Analysis of risk factors for elective surgery complications in a cancer specialty hospital based on propensity score matching by Mingfei Xiang, Anxin Luo, Yixin Wang, Junshuang Lin, Zhonghua Xiong, Fei Hou, Yinping Zhou, Chunlin Wu

“…A dataset was constructed based on propensity score matching (PSM), and the disease burden caused by surgical complications was calculated. Logistic regression analysis was used to identify factors influencing the occurrence of surgical complications. …”
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New-onset urethral stricture after transurethral holmium laser enucleation of the prostate and analysis on its influencing factors by WANG Jie, WANG Jie, YE Chenxi, YE Chenxi, HU Qiang, HU Qiang

“…Objective‍　‍To systematic analyze the risk factors for new-onset urethral stricture after transurethral holmium laser enucleation of the prostate （HoLEP） in the treatment of benign prostatic hyperplasia （BPH）. ‍…”
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The Demographic and Clinical Characteristics, Prognostic Factors, and Survival Outcomes of Head and Neck Carcinosarcoma: A SEER Database Analysis by Wanting Hou, Ouying Yan, Hong Zhu

“…The identified prognostic factors were incorporated into nomograms to predict OS and CSS. …”
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Machine Learning–Based Analysis of Lifestyle Risk Factors for Atherosclerotic Cardiovascular Disease: Retrospective Case-Control Study by Hye-Jin Kim, Heeji Choi, Hyo-Jung Ahn, Seung-Ho Shin, Chulho Kim, Sang-Hwa Lee, Jong-Hee Sohn, Jae-Jun Lee

“…However, the light gradient boosting model demonstrated better performance in accuracy, recall, and F1 ConclusionsAnalyzing lifestyle behavioral factors in ASCVD risk with an ML model improves the predictive performance compared to traditional models. …”
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Incidence and Risk Factors for Femoral Malrotation Following Intramedullary Nailing of Trochanteric Fractures: A CT-Based Analysis by Chao Han M.D, Xiao-dan Li M.D, Zhe Han M.D, Qiang Dong M.D

“…Objective To quantify rotational displacement following intramedullary nail fixation for intertrochanteric femoral fractures using three-dimensional (3D) CT imaging, analyze associated risk factors, and evaluate its clinical significance. …”
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Analysis on spatiotemporal evolution and meteorological driving factors of future meteorological drought in Northwest China based on CMIP6 by Jianan Shan, Rui Zhu, Zhenliang Yin, Xiaoya Deng, Tingting Wang, Huaqing Yang, Wei Zhang

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Analysis of self-efficacy status and related factors of breastfeeding among hospitalized primiparas in Shannan City, Tibet Autonomous Region by XUE Yanni, RENQING Qunzong, DAWA Zhuoga, LIU Juan

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Relationship Analysis of Risk Factors Concerning the Incidence of Pulmonary Tuberculosis in Children at Panti Abdi Dharma Hospital, Cirebon, Indonesia by Annisa Shintya Pratama, Defa Rahmatun Nisaa, Nanang Ruhyana

“…Methods: Using a cross sectional analytical observational approach, systematic sampling technique on 138 respondents using bivariate analysis (chi-square) and multivariate analysis (logistic regression). …”
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An analysis of the factors influencing the demand for care in the community for older adults with mild dementia in China: a cross-sectional study by Shuang Teng, Shuxin Xu, Mengmeng Liang, Jingjie Li, Dongdong Shen, Yan Cheng, Xianping Tang

“…Independent samples t-test and one-way ANOVA were used to compare the differences between demographic characteristics, impairment levels and care needs. Multiple linear regression analysis was used to analyze the independent factors influencing care needs. …”
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A comparative analysis of COVID-19 cases with comorbidities according to epidemiological and demographic characteristics in South Bengkulu Regency, Indonesia by Fiya Diniarti, Bintang Agustina Pratiwi, Fery Surahman, Tuti Rohani

“…Chi-square and multiple regression logistics were used for data analysis. Result: Most groups infected with COVID-19 were in the risk age range (46.6%), men (51.2%), low education (48.2%), had a record of contact with patients (54.6%), had a travel record (53.7%), had a record of social contact (51.5%) and had the highest comorbidities such as tuberculosis (36.2%). …”
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Trend and influencing factors of low birth weight among newborns in Chongming District of Shanghai from 2008 to 2022 by SHI Aiyu, GU Tianyi, XU Yan, HUANG Yuhua, SUN Xiaolei

“…Logistic regression analysis was used to analyze the influencing factors of LBW.ResultsThe overall incidence of LBW was 3.71% in Chongming District, Shanghai from 2008 to 2022. …”
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Epidemiological characteristics and risk factors analysis of multidrug-resistant tuberculosis among tuberculosis population in Huzhou City, Eastern China by Chen Haiyan, Tong Zhaowei, Zhong Jianfeng, Zeng Qingqiu, Shen Bin, Qian Fuchu, Xiao Xin

