Annals of Epidemiology最新文献_第4页

The probable role of barberries and herbal medicine in congenital anomalies and disorders in Darmian city, South Khorasan Province, Iran 伊朗南呼罗珊省达尔米安市的芭乐和草药对先天性畸形和疾病的可能作用

IF 3.3 3区医学

Annals of Epidemiology Pub Date : 2024-09-01 DOI: 10.1016/j.annepidem.2024.07.071

Mohammad Ismail Masinainejad , Narges Khanjani , Maryam Khodadadi , Ismail Najafi

引用次数: 0

Examining word patterns and trends in self-harm emergency department narratives 研究自我伤害急诊科叙述中的用词模式和趋势

IF 3.3 3区医学

Annals of Epidemiology Pub Date : 2024-09-01 DOI: 10.1016/j.annepidem.2024.07.062

L.E. Beagle, A.L. Cammack, G.F. Miller, N. Idaikkadar

引用次数: 0

Diagnosing irritable bowel syndrome with artificial intelligence: A systematic review and Meta-analysis 用人工智能诊断肠易激综合征：系统回顾与元分析

IF 3.3 3区医学

Annals of Epidemiology Pub Date : 2024-09-01 DOI: 10.1016/j.annepidem.2024.07.005

Nishad Badree BSc, Prakash Ramdass MD, MPH

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Investigating telehealth for Medicaid SUD and MH Services 为医疗补助计划的药物滥用、精神失常和心理健康服务开展远程保健调查

IF 3.3 3区医学

Annals of Epidemiology Pub Date : 2024-09-01 DOI: 10.1016/j.annepidem.2024.07.089

Akshaya Srikanth Bhagavathula

引用次数: 0

PREDICTING RISK OF EMPLOYEE INJURY IN A PEDIATRIC HOSPITAL 预测儿科医院员工受伤的风险

IF 3.3 3区医学

Annals of Epidemiology Pub Date : 2024-09-01 DOI: 10.1016/j.annepidem.2024.06.014

Emrah Gecili, Nancy Daraiseh, Cole Brokamp, Maurizio Macaluso

{"title":"PREDICTING RISK OF EMPLOYEE INJURY IN A PEDIATRIC HOSPITAL","authors":"Emrah Gecili, Nancy Daraiseh, Cole Brokamp, Maurizio Macaluso","doi":"10.1016/j.annepidem.2024.06.014","DOIUrl":"10.1016/j.annepidem.2024.06.014","url":null,"abstract":"<div><h3><strong>PURPOSE</strong></h3><p>Healthcare has one of the highest rates of non-fatal occupational injury as compared to other industries. Yet, evaluation of risk determinants and prediction algorithms in hospital settings remains limited.</p></div><div><h3><strong>METHODS</strong></h3><p>This study examines risk factors for employee injuries in a large pediatric hospital and evaluates prediction algorithms using hospital surveillance data, incident reports, and work unit measures such as patient density and employee workload. We employed multiple statistical models and machine learning tools, including logistic regression (LR), random forest (RF), penalized logistic regression (PLR), Naïve Bayes, neural network (NN), XGBoost (XG), and mixed-effects logistic regression (GLMER) to predict employee injury risk for specific time periods. We used cross-validation and receiver-operator characteristic (ROC) curve analyses to assess model performance.</p></div><div><h3><strong>RESULTS</strong></h3><p>GLMER, LR, and PLR were superior to other models, with higher AUC values (∼0.76), indicating good discrimination ability, though accuracy and specificity varied across models. RF showed high accuracy and specificity and comparable AUC with the top performing models. Further analyses using GLMER revealed variability in employee injury risk across months, days, and hospital units, identifying peaks on Tuesdays and Saturdays and in April and July, with lows in March and June.</p></div><div><h3><strong>CONCLUSION</strong></h3><p>Our findings highlight the significance of monitoring specific risk factors within pediatric hospital settings and pairing them with appropriate predictive algorithms to effectively predict and mitigate employee injuries. These insights indicate that continuous monitoring may help enhance employee safety. Future work should evaluate additional predictors that may be obtained from individual hospital units, which may inform targeted prevention strategies.</p></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"97 ","pages":"Page 107"},"PeriodicalIF":3.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142232771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Role of transportation on overall health in Nebraska 交通对内布拉斯加州整体健康的影响

IF 3.3 3区医学

Annals of Epidemiology Pub Date : 2024-09-01 DOI: 10.1016/j.annepidem.2024.07.056

M. Ahuja, D. Lamprecht-Carson, H. Wang, L.C. Smith, K.L. Ratnapradipa

引用次数: 0

Barriers and motivators to heart-health related research participation among women 妇女参与心脏健康相关研究的障碍和动力

IF 3.3 3区医学

Annals of Epidemiology Pub Date : 2024-09-01 DOI: 10.1016/j.annepidem.2024.07.023

Ivree Datcher MPH , Olivia Affuso PhD, FACSM , Alexandra Krallman , Bertha Hidalgo PhD, MPH, FACE

引用次数: 0

Neighborhood disadvantage, social vulnerability, and mental health 邻里劣势、社会脆弱性和心理健康

IF 3.3 3区医学

Annals of Epidemiology Pub Date : 2024-09-01 DOI: 10.1016/j.annepidem.2024.07.073

Kristyne Mansilla Dubon MD. MPH , Ariane Lisann Rung PhD, MPH , Edward S. Peters DMD, SM, ScD

引用次数: 0

Father involvement during pregnancy and maternal and neonatal health outcomes 孕期父亲参与与孕产妇和新生儿健康结果

IF 3.3 3区医学

Annals of Epidemiology Pub Date : 2024-09-01 DOI: 10.1016/j.annepidem.2024.07.084

Shelby Alderman, Katherine Bowers, Alonzo Folger

引用次数: 0

PREDICTORS OF INFLAMMATION IN FIREFIGHTERS BASED ON MACHINE LEARNING MODELS 基于机器学习模型的消防员炎症预测因子

IF 3.3 3区医学

Annals of Epidemiology Pub Date : 2024-09-01 DOI: 10.1016/j.annepidem.2024.07.021

A.C. Testoff, A.J. Green, J. Garibaldi, T. Koru-Sengul, N. Schaefer Solle, E.N. Kobetz, A.J. Caban-Martinez

引用次数: 0