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Original Articles
Demographic and epidemiological characteristics of scorpion envenomation and daily forecasting of scorpion sting counts in Touggourt, Algeria
Kaouthar Boubekeur, Mohamed L’Hadj, Schehrazad Selmane
Epidemiol Health. 2020;42:e2020050.   Published online July 6, 2020
DOI: https://doi.org/10.4178/epih.e2020050
  • 13,132 View
  • 197 Download
  • 4 Web of Science
  • 1 Crossref
AbstractAbstract PDFSupplementary Material
Abstract
OBJECTIVES
This study was conducted to provide better insights into the demographic and epidemiological characteristics of scorpion envenomation in an endemic area in Algeria and to identify the model that best predicted daily scorpion sting counts.
METHODS
Daily sting data from January 1, 2013 to August 31, 2016 were extracted from questionnaires designed to elicit information on scorpion stings from the two emergency medical service providers in Touggourt, Algeria. Count regression models were applied to the daily sting data.
RESULTS
A total of 4,712 scorpion sting cases were documented, of which 70% occurred in people aged between 10 years and 49 years. The male-to-female ratio was 1.3. The upper and lower limbs were the most common locations of scorpion stings (90.4% of cases). Most stings (92.8%) were mild. The percent of people stung inside dwellings was 68.8%. The hourly distribution of stings showed a peak between 10:00 a.m. and 11:00 a.m. The daily number of stings ranged from 0 to 24. The occurrence of stings was highest on Sundays. The incidence of scorpion stings increased sharply in the summer. The mean annual incidence rate was 542 cases per 100,000 inhabitants. The fitted count regression models showed that a negative binomial hurdle model was appropriate for forecasting daily stings in terms of temperature and relative humidity, and the fitted data agreed considerably with the actual data.
CONCLUSIONS
This study showed that daily scorpion sting data provided meaningful insights; and the negative binomial Hurdle model was preferable for predicting daily scorpion sting counts.
Summary

Citations

Citations to this article as recorded by  
  • Scorpion Envenomations in Algeria
    Schehrazad Selmane, Mohamed Lhadj
    Journal of Preventive, Diagnostic and Treatment Strategies in Medicine.2022; 1(1): 45.     CrossRef
Effects of human and organizational deficiencies on workers’ safety behavior at a mining site in Iran
Mostafa Mirzaei Aliabadi, Hamed Aghaei, Omid Kalatpour, Ali Reza Soltanian, Maryam SeyedTabib
Epidemiol Health. 2018;40:e2018019.   Published online May 18, 2018
DOI: https://doi.org/10.4178/epih.e2018019
  • 17,638 View
  • 216 Download
  • 24 Web of Science
  • 24 Crossref
AbstractAbstract PDF
Abstract
OBJECTIVES
Throughout the world, mines are dangerous workplaces with high accident rates. According to the Statistical Center of Iran, the number of occupational accidents in Iranian mines has increased in recent years. This study investigated and analyzed the human and organizational deficiencies that influenced Iranian mining accidents.
METHODS
In this study, the data associated with 305 mining accidents were analyzed using a systems analysis approach to identify critical deficiencies in organizational influences, unsafe supervision, preconditions for unsafe acts, and workers’ unsafe acts. Partial least square structural equation modeling (PLS-SEM) was utilized to model the interactions among these deficiencies.
RESULTS
Organizational deficiencies had a direct positive effect on workers’ violations (path coefficient, 0.16) and workers’ errors (path coefficient, 0.23). The effect of unsafe supervision on workers’ violations and workers’ errors was also significant, with path coefficients of 0.14 and 0.20, respectively. Likewise, preconditions for unsafe acts had a significant effect on both workers’ violations (path coefficient, 0.16) and workers’ errors (path coefficient, 0.21). Moreover, organizational deficiencies had an indirect positive effect on workers’ unsafe acts, mediated by unsafe supervision and preconditions for unsafe acts. Among the variables examined in the current study, organizational influences had the strongest impact on workers’ unsafe acts.
CONCLUSIONS
Organizational deficiencies were found to be the main cause of accidents in the mining sector, as they affected all other aspects of system safety. In order to prevent occupational accidents, organizational deficiencies should be modified first.
