Under- treatment and under diagnosis of hypertension: a serious problem in the United Arab Emirates
Bibliographic record
Abstract
BACKGROUND: Hypertension, notably untreated or uncontrolled, is a major risk factor for cardiovascular diseases (CVD) morbidity and mortality. In countries in transition, little is known about the epidemiology of hypertension, and its biochemical correlates. This study was carried out in Al Ain, United Arab Emirates, to characterize self-reported (SR) normotensives and hypertensives in terms of actual hypertension status, demographic variables, CVD risk factors, treatment, and sequalae. METHODS: A sample, stratified by SR hypertensive status, of 349 SR hypertensives (Mean age +/- SD; 50.8 +/- 9.2 yrs; Male: 226) and 640 SR normotensives (42.9 +/- 9.3 yrs, Male: 444) among nationals and expatriates was used. Hypertensives and normotensive subjects were recruited from various outpatient clinics and government organizations in Al-Ain city, United Arab Emirates (UAE) respectively. Anthropometric and demographic variables were measured by conventional methods. RESULTS: Both under-diagnosis of hypertension (33%) and under-treatment (76%) were common. Characteristics of undiagnosed hypertensives were intermediate between normotensives and SR hypertensives. Under-diagnosis of hypertension was more common among foreigners than among nationals. Risk factors for CVD were more prevalent among SR hypertensives. Obesity, lack of exercise and smoking were found as major risk factors for CVD among hypertensives in this population. CONCLUSION: Hypertension, even severe, is commonly under-diagnosed and under-treated in the UAE. Preventive strategies, better diagnosis and proper treatment compliance should be emphasized to reduce incidence of CVD in this population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".