Developing a Framework of Cost Elements of Socioeconomic Burden of Rare Disease: A Scoping Review
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Rare diseases place a significant burden on patients, families, the healthcare system, and society. Evidence on the socioeconomic burden of rare disease is limited and mostly reflects diseases where treatments are available. We developed a framework encompassing recommended cost elements for studies of the socioeconomic burden of rare diseases. METHODS: A scoping review, conducted in five databases (Cochrane Library, EconLit, Embase, MEDLINE, and APA PsycINFO), identified English language publications from 2000 to 2021 presenting frameworks developed for determining, measuring or valuing costs for rare or chronic diseases. Cost elements were extracted and used to develop a literature-informed framework. Structured feedback was gathered from experts in rare diseases, health economics/health services, and policy research to revise the framework. RESULTS: Of 2990 records identified, eight papers were included and informed our preliminary framework; three focused on rare disease and five on chronic disease. Following expert input, we developed a framework consisting of nine cost categories (inpatient, outpatient, community, healthcare products/goods, productivity/education, travel/accommodation, government benefits, family impacts, and other), with several cost elements within each category. Our framework includes unique costs, added from the expert feedback, including genetic testing to inform treatment, use of private laboratories or out-of-country testing, family involvement in foundations and organizations, and advocacy costs for special access programs. CONCLUSIONS: Our work is the first to identify a comprehensive list of cost elements for rare disease for use by researchers and policy makers to fully capture socioeconomic burden. Use of the framework will increase the quality and comparability of future studies. Future work should focus on measuring and valuing these costs through onset, diagnosis, and post-diagnosis.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".