Summary

Citations

Citations to this article as recorded by  
  • Exploring the application of PLS-SEM in construction management research: a bibliometric and meta-analysis approach
    Sachin Batra
    Engineering, Construction and Architectural Management.2024;[Epub]     CrossRef
  • Analysis of characteristics and causes of gas explosion accidents: a historical review of coal mine accidents in China
    Yunxin Wang, Gui Fu, Qian Lyu, Chenhui Yuan
    International Journal of Occupational Safety and Ergonomics.2024; 30(1): 168.     CrossRef
  • A fuzzy Bayesian network DEMATEL model for predicting safety behavior
    Mohsen Mahdinia, Iraj Mohammadfam, Ahmad Soltanzadeh, Mostafa Mirzaei Aliabadi, Hamed Aghaei
    International Journal of Occupational Safety and Ergonomics.2023; 29(1): 36.     CrossRef
  • Assessing the impact of peripheral vision on construction site safety
    Isik Ates Kiral, Sevilay Demirkesen
    Engineering, Construction and Architectural Management.2023; 30(9): 4435.     CrossRef
  • Modelling and analysis of unsafe acts in coal mine gas explosion accidents based on network theory
    Wang Yuxin, Fu Gui, Lyu Qian, Li Xiao, Chen Yiran, Wu Yali, Xie Xuecai
    Process Safety and Environmental Protection.2023; 170: 28.     CrossRef
  • Conceptual Framework for Hazards Management in the Surface Mining Industry—Application of Structural Equation Modeling
    Saira Sherin, Salim Raza, Ishaq Ahmad
    Safety.2023; 9(2): 31.     CrossRef
  • Identification and evaluation of maintenance error in catalyst replacement using the HEART technique under a fuzzy environment
    Mostafa Mirzaei Aliabadi, Iraj Mohammadfam, Keyvan Salimi
    International Journal of Occupational Safety and Ergonomics.2022; 28(2): 1291.     CrossRef
  • Influencing Factors, Formation Mechanism, and Pre-control Methods of Coal Miners′ Unsafe Behavior: A Systematic Literature Review
    Li Yang, Xue Wang, Junqi Zhu, Zhiyuan Qin
    Frontiers in Public Health.2022;[Epub]     CrossRef
  • Risk Factors Identification of Unsafe Acts in Deep Coal Mine Workers Based on Grounded Theory and HFACS
    Li Yang, Xue Wang, Junqi Zhu, Zhiyuan Qin
    Frontiers in Public Health.2022;[Epub]     CrossRef
  • Application of AHP and DEMATEL for Identifying Factors Influencing Coal Mine Practitioners’ Unsafe State
    Lei Chen, Hongxia Li, Shuicheng Tian
    Sustainability.2022; 14(21): 14511.     CrossRef
  • Integrated Method for Assessing Occupational Risks at Oil and Gas Production Facilities
    N V Gorlenko, M A Murzin
    IOP Conference Series: Earth and Environmental Science.2021; 666(6): 062141.     CrossRef
  • Integrated Method for Assessing Occupational Risks at Oil and Gas Production Facilities
    N V Gorlenko, M A Murzin
    IOP Conference Series: Materials Science and Engineering.2021; 1079(6): 062078.     CrossRef
  • Occupational Risk Assessment for Workers of Aluminum Production Using the Example of RUSAL Bratsk OJSC
    M A Murzin, M S Tepina, N V Gorlenko
    IOP Conference Series: Materials Science and Engineering.2021; 1079(6): 062080.     CrossRef
  • Zero-Emission Water Cycle When Developing Underground Gas Storage in Rock Salt Formation
    E A Lokshina, A V Kolchin, B N Mastobaev
    IOP Conference Series: Materials Science and Engineering.2021; 1079(7): 072039.     CrossRef
  • Research trends in mining accidents study: A systematic literature review
    Siti Noraishah Ismail, Azizan Ramli, Hanida Abdul Aziz
    Safety Science.2021; 143: 105438.     CrossRef
  • Investigation of the Relationship among Human Factors in Mining Accidents Using a Systematic Approach
    Mostafa Mirzaei Aliabadi, Taleb Askaripoor, Hamed Aghaei
    Journal of Occupational Hygiene Engineering.2021; 8(2): 8.     CrossRef
