آرمان امیری

Today, it is well-established that, in addition to hearing loss, noise affects the natural physiological functions of the body. The aim of this study was to determine the effect of noise on hearing loss and blood pressure in workers of a steel industry in southwest of Iran.

This study was performed on 54 steel workers. Dosimetry was performed using TES 1358 sound meter and Beurer BC16 blood pressure monitor, respectively. Statistical analysis was done using SPSS 24 software at the significant level of 0.05.

There was a significant relationship between hearing loss and sound pressure level in each of the case and control groups (P< 0.05). Despite the significant relationship between hearing loss and age, (P˂0.05), there was no significant relationship between the work experience variable and hearing loss (P> 0.05). Age and work experience had a significant relationship with hearing loss at some frequencies under the study (P˂0.05). There was also a significant difference between the mean of blood pressure in the case and control group (P˂0.05).

The results of this study indicated that exposure to noise increased blood pressure in individuals and the effects of exposure intensified with age. Therefore, control measures such as reducing sound pressure level, training programs and regular screening of the workers' blood pressure are recommended to reduce the effects of noise.

The outbreak of Covid-19 has caused a lot of stress and anxiety among people in different communities. Translation results In Iran, as in other countries, the outbreak of this virus has led to many psychological consequences for various segments of society, including nurses. The aim of this study was to evaluate the level of job stress in nurses exposed to covid-19 in Ahvaz educational hospitals.

This case-control study was conducted on 335 nurses in two sections:  a target group (exposed to covid-19 patients) and a control group (not exposed to covid-19 patients). The Osipow questionnaire was used to evaluate job stress. Appropriate statistical analyzes were conducted in a significance level of 0.05.

Job stress with a mean of 190.59 ± 22.56 and 183.07± 21.52, respectively, had a significant mean difference between two groups (target and control) (p <0.05) so that job stress in the target group was higher than the control group (p <0.05). The mean of all components of job stress was significantly different between two groups (p <0.05).Also, the mean of job stress with gender variables, physical activity and employment status was significant (p <0.05).

Due to the increase in job stress score that caused by exposing to covid-19, the availability of effective prevention or treatment strategies to deal with COVID-19, strengthening the psychological services system and also appropriate planning to control demographic factors affecting  job stress, including physical activity and improving the employment status of nurses are recommended.

Metabolic syndrome (MetS) is a cluster of interrelated metabolic risk factors. The most widely-recognized set of these risk factors are: increase in waist circumference (abdominal obesity), elevated blood pressure, elevated triglycerides, growth in serum glucose and reduction in high density lipoproteins. There have been various definitions for the diagnosis of MetS, but those which introduced by the International Diabetes Federation (IDF) and the Adult Treatment Panel (ATP III) are more widely implemented because they do not require measurement of insulin resistant. Based on the definition offered by ATP III, if a person has three of the aforementioned factors, he/she has MetS. MetS directly increased cardiovascular disease and diabetes.  The cardiovascular diseases, diabetes and obesity are the major health challenges in the 21st century. Therefore, attention to this disease must be much greater than in the past due to its prevalence in the current century.  Studies show that various factors affect MetS, the most important ones are age and body weight. Studies have also shown that lack of physical activities, alcohol consumption, unhealthy food habits and smoking increase occurrence of MetS. Gender, race, Body Mass Index (BMI) and prolonged sitting at work are among other factors affecting MetS. Various studies have reported different prevalence rates of this syndrome. The global prevalence of MetS varies from 10 to 50 percent. According to IDF, one-fourth of adults in the world are suffering from MetS and among them, the risk of death and chances of a stroke and heart attack are two and three times higher, respectively. The estimated rate for Iran is 34.4 percent, whereas (according to the ACEP criteria) 20 percent of the general population in the United States suffer from MetS. MetS is considered as an important health challenge because of its costs on healthcare systems and furthermore due to its high socioeconomic costs. Studies show that the average annual costs per person in the US is estimated at $ 4,000 for treating this syndrome. Workers are an important component of any organization, and workplace directly affects worker’s physical, mental and social condition; there for workplace is a very critical environmental and social factor affecting worker’s health. Up to now few studies with small sample size have been carried out on the prevalence of MetS in industrial workplace in Iran. Since identification of the risk factors related to MetS can play an important Role in the worker’s health and consequently increase industrial productivity, it is necessary to carry out a thorough and comprehensive study on a large sample population of the Iranian workers in order identify and control this syndrome and its related risk factors. The purpose of this study was to investigate the prevalence of metabolic syndrome and its effective demographic factors among workers in one of the petrochemical industries. 

