Prevalence and Associated Factors with Ideal Cardiovascular Health Metrics in Bangladesh: Analysis of the Nationally Representative STEPS 2018 Survey
Bibliographic record
Abstract
This study aims to find out the prevalence of the American Heart Association's (AHA)'s cardiovascular health metrics and associated socio-demographic factors. A secondary analysis of the World Health Organization (WHO) STEPwise approach to surveillance survey 2018 (STEPS 2018) data was conducted. Ideal Cardiovascular Health (ICH) was defined as the presence of 5-7 ideal metrics as defined by the AHA. Design-adjusted multivariable logistic regression was used to determine the associated factors of ICH. In total, 5930 respondents were included in our analysis, and 43.1% of the participants had ICH. The odds of ICH decreased with age [compared to 18-29 years old individuals, 30-49 years: AOR (Adjusted Odds Ratio): 0.4; 95% Confidence Interval (CI): 0.4-0.5; 50-69 years: AOR: 0.7; 95% CI: 0.6-0.8], and higher educational attainment (compared to those who received no formal education, being educated up to primary level: AOR:0.7; 95% CI: 0.6-0.8; being educated up to secondary level: AOR: 0.4; 95% CI: 0.4-0.5; being educated up to college and higher: AOR: 0.4; 95% CI: 0.3-0.5). Compared with female and urban residents, the odds were 30% and 40% less among male and rural residents, respectively. The public health promotion programs of Bangladesh should raise awareness among high-risk groups to prevent cardiovascular diseases.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".