A brief research report of suicide rates in the Brazilian elderly over a 12-year period: the lack of association of the "Setembro Amarelo" campaign for suicide prevention
Bibliographic record
Abstract
Objectives Aiming to disseminate information related to suicide prevention in Brazil, the “Setembro Amarelo” campaign has been conducted since 2015. The objective of this study is to assess the association between this campaign and elderly suicide rates over a 12-year period. Methods Data were gathered from the Mortality Information System and the Notifiable Diseases Information System, established by public institutions in Brazil. An interrupted time-series framework was applied to assess the association between the “Setembro Amarelo” campaign and suicide mortality rates in the elderly population (60 et plus) in the southeastern region of Brazil. We consider three monthly outcomes: all suicides, suicides in males and suicide in females. We operationalize the campaign assuming three effects: short-term, declining and sustained. The period of analysis was from 2011-2022. Results The suicide-mortality rate over time has remained stable; the average rate in the pre-campaign period was 0.028 and increased slightly to 0.035. Regardless of the campaign’s operationalization and the outcome used, results show no significant associations between the campaign and elderly suicide rates. The campaign was associated with non-significant decreased effects of 15% (P=0.532) in the short term, and 16% (P=0.446) assuming the campaign was sustained. Conclusions There is a lack of association between the campaign and suicide rates, among the elderly in Brazil’s southeastern region. As suicide is complex and multifactorial, more research is needed. The campaign, while raising awareness and reducing stigma, may not reduce suicides. To reduce the suicide rate in the elderly requires addressing social, economic and cultural factors, multisectoral interventions, and upholding basic human rights.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".