The prevalence and predictors of cardiovascular diseases in Kherameh cohort study: a population-based study on 10,663 people in southern Iran
Bibliographic record
Abstract
BACKGROUND: The prevalence of cardiovascular disease (CVD) is rapidly increasing in the world. The present study aimed to assess the prevalence and Predictors factors of CVD based on the data of Kherameh cohort study. METHODS: The present cross-sectional, analytical study was done based on the data of Kherameh cohort study, as a branch of the Prospective Epidemiological Studies in Iran (PERSIAN). The participants consisted of 10,663 people aged 40-70 years. CVD was defined as suffering from ischemic heart diseases including heart failure, angina, and myocardial infarction. Logistic regression was used to model and predict the factors related to CVD. Additionally, the age-standardized prevalence rate (ASPR) of CVD was determined using the standard Asian population. RESULTS: The ASPR of CVD was 10.39% in males (95% CI 10.2-10.6%) and 10.21% in females (95% CI 9.9-10.4%). The prevalence of CVD was higher among the individuals with high blood pressure (58.3%, p < 0.001) as well as among those who smoked (28.3%, p = 0.018), used opium (18.2%, p = 0.039), had high triglyceride levels (31.6%, p = 0.011), were overweight and obese (66.2%, p < 0.001), were unmarried (83.9%, p < 0.001), were illiterate (64.2%, p < 0.001), were unemployed (60.9%, p < 0.001), and suffered from diabetes mellitus (28.1%, p < 0.001). The results of multivariable logistic regression analysis showed that the odds of having CVD was 2.25 times higher among the individuals aged 50-60 years compared to those aged 40-50 years, 1.66 folds higher in opium users than in non-opium users, 1.37 times higher in smokers compared to non-smokers, 2.03 folds higher in regular users of sleeping pills than in non-consumers, and 4.02 times higher in hypertensive individuals than in normotensive ones. CONCLUSION: The prevalence of CVD was found to be relatively higher in Kherameh (southern Iran) compared to other places. Moreover, old age, obesity, taking sleeping pills, hypertension, drug use, and chronic obstructive pulmonary disease had the highest odds ratios of CVD.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".