Socio-demographic and psychiatric risk factors in incident and persistent depression: An analysis in the occupational cohort of ELSA-Brasil
Bibliographic record
Abstract
BACKGROUND: Depression is a main source of disability worldwide. Identifying risk factors associated with incident and persistent episodes could inform clinical practice and hence mitigate their burden. However, previous research has focused on populations from developed countries. Thus, we evaluated sociodemographic risk factors and psychiatric comorbidities associated with incident and persistent depression in a large Brazilian occupational cohort. METHODS: We examined baseline (2008-2010, n = 15,105) and follow-up (2012-2014) data from the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Based on the presence of depression diagnosis at two timepoints, we diagnosed persistent and incident depression. Simple and multiple logistic regression analyses were employed to explore risk factors associated with incident and persistent depression. As gender is associated with the exposure and outcome variables, analyses stratified by gender were also conducted. RESULTS: Presence of any anxiety disorder, obsessive-compulsive disorder, and female gender were significant (p < 0.001) risk factors for depression incidence (odds ratios of 2.59, 3.6, and 1.82, respectively) and persistence (odds ratios of 6.94, 14.37, and 2.85, respectively) in multiple models, whereas having university degree decreased the odds of depression incidence (0.74) and persistence (0.45). In stratified analyses, the effects of low education were only evident in women. LIMITATIONS: Brief depressive episodes could not be measured by our assessments. CONCLUSION: In this occupational cohort, female gender, low education and psychiatric comorbidities were associated with unfavorable depression courses. Interventions targeting comorbidities could prevent depression incidence and persistence.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".