Trends in maternal mortality in India over two decades in nationally representative surveys
Bibliographic record
Abstract
OBJECTIVE: To assess national and regional trends and causes-specific distribution of maternal mortality in India. DESIGN: Nationally representative cross-sectional surveys. SETTING: All of India from 1997 to 2020. SAMPLE: About 10 000 maternal deaths among 4.3 million live births over two decades. METHODS: We analysed trends in the maternal mortality ratio (MMR) from 1997 through 2020, estimated absolute maternal deaths and examined the causes of maternal death using nationally representative data sources. We partitioned female deaths (aged 15-49 years) and live birth totals, based on the 2001-2014 Million Death Study to United Nations (UN) demographic totals for the country. MAIN OUTCOME MEASURES: Maternal mortality burden and distribution of causes. RESULTS: The MMR declined in India by about 70% from 398/100 000 live births (95% CI 378-417) in 1997-98 to 99/100 000 (90-108) in 2020. About 1.30 million (95% CI 1.26-1.35 million) maternal deaths occurred between 1997 and 2020, with about 23 800 (95% CI 21 700-26 000) in 2020, with most occurring in poorer states (63%) and among women aged 20-29 years (58%). The MMRs for Assam (215), Uttar Pradesh/Uttarakhand (192) and Madhya Pradesh/Chhattisgarh (170) were highest, surpassing India's 2016-2018 estimate of 113 (95% CI 103-123). After adjustment for education and other variables, the risks of maternal death were highest in rural and tribal areas of north-eastern and northern states. The leading causes of maternal death were obstetric haemorrhage (47%; higher in poorer states), pregnancy-related infection (12%) and hypertensive disorders of pregnancy (7%). CONCLUSIONS: India could achieve the UN 2030 MMR goals if the average rate of reduction is maintained. However, without further intervention, the poorer states will not. TWEETABLE ABSTRACT: We estimated that 1.3 million Indian women died from maternal causes over the last two decades. Although maternal mortality rates have fallen by 70% overall, the poorer states lag behind.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".