Childhood and adult mortality from unintentional falls in India
Bibliographic record
Abstract
OBJECTIVE: To estimate fall-related mortality by type of fall in India. METHODS: The authors analysed unintentional injury data from the ongoing Million Death Study from 2001-2003 using verbal autopsy and coding of all deaths in accordance with the International statistical classification of diseases and related health problems, tenth revision, in a nationally representative sample of 1.1 million homes throughout the country. FINDINGS: Falls accounted for 25% (2003/8023) of all deaths from unintentional injury and were the second leading cause of such deaths. An estimated 160,000 fall-related deaths occurred in India in 2005; of these, nearly 20,000 were in children aged 0-14 years. The unintentional-fall-related mortality rate (MR) per 100,000 population was 14.5 (99% confidence interval, CI: 13.7-15.4). Rates were similar for males and females at 14.9 (99% CI: 13.7-16.0) and 14.2 (99% CI: 13.1-15.4) per 100,000 population, respectively. People aged 70 years or older had the highest mortality rate from unintentional falls (MR: 271.2; 99% CI: 249.0-293.5), and the rate was higher among women (MR: 281; 99% CI: 249.7-311.3). Falls on the same level were the most common among older adults, whereas falls from heights were more common in younger age groups. CONCLUSION: In India, unintentional falls are a major public health problem that disproportionately affects older women and children. The contexts in which these falls occur and the resulting morbidity and disability need to be better understood. In India there is an urgent need to develop, test and implement interventions aimed at preventing falls.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".