Awareness and attitude of radiographers towards radiation protection

    Whereas radiology departments have potential to present hazardous effects due of ionizing radiations, awareness and knowledge of application protection guidelines and instruments among radiology technicians has an important role to safe working in these places. Therefore radiographers' knowledge regarding radiation and their healthy behaviors during work time evaluated by a special questionnaire form including different relative questions. The level of participants' awareness about necessity of application film-badge and following the periodical examination were 70% and 63% respectively. Most of them are familiar with radiation adverse effects and they apply the protection devices for themselves and patients by 83.1% and 78.9%. based on the obtained data, the employees have a good awareness about construction protection especially in door shielding and wall. Their knowledge around dose limit was acceptable and there is a significant relationship between their awareness about Maximum permissible dose and their education level (p< 0.008). Taking part in different relative courses and continuously educations will affected on radiographers' awareness about  important aspects of their activities in workplace and will be ensured working with ionizing radiation.

The effect of viewing conditions on reader performance in radiographic images

Accurate interpretation of radiographic images is dependent on viewing conditions. Recently the number of radiology departments has been increased so it needs to use a workstation for reporting. The aim of this study was assess monitor performance and the effect of viewing conditions on object detection. This investigation aimed to quantify the effects of changes in box brightness and ambient light level on reader performance. Radiographs of the contrast-details phantom were taken in multiple exposures and were viewed by six observers. The viewing test was performed in 50,100 and 150 lux of ambient light in compound with 1000,1500 and 2000 cd m-2 box brightness. The percentage of uniformity was also 85. The results were analyzed by SPSS software. Low contrast visibility generally increased when the ambient light was 100 lux. The greatest performance was obtained in 2000 cd m-2 brightness and 15% non uniformity in mentioned ambient lighting. Reader performance not affected by ambient light and view box luminance although it seems those factors influenced on detection of low-contrast features in some studies.

Detecting Rates, Trends and Determinants of Cesarean Section Deliveries in Iran Using Generalized Additive Mixed Models

Background: The present study aimed to investigate the trend of cesarean section and its related factors through the recent years. Methods: The study data containing delivery information from Hamadan hospitals and are recorded from 2001 to 2014. The data were analyzed through the generalized additive mixed models using R software (v. 3.2.2). Results: cesarean rate in this study was about 42%. According to the results, the trend of cesarean deliveries almost increased in the recent years. A significant relationship was found between the average age and elective cesarean rate, but, the pregnancy rate didn’t have a significant effect on the elective cesarean rate. Conclusion: Cesarean section rate was more than the allowed limit by world health organization (WHO) that is 15%. Although cesarean delivery is preferred to natural vaginal delivery in the case the mother’s or infant’s life is in danger, it should not replace natural delivery for any reason. Natural vaginal delivery can be promoted by providing the society with the knowledge about the advantages of natural delivery and complications of cesarean section

Exploring the spatial patterns of three prevalent cancer latent risk factors in Iran; Using a shared component model

Background and aims: The aim of this study was the modeling of the incidence rates of Colorectal, breast and prostate cancers using a shared component model in order to explore the spatial pattern of their shared risk factors (i.e., obesity and low physical activity) affecting on cancer incidence, and also to estimate the relative weight of these shared components. Methods: In this study, the new cases of colorectal, breast and prostate cancers information provided by the Management Center of Ministry of Health and Medical Education in 2009 were analyzed. The Bayesian shared component model was used. In addition, BYM (Besag, York and Mollie) model was applied to investigate the geographical pattern of disease incidence rates, individually. Results: The larger effect of obesity on the incidence of the relevant cancers was found in Ardabil, West Azarbaijan, Gilan, Zanjan, Kurdistan, Qazvin, Tehran, Mazandaran, Hamadan, Kermanshah, Semnan, Golestan, Yazd and Kerman, and this component was more important for prostate cancer compared to colorectal and breast cancers. In addition, low physical activity shared component had more effect on the incidence of colorectal and breast cancers in Ardabil, Zanjan, Qazvin, Tehran, Mazandaran, Markazi, Lorestan, Kermanshah, Ilam, Khuzestan, South Khorasan, Yazd, Kerman and Fars, and also, this component was more important for Breast cancer compared to Colorectal cancer. Conclusion: Based on deviance Information criterion, combined modeling of three understudy cancers using a shared component model was better than modeling them individually using BYM model

