Healthcare costs among patients with macular oedema associated with non-infectious uveitis: a US commercial payer’s perspective
Bibliographic record
Abstract
OBJECTIVE: To describe patient characteristics and healthcare costs associated with uveitic macular oedema (UME) in US clinical practices from a commercial payer's perspective. METHODS AND ANALYSIS: The IBM MarketScan Commercial Subset (1 October 2015-31 March 2020) was used to identify patients with non-infectious uveitis (NIU), with or without UME. Patients with UME at any time were further classified into subgroups of patients who received a UME diagnosis during the study period and those who received a UME diagnosis and local steroid injection (LSI) during the study period. Demographic and clinical characteristics, NIU-related treatments and healthcare costs were described for each cohort and subgroup during the most recent 12 months of continuous health plan enrolment. Healthcare costs were also described by vision status among all patients with NIU. RESULTS: A total of 36 322 patients with NIU were identified, of whom 3 301 (9.1%) had UME and 33 021 (90.9%) had no UME. Patients with UME more frequently received NIU-related treatment compared with those without UME (64.6% vs 45.0%), particularly LSI treatment (12.5% vs 0.7%). Mean total all-cause healthcare costs per-patient-per-year (PPPY) were higher among patients with UME ($19 851) than patients without UME ($16 188) and were especially high among those with bilateral UME ($24 162). Further, vision loss was more commonly observed in those with UME versus those without UME (5.7% vs 2.2%) and a trend of increasing healthcare costs with increasing vision loss was observed. CONCLUSION: NIU is associated with substantial clinical and economic burden, particularly when UME is present.
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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 | 0.004 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".