The Impact of Inherited Retinal Diseases in the United States of America (US) and Canada from a Cost-of-Illness Perspective
Bibliographic record
Abstract
OBJECTIVE: To estimate the annual cost of inherited retinal diseases (IRDs) in the United States of America (US) and Canada from a societal perspective - including costs to the health system, individual and family productivity costs, lost wellbeing and other societal economic costs - by setting and payer. Findings will inform the need for policy action to mitigate the impact of IRDs. METHODS: The costs of IRDs were estimated using a cost-of-illness methodology, based on the prevalence of IRDs in each country. Intangible costs of reduced wellbeing were also estimated using disability-adjusted life years which were then converted to monetary values using the value of a statistical life. RESULTS: Using base prevalence rates, total costs attributable to IRDs in the US were estimated to range between US$13,414.0 and US$31,797.4 million in 2019, comprising both economic costs (between US$4,982 and US$11,753.9 million; 37% of total costs) and wellbeing costs (between US$8,431.7 and US$20,043.6 million; 63%). Total costs attributable to IRDs in Canada were estimated to range between CAN$1637.8 and CAN$6687.5 million in 2019, comprising both economic costs (between CAN$566.6 and CAN$2,305.7 million; 34%) and wellbeing costs (between CAN$1,071.4 and CAN$4,381.9 million; 66% of total costs). CONCLUSION: The impact of IRDs in the US and Canada is substantial when considering both economic costs and reduced wellbeing. The wellbeing costs due to IRDs in the US and Canada are considerable, accounting for over 60% of total costs. Vision loss from IRDs often manifests in childhood, meaning some people live with vision impairment and blindness for their whole lives. Further research into current and emerging cost-effective therapies and interventions is required given the substantial economic burden faced by those living with vision loss.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| 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".