Abstract P031: Does The Place Of Residence Influence Your Risk Of Being Hypertensive? A Case Study From Nepal
Bibliographic record
Abstract
Introduction: Area level factors could be an independent risk factor for disease and negative health outcomes. Despite the multiple studies on risk factors for hypertension, area-level influence is poorly investigated especially in low- and middle-income countries. Hypothesis: This study aims to test the hypothesis that area-level deprivation (AD) is significantly associated with the risk of hypertension independent of individual risk factors in the context of Nepal. Methods: The study is based on the latest Nepal Demographic Health Survey conducted in 2016. The area-level deprivation index was constructed and validated based on the material (household assets, household structure), social and geospatial features. The index ranged from 52 to 146 with higher scores representing higher levels of deprivation. Individuals aged 15 and above were identified as hypertensive if they met any of the following criteria: i) systolic blood pressure ≥140 mmHg; ii) diastolic blood pressure ≥90 mmHg; iii) taking antihypertensive medication irrespective of their blood pressure readings during the survey; iv) history of earlier diagnosis of hypertension by any health care provider. Associations between the quartiles of AD and hypertension were investigated using two-level logistic regression. Further, potential cross-level interaction and mediated pathways between AD and hypertension were explored. Results: The overall prevalence of hypertension was approximately 23% of which nearly 50% were unaware of their high blood pressure status at the time of the survey. Individuals from the least deprived areas seemed to have higher odds of hypertension compared to highly deprived areas (Odds Ratio=1.54 (95% CI 1.26, 1.89); p-trend=<0.001). Literate individuals from deprived areas were likely to have a higher risk of hypertension compared to those without formal education. However, the association was not significant in the least deprived areas (p-interaction <0.001). Overweight/obesity seems to explain a significant magnitude of the indirect pathway (67%) between AD and hypertension in the context of Nepal. Conclusion: Residing in the least deprived or more affluent areas might increase an individual’s risk of being hypertensive. Association between individual risk factors and hypertension is likely to vary across the areas with varying levels of deprivation. These results seem paradoxical and are inconsistent with epidemiological data from high-income countries. Over-nutrition and changing lifestyle, driven by affluence and higher disposable income, could explain this counterintuitive association in the context of a country like Nepal which is undergoing a rapid socio-demographic and epidemiological transition.
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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 | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".