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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".