Prediction of Metabolic Syndrome based on Sleep and Work-related Risk Factors using an Artificial Neural Network.

Background Metabolic syndrome (MetS) is a major public health concern due to its high prevalence and association with heart disease and diabetes. Artificial neural networks (ANN) are emerging as a reliable means of modelling relationships towards understanding complex illness situations such as MetS. Using ANN, this research sought to clarify predictors of metabolic syndrome (MetS) in a working age population. Methods 468 employees of an oil refinery in Iran consented to providing anthropometric and biochemical measurements, and survey data pertaining to lifestyle, work-related stressors and sleep variables. National Cholesterol Education Programme Adult Treatment Panel ІІI criteria was used for determining MetS status. The Management Standards Indicator Tool and STOP-BANG questionnaire were used to measure work-related stress and obstructive sleep apnoea respectively. With 17 input variables, multilayer perceptron was used to develop ANNs in 16 rounds of learning. ANNs were compared to logistic regression models using the mean squared error criterion for validation. Results Sex, age, exercise habit, smoking, high risk of obstructive sleep apnoea, and work-related stressors, particularly Role, all significantly affected the odds of MetS, but shiftworking did not. Prediction accuracy for an ANN using two hidden layers and all available input variables was 89%, compared to 72% for the logistic regression model. Sensitivity was 82.5% for ANN compared to 67.5% for the logistic regression, while specificities were 92.2% and 74% respectively. Conclusions Our analyses indicate that ANN models which include psychosocial stressors and sleep variables as well as biomedical and clinical variables perform well in predicting MetS. The findings can be helpful in designing preventative strategies to reduce the cost of healthcare associated with MetS in the workplace

Risk assessment of exposure to needle-stick injuries by Healthcare Failure Mode and Effect Analysis Method in a large Hospital

Article in Persian Background and Objectives: Healthcare workers (HCWs) are one of the most important jobs in exposure to Needle Stick (NS) and, therefore, are in risk of infection to diseases such as Hepatitis B (HB) and AIDS. The aim of this study was to identify and assess risk of injuries caused by needle and sharps in healthcare personnel of a hospital in Tehran. Methods: In this study, hazard analysis method of Healthcare Failure Mode and Effect Analysis (HFMEA) was selected. After several meetings with related experts, by the help of a provided checklist, hazardous processes related to NS injuries were identified and finally assessed by HFMEA method. Results: Potential causes of injuries included recapping, sudden shake of patient organs, lack of personnel, and slip. For most of the causes there were no control measures. Sudden shake of patient\u27s arm and heavy workload were recognized as high risk scores due to unskilled injector. Also for 53% of the studied cases, a score risk of more than 8 was recorded. Conclusion: As the results show, a major portion of potential causes of injuries had a high risk score, that is due to lack of a clear safety program or system to control the risk. Thus hospital manager should consider and control the causes according to the recommendations from risk assessment team. Totally HFMEA is an appropriate technique to analyze hazards related to NS injuries and predict effective measures to reduce related risks