Background & Aim: One of the basic assumptions in simple linear regression models is the statistical independence of observations. Sometimes this assumption is not true for study subject and consequently the use of general regression models may not be appropriate. In this case, one of the leading methods is the use of multilevel models. The present study utilizes Multivariate logistic regression model using a multilevel model to exhibit the chance of having elbow, wrist and knee disorders over the past year based on elbow, wrist and disorders during the past week. Methods & Materials: This study is a cross-sectional study that was carried out from April 2015 to May 2016 in Mobarakeh Steel Company, Isfahan. The study population includes 300 male employees of Mobarakeh Steel Company, with a mean age of 41. 40± 8. 17 years and an average working experience of 16. 0± 7. 66 years. Data were analyzed using SPSS (version 24) and MLwiN software. Results: Based on this study, results obtained from single variable and multivariable regression were different. Conclusion: Based on this study, it can be suggested that multivariable regression cause a better and more accurate deduction compared to single variable method.

Background: It is known that risk factors related to neonatal bacterial sepsis are complex.Objectives: The current study aimed to determine the risk factors of neonatal sepsis.Materials and Methods: This study was conducted on 1023 neonates admitted to neonatal intensive care units (NICUs) in South Sinai governorate hospitals from January 2010 till September 2014. Demographic data of the neonates and the potential risk factors for neonatal septicemia were collected and analyzed.Results: The incidence of septicemia in the NICUs of South Sinai governorate hospitals was 8.6% and a mortality rate among sepsis cases (sepsis fatality rate) was about 25%. The percentage of cases with late-onset sepsis (68.2%) was predominant than early-onset sepsis (31.8%). Hospital acquired infections represented 30.7% of the sepsis cases. A Multivariate logistic regression analysis for the significant neonatal and maternal risk factors related to neonatal sepsis was performed to explain the interaction between these risk factors and order these risk factors regarding statistical significance. The results showed that the highest effect on sepsis was for rupture of membranes>18 hours then the presence of twin deliveries came next, followed by multipara mothers then normal vaginal delivery came 4th in order followed by male gender, low birth weight babies and preterm neonates which became no nsignificant in this Multivariate logistic regression analysis.Conclusions: Rupture of membranes>18 hours, the presence of twin deliveries came next, multipara mothers, normal vaginal delivery came 4th in order, followed by male gender are the most common risk factors for neonatal sepsis in Sinai governorate hospitals, Egypt.

In this article, while studying 125 pediatric patients who have not descended testicles, we statistically examine the consequences of undescended testicular surgery in children referred to 17 Shahrivar Hospital in Rasht in the period 1394-1400. Then, while finding the most effective variables, we analyze them regression and arrive at a predictive model based on the adjusted variables in backward LR regression analysis.

Background: Obesity and hypertension are the most important non-communicable diseases that in many studies, the prevalence and their risk factors have been performed in each geographic region univariately. Study of factors affecting both obesity and hypertension may have an important role which to be addressed in this study.Materials & Methods: This cross-sectional study was conducted on 1000 men aged 20-70 living in Bushehr province. Blood pressure was measured three times and the average of them was considered as one of the response variables. Hypertension was defined as systolic blood pressure³140 (and-or) diastolic blood pressure³90 and obesity was defined as body mass index ³25. Data was analyzed by using multilevel, Multivariate logistic regression model by MlwiN software.Results: Intra class correlations in cluster level obtained 33% for high blood pressure and 37% for obesity, so two level model was fitted to data. The prevalence of obesity and hypertension obtained 43.6% (0.95%CI; 40.6-46.5), 29.4% (0.95%CI; 26.6-32.1) respectively. Age, gender, smoking, hyperlipidemia, diabetes, fruit and vegetable consumption and physical activity were the factors affecting blood pressure (p£0.05). Age, gender, hyperlipidemia, diabetes, fruit and vegetable consumption, physical activity and place of residence are effective on obesity (p£0.05).Conclusion: The multilevel models with considering levels distribution provide more precise estimates.As regards obesity and hypertension are the major risk factors for cardiovascular disease, by knowing the high-risk groups we can d careful planning to prevention of non-communicable diseases and promotion of society health.

Streptococcusis is the one of the most important bacterial fish diseases with outbreak in rainbow trout farms in Iran. The fish farmers have been largely suffered from huge economic losses due to the Streptococcusis outbreaks in different rainbow trout farms in Iran. The present study assessed the effects of some environmental risk factors on incidence of streptococcusis in rainbow trout farms in Haraz River in Mazandaran Province, Iran. A suit of environmental factors including water temperature, nitrite, nitrate, ammonium, water turbidity, DO, water Debi and total count of bacteria were explored as influential factors. Fish and water samples were randomly collected from 10 farms on a monthly basis throughout a year. Isolation and recognition of strep strains were made using biochemical and PCR tests and the data were analyzed by logistic regression method. According to the results, 20% of the differences were explained by the logistic model. Management of these factors might decline the rate of disease outbreak.

Present study seeks to identify effective factors in landslide occurrence and landslide sensitivity zonation using logistic regression and Multivariate linear regression. Accordingly, through the interpretation of arial photos with scale of 1: 40000, geological, topographic maps, and field survey using GPS, landslide hazard map was prepared as dependent variables. For determination of effective factors in landslide occurrence, using Support Vector Machines in Rapid Miner Software, the numerical values of the parameters were analyzed and from 21 selective data layers, 15 data layers were selected and were prepared and digitized for zonation map as the independent variable in ArcGIS 10.1. After weighing the layers, zonation map was prepared using selective method in five classes: very low, low, moderate, high and very high. Result of weighting layers showed that in both methods, land use and aspect have the greatest impact on landslides. The ROC (Receiver operating characteristic) curves and area under the curves (AUC) for landslide susceptibility maps were constructed and the areas under curves was assessed for validation purpose and its values showed that Multivariate linear regression model (0.890) has a higher efficiency than the logistic model (0.829) for landslide hazard zonation. According to result of superior model (Multivariate linear regression), 16046.1 hectare (20.13%) of the region was found to be located in high risk class and 15671.2 hectare (19.66%) was in very high risk class.