Cost-of-illness studies of inherited retinal diseases: a systematic review
Bibliographic record
Abstract
BACKGROUND: While health care and societal costs are routinely modelled for most diseases, there is a paucity of comprehensive data and cost-of-illness (COI) studies for inherited retinal diseases (IRDs). This lack of data can lead to underfunding or misallocation of resources. A comprehensive understanding of the COI of IRDs would assist governmental and healthcare leaders in determining optimal resource allocation, prioritizing funding for research, treatment, and support services for these patients. METHODS: Following PRISMA guidelines, a literature search was conducted using Medline, EMBASE and Cochrane databases, from database inception up to 30 Jun 2023, to identify COI studies related to IRD. Original studies in English, primarily including patients with IRDs, and whose main study objective was the estimation of the costs of IRDs and had sufficiently detailed methodology to assess study quality were eligible for inclusion. To enable comparison across countries and studies, all annual costs were standardized to US dollars, adjusted for inflation to reflect their current value and recalculated on a "per patient" basis wherever possible. The review protocol was registered in PROSPERO (registration number CRD42023452986). RESULTS: A total of nine studies were included in the final stage of systematic review and they consistently demonstrated a significant disease burden associated with IRDs. In Singapore, the mean total cost per patient was roughly US$6926/year. In Japan, the mean total cost per patient was US$20,833/year. In the UK, the mean total cost per patient with IRD ranged from US$21,658 to US$36,549/year. In contrast, in the US, the mean total per-patient costs for IRDs ranged from about US$33,017 to US$186,051 per year. In Canada, these mean total per-patient costs varied between US$16,470 and US$275,045/year. Non-health costs constituted the overwhelming majority of costs as compared to healthcare costs; 87-98% of the total costs were due to non-health costs, which could be attributed to diminished quality of life, poverty, and increased informal caregiving needs for affected individuals. CONCLUSION: IRDs impose a disproportionate societal burden outside health systems. It is vital for continued funding into IRD research, and governments should incorporate societal costs in the evaluation of cost-effectiveness for forthcoming IRD interventions, including genomic testing and targeted therapies.
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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 | 0.013 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".