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 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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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 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".