Escalating costs of self-injury mortality in the 21st century United States: an interstate observational study
Bibliographic record
Abstract
BACKGROUND: Estimating the economic costs of self-injury mortality (SIM) can inform health planning and clinical and public health interventions, serve as a basis for their evaluation, and provide the foundation for broadly disseminating evidence-based policies and practices. SIM is operationalized as a composite of all registered suicides at any age, and 80% of drug overdose (intoxication) deaths medicolegally classified as 'accidents,' and 90% of corresponding undetermined (intent) deaths in the age group 15 years and older. It is the long-term practice of the United States (US) Centers for Disease Control and Prevention (CDC) to subsume poisoning (drug and nondrug) deaths under the injury rubric. This study aimed to estimate magnitude and change in SIM and suicide costs in 2019 dollars for the United States (US), including the 50 states and the District of Columbia. METHODS: Cost estimates were generated from underlying cause-of-death data for 1999/2000 and 2018/2019 from the US Centers for Disease Control and Prevention's (CDC's) Wide-ranging ONline Data for Epidemiologic Research (WONDER). Estimation utilized the updated version of Medical and Work Loss Cost Estimation Methods for CDC's Web-based Injury Statistics Query and Reporting System (WISQARS). Exposures were medical expenditures, lost work productivity, and future quality of life loss. Main outcome measures were disaggregated, annual-averaged total and per capita costs of SIM and suicide for the nation and states in 1999/2000 and 2018/2019. RESULTS: 40,834 annual-averaged self-injury deaths in 1999/2000 and 101,325 in 2018/2019 were identified. Estimated national costs of SIM rose by 143% from $0.46 trillion to $1.12 trillion. Ratios of quality of life and work losses to medical spending in 2019 US dollars in 2018/2019 were 1,476 and 526, respectively, versus 1,419 and 526 in 1999/2000. Total national suicide costs increased 58%-from $318.6 billion to $502.7 billion. National per capita costs of SIM doubled from $1,638 to $3,413 over the observation period; costs of the suicide component rose from $1,137 to $1,534. States in the top quintile for per capita SIM, those whose cost increases exceeded 152%, concentrated in the Great Lakes, Southeast, Mideast and New England. States in the bottom quintile, those with per capita cost increases below 70%, were located in the Far West, Southwest, Plains, and Rocky Mountain regions. West Virginia exhibited the largest increase at 263% and Nevada the smallest at 22%. Percentage per capita cost increases for suicide were smaller than for SIM. Only the Far West, Southwest and Mideast were not represented in the top quintile, which comprised states with increases of 50% or greater. The bottom quintile comprised states with per capita suicide cost increases below 24%. Regions represented were the Far West, Southeast, Mideast and New England. North Dakota and Nevada occupied the extremes on the cost change continuum at 75% and - 1%, respectively. CONCLUSION: The scale and surge in the economic costs of SIM to society are large. Federal and state prevention and intervention programs should be financed with a clear understanding of the total costs-fiscal, social, and personal-incurred by deaths due to self-injurious behaviors.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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 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".