Suicide trends in Germany from 1991-2022, considering misclassification of undetermined intent deaths – a time-series analysis
Bibliographic record
Abstract
Changes over time in age-standardised death rates of undetermined intent (USDR) and suicide (SSDR), the sum of suicide and undetermined rates (SUSDR), and the USDR to SSDR rates ratio will point to variations in “hidden suicides” caused by disparities in the death registration procedures. We aim to analyse from 1991 to 2022: 1) trends and differences for suicide (SSDR) and suicide plus undetermined rates (SUSDR), 2) trends and breakpoints in the USDR to SSDR ratio. Suicide and undetermined death registration data for German inhabitants were obtained from the Federal Health Monitoring website from 1991 to 2022. SDRs were calculated and analyzed by joinpoint regression analysis. Rate ratios were calculated by dividing USDR by SSDR. A time-series analysis was then applied to detect structural changes in the USDR-to-SSDR ratio. In the last 32 years, SSDR and USDR declined by 41.01 % and 19.55 %. The trends for SSDR and SUSDR are not identical ( p < 0.001). The ratio of USDR to SSDR varied from 0.11 to 0.37. Breakpoints were identified in 1997, 2010, 2016, and 2005 for males. Analyses are post-hoc, and causal relationships cannot be identified. Unequal trend declines for SSDR and SUSDR could indicate hidden suicide. The breakpoint in 1997 could be due to registry variability when ICD9 gave place to ICD10; the breakpoint in 2010 could be attributed to boosted suicide awareness after national media reporting about Robert Enke's suicide; other factors could explain the breakpoints in 2016, and for males in 2005. • Suicide and undetermined death rates declined in Germany from 1991 to 2022. • Female suicide rates show a real increase starting before the COVID-19 pandemic. • No rise in suicide between 2007 and 2011; data artefacts misled earlier conclusions. • Breakpoints in data trends suggest shifts in suicide misclassification practices. • The ratio of undetermined to official suicides reveals data structure changes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".