Use of traditional medicine for hypertension, diabetes, and hypercholesterolaemia measured in 71 surveys
Bibliographic record
Abstract
Objective: To assess the pattern of traditional medicine use globally for treating hypertension, diabetes and hypercholesterolaemia. Methods: We pooled individual-level data from 309 745 non-pregnant people aged ≥ 15 years from 71 nationally representative surveys conducted in low- and middle-income countries between 2005 and 2021. We identified individuals with diagnosed hypertension, diabetes and hypercholesterolaemia who reported use of traditional medicine. For each condition, we estimated the prevalence of traditional medicine use at the global, regional and country-income level and the proportion using traditional medicine and biomedicine. We estimated the association between traditional medicine use and individual characteristics. Findings: The prevalence of traditional medicine use was 14.7% (95% confidence interval, CI: 12.7-16.9) for diabetes, 12.4% (95% CI: 10.0-15.3) for hypercholesterolaemia and 8.1% (95% CI: 7.3-9.0) for hypertension. Most individuals using traditional medicine for diabetes or hypercholesterolaemia also used biomedicine. Associations between sociodemographic characteristics and traditional medicine use varied between regions and health conditions. In the World Health Organization's (WHO) Western Pacific Region, traditional medicine use for diabetes was significantly higher in males and younger adults, whereas use for hypertension was significantly higher in females and older adults. In the WHO African Region, traditional medicine use for diabetes and hypertension was higher in males and individuals with lower education. Conclusion: Our study shows a high prevalence of traditional medicine use for treating hypertension, diabetes and hypercholesterolaemia in low- and middle-income countries. Our results highlight the need to better understand the clinical interactions and risks of traditional medicine for improved cardiometabolic treatment.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".