Maternal metabolic health conditions and risk of stillbirth in India: evidence from a nationwide survey
Bibliographic record
Abstract
BACKGROUND: Stillbirth, defined by foetal death at or beyond 28 weeks of gestation, represents a significant challenge in India, contributing to approximately 500,000 foetal deaths each year. The country's stillbirth rate of 12.2 per 1000 births underscores the imperative to address this preventable occurrence. While maternal metabolic conditions diabetes, and hypertension, are widely recognized as established risk factors for stillbirth worldwide, the extent of their impact on India's stillbirth burden remains inadequately elucidated due to limited evidence. METHODS: This cross-sectional study utilized NFHS-5 data to examine stillbirths in the most recent pregnancy outcomes of 204,723 women aged 15-49 years, sampled from all states and union territories of India. The primary exposures assessed were diabetes, and hypertension. Descriptive analyses were conducted to determine the prevalence of diabetes, hypertension and stillbirths. Logistic regression was used to quantify the association between diabetes, hypertension and the risk of stillbirth, indicated by adjusted odds ratios (AOR) with 95% confidence intervals (CI). The study also assessed effect modification by maternal age, education, wealth quintile, and social category. RESULTS: The prevalence of diabetes and hypertension was 1% and 3% respectively, while the stillbirth rate was 1%. diabetes conferred a significantly higher risk of stillbirth with an increase of 74% (AOR 1.74, CI 1.14-2.67) as compared to women without diabetes. The risk was potential among mothers with hypertension with an increase of 50% (AOR 1.50, CI 1.16-1.95) on contrary to women without hypertension. The combined model (i.e. having diabetes or hypertension) also showed a significant risk of stillbirth with a higher risk of 58% (AOR 1.58, CI 1.25-1.99) indicating a synergistic interaction. Stratified analyses revealed the stillbirth risk among mothers belonging to the scheduled caste category (AOR 1.30, CI 1.10-1.53). CONCLUSION: Diabetes, and hypertension, increase stillbirth risk in India, highlighting the need for better metabolic health management pre- and during pregnancy. Our research highlights the need of integrated care for diabetes and hypertension is crucial. Targeted interventions for high-risk mothers and improved screening are vital to reduce stillbirth rates. More research is needed to understand these risks better. Collaboration across medical fields is essential to save lives and improve pregnancy outcomes.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 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".