Cross‐sectional and longitudinal analysis of the association between hemogloblin concentrations and depression in older adults: The International Mobility in Aging Study
Bibliographic record
Abstract
Abstract Background Anemia is a common hematological condition in older adults, with estimated prevalence increasing with age after the fifth decade of life. 1 We examined the cross‐sectional and longitudinal relationships between hemoglobin concentrations and depression in older adults and whether these relationships were modified by sex, social context, or cognitive function. Method A total of 1608 community‐dwelling older adults from the International Mobility in Aging Study (IMIAS) aged 65 to 74 years were recruited in Natal (Brazil), Manizales (Colombia), Kingston (Ontario, Canada), and Saint‐Hyacinthe (Quebec, Canada). The study outcome was depression, defined by a score of 16 or over in the Center for Epidemiologic Studies Depression Scale (CES‐D). Poisson regression models were used to estimate prevalence ratios (PR) of depression, while longitudinal associations over four years follow‐up were examined using generalized estimating equations. Hemoglobin concentrations were used either as a continuous measure or categorized according to the severity of anemia. Models reported were adjusted for research sites, alcohol drinking status, body mass index, chronic conditions, activities of daily life disabilities, cognitive function, and polypharmacy. Result In multivariate cross‐sectional and longitudinal analyses, for every 1g/dL increase in hemoglobin concentrations there was a significant reduction in the prevalence (PR=0.90, 95% confidence interval (CI): 0.83 ‐ 0.97) and incidence (Odds Ratio (OR)=0.85, 95% CI: 0.77‐0.92) of depression. Moderate anemia was also a strong predictor of depression (OR=8.24, 95% CI: 1.45 ‐ 47.02). None of the multiplicative interactions by sex, research sites, or cognitive function were statistically significant. Conclusion In international samples of older adults, hemoglobin concentrations, as well as the severity of anemia, were independent risk factors for depression. References: 1‐ Guralnik JM, Eisenstaedt RS, Ferrucci L, Klein HG, Woodman RC. Prevalence of anemia in persons 65 years and older in the United States: evidence for a high rate of unexplained anemia. Blood. 2004;104(8):2263‐2268.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".