محمد آرام احمدی

Diabetes is considered as a major public health problem all over the world. Self-care behaviors is the most important strategy for controlling chronic diseases, such as diabetes. Therefore, this study was conducted to determine the self-care agency and some associated factors in patients with type 2 diabetes.

This cross-sectional study was performed on a descriptive-analytic approach on 374 patients referring to the Diabetes Clinic of Tohid Hospital in Sanandaj, selected using systematic sampling. Data were collected using a questionnaire including demographic and background information questionnaire and a valid and reliable questionnaire for assessing the self-care agency of diabetic patients. Data were analyzed by using SPSS software version 16 and appropriate tests. All stages of the study were conducted according to moral standards.

The mean and standard deviation of self-care agency score was 58.40 ± 12.49 from 105, that is considered moderate. There was a significant difference in self-care agency of patients according to variables such as gender, ability to measure blood glucose, occupational status, history of education about diabetes, regular dental examinations and annual infusion of influenza vaccine (P<0.05). There was a significant and inverse correlation between the number of years elapsed since diabetes diagnosis and self-care agency (P<0.05 and r=0.24).

Regarding the average self-care level in the majority of patients and the important role of self-care in controlling diabetes, the need to implement self-care education is increasingly felt in diabetic patients.

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.
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.
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.
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.

Diabetes is one of the chronic diseases with no curative treatment; also, it is the most common cause of amputation, blindness and chronic renal failure and the most important risk factor of heart diseases. Logistic regression is one of the statistical analysis models for predicting that can be used to find out the relationship between dependent and predictor independent variables and control of the confounding variables. The aim of this study was to determine the rate of effective variables on diabetes and estimation of the logistic regression model for predicting. 5357 persons in Kerman city, Iran, were enrolled. Diabetes considered as the response variable and weight, height, body mass index (BMI), waist circumference, hip circumference, waist-to-hip ratio (WHR), age, gender, occupation, education, drugs, drug abuse, activities, systolic and diastolic blood pressure, and levels of total cholesterol, the high-density lipoprotein (HDL), the low-density lipoprotein (LDL), and triglycerides were considered as independent variables in the model. Measures of sensitivity, specificity, accuracy, Kappa measure of agreement and ROC (receiver operating characteristic) curve was applied for determining the power of test. The Sensitivity, specificity, accuracy rate, Kappa measure of agreement and area under the ROC curve for the model were 0.764, 0.725, 0.731, 0.312 and 0.822, respectively. The following variables were significant according to their impact and their importance, respectively: WHR (? = 2.66, OR=14.32), anti-hypertensive drug (? =1.279, OR= 3.59), sex (? =0.707, OR= 2.028), level of education, walking and cycling (? = 0.136, OR= 1.146), waist circumference (? =0.12, OR= 1.127), weight (? = 0.112, OR= 1.118), BMI (? = 0.053, OR= 1.054), systolic blood pressure (? =0.052, OR= 1.054), age (? =0.046, OR= 1.047), diastolic blood pressure (? =0.043, OR= 1.044), total cholesterol (? = 0.003, OR= 1.003), triglycerides (? =0.01, OR= 1.011), LDL (? = 0.001, OR= 1.001), hip circumference (? = - 0.025, OR= 1.025), height (? = -0.071, OR= 0.932), HDL (? = -0.078, OR= 0.925), an intense 10-minute work activities (? = -0.507, OR=0.602). According to the criteria of accuracy and power of prediction, and considering ROC curve value (0.822) which could perform test accuracy as well for the diagnosis of diabetes, the logistic regression model was an appropriate model for the prediction of diabetes in this study.