The association between anemia and depression in older adults and the role of treating anemia
Bibliographic record
Abstract
OBJECTIVES: To investigate the association between anemia and depression and whether the treatment of anemia modifies the effect of the association between anemia and depression. METHODS: This secondary data analysis is based on data from the Enquête sur la santé des aînés (ESA)-Services study conducted in 2011-2013 on community-dwelling older adults recruited in primary care and have given access to their medico-administrative data (n = 1447). The presence of anemia was self-reported, as was depression (major and minor) aligned with symptoms of the DSM-5. Treated anemia was based on the presence of medications delivered to participants. Cross-sectional associations were analyzed using multivariable logistic regression, controlling for confounders. RESULTS: The prevalence of self-reported anemia in our sample was estimated at 6.7%. Self-reported anemia was associated with increased odds of depression. Individuals with untreated anemia had a 2.6-fold increased odds of depression compared to those with no anemia. In contrast, the odds of depression in individuals with treated anemia were not different from individuals with no anemia. CONCLUSION: The findings underline the importance of treating anemia in older adults. Future longitudinal studies are needed to replicate the findings and further explore the role of treating anemia on symptoms of depression.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".