Mediating pathways between area-level deprivation and hypertension in Nepal: the role of nutritional status, food insecurity, and health behaviors
Bibliographic record
Abstract
BACKGROUND: Hypertension is a major contributor to global disease burden, and the prevalence is associated to various social determinants, including area-level deprivation. However, the mechanisms underlying this association remain unclear, particularly in low- and middle-income countries (LMICs). This study explores how household food insecurity, individual behaviors (smoking and alcohol use), and nutritional status may mediate the relationship between area-level deprivation and hypertension in Nepal. METHODS: We used nationally representative data from the 2016 Nepal Demographic and Health Survey. Area-level deprivation was measured using a validated 15-item composite index. A two-level structural equation model was employed to analyze both the direct and indirect (mediated) relationships between deprivation and hypertension, with individuals (aged ≥ 15 years) and households nested within geographic clusters/areas. RESULTS: The overall prevalence of hypertension was 22.9%, with a higher rate in less deprived (more affluent) areas (27.5%) compared to highly deprived ones (17.9%). When mediators were not included, area-level deprivation was inversely associated with hypertension. However, when potential mediators—Body Mass Index (BMI; as a proxy for nutritional status), household food insecurity, and individual behaviors (smoking and alcohol use) were included, the direct effect of deprivation on hypertension was no longer significant. BMI emerged as the only significant mediator, accounting for approximately 70% of the total indirect effect. Food insecurity and individual behaviors did not significantly mediate the relationship; although, food insecurity was associated with lower hypertension risk and individual health behaviors (smoking and alcohol use) with higher risk at the individual level. CONCLUSION: In the context of Nepal, hypertension is more prevalent in affluent areas, and BMI- a proxy for nutritional status significantly mediates the link between area-level deprivation and hypertension risk. These findings highlight the role of nutritional transitions in LMICs and the need for context-specific public health strategies targeting both environmental and individual-level 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".