Absolute and relative declines in child mortality in India's districts during 2001–12 – Authors' reply
Bibliographic record
Abstract
We thank Rajatashuvra Adhikary for his comments on our study1Ram U Jha P Ram F et al.Neonatal, 1–59 month, and under-5 mortality in 597 Indian districts, 2001 to 2012: estimates from national demographic and mortality surveys.Lancet Glob Health. 2013; 1: e219-e226Summary Full Text Full Text PDF Scopus (87) Google Scholar assessing progress from 2001 to 2012 by India's 597 districts towards achievement of the Millennium Development Goal for child mortality (MDG 4). Our classification of poorer states is based on the same system the Indian government uses to define priority states under the National Rural Health Mission, and is similar to the lowest ranking states on the Human Development Index of 2007–082Gandhi A Kumar C Saha P Sahoo BK Sharma A India Human Development Report 2011: Towards social inclusion. Institute of Applied Manpower Research, Planning Commission, Government of India. Oxford University Press, Oxford2011Google Scholar and to development rankings by Jean Drèze and Amartya Sen.3Drèze J Sen A An uncertain glory: India and its contradictions. Princeton University Press, Princeton2013Google Scholar We used absolute goals for 2015 (ie, under-5 mortality of 38 per 1000 livebirths, neonatal mortality of 20 per 1000 livebirths, and 1–59 month mortality of 18 per 1000 livebirths). Declines in child mortality to these absolute levels by 2015 will probably reduce geographical and social inequalities across India. In our analysis that used relative declines, we reached similar conclusions in terms of the number of lagging districts (appendix1Ram U Jha P Ram F et al.Neonatal, 1–59 month, and under-5 mortality in 597 Indian districts, 2001 to 2012: estimates from national demographic and mortality surveys.Lancet Glob Health. 2013; 1: e219-e226Summary Full Text Full Text PDF Scopus (87) Google Scholar). Whereas relative declines are similar across states (figure), the proportion of districts on track to achieve MDG 4 by 2015 varies substantially. Among the richer states, 100% of districts in Kerala and Tamil Nadu are on track, whereas only 43% of districts are on track in Andhra Pradesh. Among the poorer states, the proportion of districts on track varied between 15% in Assam to 0% in Uttar Pradesh and Orissa. Nearly 13 million of the 26 million livebirths in 2012 in India occurred in Uttar Pradesh, Bihar, Madhya Pradesh, Rajasthan, and Orissa, in which fewer than 5% of districts are on track to achieve MDG 4. The National Rural Health Mission and states have, as of 2005, begun to allocate more funding and attention to districts with less progress. Thus, absolute progress during 2001–12 provides the more appropriate indicator for these decisions. Similarly, the districts lagging behind the MDG by less than 5 years can be motivated to accelerate progress. Our provision of these estimates is factual, without a negative or positive tenor. Maharaj K Bhan's accompanying Comment4Bhan MK Accelerated progress to reduce under-5 mortality in India.Lancet Glob Health. 2013; 1: e172-e173Summary Full Text Full Text PDF Scopus (15) Google Scholar correctly identifies that the main need is to obtain estimates on cause-specific mortality that are specific to each district. In particular, improved understanding is needed of how three causes, which account for 80% of neonatal deaths (birth asphyxia or trauma, low birthweight or prematurity, and infection), and two causes, which account for half of deaths at age 1–59 months (pneumonia and diarrhoea), are distributed among the districts of India.5The Million Death Study CollaboratorsCauses of neonatal and child mortality in India: a nationally representative mortality survey.Lancet. 2010; 376: 1853-1860Summary Full Text Full Text PDF PubMed Scopus (335) Google Scholar Direct estimates from representative, rapid, and low-cost surveys of cause of death are an emerging global priority for the post-2015 agenda.6Birbeck GL Wiysonge CS Mills EJ Frenk J Xiao-Nong Z Jha P Global health: the importance of evidence-based medicine.BMC Med. 2013; 11: 223Crossref PubMed Scopus (26) Google Scholar We declare that we have no conflicts of interest. Absolute and relative declines in child mortality in India's districts during 2001–12I greatly appreciate the publication of an excellent and insightful report on childhood mortality in India by Usha Ram and colleagues (October, p e219).1 This report is valuable because it provides, perhaps for the first time in Indian history, reliable estimates of neonatal, 1–59 month, and under-5 mortality for every district of India. The analysis of whether India is on track to meet the UN 2015 Millennium Development Goal for under-5 mortality (MDG 4), or how far India is from the MDG 4 target, is simple but brilliant. Full-Text PDF Open Access
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".