Anemia in the Elderly and the Risk of Injurious Falls.
Bibliographic record
Abstract
Abstract Background: Anemia commonly occurs in the elderly (≥65), and has been associated with a number of adverse consequences. Thirty percent of the community-dwelling elderly fall annually and this risk increases to 50% by the age of 80. Serious injuries caused by a fall, such as fractures and head injuries, are sustained by about 10% of the elderly and often lead to functional disability, increased health care costs, and increased mortality. Identification of reversible risk factors is critical for the management of falls and related injuries. The purpose of the current study is to investigate whether anemia increases the risk of injurious falls (IF) in the elderly. Methods: Health claims data from over 30 health plans from 01/1999 through 04/2004 were used. Patients ≥65 years with ≥1 hemoglobin (Hb) measurement were selected. IF were defined as a fall claim followed by an injurious event claim within 30 days after the fall. Injurious events were defined as fractures of the hip, pelvis, femur, vertebrae, ribs, humerus, and lower limbs, Colle’s fracture, head injuries, or hematomas. An open-cohort design was employed to classify patients’ observation periods by: (1) by anemia status based on WHO criteria (< 12 g/dL for women; < 13 g/dL for men), and (2) by Hb level: <10, 10-<12, 12-<13, and ≥13 g/dL. The incidence rates (IF events / person-years of observation) were compared by anemia status and Hb levels, respectively. Subset analyses based on IF of the hip (including pelvis and femur) and the head were further conducted. The association of IF with anemia and Hb levels, respectively, was analyzed using both univariate and multivariate (adjusted for age, gender, health plan, comorbidities, concomitant medications) approaches. Results: Among the 47,530 study subjects, a statistically significant linear trend of increasing risk of falls (i.e., IF and non-IF events) with decreasing Hb was observed (p<.0001). The incidence of IF was 15.8, 14.0, 9.8, and 6.5 per 1,000 person-years for Hb levels of <10, 10-<12, 12-<13, and ≥13 g/dL, respectively (trend: p<.0001). Based on the univariate analysis, anemia increased the risk of IF by 1.66 times (95% CI: 1.41–1.95) compared to no anemia, and the effects of anemia on IF of the hip and head were more pronounced (rate ratio (RR)=2.25 [95% CI: 1.74–2.89] and 1.77 [95% CI: 1.22–2.55], respectively, (p<.01 for both)). Multivariate analysis revealed that Hb levels were significantly associated with the risk of IF (RR = 1.57, 1.48, 1.17 for Hb levels of <10, 10-<12, 12-<13 g/dL, respectively, compared to Hb≥ 13 g/dL), and the negative linear trend of the risk of IF by Hb levels remained statistically significant (p<.0001). In the subset of hip and head IF, the association with anemia was even stronger (Hip: RR=3.37, 1.83, 1.36 for Hb levels of <10, 10-<12, 12-<13 g/dL, respectively; Head: RR=1.65, 1.47, 1.18, respectively), with a statistically significant linear trend observed (Hip: p<.0001; Head: p=0.07). Anemia (esp. Hb < 10) had comparable risk to other well-known risk factors for falls such as Alzheimer’s disease, Parkinson’s disease, and osteoarthritis. Conclusion: Anemia was significantly and independently associated with an increasing risk for IF, especially IF to the hip and head, in elderly persons. Furthermore, the risk of IF increased as the anemia worsened. The impact of anemia correction on the risk of falls and IF needs to be evaluated.
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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 | 0.003 |
| 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 | 0.000 | 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".