Association of household wealth and education level with hypertension and diabetes among adults in Bangladesh: a propensity score‐based analysis
Bibliographic record
Abstract
OBJECTIVE: To determine the association of household wealth and education level with hypertension and diabetes in Bangladesh using propensity score (PS) analyses. METHODS: A nationally representative sample of the Bangladesh Demographic and Health Survey 2017-18 was analysed to explore the research question. A weighted sample of 11 320 individuals was considered. Hypertension and diabetes were the outcomes of interest, and household wealth status (non-poor and poor) and education level (secondary/higher education and no secondary/higher education) were the exposure variables of interest. A person was defined as hypertensive if their average blood pressure was ≥140/90 mmHg or self-reported history of taking antihypertensive medications. Individuals were classified as diabetic if they had a Fasting Blood Glucose level of ≥7 mmol/l or reported taking prescribed medication for reducing high blood glucose or diabetes. We used the 1:1 nearest neighbour PS matching without replacement and PS weighting approaches to assess the association between the exposures and the outcome variables. RESULTS: Wealth status was significantly associated with diabetes but not with hypertension, while education status was significantly associated with neither diabetes nor hypertension. We also observed a significant interaction effect between household wealth status and education level with diabetes. The odds of diabetes were approximately 60% higher among adults from non-poor households and those without secondary/higher education. CONCLUSION: Diabetes prevention and control programs should focus on non-poor individuals, while hypertension prevention programs should target populations irrespective of educational attainment and wealth status.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".