The following cross sectional study was conducted among 692 male workers of a petrochemical company in south-west Iran. All participants had at least one-year work experience without any history of congenital disease, heart failure, kidney problem or hypertension. Stratified random sampling method was used to select participants. The procedure and aims of the study were explained to the participants and they were assured that their personal information will be kept confidential. Also all participants signed an informed written consent form prior to their participation. A data was collected by a two-part questionnaire in this study. The first part was focused on demographic information (age, marital status, education, and smoking status) and included questions on inclusion and exclusion criteria, the second part collected anthropometric information (height, weight, and waist circumference), blood pressure, and the parameters obtained through performing blood tests and BMI values (BMI obtained through dividing body weight in kilograms by height in meters squared). Based on criteria defined by World Health Organization, the BMI values were classified into four groups: less than 18.5 (underweight), 18.5-24.9 (normal range), 25-29.9 (overweight), and higher than 30 (obese). Since few participants had BMI values lower than 18.5, the BMI values of underweight participants and the BMI values in the normal range were categorized into one group. The participantchr('39')s blood pressure was measured by a standard mercury manometer. Each participant’s blood pressure was measured twice in a sitting position on the right arm, with a 5-minute rest between the twomeasurements, also each participant was asked to sit down for at least five minutes and then theirblood pressure was measured. The first part of the questionnaire was answered by the participants, then their anthropomorphic information and their blood pressure were measured by a researcher. After that they were asked to visit the laboratory to have their blood samples taken (after fasting for 12-14 hours). Three markers from the blood sample tests were recorded: fasting blood sugar, triglycerides and HDL-C. After blood samples were taken, tests to measure fasting plasma glucose and triglycerides levels were carried out using the enzymatic colorimetric method, employing Pars Azmoon standard kits. HDL cholesterol tests were performed employing the antibody-enzyme method and using Pars Azmoon standard kits. Adult Treatment Panel III (ATP III) method was used to diagnose MetS in the study population. MetS was diagnosed based on abdominal obesity, increased blood pressure, increased triglycerides, low HDL cholesterol level, and increased fasting blood sugar. Based on the ATP III method the copresence of at least three of the following indicators is considered diagnostic for MetS: waist circumference ≥ 102 cm in men and ≥ 88 cm in women, triglycerides level ≥ 150 mg/dl, HDL level ≤40mg/dl in men and ≤50 mg/dl in women, systolic blood pressure ≥130.85 mm Hg, diastolic pressure ≥85 mm Hg and fasting blood sugar ≥ 100mg/dl. Using SPSS version 24, descriptive statistics were analyzed. Mean, percentage, range and standard deviation were calculated. In order to determine correlation between MetS and its factors with shift work odds ratio (ORs) for the MetS, 95% confidence level (95% CL), chi-square test, Spearman correlation test and logistic regression analysis were performed. To assess the effect of shift work on NCEP ATP III factors and demographic factors, independent samples t test and Cramerchr('39')s V were implemented. The level of statistical significance for p-value was set at <0.05..

The findings show that the prevalence of metabolic syndrome was 15.1%, elevated waist circumference 27.3%, high-density lipoprotein deficiency 71.6%, elevated triglycerides 49.5%, hypertension 34.1%, elevated cholesterol 37.8 % and elevated fasting plasma glucose 13.4%.The correlation tests for demographic factors and metabolic syndrome indicate relationships between metabolic syndrome and each of the factors: age, hypertension, body mass index, shift work and high-density lipoprotein deficiency. So that metabolic syndrome had a moderate relationship with hypertension, body mass index and shift work (s=0.316, s= 0.371, and s=0.452 respectively) and while, the extent of this relation for age and high-density lipoprotein deficiency were small (s= 0.18 and, s= 0.15), other demographic factors, namely, marriage status, being a smoker, and education level found to be unrelated with metabolic syndrome (P>0.05).A significant difference between mean values of body mass index, high-density lipoprotein, shift work, waist circumference, triglycerides, blood pressure and plasma glucose was identified among people diagnosed with metabolic syndrome and healthy individuals (p<0.001).The probability of metabolic syndrome occurrence found to be affected by blood pressure with OR=3.12 (C.I. 95% = 4.1 – 2.37), body mass index with OR=1.301 (C.I. 95% =1.38 – 1.22), age with OR=1.09 (C.I. 95% =1.088 – 1.031), high-density lipoprotein deficiency with OR=1.015 (C.I. 95% =1.01 – 1.019), fasting plasma glucose with OR=2.78 (C.I. 95% =1.786 – 4.343), waist circumference with OR=9.830 (C.I. 95% =6.105 – 15.829), shift work with OR=7.309 (C.I. 95% =4.249 – 12.290), and triglyceride with OR=1.009 (C.I. 95% = 1.006-1.011), which results in increases in Odds Ratio (95% Confidence Interval).

Due to high metabolic syndrome prevalence and abnormal levels of waist circumference, triglyceride, blood pressure, high-density lipoprotein, body mass index, and fasting plasma glucose, as well as, the significant relationship between syndrome prevalence and age, body mass index, and shift work, raising awareness to change work conditions in order to live a healthy lifestyle and controlling demographic factors related with metabolic syndrome might reduce the consequences of metabolic syndrome and promote the overall health of people.

job stress is one of the factors affecting workers occupational productivity and health. In recent years, stress has been emphasized as an alarm that results in loss of productivity and efficiency. The purpose of this study was to investigate the effect of job stress on worker productivity in one of the Southwest Oil and Gas Companies.

In this study, two separate questionnaires were used to collect job stress and productivity information. The validity and reliability of these questionnaires have been reviewed in previous studies. All data was processed by SPSS 24 software. 

The mean score of job stress was 3.68 ± 0.43. The results showed that 36.4% and 63.6% of workers had low and no stress, respectively. It also found that productivity with job stress and each of the indicators related to  role, relationship, support of authorities, support of coworkers, demand had a significant and inverse correlation (p <0.05). Productivity was also significantly correlated with age, work experience and education (p <0.05).

Controlling occupational stress and related factors in the workplace is one of the strategies to increase productivity, because there is a significant and inverse relationship between the mean of job stress and productivity.