Economic Burden and Cost-Effectiveness of Management of Non-Infectious Uveitis: A Systematic Review
Bibliographic record
Abstract
PURPOSE: To evaluate the economic burden and cost-effectiveness of interventions and management of non-infectious uveitis (NIU). METHODS: A comprehensive search was conducted across Medline, Embase, and Scopus databases from inception to March 2023. Risk of bias assessments were conducted using the Joanna Briggs Institute critical appraisal tools. RESULTS: A total of 24 articles consisting of 16 economic burden studies (67%) and 9 cost-effectiveness or cost-utility studies (38%) met the inclusion criteria. Annual direct medical costs ranged from $16,428 to $134,135 USD 2023, with costs being 4.3 times higher for those with blindness compared to those without vision loss. Direct medical costs for corticosteroid, immunosuppressive, and biologic therapies were $19,497, $29.979, and $45,830, respectively. Indirect costs ranged from $806 to $57,170, with costs being 2.1 times higher for persistent NIU and 2.3 times higher for those with blindness. Annual medication and intervention costs ranged from $345 to $13,134, with prescription drug costs being 60% higher for blind patients compared to those with moderate vision loss. Overall, cost-effectiveness analyses show promise for treatments like adalimumab and certain implants, though the extent of economic benefit depends on price reductions and healthcare system variations. Varying parameters like willingness-to-pay (WTP) thresholds and input parameters further complicated comparability. CONCLUSIONS: NIU poses a significant economic impact, particularly in patients with blindness and those on advanced therapies. While evidence is growing in Western countries like the US and UK, further research in non-westernized countries is warranted for a comprehensive, global understanding of the disease's economic burden.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".