“…Multivariate logistic regression analysis showed that the risk of developing MDR-TB in individuals with comorbidities was 9.17 times higher than in individuals without comorbidities (odds ratio [OR] = 9.17, 95% confidence interval [CI]: 6.5–12.93, P < 0.001). …”
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Epidemiological investigation of urinary calculi among the permanent residents in Basu County,Tibet Autonomous Region and analysis on its influencing factors by YU Min, BAIMA Ga-jin, LUOSONG Qu-cuo, XIANG Xi, BAIMA Gong-sang, LIU Nan-mei

“…Objective This paper tries to investigate the epidemiological characters of urinary calculi among the permanent residents in Basu County,Tibet Autonomous Region and to analyze the influencing factors.These results are expected to provide a basis for the prevention and treatment of urinary calculi in this area and the whole Tibetan plateau.Methods By using stratified cluster random sampling method,we enrolled 7359 permanent residents in 34 administrative villages from 3 towns and 1 townships in Basu County,Tibet Autonomous Region from September to December in 2019.The questionnaire was employed to investigate the demographic data,the living and dietary habits,and the family history of disease.Urinary calculi was diagnosed by type B ultrasonography.Local drinking water quality was also analyzed.Univariate analysis and multivariate Logistic regression analysis were conducted on the influencing factors for urinary calculi in local residents.Results Among the 7 359 subjects,1 119 subjects were diagnosed with urinary calculi with a prevalence rate of 15.21%.The prevalence rate of urinary calculi has statistically significant differences among populations with different genders,ages,marital status(P<0.05).Univariate analysis also showed that smoking,BMI,drinking water amount,drinking before sleeping,the frequency of eating green vegetables also influenced the incidence of urinary calculi in the local residents(P<0.05).The investigation on the special dietary habits in Tibetan areas also showed that the prevalence rate of urinary calculi was significantly different among populations with different daily drinking amount of buttered tea and highland barley wine,different frequency of eating beef and mutton,and different drinking water quality(P<0.05).Multivariate Logistic regression analyses showed that age,gender,smoking,drinking before sleeping,the daily drinking amount of buttered tea and highland barley wine,and the drinking water quality were the independent influencing factors of urinary calculi(P<0.05).The test of drinking water quality showed that the content of calcium and magnesium exceeded the standard in the drinking water in Basu County,especially in the mountain spring water and the well water(P<0.05).Conclusions The prevalence of urinary calculi among the permanent residents in Basu County,Tibet Autonomous Region is relatively higher.Some influencing factors are consistent with the plain area,and there are also other factors including the environment,the dietary habits unique to Tibetan areas.The prevention and treatment of urinary calculi in Tibet should be carried out by pointing to these high risk factors.…”
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Geospatial mapping to assess the distribution and determinants of zero dose vaccination status hot spots among children in Ethiopia using EDHS 2019: Spatial and geographical weight... by Muluken Chanie Agimas, Meron Asmamaw, Melese Kebede Hailu, Tigabu Kidie, Habtamu Wagnew Abuhay, Getaneh Awoke Yismaw, Nebiyu Mekonnen Derseh

“…The efforts to identify the factors contributing to the zero-dose vaccine have not yet been addressed in Ethiopia.…”
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Factors Affecting Labour Productivity in Manufacturing Enterprises by Zbigniew Gołaś

“…The article presents the results of the analysis of the factors influencing labour productivity in the manufacturing business sector in 2004–2008. …”
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Spatiotemporal Variation Characteristics and Influencing Factors of Water Transparency in the Pilot Zone of Yangtze River Delta by WANG Yu, ZHOU Wei-qi, BAI Meng-yu, SU Han, HU Wei

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Characteristics and Evolution Trends of Extreme Precipitation in Southern Gaoligong Mountain from 1981 to 2020 by CHEN Wen-hua, ZHANG Ning, FENG Chun-hong, ZHAO Wei-hua, YANG Min

“…[Methods] Using daily precipitation data from 8 meteorological stations, this study selected five EPIs: total wet-day precipitation (PTOT), maximum consecutive dry days (CDD), maximum 1-day precipitation (RX1day), number of heavy precipitation days (R10mm), and extreme precipitation intensity (SDII). Innovative trend analysis (ITA) and linear regression (LR) were used to analyze long-term trends, and composite analysis was employed to examine the impact of ENSO events (represented by ONI and DMI) on extreme precipitation. …”
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Influencing factors for influenza vaccination among the elderly by LI Yiyao, LI Xiaoju, SHEN Xiaoying, ZHANG Xianqi, ZHAO Li, ZHANG Yuhan, WANG Xinmeng

“…Demographic information, knowledge about influenza and influenza vaccines, vaccine literacy and influenza vaccination status in the past year were collected through questionnaire surveys. Factors affecting influenza vaccination among the elderly were analyzed using a multivariable logistic regression model. …”
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Clinical Characteristics and Independent Risk Factors for Multidrug-Resistant Klebsiella pneumoniae Bloodstream Infections: A Retrospective Analysis from China by Xu P, Mao Y, Chen Q, Luo X, Lin R, Zheng C

“…Clinical data were analyzed using multivariable logistic regression to identify risk factors.Results: MDR KP-BSI prevalence was 22.5% (54/240). …”
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Factors That Influence the Level of Banking Profitability in Indonesia by Erliana Aditya, Karin Resnik, Susy Muchtar

“… This study aims to analyze the level of banking profitability in Indonesia using panel data analysis. …”
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