Economic burden of suicide deaths in India (2019): a retrospective, cross-sectional study
Bibliographic record
Abstract
Background: India has the highest number of suicide deaths in the world. Suicide prevention requires policy attention and resource allocation. Evidence of economic losses due to disease burden can influence such allocations. We assessed the economic burden and its distribution across states and demographic groups in India. Methods: We used the human capital approach in this retrospective cross-sectional analysis to assess the economic burden of suicide in India for the year 2019 for 28 Indian states and 3 union territories (UTs). We calculated the monetary value for the years of life lost disaggregated by states, age groups, and sexes. For sensitivity, we present a library of estimates using different discount rates, life expectancy thresholds, and estimates specific to the populations that can participate in the workforce. Findings: The national economic burden of suicide was US$ 16,749,079,455 (95% Uncertainty Interval: 11,913,034,910-22,404,233,468). The top three states, Karnataka, Tamil Nadu, and Maharashtra, contributed to 44.82% of the total burden in India. The age group 20-34 years had the largest suicide burden and contributed to 53.05% of the overall national economic burden (US$ 8,885,436,385 [6,493,912,818-11,694,138,884]). Twenty states and UTs had a greater economic burden for females than males. Interpretation: The current analysis ascertains a high economic burden of suicide among the Indian youth and females, necessitating concerted multisectoral efforts and immediate investments. Funding: None.
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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 | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".