Objectives: To study the effect of providing tertiary (specialized) health care for type 2 diabetic patients to meet the WHO and ADA standards and glycemic targets.
Method: Six months, Jan. – Jun. 2010, cohort study was conducted on 600 adult diabetics who registered in the National Diabetes Center (NDC) / Al-Mustansiriya University, Baghdad – Iraq. They were followed for 3- 6 months; each time patients were examined physically and their blood pressure, height, weight and BMI were measured. Fasting blood samples were taken from all patients to test the FPG, HbA1c, T.Chol, TG, HDL and LDL.
Results: Patients’ age was 52.85±15.56 year and the male/female ratio was 1.01, the median duration of disease was 7 years and their BMI was 28.80 ± 13.02 kg/m2. Patients’ achievement during study period, of glycemic and cardiovascular risk factors, meet the targets of ADA, NHANES and NCEP/ATP III Guidelines of FPG, PPG, HbA1c, T.Chol, TG, LDL, HDL, systolic and diastolic blood pressure by 26.74%, 29.09%, 32.78%, 61.0%, 60.86%, 76.19%, 74.35%, 52.54% and 62.50% respectively.
Conclusions: We concluded that tertiary health service can help to meet the international guidelines and recommended targets for type 2 diabetes. Improving quality and coverage of tertiary health services may help in achieve and sustain targets; and afterward close adhering to the WHO and ADA standards and glycemic targets.
Abbreviations: BMI=Body Mass Index, FPG= Fasting Plasma Glucose, PPG = post prandial plasma glucose, HbA1c =glycated hemoglobin, T.Chol. = total cholesterol, TG = Triglyceride, LDL = low-density lipoprotein, HDL = high-density lipoprotein, ADA= American Diabetes Association, NHANES = National Health and Nutrition Examination Survey, NCEP/ATP III = National Cholesterol Education Program/Adult Treatment Panel
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Thyroid disease is a common disease affecting millions worldwide. Early diagnosis and treatment of thyroid disease can help prevent more serious complications and improve long-term health outcomes. However, thyroid disease diagnosis can be challenging due to its variable symptoms and limited diagnostic tests. By processing enormous amounts of data and seeing trends that may not be immediately evident to human doctors, Machine Learning (ML) algorithms may be capable of increasing the accuracy with which thyroid disease is diagnosed. This study seeks to discover the most recent ML-based and data-driven developments and strategies for diagnosing thyroid disease while considering the challenges associated with imbalanced data in thyroid dise
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