for Cell Biology and Cutaneous Research Blizard Institute Barts and the London School of Medicine and Dentistry Queen Mary University of London London United Kingdom Department of Dermatology The Royal London Hospital Barts Health NHS Trust London United Kingdom School of Biological and Behavioural Sciences Queen Mary University of London London United Kingdom

Atopic eczema is a common and complex disease. Missing genetic hereditability and increasing prevalence in industrializing nations point toward an environmental driver. We investigated the temporal association of weather and pollution parameters with eczema severity. This cross-sectional clinical study was performed between May 2018 and March 2020 and is part of the Tower Hamlets Eczema Assessment. All participants had a diagnosis of eczema, lived in East London, were of Bangladeshi ethnicity, and were aged <31 years. The primary outcome was the probability of having an Eczema Area and Severity Index score > 10 after previous ambient exposure to commonly studied meteorological variables and pollutants. There were 430 participants in the groups with Eczema Area and Severity Index ≤ 10 and 149 in those with Eczema Area and Severity Index > 10. Using logistic generalized additive models and a model selection process, we found that tropospheric ozone averaged over the preceding 270 days was strongly associated with eczema severity alongside the exposure to fine particles with diameters of 2.5 μm or less (fine particulate matter) averaged over the preceding 120 days. In our models and analyses, fine particulate matter appeared to largely act in a supporting role to ozone. We show that long-term exposure to ground-level ozone at high levels has the strongest association with eczema severity. © 2021

关键词： AIC Akaike Information Criterion EASI Eczema Area and Severity Index EseC European Socio-Economic Classification GAM generalized additive model IGA Investigators Global Assessment MAv moving average NO nitric oxide NO 2 nitrogen dioxide NO x nitrogen oxide O 3 ozone PM particulate matter SCORAD SCORing Atopic Dermatitis SE standard error THEA Tower Hamlets Eczema Assessment VOC volatile organic compound

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The burden associated with ambient PM_2.5 and meteorological factors in Guangzhou, China, 2012-2016: A generalized additive modeling of temporal years of life lost

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CHEMOSPHERE 2018年 212卷 705-714页

作者： Lin, Xiao Liao, Yu Hao, Yuantao Sun Yat Sen Univ Sch Publ Hlth Dept Med Stat & Epidemiol Guangzhou 510080 Guangdong Peoples R China Sun Yat Sen Univ Sun Yat Sen Global Hlth Inst Guangzhou 510080 Guangdong Peoples R China

Background: Daily exposure to ambient particulate matter with aerodynamic diameter <2.5 Am (PM2.5) increases deaths and is an important contributor to burden of disease in population. To better understand the disease burden associated with PM2.5, we examined the effects of PM2.5 on daily years of life lost (YLL) in Guangzhou, China. Methods: Using Guangzhou death registry, air pollution and meteorological database, we applied generalized additive models (GAM) to the relationships between YLL and PM2.5. We then adjusted the models for age, gender, seasonality and meteorological variables. We also conducted within -data prediction of YLL while setting 2012-2014 as baseline. Results: Over 2 million YLLs (800,137 YLLs for females and 1,212,040 YLLs for males) were observed during 2012-2016. YLL was higher for the elderly people. Mean daily average PM2.5 concentration was 47.3 mu g/m(3). In model comparisons, the GAM with six meteorological variables (sunshine hours, relative humidity, precipitation, atmospheric pressure, wind speed, evaporation) outperformed the others. The R-2 and total deviance were 0.542 and 53.0%, respectively. Non-linear trends were observed for PM2.5 and meteorological variables. Fitted daily YLL increased to the highest level, when PM2.5 concentration reached 134.3 g/m(3) and atmospheric pressure reached 99.4 kPa. Within-data prediction supported the fitted GAM, where low mean absolute percentage errors were observed. Conclusions: Daily PM2.5 exposure has a nonlinear effect on YLL and increased levels of PM2.5 may lead to increased YLL. This study highlights the urge to reduce ambient PM2.5 pollution in Guangzhou, in order to promote environmental health. (C) 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license

关键词： Disease burden Years of life lost PM2.5 Meteorological factor generalized additive model

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PREDICTION OF GAS TURBINE PERFORMANCE USING MACHINE LEARNING METHODS

PREDICTION OF GAS TURBINE PERFORMANCE USING MACHINE LEARNING...

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ASME Turbomachinery Technical Conference and Exposition (Turbo Expo)

作者： Goyal, Vipul Xu, Mengyu Kapat, Jayanta Vesely, Ladislav Univ Cent Florida Ctr Adv Turbomachinery & Energy Res Dept Mech & Aerosp Engn Orlando FL 32816 USA Univ Cent Florida Ctr Adv Turbomachinery & Energy Res Dept Stat Orlando FL 32816 USA

ISBN: (纸本)9780791884157

The current study is based on multiple machine learning algorithms to predict the normal behavior of operational parameters including power generated and blade path temperature spread. The predictions can be used to identify anomalies and probable failures in the gas turbine performance. The data used in the study is taken from multiple heavy-duty gas turbine units of combined cycled utility power plants which are known to contain operational failures. The predictors include operational parameters such as fuel flow, various thermodynamic variables, etc. In the first step, we cluster the observations into different working modes, because of the heterogeneous behavior of the gas turbine parameters under various modes. Then we consider predicting the operational parameters under each mode respectively, via algorithms including random forest, generalized additive model, and neural networks. The models are trained and parameters are selected based on the overall prediction performance on the validation set. The comparative advantage based on prediction accuracy and applicability of the algorithms is discussed for real-time use and post processing. The advantage of our method is that they achieve high predictive power and provide insight into the behavior of specific gas turbine variables, e.g.- turbine blade path temperature spread, which are not explicitly known to have any correlation with other thermodynamic variables.

关键词： Anomaly detection Hidden Markov model Random Forest generalized additive model Neural Network

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