Renal failure deaths and their risk factors in India 2001–13: nationally representative estimates from the Million Death Study
Bibliographic record
Abstract
BACKGROUND: Renal failure represents a growing but mostly undocumented cause of premature mortality in low-income and middle-income countries. We investigated changes in adult renal failure mortality and its key risk factors in India using the nationally representative Million Death Study. METHODS: In this cross-sectional analysis of population-based data, two trained physicians independently assigned underlying causes to 150 018 deaths at ages 15-69 years from a nationally-representative mortality survey in India for 2001-03 and 2010-13, using the International Classification of Diseases, 10th version (ICD-10). We applied the age-specific proportion of renal failure deaths for the 2010-13 period to the 2015 UN estimates of total deaths in India and calculated age-standardised death rates for renal failure by rural or urban residence, state, and age group. We used proportional mortality of renal deaths (cases) to injuries (controls) to calculate the odds of renal death in the presence of different comorbidities and stratified risks by decade of birth. FINDINGS: In 2001-03, 2·1% of total deaths among 15-69 year olds were from renal failure (1266 [2·2%] of 58 871; unweighted). By 2010-13, the proportion of deaths from renal failure had risen to 2·9% (2943 [3·2%] of 91 147; unweighted) of total deaths and corresponding to 136 000 renal failure deaths (range 108 000-150 000) of 4 688 000 total deaths nationally in 2015. Age-standardised renal death rates were highest in the southern and eastern states, particularly among adults aged 45-69 years in 2010-13. Diabetes, hypertension, and cardiovascular disease were all significantly associated with increased renal failure deaths, with diabetes the strongest predictor-odds ratio (OR) vs control 9·2 (95% CI 6·7-12·7) in 2001-03, rising to 15·1 (12·6-18·1) in 2010-13. In the 2010-13 study population, the diabetes to non-diabetes OR was twice as large in adults born in the 1970s (25·5, 95% CI 17·6-37·1) as in those individuals born during or before the 1950s (11·7, 9·1-14·9). INTERPRETATION: Renal failure is a growing cause of premature death in India. Poorly treated diabetes is the most probable reason for this increase. Strategies aimed at diabetes prevention, and early detection and treatment are urgently needed in India, as well as greater access to renal replacement therapy. FUNDING: US National Institutes of Health, International Development Research Centre, Centre for Global Health Research, University of Toronto.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".