Prevalence and determinants of hypertension in underrepresented indigenous populations of Nepal
Bibliographic record
Abstract
Indigenous populations residing in low- and middle-income countries (LMICs) are highly underrepresented in medicine and public health research. Specifically, data on non-communicable diseases (NCDs) from indigenous populations remains scarce. Despite the increasing burden of NCDs in the Himalayan region, their prevalence in many indigenous populations remains understudied. The nationally representative public health surveys often do not include the indigenous communities, especially those that reside in rural areas or exist in small numbers. This observational cross-sectional survey study aimed to assess the prevalence of three NCD risk factors namely obesity, hypertension, and tachycardia and identify dietary and lifestyle variables associated with them across underrepresented indigenous populations of Nepal. A total of 311 individuals (53.3% women, 46.7% men) with mean age 43±15 years from 12 indigenous Nepali communities residing in rural (47.9%) or semi-urban (52.1%) areas volunteered to participate in this study. Univariate tests and multivariable logistic regressions were used to analyze the survey data. The mean systolic and diastolic blood pressures were 121.3±19.5 mmHg and 81.3±11.8 mmHg respectively. Overall, the prevalence of obesity and tachycardia was low (0.64% and 3.22%, respectively) but hypertension was prevalent at 23.8%. Hypertension was not significantly different across populations, but it was associated with age, BMI, and tobacco use, and collectively, these variables explained 13.9% variation in hypertension prevalence. Although we were unable to detect direct associations between individual determinants of hypertension identified in non-indigenous Nepalis, such as education levels, alcohol consumption, and smoking in this study, having one or more determinants increased the odds of hypertension in the indigenous participants. Furthermore, ~14% of the hypertensive individuals had none of the universally identified hypertension risk factors. The lack of association between previously identified risk factors for hypertension in these individuals indicates that the additional determinants of hypertension remain to be identified in indigenous Nepali populations.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".