Examining risk factors for hypertension in Ghana: evidence from the Study on Global Ageing and Adult Health
Bibliographic record
Abstract
Like most countries in sub-Saharan Africa, hypertension contributes substantially to morbidity and mortality in Ghana, yet nationally representative studies that examine the odds of becoming hypertensive among Ghanaians are conspicuously missing. We aimed to fill this void in the literature. The data used for analysis came from the first wave of the Study on Global Ageing and Adult Health (SAGE), collected in Ghana from January 2007 to December 2008 by the World Health Organization (WHO). A total of 5573 respondents were sampled for the study. Random-effects C-log-log models were employed in examining socio-economic, lifestyle and psychosocial factors on the odds of becoming hypertensive in Ghana. Separate models were run for male and females. Results indicated there were strong significant associations between socio-economic, lifestyle and psychosocial factors on the likelihood of becoming hypertensive, among Ghanaian men and women. Compared with the poorest, Ghanaians from wealthy households were significantly more likely to be hypertensive. Educated women, as compared with the uneducated, were also more likely to be hypertensive. Ghanaians who engaged in vigorous or intensive activities continuously, for at least 10 minutes, were significantly less likely to be hypertensive, compared to those who did not. Happier men had lower odds of becoming hypertensive, and depressed women had increased odds of reporting they were hypertensive.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".