Association of hypertension and hyperglycaemia with socioeconomic contexts in resource-poor settings: the Bangladesh Demographic and Health Survey
Bibliographic record
Abstract
BACKGROUND: Cardiovascular diseases and risk factors are disproportionally concentrated among the socioeconomically disadvantaged in high-income countries; however, this relationship is not well-understood or documented in resource-limited countries. METHODS: We analysed data from the 2011 Bangladesh Demographic and Health Survey to estimate age-, sex- and location-adjusted differences in blood pressure and blood glucose outcomes by categories of a standardized wealth index and education levels. Body mass index (BMI) was examined as a secondary outcome and also assessed as a potential confounder. RESULTS: There was strong evidence that the prevalence of hypertension was higher among Bangladeshi women than among men (33.6% vs 19.6%, P < 0.001), whereas the overall prevalence of hyperglycaemia was 7.1% with no evidence of sex differences. The likelihood of having hypertension was more than double for individuals in the highest vs lowest wealth quintile [odds ratio (OR) for men: 2.82, 95% confidence interval (CI): 2.32-3.44; OR for women: 2.25, 95% CI: 1.90-2.67], and for individuals with the highest level of education attained vs those with no education (OR for men: 2.55, 95% CI: 2.06-3.16; OR for women: 1.42, 95% CI: 0.99-2.03). Likewise, the likelihood of having hyperglycaemia was more than four times higher in the wealthiest compared with the poorest individuals (OR for men: 6.48, 95% CI: 5.11-8.22; OR for women: 4.77, 95% CI: 3.72-6.12), and in individuals with the highest level of education attained vs those with no education (OR for men: 4.68, 95% CI: 3.56-6.15; OR for women: 5.02, 95% CI: 3.30-7.64). There were no appreciable differences in these trends when stratified by geographical location. BMI did not attenuate these associations and exhibited similarly positive associations with education and wealth. CONCLUSIONS: Increasing levels of wealth and educational attainment were associated with an increased likelihood of having hypertension and hyperglycaemia in Bangladesh.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".