Cardiovascular health through a sex and gender lens in six South Asian countries: Findings from the WHO STEPS surveillance
Bibliographic record
Abstract
Background Sex and gender-based differences in cardiovascular health (CVH) has been explored in the context of high-income countries.However, these relationships have not been examined in low-and middle-income countries.The main aim of this study was to examine how sex and gender-related factors are associated with cardiovascular risk factors of people in South Asian countries. MethodsWe conducted a retrospective analysis of the World Health Organization's "STEPwise approach to surveillance of risk factors for non-communicable disease" or "STEPS" from six South Asian countries, surveys conducted between 2014-2019.The main outcomes were CVH as measured by a composite measure of STEPS-HEART health index (smoking, physical activity, fruit and vegetable consumption, overweight/obesity, diabetes and hypertension), values ranging from 0 (worst) to 6 (best or ideal) and self-reported occurrence of cardiovascular disease (ie, heart attack and stroke).Multivariate linear and logistic regression models were performed.Multiple imputation with chained equations was performed. ResultsThe final analytic sample consisted of 33 106 participants (57.5% females).The mean STEPS-HEART index score in the South Asian population was 3.43 [SD: 0.92].Female sex (β: 0.05, 95% confidence interval (CI) = 0.01-0.08,P < 0.05) was significantly associated with better CVH compared to males.Being married (β male = -0.30,95% CI = -0.37,-0.23 vs β female = -0.23,95% CI = -0.29,-0.17; P < 0.001) and having a household size ≥5 (β male = -0.15,95% CI = -0.24,-0.06 vs β female = -0.11,95% CI = -0.16,-0.04; P < 0.01) were associated with poorer CVH, more so in males.Being married was also associated with high risk of CVD (OR male = 2.54, 95% CI = 1.68-3.86,P < 0.001 vs OR female = 1.19, 95% CI = 0.84-1.68,P = 0.31), significant in males.Conclusions Among the South Asian population, being female may be advantageous in having an ideal CVH.However, gender-related factors such as marital status and large household size were associated with poorer CVH and greater risk of CVD, regardless of sex.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".