Hypertension and undiagnosed hypertension among Bangladeshi adults: Identifying prevalence and associated factors using a nationwide survey
Bibliographic record
Abstract
Background: Although undiagnosed hypertension (HTN) is a serious concern worldwide, it is less of an importance in Bangladesh, where there is a dearth of research on the subject. So, we aimed to identify the prevalence and associated factors for diagnosed and undiagnosed HTN. Methods: We analyzed the recent 2017-2018 Bangladesh Demographic and Health Survey data. We included 11,981 participants aged 18 years and above for the analysis. The prevalence rates of both diagnosed and undiagnosed hypertension were computed for all individuals and subgroups. The influence of socio-demographic, household, and community-related variables on HTN and undiagnosed HTN was investigated using multinomial regression analysis. Results: The study finds 1,464 (12.2%) of the 11,981 respondents [6,815 females [56.9 %]; mean age 39.4 years] had diagnosed HTN, whereas 1 898 (15.8%) had undiagnosed HTN. The HTN and undiagnosed HTN were significantly prevalent in the elderly, type 2 diabetic (T2DM), and overweight and obese individuals. In terms of residential regions, people from coastal region had a significantly higher prevalence of both HTN (RRR: 1.37; 95% CI: 1.17-1.62) and undiagnosed HTN (RRR: 1.35; 95% CI: 1.17-1.56) compared to those from the central region of Bangladesh. Conclusions: The high prevalence of undetected hypertension in Bangladesh suggests that screening procedures for the current chronic illness may be inadequate in routine clinical practice. All populations should have access to hypertension screening, but it is especially crucial for the elderly, those with diabetes, those who are overweight or obese, and those from coastal and northern regions of Bangladesh.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".