A Neural Network Classifier Model for Forecasting Safety Behavior at Workplaces

The construction industry is notorious for having an unacceptable rate of fatal accidents. Unsafe behavior has been recognized as the main cause of most accidents occurring at workplaces, particularly construction sites. Having a predictive model of safety behavior can be helpful in preventing construction accidents. The aim of the present study was to build a predictive model of unsafe behavior using the Artificial Neural Network approach. A brief literature review was conducted on factors affecting safe behavior at workplaces and nine factors were selected to be included in the study. Data were gathered using a validated questionnaire from several construction sites. Multilayer perceptron approach was utilized for constructing the desired neural network. Several models with various architectures were tested to find the best one. Sensitivity analysis was conducted to find the most influential factors. The model with one hidden layer containing fourteen hidden neurons demonstrated the best performance (Sum of Squared Errors=6.73). The error rate of the model was approximately 21 percent. The results of sensitivity analysis showed that safety attitude, safety knowledge, supportive environment, and management commitment had the highest effects on safety behavior, while the effects from resource allocation and perceived work pressure were identified to be lower than those of others. The complex nature of human behavior at workplaces and the presence of many influential factors make it difficult to achieve a model with perfect performance

Prevalence of Depression among Iranian Elderly: Systematic Review and M

Objective: depression is one of the most serious and prevalent mood disorders. Aging population is an important economic, social, and health challenge of the 21st century. The present study aimed at estimating the prevalence of depression among the Iranian elderly through meta-analysis method. Method: Studies were searched in ISI, Scopus, Pub Med, Google Scholar, and in Iranian databases including Iran Medex, Magiran, SID, and Med Lib using the following keywords: "depression", "prevalence", and "elderly". Data were analyzed using meta-analysis (random effects model). Heterogeneity among the results of the studies was examined by "I2" index. Beck, DASS-21, GHQ-28, and G DS questionnaires were used in this study, and analyses were performed using STATA Ver.11. Results: A total of 26 studies in Iran with a sample size of 5781 individuals had been found during 2001 and 2015. Prevalence of depression among Iranian elderly was estimated to be 43% (95% confidence interval (CI):30% - 55%). The findings showed that the prevalence of depression among Iranians were49% in women, 48% in men, 37% in unmarried, and 45%in the married. In addition, the prevalence of very severe, severe, moderate, and mild depression levels were estimated to be 5%, 19%, 33%, and 38% of the participants, respectively. No significant difference was observed between married and unmarried individuals. Most of Iranian elderly suffered from mild depression. Conclusion: There was high level of depression prevalence among Iranian elderly, and women were more depressed than men. So, policy makers must design and run mental health programs to decrease the prevalence of depression among Iranian elderly

Primjena regresijskog modela u analizi ključnih čimbenika koji pridonose težini nesreća u građevinskoj industriji u Iranu

Construction industry involves the highest risk of occupational accidents and bodily injuries, which range from mild to very severe. The aim of this cross-sectional study was to identify the factors associated with accident severity rate (ASR) in the largest Iranian construction companies based on data about 500 occupational accidents recorded from 2009 to 2013. We also gathered data on safety and health risk management and training systems. Data were analysed using Pearson’s chi-squared coefficient and multiple regression analysis. Median ASR (and the interquartile range) was 107.50 (57.24-381.25). Fourteen of the 24 studied factors stood out as most affecting construction accident severity (p<0.05). These findings can be applied in the design and implementation of a comprehensive safety and health risk management system to reduce ASR.Građevinska se industrija povezuje s najvišim rizikom od nesreća na radu i tjelesnih ozljeda u rasponu od blagih do vrlo teških. Cilj ovoga presječnog istraživanja bio je utvrditi čimbenike povezane s indeksom težine nesreća među najvećim građevinskim tvrtkama u Iranu na temelju podataka iz 500 izvještaja o nesrećama na radu prikupljanih od 2009. do 2013. Usto smo prikupili podatke o upravljanju rizikom za sigurnost i zdravlje radnika te o njihovu obrazovanju u tom pogledu. Podaci su analizirani Pearsonovim hi-kvadratnim testom i modelom višestruke regresije. Medijan indeksa težine nesreća (i interkvartilni raspon) iznosio je 107,50 (57,24-381,25). Na težinu nesreća najviše je utjecalo četrnaest od 24 ispitana čimbenika (p<0,05). Ovi rezultati mogu biti korisni u osmišljavanju i uspostavi obuhvatnih sustava upravljanja rizikom za sigurnost i zdravlje radnika kako bi se smanjio indeks težine nesreća na radu

