Prevalence of high waist to hip ratio and its association with hypertension among married couples in India: A cross-sectional study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
• Data were extracted from the fifth round of the National Family Health population-based, cross-sectional survey. • Around 36.3 % of Indian couples have high waist to hip ratio. • High Waist to hip ratio in one spouse increases hypertension risk in both partners. • Risk of hypertension is even higher when both spouses have high waist to hip ratio. • Waist to-hip ratio should be used in routine screening to prevent hypertension. The study aims to explore the prevalence of high waist-to-hip ratio (WHR) and examine its association with hypertension and various socio-demographic factors among Indian couples with a focus on health implications. Data were extracted from the fifth round of the National Family Health Survey (NFHS) - a comprehensive population-based, cross-sectional survey conducted from 2019 to 2021. A total sample of 51,797 couples was analysed using bivariate and multivariable techniques to address the study objectives. The prevalence of high WHR among Indian couples was 36.3%. After adjusting for significant background factors, both female and male spouses had higher odds of hypertension when one spouse had a high WHR (female: OR = 1.19, p < 0.001, 95% CI: 1.13–1.25; male: OR = 1.30, p < 0.001, 95% CI: 1.22–1.38), compared to those with normal WHR. The risk increased when both spouses had high WHR, with odds of 1.44 in female spouses (OR = 1.44, p < 0.001, 95% CI: 1.37–1.52) and 1.56 in male spouses (OR = 1.56, p < 0.001, 95% CI: 1.47–1.66). The study highlights the significant health implications of high WHR in Indian couples. The findings emphasize the importance of monitoring WHR as an anthropometric measure for assessing hypertension risk and managing related health conditions in both clinical and public health settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it