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Background and Objectives: Psychological distress is one of the minor psychiatric disorders that can be used as a predictor of serious mental illnesses such as depressive and anxiety disorders. These disorders are predictors of death from cardiovascular disease, stroke, and cancer. This study was conducted to evaluate the prevalence of psychological distress and its related factors. Methods: This descriptive- analytical cross-sectional study was conducted in 2018. The study population included 1103945 subjects aged 18-65 years old that presented to the health centers of Khorasan Razavi. In this study, demographic checklists and the six-item Kessler Psychological Distress Scale (K-6) were used. Results: The prevalence of psychological distress was 10.10%. The mean age of the subjects was 35.99 ± 10.46 years old, 62.10% were female, 86% were married, and 78.90% had a high school education or less. The response rate was 98.6%. Logistic regression analysis showed a significant relationship between psychological distress and age, gender, marital status, education, location, and occupation. Conclusion: In this study, the prevalence of psychological distress was low. According to the odds ratio (OR), divorced women, illiterate women, housewives, and big city dwellers were high-risk groups for psychological distress. Considering the relatively large sample size, it is necessary to develop appropriate strategies for prevention and treatment, and training of these groups.

Background and Objectives: Diabetes is considered as one of the most common endocrine disorders worldwide. The aim of this study was to investigate the factors associated with diabetic complications. Methods: A case-control study was performed on the data of 70089 diabetic patients (4622 cases and 53613 controls) extracted from the SINA Electronic Health Record (SinaEHR®) in a population covered by Mashhad University of Medical Sciences in 2018. The effect of independent variables on the likelihood of diabetic complications was investigated using single-variable and multivariate logistic regression models with the control of the potential confounding effects. Results: Using the multivariate logistic regression, the odds of developing diabetic complications were 0.35 (0.31-0.38) for living in the city, 0.73(0.67-0.79) for living in the suburbs and 0.31(0.28-0.33) for living in rural areas relative to the metropolises, 0.84 (0.78-0.91) for illiterate subjects, 0.70 (0.66-0.75) for physical activity, 1.51(1.34-1.71) for stage 1 hypertension and 1.87 (1.43-2.44) for stage 2 hypertension relative to normal blood pressure, 0.79(0.74-0.85) for uncontrolled low density lipoprotein and 1.42(1.33-1.51) for uncontrolled hemoglobin A1C. Conclusion: Various risk factors were identified to increase the odds ratio of diabetic complications. The most important risk factors were uncontrolled glycosylated hemoglobin and stage 1 and 2 hypertension. Control of these factors can reduce the chance of diabetic complications in diabetic patients.

Background: Discovery of new risk factors poses new challenges on how to quantify their added value and importance in risk prediction improvement., Objectives: The aim of this study was to apply different statistics and to quantify the importance of some risk factors in acute myocardial infarction (AMI)., Patients and Methods: In a retrospective cohort study, 607 patients with AMI, aged more than 25 years were studied. They were admitted to the CCU of Imam Reza hospital in Mashhad, Iran from 2007 to 2012. Health information and death registration systems were used to identify patients and to assess their outcome. At first a model containing all variables was fitted (full model). Importance of variables was compared in terms of standardized regression coefficient and inclusion frequency in bootstrap samples. Then, a series of reduced models were fitted, where in each of them only one of the independent variables was excluded. Models were compared in terms of goodness of fit, accuracy (Cindex, R square), separation of patients into risk groups (SEP), and net reclassification improvement (NRI)., Results: Age was selected as the important factor based on all 7 statistics. Exclusion of age variable decreased C index from 0.75 to 0.68 and R square from 0.25 to 0.15. Duration of hospitalization was important based on 4 statistics. Exclusion of this variable decreased R square from 0.25 to 0.21. While gender was a useful variable in separation of patients into risk groups, its omission did not reduce model likelihood. The opposite was true in the case of using streptokinase during hospitalization., Conclusions: Our results revealed that a variable with high separation ability might not necessarily be useful in terms of goodness of fit. Therefore, importance should be defined carefully based on clinical objectives of the study.