Intentional Drug Overdose Deaths and Mood Disorders in Brazil - A 20-year overview
Bibliographic record
Abstract
BACKGROUND: Drug overdose deaths remain a significant and understudied public health concern in Brazil. This study aimed to examine drug overdose death records from 2000 to 2020, focusing on the relationship between mood disorders and intentionality, along overall sociodemographic characteristics. METHODS: Data from the Brazilian Mortality Information System from 2000-2020 were analyzed. Records with causes of death coded as X40-X45 (accidental poisoning), X60-X65 (intentional poisoning), or Y10-Y15 (undetermined intentionality poisoning) were included. The dataset comprised 21,410 deaths, including 933 subjects with mood disorders (ICD-10 codes F30-F39). A descriptive analysis of sociodemographic variables, stratified by mood disorders, was conducted. Logistic regression models identified independent associations with intentional overdose deaths. RESULTS: People who died of a drug overdose were mostly men (61.55%), non-white (52.45%), and single (59.33%). Most drug overdose deaths were intentional (44.70%). Compared to overall overdoses, the subset with mood disorders included a higher share of women (67.95%), whites (63.88%), and intentional overdoses (75.24%). Female gender (OR 1.30), mood disorder (OR 2.0), non-white race (OR 0.56), high school graduates (OR 0.93), some college education (OR 1.28), and divorced or widowed (OR 0.73) were independently associated with intentional overdose deaths. CONCLUSION: The sociodemographic characteristics of people who died of overdoses must guide national public policies. Strategies might involve conducting screenings for mental health disorders and drug-related problems in primary care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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 teacher head, 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".