Epidemiology and temporal trend of suicide mortality in the elderly in Jiading, Shanghai, 2003–2013: a descriptive, observational study
Bibliographic record
Abstract
OBJECTIVES: To investigate and describe the epidemiological characteristics of suicide in the elderly in Jiading, Shanghai, for the period 2003-2013. DESIGN: Retrospective, observational, epidemiological study using routinely collected data. SETTING: Jiading District, Shanghai. METHODS: Suicide data were retrieved from the Shanghai Vital Registry database for the period 2003-2013. Crude and age-standardised mortality rates were calculated for various groups according to sex and age. Joinpoint regression was performed to estimate the percentage change (PC) and annual percentage change (APC) for suicide mortality. RESULT: A total of 956 deaths due to suicide occurred among people aged ≥65 years during the study period, accounting for 76.7% (956/1247) of all suicide decedents. Among the 956 people with suicide deaths, 88.7% (848/956) had a history of a psychiatric condition. The age-standardised mortality rates for suicide without and with a psychotic history in people aged ≥65 years were much higher than those for people aged <65 years in both genders. Suicide mortality in the elderly showed a declining trend, with a PC of -51.5% for men and -47.5% for women. The APC was -29.1 in 2003-2005, 4.6 in 2005-2008 and -9.7 in 2008-2013 for aged men, and -12.2 in 2003-2006 and -5.2 in 2006-2013 for aged women, respectively. Women living in Jiading had a higher risk of suicide death than men, especially among the elderly. The mortality rate for suicide increased with age in the elderly, and was more marked for those with a psychiatric history than for those without. CONCLUSIONS: Suicide mortality declined in Jiading during the study period 2003-2013 overall, but remained high in the elderly, especially those with a psychiatric history.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".