Sex and Gender Influence on Cardiovascular Health in Sub-Saharan Africa: Findings from Ghana, Gambia, Mali, Guinea, and Botswana
Bibliographic record
Abstract
<strong>Background:</strong> There is an upsurge of cardiovascular diseases (CVDs) in sub-Saharan Africa (SSA). Irrespective of biological sex, gender-related factors could be the precursor of these conditions. <strong>Objective:</strong> To examine the associations between biological sex, gender-related variables, and cardiovascular health (CVH) risk factors in SSA countries. <strong>Methods:</strong> We used data from the STEPwise approach to surveillance of risk factors for non-communicable disease survey, conducted in adults from Ghana, Gambia, Mali, Guinea, and Botswana. The main outcome was CVH, measured through the health index with values ranging from 0 (worst) to 5 (best or ideal) CVH. Multivariable logistic regression was applied to determine the gender-related factors related to poorer CVH (index less than 4). <strong>Results:</strong> Data included 15,356 adults (61.4% females, mean age 36.9 years). The prevalence of hypertension (21.6% vs. 13.8%) and overweight/obesity (48.3% vs. 27.5%) was higher among females as compared to males. Females were more likely to be unemployed (17.3% vs. 9.7%) or reported unpaid work (36.8% vs. 15.2%). Overall, females showed worse CVH than males (OR<sub>female</sub> = 0.95, 95% CI:0.91–0.99). Being married was associated with better CVH compared with being single, more so for males (OR<sub>male</sub> = 1.09, 95% CI:0.96–1.24, p<sub>interaction</sub> < 0.01). Males with unpaid work (OR<sub>male</sub> = 1.28, 95% CI:1.12–1.47) had better CVH than their unpaid female counterparts (OR<sub>female</sub> = 1.08, 95% CI:1.01–1.17). <strong>Conclusion:</strong> In SSA populations, being female was associated with poorer CVH given the disproportionate burden of hypertension and overweight/obesity. Gender-related factors such as marital status and unpaid work were associated with better CVH in males compared to females.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".