P2-526 Social determinants of depressive symptoms among older adults in low- and middle-income countries
Bibliographic record
Abstract
Background Consistent evidence has linked depression to social factors among older people in rich countries; however, little is known for low- or middle-income countries (LMIC) and virtually no research exists that is comparable across LMIC. Understanding the relationship between key socioeconomic factors and depressive symptoms among older people in resource-poor contexts is essential for developing mental health policies. Methods To investigate social determinants of depressive symptoms among older adults in LMIC, we conducted a cross-sectional analysis of adults age 50+ from 51 countries that participated in the World Health Survey in 2002–2003. Using multivariable ordinal logistic regression models, we examined the association between socioeconomic predictors and the severity of depressive symptoms. Results Similar to patterns from rich countries, more severe depressive symptoms were reported among older, female, less-educated, poorer, and urban-dwelling individuals. Living arrangement also emerged as an important predictor that exhibited substantial heterogeneity across countries. In Southeast Asian countries, the odds of reporting more severe depressive symptoms was 2.6 (95% CI 1.5 to 4.7) times higher for individuals living alone compared to in intergenerational households. In African countries, individuals living in skipped-generation households (only older people and dependent children) reported significantly worse symptoms. Further analyses will incorporate country-level predictors (eg, availability of pensions, HIV/AIDS mortality) to explain some between-country variation. Conclusions In addition to established socioeconomic determinants, living alone or in skipped generation households is associated with an increased risk of depressive symptoms among older people.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".