Neonatal, 1–59 month, and under-5 mortality in 597 Indian districts, 2001 to 2012: estimates from national demographic and mortality surveys
Bibliographic record
Abstract
BACKGROUND: India has the largest number of child deaths of any country in the world, and has wide local variation in under-5 mortality. Worldwide achievement of the UN 2015 Millennium Development Goal for under-5 mortality (MDG 4) will depend on progress in the subregions of India. We aimed to estimate neonatal, 1-59 months, and overall under-5 mortality by sex for 597 Indian districts and to assess whether India is on track to achieve MDG 4. METHODS: We divided the 2012 UN sex-specific birth and mortality totals for India into state totals using relative birth rates and mortality from recent demographic surveys of 24 million people, and divided state totals into totals for the 597 districts using 3 million birth histories. We then split the results into neonatal mortality and 1-59 month mortality using data for 109,000 deaths in children younger than 5 years from six national surveys. We compared results with the 2001 census for each district. FINDINGS: Under-5 mortality fell at a mean rate of 3·7% (IQR 3·2-4·9) per year between 2001 and 2012. 222 (37%) of 597 districts are on track to achieve the MDG 4 of 38 deaths in children younger than 5 years per 1000 livebirths by 2015, but an equal number (222 [37%]) will achieve MDG 4 only after 2020. These 222 lagging districts are home to 41% of India's livebirths and 56% of all deaths in children younger than 5 years. More districts lag behind the relevant goal for neonatal mortality (251 [42%]) than for 1-59 month mortality (197 [33%]). Just 81 (14%) districts account for 37% of deaths in children younger than 5 years nationally. Female mortality at ages 1-59 months exceeded male mortality by 25% in 303 districts in nearly all states of India, totalling about 74,000 excess deaths in girls. INTERPRETATION: At current rates of progress, MDG 4 will be met by India around 2020-by the richer states around 2015 and by the poorer states around 2023. Accelerated progress to reduce mortality during the neonatal period and at ages 1-59 months is needed in most Indian districts. FUNDING: Disease Control Priorities 3, Canadian Institutes of Health Research, International Development Research Centre, US National Institutes of Health.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".