Unmasking anemia in the elderly: A systematic review of prevalence, causes, and consequences
Bibliographic record
Abstract
Background: Anemia is a prevalent condition in the elderly (aged ≥65 years), associated with increased morbidity, mortality, and reduced quality of life. Despite its impact, comprehensive data on its prevalence, etiology, and outcomes in this population are limited. Objective: To synthesize evidence on the prevalence, etiology, and clinical outcomes of anemia in individuals aged 65 years and older, informing clinical practice and future research. Methods: A systematic search was conducted across PubMed, Scopus, Web of Science, and Google Scholar (2004-2024), identifying 47 studies (observational, cohort, cross-sectional, and review). Inclusion criteria focused on anemia in the elderly, reporting prevalence, etiology, or outcomes. Quality was assessed using the Newcastle-Ottawa Scale and AMSTAR 2. Narrative synthesis, supported by biostatistical analyses (pooled prevalence, hazard ratios [HRs], odds ratios, 95% confidence intervals [CIs]), was performed, with heterogeneity assessed via I² statistics. Results: Anemia prevalence ranged from 10% to 50% (pooled prevalence 27.8%, 95% CI: 22.1-33.5, I² =92%), higher in hospitalized patients (37.4%, 95% CI: 29.8-45.0) than community-dwelling elderly (23.1%, 95% CI: 18.2-28.0). Iron deficiency anemia (27.5%, 95% CI: 21.3-33.7), anemia of chronic disease (20.1%, 95% CI: 15.6-24.6), and unexplained anemia (34.2%, 95% CI: 28.0-40.4) were predominant. Anemia increased all-cause mortality (pooled HR: 2.85, 95% CI: 2.10-3.87, I² =78%) and cardiovascular events (pooled HR: 3.12, 95% CI: 2.34-4.16, I² =65%). Conclusion: Anemia in the elderly, particularly unexplained anemia, is a significant health burden, necessitating targeted diagnostic and therapeutic strategies.
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 | 0.010 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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 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".