Increased hypertension following hysterectomy among reproductive women in India
Bibliographic record
Abstract
BACKGROUND: In recent years, hysterectomy has received increased attention in health policy debates in India. On the other hand, based non-communicable disease specific data for India, in 2011, WHO portray a grim picture and recommended to the government a 20% reduction in hypertension by 2020; however, the trends show that it is increasing. Yet, to date, there has not been a single nationally representative study of hypertension prevalence among women who undergo a hysterectomy. METHODS: The study has used the Indian fourth round of National Family Health Survey data, which is a cross-sectional nationally representative sample of 699,686 women in the age group 15-49 years and conducted during 2015-16. Bivariate and multivariate logistic regressions were used to examine the effect of hysterectomy on increased odds of hypertension among women of reproductive age groups. RESULTS: The age adjusted prevalence of hypertension was higher among women those who undergone hysterectomy (11.9%) compared to non-hysterectomy women (10.6%). The pattern holds true among relevant background characteristics such as age, place of residence, education, caste, religion, wealth, family size, years since hysterectomy, body mass index (BMI), anaemia and consumption of tobacco. The adjusted odds of hypertension among women who underwent hysterectomy compared to those who did not was 1.72 (95% CI: 1.14-2.58). CONCLUSIONS: The results indicated increased hypertension level among hysterectomy women. However, these results are based on a cross-sectional study, and hence, further through investigation based on a prospective study is necessary before undertaking any policy changes. Meanwhile, the government of India may like to suggest surveillance to the general practitioners as well as obstetricians and gynaecologists following a hysterectomy in order to better understand the effect of hysterectomy on hypertension.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".