High blood pressure and associated risks in an Indian industrial population
Bibliographic record
Abstract
High blood pressure, and other cardiovascular disese (CVD) risk factors are increasingly prevalent in the urban Indian population. Data regarding this clustering of risk factors is scarce from developing countries. We carried out a comprehensive cross-sectional CVD risk factor prevalence study on 2200 male employees aged 21–59 years of an urban industrial population in India employing questionnaire, clinical examination and biochemical estimations. Most of the employees were below 45 years of age and the mean age was 42 years. The prevalence of hypertension (as per JNC VI criteria) and high-normal blood pressure was 30% and 17% respectively. Only 25% of the population had optimal blood pressure. Only a third of the hypertensives were aware of their status, while just 8% had their blood pressure controlled. Even in the age group 21–29 years, the prevalence of hypertension was 18%. The prevalence of other CVD risk factors in the hypertensive population was as follows: diabetes 20%, impaired fasting glucose 23%, total cholesterol>200mg/dl 35%, total cholesterol/HDL-C ratio≥4.5 67%, hypertriglyceridemia 48%, and central obesity (waist hip ratio≥ 0.95) 80% . The mean BMI for this population was 24 kg/m 2 and the mean waist circumference was high at 97 cm. . 41% of the study population was actively smoking at the time of the study. While the overall population had high prevalence of these risk factors, adverse trends in risk factors were significantly higher in the hypertensives as compared to non-hypertensives. As per the NCEP guidelines, about 25% of the overall population had metabolic syndrome. The prevalence rose further if the proposed lower cut-offs for obesity in Asian population were employed. While only 15% of the study population was free of any of traditional four risk factors, 45% had at least two risk factors. The prevalence of coronary heart disease, using modified Rose angina questionnaire and/or Minnesota-coded ECG abnormalities, in this population was 7.3%. This study demonstrates the high prevalence of adverse blood pressure profile in this comparatively young population and a low awareness of the same. The significant clustering of risk factors in this population is likely to increase greatly the absolute cardiovascular risk and underscores the urgent preventive measures which need to be implemented in developing countries like India.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".