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Impact of Anemia on the Medical Cost of Injurious Falls in the Elderly.

2005· article· en· W2538424099 on OpenAlexaff
Patrick Lefèbvre, Samir H. Mody, Mei Sheng Duh, Richard C. Woodman, Sharon Buteau, Catherine Tak Piech

Bibliographic record

VenueBlood · 2005
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsGroup for Research in Decision Analysis
Fundersnot available
KeywordsMedicineAnemiaHip fracturePediatricsRetrospective cohort studyPhysical therapyOsteoporosisEmergency medicineSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background: Anemia is common in the elderly (≥65) and increases with age, reaching 26% in community dwelling (CD) men 85 yrs and older. Falls, especially those leading to serious injuries such as a fracture or head injury, affect about 10% of the CD elderly. The prevalence of anemia and the occurrence of falls is even higher in nursing home residents. Falls in the elderly are a significant cause of functional disability and significantly increase health care burden. This analysis was performed to determine if the presence of anemia significantly increases medical costs related to injurious falls (IF) in the CD elderly. Methods: A retrospective economic analysis of medical claims from over 30 health plans from 01/1999 through 04/2004 was conducted. Patients ≥65 years with ≥1 hemoglobin (Hb) reading were selected. An open-cohort design was employed to classify patients’ observation period into anemic and non-anemic periods. Anemia was defined as Hb<12 g/dL for women and Hb<13 g/dL for men based on the WHO criteria. IF were defined as a fall claim followed by an injurious event claim (fractures of the hip, pelvis, femur, vertebrae, ribs, humerus, and lower limbs, Colle’s fracture, head injuries, or hematomas) within 30 days after the fall. Anemia status was determined at the date of the IF and average monthly direct medical costs (outpatient, inpatient, and pharmacy costs in US dollars) were calculated for six months before and after the IF. A difference-in-difference approach was used to calculate the incremental costs and cost ratios of IF associated with anemia status. Results: 620 subjects with at least one IF were identified. Mean age for these patients was 76.3 ± 2.9 years; 70.2% were women. At the date of IF, 3%, 21%, 25%, and 51% of patients had Hb level of <10, 10–<12, 12–<13, and ≥ 13 g/dL, respectively. Table 1 shows the total medical costs in the pre-injurious and post-injurious fall periods by anemia status. Results indicate the difference in medical costs between patients with and without anemia increased significantly in the post-injurious fall period, compared to the pre-injurious fall period (incremental cost for anemia: $1,855; costs ratio: 3.7, p=0.030), with 90% due to increased costs for inpatient services ($1,675/patient/month) The economic impact of anemia in the subset of injurious falls of the hip was more pronounced (incremental cost: $2,811; costs ratio: 12.0, p=0.049). Conclusion: Anemia in the elderly is an important cost multiplier in the post-injurious fall period. Anemia contributes to an average increase of $1,855 and $2,811 per patient per month for all injurious falls and hip-specific injurious falls, respectively. It remains to be determined if anemia correction in the elderly will impact the medical costs associated with injurious falls. Table 1. Monthly Direct Healthcare Utilization Costs Pre-Injurious Fall Period Post-Injurious Fall period Inc. Cost {([C]−[D]) −([A]−[B])} Cost Ratio {([C]−[D]) /([A]−[B])} Anemia ([A]) Non-Anemia ([B]) Anemia ([C]) Non-Anemia [D] All IF $2,151 $1,467 $8,640 $6,101 $1,855 3.7 (p=0.030) Hip IF $1,753 $1,499 $12,446 $9,380 $2,811 12.0 (p=0.049)

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.014
GPT teacher head0.303
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2005
Admission routes1
Has abstractyes

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