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Record W4303650528 · doi:10.1186/s13023-022-02534-z

Historical and projected public spending on drugs for rare diseases in Canada between 2010 and 2025

2022· article· en· W4303650528 on OpenAlexaffabout
Richard Lech, Gideon Chow, Kamalpreet Mann, Patrick J. Mott, Christine Malmberg, Lindy Forte

Bibliographic record

VenueOrphanet Journal of Rare Diseases · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsEVERSANA (Canada)
Fundersnot available
KeywordsHuman geneticsOrphan drugPublic economicsBusinessEconomicsChemistryBiologyBioinformaticsBiochemistry

Abstract

fetched live from OpenAlex

OBJECTIVE: Rare diseases are life-threatening, debilitating, or serious chronic conditions that affect < 50/100,000 people. Canadians can only access approximately 60% of drugs for rare diseases (DRDs), which is partially related to high per-patient costs and payers' affordability concerns. However, limiting access to DRDs can reduce survival and quality of life among patients and caregivers. Therefore, we projected Canadian non-oncology DRD spending relative to total public drug spending to provide perspective for decision makers. METHODS: Candidate historical (2010-2020) and pipeline (2021-2025) Canadian-marketed non-oncology DRDs were identified using definitions from the European Medicines Agency and the US Food and Drug Administration databases. Inclusion and exclusion criteria were applied to identify eligible DRDs. Public payer claims data, prevalence rates, regulatory, and health technology assessment factors were used to project DRD spending in relation to total Canadian public drug spending. RESULTS: We included 42 historical DRDs and 122 pipeline DRDs. Public spending on DRDs grew from $14.8 million in 2010 (11 DRDs) to $380.9 million in 2020, then a projected $527.6 million in 2021 (59 potential DRDs) and $1.6 billion in 2025 (164 potential DRDs). Projected DRD spending increased from 3.2% of $16.5 billion public drug spending in 2021 to 8.3% of $19.4 billion in 2025. These projections do not include confidential manufacturer discounts, health outcome-related offsets, or additional safety-related costs. CONCLUSIONS: Projected DRD spending shows robust growth but remains a fraction of total public drug spending. Limiting DRD access because of this growth is not aligned with Canadian patient or societal values. Given the renewed interest in a Canadian DRD framework, our results may help guide discussions that aim to balance control of public drug spending with the well-being of patients with rare diseases.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.913
Threshold uncertainty score0.630

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.007
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.185
GPT teacher head0.355
Teacher spread0.169 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2022
Admission routes2
Has abstractyes

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