Use of traditional medicine and control of hypertension in 12 African countries
Bibliographic record
Abstract
BACKGROUND: Use of traditional medicine (TM) is widespread in sub-Saharan Africa as a treatment option for a wide range of disease. We aimed to describe main characteristics of TM users and estimate the association of TM use with control of hypertension. METHODS: We used data on 2128 hypertensive patients of a cross-sectional study (convenience sampling), who attended cardiology departments of 12 sub-Saharan African countries (Benin, Cameroon, Congo, Democratic Republic of the Congo, Gabon, Guinea, Côte d'Ivoire, Mauritania, Mozambique, Niger, Senegal, Togo). To model association of TM use with odds of uncontrolled, severe and complicated hypertension, we used multivariable mixed logistic regressions, and to model the association with blood pressure (systolic (SBP) and diastolic (DBP)) we used mixed linear models. All models were adjusted for age, sex, wealth, adherence to hypertension conventional treatment and country (random effect). RESULTS: A total of 512 (24%) participants reported using TM, varying across countries from 10% in the Congo to 48% in Guinea. TM users were more likely to be men, living in rural area, poorly adhere to prescribed medication (frequently due to its cost). Use of TM was associated with a 3.87 (95% CI 1.52 to 6.22)/1.75 (0.34 to 3.16) mm Hg higher SBP/DBP compared with no use; and with greater odds of severe hypertension (OR=1.34; 95% CI 1.04 to 1.74) and of any hypertension complication (OR=1.27; 95% CI 1.01 to 1.60), mainly driven by renal complication (OR=1.57; 95% CI 1.07 to 2.29) after adjustment for measured confounders. CONCLUSIONS: The use of TM was associated with higher blood pressure, more severe hypertension and more complications in Sub-Saharan African countries. The widespread use of TM needs to be acknowledged and worked out to integrate TM safely within the conventional healthcare.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".