Does the place of residence influence your risk of being hypertensive? A study-based on Nepal Demographic and Health Survey
Bibliographic record
Abstract
Even though several studies have examined various risk factors for hypertension, residential influence is poorly explored especially in the low-income countries. We aim to investigate the association between residential characteristics and hypertension in resource limited and transitional settings like Nepal. A total of 14,652 individuals aged 15 and above were selected from 2016-Nepal Demographic and Health Survey. Individuals with blood pressure ≥140/90 mmHg or a history of hypertension (as identified by physicians/health professionals) or under antihypertensive medication were defined as hypertensive. Residential characteristics were represented by area level deprivation index, with a higher score representing higher level of deprivation. Association was explored using a two-level logistic regression. We also assessed if residential area modifies the association between individual socio-economic status and hypertension. Area deprivation had a significant inverse association with the risk of hypertension. Individuals from the least deprived areas had higher odds of hypertension compared to highly deprived areas 1.59 (95% CI 1.30, 1.89). Additionally, the association between literacy a proxy of socio-economic status and hypertension varied with a place of residence. Literate individuals from highly deprived areas were likely to have a higher odds of hypertension compared to those with no formal education. In contrast, literate from the least deprived areas had lower odds of hypertension. These results identify counterintuitive patterns of associations between residential characteristics and hypertension in Nepal, as compared with most of the epidemiological data from high-income countries. Differential stages of demographic and nutritional transitions between and within the countries might explain these associations.
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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 | 0.001 |
| 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) | 0.002 | 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".