  • Contributory factors interactions model: A new systems‐based accident model
    Linlin Jing, Qingguo Bai, Weiqun Guo, Yan Feng, Lin Liu, Yingyu Zhang
    Systems Research and Behavioral Science.2020; 37(2): 255.     CrossRef
  • Game Modelling and Strategy Research on Trilateral Evolution for Coal-Mine Operational Safety Production System: A Simulation Approach
    Yan Li, Yan Zhang, Haifeng Dai, Ziyan Zhao
    Complexity.2020; 2020: 1.     CrossRef
  • A Discourse on the Incorporation of Organizational Factors into Probabilistic Risk Assessment: Key Questions and Categorical Review
    Justin Pence, Zahra Mohaghegh
    Risk Analysis.2020; 40(6): 1183.     CrossRef
  • Occupational Risks in the Extraction and Processing of Mineral Raw Materials
    N V Gorlenko, M S Leonova, M A Murzin
    IOP Conference Series: Earth and Environmental Science.2020; 459(3): 032023.     CrossRef
  • Structural equation modeling of risk-taking behaviors based on personality dimensions and risk power
    MostafaMirzaei Aliabadi, Elnaz Taheri, Kamran Najafi, Farzaneh Mollabahrami, Sajjad Deyhim, Maryam Farhadian
    International Archives of Health Sciences.2020; 7(3): 119.     CrossRef
  • The Relationships Among Occupational Safety Climate, Patient Safety Climate, and Safety Performance Based on Structural Equation Modeling
    Hamed Aghaei, Zahra Sadat Asadi, Mostafa Mirzaei Aliabadi, Hassan Ahmadinia
    Journal of Preventive Medicine and Public Health.2020; 53(6): 447.     CrossRef
  • Analysis of the severity of occupational injuries in the mining industry using a Bayesian network
    Mostafa Mirzaei Aliabadi, Hamed Aghaei, Omid kalatpuor, Ali Reza Soltanian, Asghar Nikravesh
    Epidemiology and Health.2019; 41: e2019017.     CrossRef
  • Cause Analysis of Unsafe Behaviors in Hazardous Chemical Accidents: Combined with HFACs and Bayesian Network
    Xiaowei Li, Tiezhong Liu, Yongkui Liu
    International Journal of Environmental Research and Public Health.2019; 17(1): 11.     CrossRef
Application of an artificial neural network model for diagnosing type 2 diabetes mellitus and determining the relative importance of risk factors
Shiva Borzouei, Ali Reza Soltanian
Epidemiol Health. 2018;40:e2018007.   Published online March 10, 2018
DOI: https://doi.org/10.4178/epih.e2018007
  • 17,323 View
  • 288 Download
  • 16 Web of Science
  • 15 Crossref
AbstractAbstract PDF
Abstract
OBJECTIVES
To identify the most important demographic risk factors for a diagnosis of type 2 diabetes mellitus (T2DM) using a neural network model.
METHODS
This study was conducted on a sample of 234 individuals, in whom T2DM was diagnosed using hemoglobin A1c levels. A multilayer perceptron artificial neural network was used to identify demographic risk factors for T2DM and their importance. The DeLong method was used to compare the models by fitting in sequential steps.
RESULTS
Variables found to be significant at a level of p<0.2 in a univariate logistic regression analysis (age, hypertension, waist circumference, body mass index [BMI], sedentary lifestyle, smoking, vegetable consumption, family history of T2DM, stress, walking, fruit consumption, and sex) were entered into the model. After 7 stages of neural network modeling, only waist circumference (100.0%), age (78.5%), BMI (78.2%), hypertension (69.4%), stress (54.2%), smoking (49.3%), and a family history of T2DM (37.2%) were identified as predictors of the diagnosis of T2DM.
CONCLUSIONS
In this study, waist circumference and age were the most important predictors of T2DM. Due to the sensitivity, specificity, and accuracy of the final model, it is suggested that these variables should be used for T2DM risk assessment in screening tests.
