Real-World Healthcare Resource Utilization, Healthcare Costs, and Injurious Falls Among Elderly Patients with Geographic Atrophy
Bibliographic record
Abstract
Purpose: This study assessed the clinical and economic burden of geographic atrophy (GA) using real-world data from elderly patients with Medicare Advantage plans in the United States. Patients and Methods: A retrospective cohort design of patients with GA only, GA + visual impairment (GA + VI), GA + blindness (GA + B), and patients without GA were identified using administrative healthcare claims data from Optum Clinformatics Data Mart. Inverse probability of treatment weighting controlled for confounding when comparing patients with GA only vs without GA, GA + VI vs GA only, and GA + B vs GA only. Endpoints included all-cause and ophthalmic condition-related healthcare resource utilization (HRU), injurious falls, and healthcare costs. HRU and injurious falls were assessed per-person-per-year and per 100 person-years, respectively. Cohorts were compared using rate ratios, 95% confidence intervals (CIs), and p-values from weighted Poisson regression models. Healthcare costs were evaluated per-person-per-year using mean cost differences, 95% CIs, and p-values from weighted linear regression. Results: The study included 18,119 patients with GA only, 2,285 with GA + VI, 1,716 with GA + B, and 72,476 patients without GA. Higher rates of all-cause hospitalizations (RR [95% CI]: 1.08 [1.03, 1.12]), outpatient visits (1.08 [1.05, 1.10]), other visits (1.14 [1.08, 1.21]), and falls with head injuries (1.24 [1.05, 1.45]) were observed in patients with GA vs without GA (P<0.05). GA was associated with higher annual all-cause total healthcare costs, spending an average of $1,171 more after adjustment (P<0.05). Progression to GA + VI and GA + B was associated with a more pronounced burden. Conclusion: The clinical and economic burden of GA is substantial and escalates as the disease advances. These findings suggest early intervention aimed at slowing GA progression may help to mitigate the healthcare burden associated with advancement of GA to visual impairment and blindness.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".