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Assessing the Performance of the Health, Safety and Environment Management System (HSE) in the Area of Human Resources in the 22 Districts of Tehran
Hamid Choobineh, Zeinab Parisay, Fatemeh Shahbazi, Gazaleh Danesh, Mahdi Nasri, Sayed-Saeed Hashemi-Nazari,
Volume 18, Issue 2 (Vol.18, No.2, Summer 2022 2022)
Abstract
Background and Objectives: Human resources are the center of sustainable development in advanced management. Health, Safety and Environment (HSE) is a system that consistently and by means of human resources, facilities and equipment and tries to create a healthy, pleasant, fresh environment away from accident, damage and waste. This study was conducted to assess the health performance, Safety and Environmental Indicators (HSE) in the field of human resources development of Tehran Municipality.
Methods: This study was conducted as a descriptive cross-sectional and, the study base was all 22 districts of Tehran municipality. After designing and validating of HSE performance evaluation protocol, HSE status was evaluated in 22 districts of Tehran. The aforementioned protocol contained seven sections: leadership and commitment, policy and strategic goals, organization and documentation, risk management, planning, implementation and monitoring, audit and review). Its validity and reliability were determined by obtaining corrective opinions from specialists and experts inside and outside the municipal organization.
Results: In most areas of Tehran municipality, the inter-organizational communication index was the highest score. The highest score (68%) is related to this index. The mean overall score for performance evaluation was 46.6.
Conclusion: The HSE situation was undesirable (less than 70%) in more than half of Tehran's municipalities. Regions 4 and 7 were in desirable status (above 70%). Thus, for current situation improvement, we should use long-term strategy planning in the field of HSE.
 

Evaluation of Tuberculosis Status in Family Members of Patients with Pulmonary Tuberculosis in Bam City in 2019
Ali Hasanabadi, Shirin Nasri, Elaheh Salarpour, Naser Nasiri, Hamid Sharifi,
Volume 18, Issue 3 (Vol.18, No.3, Autumn 2022)
Abstract
Background and Objectives: Screening for home contact with TB patients is essential to identify new infections. This study aimed to evaluate the tuberculosis status in family members of patients with pulmonary tuberculosis in Bam.
Methods: This cross-sectional study was conducted as a census of patients' family members whose records are registered during 2013-2019 in Bam Health Center. Patients' information was collected based on a checklist, and then sputum smear-positive patients were identified using tuberculin and sputum smear tests.
Results: Ninety-seven of the patients had a positive sputum smear test result. Based on the records of these patients, 237 members of their families were examined as contact persons. Most of the patients were female and in the age group of 40-50 years; 76.8% of them had unprotected close contact, and 78.9% had a history of permanent contact with patients. Five (2.1%; 95% confidence intervals: 0.7-4.9) sputum-positive smear pulmonary tuberculosis cases were found in contacts of patients, most of them were over 50 years old and primarily women. 40% of these people had unprotected close contact, and 80% had a house with less than 70 square meters.
Conclusion: in this study, 2.0% of the family members of patients were sputum smear-positive. Identifying patients and following them up is essential to prevent the spread of tuberculosis in those people around them. Therefore, screening the patient's family members can significantly help ease the disease burden in Iran.

Identification Symptoms and Underlying Diseases Related to COVID-19 And Prediction of Death Status Using Artificial Neural Network and Logistic Regression: A Data Mining Approach
Nasrin Talkhi, Nooshin Akbari Sharak, Zahra Rajabzadeh, Maryam Salari, Seyed Masoud Sadati, Mohammad Taghi Shakeri,
Volume 18, Issue 3 (Vol.18, No.3, Autumn 2022)
Abstract
Background and Objectives: Due to the high prevalence of COVID-19 disease and its high mortality rate, it is necessary to identify the symptoms, demographic information and underlying diseases that effectively predict COVID-19 death. Therefore, in this study, we aimed to predict the mortality behavior due to COVID-19 in Khorasan Razavi province.
Methods: This study collected data from 51, 460 patients admitted to the hospitals of Khorasan Razavi province from 25 March 2017 to 12 September 2014. Logistic regression and Neural network methods, including machine learning methods, were used to identify survivors and non-survivors caused by COVID-19.
Results: Decreased consciousness, cough, PO2 level less than 93%, age, cancer, chronic kidney diseases, fever, headache, smoking status, and chronic blood diseases are the most important predictors of death. The accuracy of the artificial neural network model was 89.90% in the test phase. Also, the sensitivity, specificity and area under the rock curve in this model are equal to 76.14%, 91.99% and 77.65%, respectively.
Conclusion: Our findings highlight the importance of some demographic information, underlying diseases, and clinical signs in predicting survivors and non-survivors of COVID-19. Also, the neural network model provided high accuracy in prediction. However, medical research in this field will lead to complementary results by using other methods of machine learning and their high power.

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