BackgroundOver the last 30 years the citizens of the United Arab Emirates have experienced major changes in life-style secondary to increased affluence. Currently, 1 in 5 adults have diabetes mellitus, but the associations (clustering) among risk factors, as well as the relevance of the concept of the metabolic syndrome, in this population is unknown.AimTo investigate the prevalence and associations among cardiovascular risk factors in this population, and explore to what extent associations can be explained by the metabolic syndrome according to ATP-III criteria.MethodA community based survey, of conventional risk factors for cardiovascular disease was conducted among 817 national residents of Al Ain city, UAE. These factors were fasting blood sugar, blood pressure, lipid profile, BMI, waist circumference, smoking, or CHD family history. Odds ratios between risks factors, both unadjusted and adjusted for age and sex as well as adjusted for age, sex, and metabolic syndrome were calculated.ResultsVarious risk factors were positively associated in this population; associations that are mostly unexplained by confounding by age and sex. For example, hypertension and diabetes were still strongly related (OR 2.5; 95% CI 1.7–3.7) after adjustment. An increased waist circumference showed similar relationship with hypertension (OR 2.3; 95% CI 1.5–3.5). Diabetes was related to an increased BMI (OR 1.5; 96% CI 1.0–2.3). Smoking was also associated with diabetes (OR 1.9, 95% CI 1.0–3.3).Further adjustment for metabolic syndrome reduced some associations but several remained.ConclusionIn this population risk-factors cluster, but associations do not appear to be explained by the presence/absence of the ATP-III metabolic syndrome. Associations provide valuable information in planning interventions for screening and management.
BackgroundThe cost effective provision of quality care for chronic diseases is a major challenge for health care systems. We describe a project to improve the care of patients with the highly prevalent disorders of diabetes and hypertension, conducted in one of the major cities of the United Arab Emirates.Settings and MethodsThe project, using the principles of quality assurance cycles, was conducted in 4 stages.The assessment stage consisted of a community survey and an audit of the health care system, with particular emphasis on chronic disease care. The information gleaned from this stage provided feedback to the staff of participating health centers. In the second stage, deficiencies in health care were identified and interventions were developed for improvements, including topics for continuing professional development.In the third stage, these strategies were piloted in a single health centre for one year and the outcomes evaluated. In the still ongoing fourth stage, the project was rolled out to all the health centers in the area, with continuing evaluation. The intervention consisted of changes to establish a structured care model based on the predicted needs of this group of patients utilizing dedicated chronic disease clinics inside the existing primary health care system. These clinics incorporated decision-making tools, including evidence-based guidelines, patient education and ongoing professional education.ResultsThe intervention was successfully implemented in all the health centers. The health care quality indicators that showed the greatest improvement were the documentation of patient history (e.g. smoking status and physical activity); improvement in recording physical signs (e.g. body mass index (BMI)); and an improvement in the requesting of appropriate investigations, such as HbA1c and microalbuminurea. There was also improvement in those parameters reflecting outcomes of care, which included HbA1c, blood pressure and lipid profiles. Indicators related to lifestyle changes, such as smoking cessation and BMI, failed to improve.ConclusionChronic disease care is a joint commitment by health care providers and patients. This combined approach proved successful in most areas of the project, but the area of patient self management requires further improvement.
Neurological emergencies carry significant morbidity and mortality, and it is necessary to have a multidisciplinary approach involving the emergency physician, the neurologist, the intensivist, and the critical care nursing staff. These disorders can be broadly divided into noninfectious and infectious etiologies. In this article, we review a few of the neurological emergencies that present to the neurological intensive unit, with emphasis on convulsive status epileptics, myasthenia gravis, Guillain-Barré syndrome, meningitis, encephalitis, and brain abscess.
Background Electronic Health Record (EHR) implementation has created an unprecedented library of patient data. Data extraction tools provide an opportunity to retrieve clinico-epidemiological information on a wide scale. Slicer Dicer is a data exploration tool in the EPIC EHR that allows one to customize searches on large patient populations. This software contains a variety of models that present de-identified information from EPIC’s Caboodle database. We explored the applicability and potential utility of this tool utilizing the diagnosis of Lyme disease as an example. Methods The following steps outline an overview of data extraction utilizing ICD-10 codes around Lyme disease at our health system. Step 1-3: Denominator chosen as ‘All Patients’ over a 3-year period, ‘Slicing’ of the data by ‘Lyme disease, unspecified’ was applied to these results, and the ‘sliced’ data was categorized by year of diagnosis (Slide 1). Step 4: This data was further arranged by month of diagnosis for trend analysis (Slide 2). Step 5: Sub-diagnosis was applied for Lyme arthritis (Slide 3). Step 6: Further ‘slicing’ was/can be done by other variables, such as ‘Hospitalization,’ ‘Encounter Diagnosis,’ and ‘ED Diagnosis’ (Slide 4). Step 7-8: Output was ‘sliced’ by ‘Age’ (Slide 5) and ‘Postal Code’ (Slide 6). Slide 1. EPIC EHR screen capture showing 3-year period data Data shown here represents 'All patients' chosen as the denominator further sliced by 'Lyme disease, unspecified' and categorized by the year of diagnosis. Slide 2. EPIC EHR screen capture showing data further arranged by month of diagnosis Results Macro-level data of period prevalence on Lyme disease over 3 years (Slide 1), seasonal trends (Slide 2), specific sub-diagnosis (Slide 3), output by setting of diagnosis (Slide 4), and demographic information of our patient population (Slides 5, 6) was revealed by application of these parameters. Slide 3. EPIC EHR screen capture showing application of sub-diagnosis for Lyme arthritis Slide 4. EPIC EHR screen capture showing further slicing by multiple variables like hospitalization and diagnosis Slide 5. EPIC EHR screen capture showing slicing of data by demographic information (Age) Conclusion Slicer Dicer can provide a snapshot for preliminary data analysis prior to investing time and commitment to a project. The appeal of this tool is that it mines de-identified data and thus does not require initial IRB approval. This opens an avenue for potential full research projects based on the results obtained and helps generate preliminary hypotheses through analysis of healthcare. Slide 6. EPIC EHR screen capture showing slicing of data by demographic information (Postal Code) Disclosures All Authors: No reported disclosures
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