Diabetic peripheral neuropathy class prediction by multicategory support vector machine model: a cross- sectional study

OBJECTIVES Diabetes is increasing in worldwide prevalence, toward epidemic levels. Diabetic neuropathy, one of the most common complications of diabetes mellitus, is a serious condition that can lead to amputation. This study used a multicategory support vector machine (MSVM) to predict diabetic peripheral neuropathy severity classified into four categories using patients’ demographic characteristics and clinical features. METHODS In this study, the data were collected at the Diabetes Center of Hamadan in Iran. Patients were enrolled by the convenience sampling method. Six hundred patients were recruited. After obtaining informed consent, a questionnaire collecting general information and a neuropathy disability score (NDS) questionnaire were administered. The NDS was used to classify the severity of the disease. We used MSVM with both one-against-all and one-against-one methods and three kernel functions, radial basis function (RBF), linear, and polynomial, to predict the class of disease with an unbalanced dataset. The synthetic minority class oversampling technique algorithm was used to improve model performance. To compare the performance of the models, the mean of accuracy was used. RESULTS For predicting diabetic neuropathy, a classifier built from a balanced dataset and the RBF kernel function with a one-against-one strategy predicted the class to which a patient belonged with about 76% accuracy. CONCLUSIONS The results of this study indicate that, in terms of overall classification accuracy, the MSVM model based on a balanced dataset can be useful for predicting the severity of diabetic neuropathy, and it should be further investigated for the prediction of other diseases

Longitudinal Joint Modelling of Binary and Continuous Outcomes: A Comparison of Bridge and Normal Distributions

Background: Longitudinal joint models consider the variation caused by repeated measurements over time as well as the association among the response variables. In the case of combining binary and continuous response variables using generalized linear mixed models, integrating over a normally distributed random intercept in the binary logistic regression sub-model does not yield to a closed form. In this paper, we assessed the impact of assuming a Bridge distribution for the random intercept in the binary logistic regression submodel and compared the results to that of normal distribution. &nbsp;Method: The response variables are combined through correlated random intercepts. The random intercept in the continuous outcome submodel follows a normal distribution. The random intercept in the binary outcome submodel follows a normal or Bridge distribution. The estimations were carried out using a likelihood-based approach in direct and conditional joint modeling approaches. To illustrate the performance of the models, a simulation study was conducted Results: Based on the simulation results and regardless of the joint modeling approach, the models with a Bridge distribution for the random intercept of the binary outcome resulted in a slightly more accurate estimations and better performance. Conclusion: In addition to the fact that assuming a bridge distribution for the random intercept in binary logistic regression yields to the same interpretation of parameter estimates in marginal and conditional forms, our study revealed that even if the random intercept of binary logistic regression is normally distributed, assuming a Bridge distribution in the model will result in more accurate results.&nbsp

Longitudinal Machine Learning Model for Predicting Systolic Blood Pressure in Patients with Heart Failure

Objective: Systolic blood pressure (SBP) is a powerful prognostic factor in heart failure (HF) patients, which is associated with death and readmission. Therefore, control of blood pressure is an important element for managing these patients. The goal of this study was to compare the performance of classical and machine learning models for predicting SBP and identify important variables related to SBP changes over time. Methods: The information of 483 HF patients was analyzed in this retrospective cohort study. These patients were hospitalized at least twice in Farshchian Heart Center Hamadan province, the west of Iran, between October 2015 and July 2019. We applied a linear mixed-effects model (LMM) and mixed-effects least-square support vector regression (MLS-SVR) for predicting SBP. The performance of both models was assessed by mean absolute error, and root mean squared error. Results: Based on LMM results, there was a significant association between sex, body mass index (BMI), sodium, time, and history of hypertension with SBP changes over time (P-value &lt;0.05). Also, MLS-SVR indicated that the four most important variables were history of hypertension, sodium, BMI, and triglyceride. The performance of MLS-SVR compared to LMM was better in both training and testing datasets. Conclusions: According to our results, BMI, sodium, and history of hypertension were the important variables on SBP changes in both LMM and MLS-SVR models. Also, it seems that MLS-SVR can be used as an alternative for classical longitudinal models for predicting SBP in HF patients