Comparing gender-specific suicide mortality rate trends in the United States and Lithuania, 1990–2019: putting one of the “deaths of despair” into perspective
Bibliographic record
Abstract
INTRODUCTION: The increase in the suicide mortality rate among middle-aged adults in the United States (US) has been well documented. Aside from a few studies from the United Kingdom, it is unclear whether the suicide mortality rate trend in the US is also occurring in other developed countries. Accordingly, we aimed to compare the suicide mortality rate trends over the past 30 years in the US to a country in the European Union-Lithuania. METHODS: Joinpoint regression analyses were performed to identify secular trends in the gender-specific age-standardized suicide mortality rate among individuals 15 + years of age, as well as middle-aged adults (45-54 years of age), and suicide mortality rate ratio for men-to-women. RESULTS: Age-standardized suicide mortality rates among middle-aged adults in the US increased annually, on average, by 0.89% (95% CI: 0.66%, 1.12%) among men and 1.21% (95% CI: 0.75%, 1.66%) among women between 1990 and 2019. In contrast to the US, there was an overall downward trend in the suicide mortality rates among middle-aged adults in Lithuania across the study period. The average annual percent change in the suicide mortality rate ratio for men-to-women were not statistically significant for either country. CONCLUSION: The suicide mortality rate trend in the US does not appear to be an indicator of an upcoming global trend, but rather should be regarded as a cautionary example of what other countries should strive to avoid.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".