Assessment and prediction of cardiovascular risk and associated factors among tribal population of Assam and Mizoram, Northeast India: A cross-sectional study
Bibliographic record
Abstract
Background Cardiovascular diseases (CVD) are major health concerns and the leading cause of mortality globally. In India, tribal people are limited to rural areas and often associated with undiagnosed, uncontrolled disease risk factors. In this study, we explore the CVD risk factors and predict the ten-year CVD risk in tribal populations of Assam and Mizoram, Northeast India. Methods This community-based cross-sectional study was conducted in Assam and Mizoram from 2019 to 2022. The details of demographics, socioeconomic status, and anthropometric data were collected, and participants were evaluated for cardiometabolic risk factors using serum samples. To identify cardio-metabolic risk-associated factors, we performed a logistic regression analysis. The ten-year CVD risk was calculated using the Framingham general cardiovascular risk prediction equations. Results The study included 1812 participants from the villages of Assam (n = 708) and Mizoram (n = 1104). It was observed that Mizoram's tribal males who were overweight, >35 years of age, with higher systolic (SBP) and diastolic blood pressure (DBP), and low levels of high-density lipoproteins (HDL) had a higher chance of developing cardiovascular disease over the next ten years. Multiple regression analysis revealed that age, gender, body mass index (BMI), smoking habits, and alcohol consumption were the risk factors that elevate SBP, DBP, blood glucose, and lipid levels and contribute to CVD risk among the tribal population. Conclusion Our findings highlight distinct risk factors contributing to cardiovascular risks within the tribal communities of Assam and Mizoram. Hence, it is essential to raise awareness among the tribal population and educate them on adopting a healthy lifestyle.
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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 | 0.001 | 0.000 |
| 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".