Socioeconomic risk factors of hypertension and blood pressure among persons aged 15–49 in Nepal: a cross-sectional study
Bibliographic record
Abstract
OBJECTIVES: This study estimated the prevalence of hypertension, in accordance with the American College of Cardiology and American Heart Association's 2017 guidelines, and examined the association between various socioeconomic factors and systolic blood pressure (SBP), diastolic blood pressure (DBP) and hypertension. SETTING AND DESIGN: We used nationally representative data from the 2016 Nepal Demographic and Health Survey. Multivariate analysis was used to study the association of hypertension with socioeconomic factors: logistic regression was used for hypertension and linear regression was used for DBP and SBP. PARTICIPANTS: Our sample consisted of 9827 adults between the ages of 15 and 49 years. RESULTS: The prevalence of hypertension was 36%. The mean DBP and SBP were 76.4 and 111.5, respectively. Janjatis (adjusted OR (AOR): 1.34, CI: 1.12 to 1.59), Other Terai castes (AOR: 1.38, CI: 1.03 to 1.84), Muslim and other ethnicities (AOR: 1.64, CI: 1.15 to 2.33) and Dalits (AOR: 1.26, CI: 1.00 to 1.58) had higher odds of hypertension. Individuals employed in professional, technical and managerial professions collectively (AOR: 1.62; CI: 1.18 to 2.21) also had higher odds of hypertension. Moderately food insecure household had lower odds of hypertension (AOR: 0.84; CI: 0.72 to 0.99) compared with households with no issue of food insecurity. Results were similar for SBP and DBP. When stratified by sex, there were differences mainly in terms of occupation and ethnicity. CONCLUSION: There are substantial disparities in hypertension prevalence in Nepal. These disparities extend across ethnic groups, occupational status and food security status. Differences also persist across different provinces. As hypertension continues to be increasingly more significant, more research is needed to better understand the disparities and gradients that exist across various socioeconomic factors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".