Summary

Citations

Citations to this article as recorded by  
  • Predicting stunting status among under-5 children in Rwanda using neural network model: Evidence from 2020 Rwanda demographic and health survey
    Similien Ndagijimana, Ignace Kabano, Emmanuel Masabo, Jean Marie Ntaganda
    F1000Research.2025; 13: 128.     CrossRef
  • Predicting Type 2 diabetes onset age using machine learning: A case study in KSA
    Faten Al-hussein, Laleh Tafakori, Mali Abdollahian, Khalid Al-Shali, Ahmed Al-Hejin, Salim Heddam
    PLOS ONE.2025; 20(2): e0318484.     CrossRef
  • Multi‐feature, Chinese–Western medicine‐integrated prediction model for diabetic peripheral neuropathy based on machine learning and SHAP
    Aijuan Jiang, Jiajie Li, Lujie Wang, Wenshu Zha, Yixuan Lin, Jindong Zhao, Zhaohui Fang, Guoming Shen
    Diabetes/Metabolism Research and Reviews.2024;[Epub]     CrossRef
  • Artificial intelligence-driven transformations in diabetes care: a comprehensive literature review
    Muhammad Iftikhar, Muhammad Saqib, Sardar Noman Qayyum, Rehana Asmat, Hassan Mumtaz, Muhammad Rehan, Irfan Ullah, Iftikhar Ud-din, Samim Noori, Maleeka Khan, Ehtisham Rehman, Zain Ejaz
    Annals of Medicine & Surgery.2024; 86(9): 5334.     CrossRef
  • A Review on Trending Machine Learning Techniques for Type 2 Diabetes Mellitus Management
    Panagiotis D. Petridis, Aleksandra S. Kristo, Angelos K. Sikalidis, Ilias K. Kitsas
    Informatics.2024; 11(4): 70.     CrossRef
  • Bioinformatics Analysis of Next Generation Sequencing Data Identifies Molecular Biomarkers Associated With Type 2 Diabetes Mellitus
    Varun Alur, Varshita Raju, Basavaraj Vastrad, Chanabasayya Vastrad, Satish Kavatagimath, Shivakumar Kotturshetti
    Clinical Medicine Insights: Endocrinology and Diabetes.2023;[Epub]     CrossRef
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    Kyungjin Chang, Songmin Yoo, Simyeol Lee
    Nutrition Research and Practice.2023; 17(6): 1255.     CrossRef
  • Evaluation of the Risk Factors for Type 2 Diabetes Using the Generalized Structure Equation Modeling in Iranian Adults based on Shahedieh Cohort Study
    Marzieh Farhadipour, Hossien Fallahzadeh, Akram Ghadiri-Anari, Masoud Mirzaei
    Journal of Diabetes & Metabolic Disorders.2022; 21(1): 919.     CrossRef
  • Predicting the Risk of Incident Type 2 Diabetes Mellitus in Chinese Elderly Using Machine Learning Techniques
    Qing Liu, Miao Zhang, Yifeng He, Lei Zhang, Jingui Zou, Yaqiong Yan, Yan Guo
    Journal of Personalized Medicine.2022; 12(6): 905.     CrossRef
  • Establishment and Evaluation of Artificial Intelligence-Based Prediction Models for Chronic Kidney Disease under the Background of Big Data
    Xiaoqian Yan, Ximin Li, Ying Lu, Dongfang Ma, Shenghong Mou, Zhiyuan Cheng, Yuan Ding, Bin Yan, Xianzhen Zhang, Gang Hu, Muhammad Zia-Ul-Haq
    Evidence-Based Complementary and Alternative Medicine.2022; 2022: 1.     CrossRef
  • Diagnosis of Addison's disease Using Artificial Neural Network
    S. Džaferović, D. Melić, M. Mihajlović, A. Smajović, E. Bečić, L. Spahić Bećirović, L. Gurbeta Pokvić, A. Badnjević
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    Ankit Sharma, Harendra Pal Singh, Nilam
    International Journal of Biomathematics.2022;[Epub]     CrossRef
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    Xinrui Jin, Zixuan Ding, Tao Li, Jie Xiong, Gang Tian, Jinbo Liu
    The American Journal of Emergency Medicine.2021; 44: 85.     CrossRef
  • Modeling the Research Landscapes of Artificial Intelligence Applications in Diabetes (GAPRESEARCH)
    Giang Thu Vu, Bach Xuan Tran, Roger S. McIntyre, Hai Quang Pham, Hai Thanh Phan, Giang Hai Ha, Kenneth K. Gwee, Carl A. Latkin, Roger C.M. Ho, Cyrus S.H. Ho
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