Iranian Journal of

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Background & Objectives: Early detection can improve the outcome of visual impairment in children, and one method for early detection could be screening of pre-school children with visual acuity tests. The aim of this study was to determine the validity of these tests when they are used on children entering grade school.
Methods: For this cross-sectional study we drew 39 random clusters, comprising a total of 5721 school children. We then used 2158 student files to extract data on tests of vision performed by school health officers and compared these data with data generated by optometrists. Measurement of uncorrected visual acuity was done with the E Chart by both teachers and optometrists.
Results: The sensitivity and specificity of teacher-administered tests were 25.0% and 96.6%, yielding positive and negative predictive values of 13.4% and 98.4%, respectively. Sensitivity and specificity rates did not show any significant difference between male and female populations (P=0.356, P=0.258), but the difference between specificity in urban and rural areas was significant (P<0.001).
Conclusions: Screening tests for visual impairment did not attain the desired level of sensitivity or specificity for case detection in school children. More accurate procedures are required to minimize the number of false negative results.

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Methods: Thirty female Wistar rats were randomly allocated to three dietary groups: a standard diet (containing soy bean-oil), diet containing fish oil and diet containing mixed oil which was designed based on Iranian population fatty acid intake. Dams in each group were fed one of the diets during pregnancy and lactation and the pups were also weaned onto the same diet. Fasting serum glucose (Photometry) and insulin (ELISA) in pups were assessed and insulin sensitivity calculated on puberty.
Results: Fasting serum insulin in fish oil-fed group was significantly less than two other groups (P=0.018) and insulin sensitivity in fish oil-fed rats was significantly more than two other groups (P=0.002).
Conclusions: It seems a diet containing fish oil (rich in long chain omega-3 fatty acids) causes more insulin sensitivity comparing to diet containing soy bean oil (rich in omega-6 fatty acids) and diet with Iranian population fatty acid intake pattern (rich in saturated fatty acids).

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Background and Objectives: Diabetes is a chronic and common metabolic disease which has no curative treatment. Logistic regression (LR) is a statistical model for the analysis and prediction in multivariate statistical techniques. Discriminant analysis is a method for separating observations in terms of dependent variable levels which can allocate any new observation after making discriminating functions. The aim of this study was to compare and determine the effective variables in type 2 diabetes.

Methods: The data included 5357 persons obtained through a cohort study in Kerman, southeastern Iran, in 2009-11. Diabetes was considered the response variable. The independent variables after deleting colinearity and correlated variables included height, waist circumference, age, gender, occupation, education, drugs, systolic blood pressure, HDL, LDL, drug abuse, activities, and triglyceride. Sensitivity, specificity, accuracy, and ROC curve were applied for determining and comparing the prediction power of the models.

Results: The results in the reduced model with extracted significant variables from the full model, the sensitivity of the LR model and DA was 74% and 22.4%, the specificity of the LR model and DA was 71.1 % and 95.4 %, the prediction accuracy of the LR model and DA was 71.5% and 85.3%, and the ROC curve of the LR model and DA was 80.3% and 80.1%, respectively.Simulation showed the sensitivity, specificity, accuracy, and ROC curve was 99.18%, 98.49%, 98.59%, and 99.9% for the LR model and 92.62%, 99.19%, 98.26%, and 99.56% for DA, respectively.

Conclusion: The results showed that the risk factors of diabetes in the logistic regression reduced model were waist circumference, age, gender, LDL level, systolic pressure, and drugs. Also, the sensitivity of the LR model was more than DA while DA had a higher specificity and prediction accuracy. Comparison of the ROC curve showed that the prediction estimated values were rather similar in both models, but the two models were the same asymptotically.

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Background and Objectives: Gestational Diabetes Mellitus (GDM) is the most common metabolic disorder in pregnancy. In case of early detection, some of its complications can be prevented. The aim of this study was to investigate early prediction of GDM by logistic regression (LR), discriminant analysis (DA), decision tree (DT) and perceptron artificial neural network (ANN) and to compare these models.
 
Methods: The medical files of 420 pregnant women (2010-12) in Kermanshah health centers were evaluated using convenience sampling. Demographic data, pregnancy-related variables, lab tests results, and a diagnosis of GDM according to a fasting blood sugar level of 92 or more were collected from their files. After fitting the four models, the performance of the models was compared and according to the criteria of accuracy, sensitivity and specificity (based on the ROC curve), the superior model was introduced.
 
Results: Following the fitting of LR, DA, DT and perceptron ANN models, the following results were obtained. The accuracy of the above models was 0.81, 0.83, 0.78 and 0.83, respectively, the sensitivity of the models was 0.50, 0.63, 0.58 and 0.58, the specificity of the models was 0.96, 0.93, 0.87 and 0.94, and the area under the ROC curve was 0.86, 0.78, 0.73 and 0.87, respectively.
 
Conclusion: In predicting and categorizing the presence of GDM, the ANN model had a lower error rate and a higher area under the ROC curve compared to other models. It can be concluded that this model offers better predictions and is closer to reality than other models.

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Background and Objectives: Environmental conditions in different geographical areas provide a basis for the spread of some diseases. Cutaneous leishmaniasis is a serious threat to public health and is one of the arthropod-borne diseases. The prevalence and distribution of this disease is affected by environmental and climatic factors. The aim of this study was to model the Spatio-temporal variations in the incidence rate of this disease based on environmental and ecological criteria.
 
Methods: The northeast of Iran was selected as the study area. The data used in this study included vegetation, surface temperature, precipitation, evapotranspiration, soil moisture, digital elevation model and sunny hours. The artificial neural network method was used to model the spatio-temporal changes of cutaneous leishmaniasis.
 
Results: Spatial variations in the incidence of the disease had a north-south trend and decreased from north to south. In addition, two foci were identified in the medium altitude areas in North and South Khorasan provinces. Temporal variations in the incidence of disease in the study period showed that the incidence rate decreased in the two identified foci from 2011 to 2016.
 
Conclusion: The modeling results showed that the estimated regression coefficient was 0.92 for neural network based on all three types of data (training, validation, test) indicating good quality of constructed neural network.  In addition, sensitivity analysis results showed that sunny hours and soil moisture were the most important factors in the model function.