Sex and gender influence on cardiovascular health in sub-saharan Africa: findings from Ghana, Gambia, Mali, Guinea, and Botswana
Bibliographic record
Abstract
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. 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. Purpose To examine the associations between biological sex, gender-related variables, and CVH risk factors in SSA countries. Methods We conducted a retrospective analysis of the World Health Organization's “STEPwise approach to surveillance of risk factors for non-communicable disease” or “STEPS” survey, conducted in adults aged 18–69 years from Ghana, Gambia, Mali, Guinea, and Botswana. The surveys were conducted between 2006 and 2014. The main outcome was CVH, as measured by a composite measure of STEPS-HEART health index (smoking, hypertension, diabetes, obesity/overweight, and daily consumption of fruits and vegetables), values ranging from 0 (worst) to 5 (best or ideal). Multivariable logistic regression was applied to determine the gender-related factors related to poorer CVH (index less than 4). Two-way interaction between the sex and gender-related factors were tested by including an interaction term in bivariate models. Results 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 female = 0.95, 95% CI: 0.91–0.99). Being married was associated with better CVH compared with being single, more so for males (OR male = 1.09, 95% CI: 0.96–1.24, p interaction <0.01). Males with unpaid work (OR male = 1.28, 95% CI: 1.12–1.47) had better CVH than their unpaid female counterparts (OR female = 1.08, 95% CI: 1.01–1.17). Conclusion This study highlights an alarmingly high prevalence of CVD risk factors, mainly overweight/obesity and hypertension among females in SSA population. 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. With the rising prevalence of CVDs in SSA, it will be important to consider the gender-related factors while implementing preventive programs and creating effective health policies. Funding Acknowledgement Type of funding sources: Foundation. Main funding source(s): The GENDER– NET Plus ERA-NET Initiative (project ref. number: GNP-78): The Canadian Institutes of Health Research (GNP-161904)
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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 | 0.002 |
| 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".