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Record W3211765601 · doi:10.1182/blood-2021-151271

Cost of Care of Rare Hematologic Disorders in the United States: Call to Action for Policy Supporting Pharmacologic Innovation

2021· article· en· W3211765601 on OpenAlexaboutno aff
Pedro Andreu Perez, Gina Cioffi, Jenny Karam, Caroline Child, Fernando Tricta, Giacomo Chiesi

Bibliographic record

VenueBlood · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPorphyrin Metabolism and Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineIndirect costsDiseaseThalassemiaSpecialtyHealth carePediatricsIntensive care medicineFamily medicineInternal medicine

Abstract

fetched live from OpenAlex

Abstract BACKGROUND: Rare diseases (RD) present a societal concern because of lack of treatment availability and difficulty developing new treatments. Even when treatment options exist, there are considerable barriers to diagnosis and access to specialty care. OBJECTIVES: To estimate direct (attributable to patient care), indirect (patients' and caregivers' loss of productivity), and mortality-related costs of 5 rare hematologic disorders (atypical hemolytic uremic syndrome [aHUS], acute intermittent porphyria, acquired aplastic anemia, beta thalassemia major, and sickle cell disease) and evaluate burden of care, both when treatment is available and when no treatment exists. We compared these costs with mass market (MM) diseases, including a common hematologic disorder, deep vein thrombosis (DVT), to highlight the need to better serve RD individuals. METHODS: We evaluated peer-reviewed published articles and databases (e.g., Orphanet, the Genetic and Rare Disease Information Center, NORD, NIH), conducted interviews with patient advocacy groups (e.g., Global Gene, the EveryLife Foundation for Rare Diseases, NORD) and key opinion leaders (e.g., Penn Blood Disorders Center), and referred to the US Bureau of Labor Statistics and Medi-Span Price Rx. We performed a statistical analysis to confirm that the sample size of patients covered in our disease selection was significant. In-depth analyses were performed to assess the per patient per year (PPPY) direct, indirect, and mortality costs associated with the 5 disorders, as well as costs for MM diseases, including DVT. While treatments exist for each of these 5 rare disorders, there are no universal curative options. Cost data for MM diseases, including DVT, were derived from literature reviews. RESULTS: The economic burden of rare hematologic disorders in the US is considerable. For most diseases, treatment costs account for the majority of total direct costs (52-90%). Indirect and mortality costs account for 4% and 22% of the total burden cost, respectively, but mortality costs vary widely (4-74% of total costs). Highest overall direct cost observed was for aHUS ($530k) due to challenging diagnosis, persistent treatment, and poor prognosis. Productivity loss is 2 hours/week for patients and 2-4 hours/week for caregivers. The life expectancy of aHUS patients is ~60 years, but if untreated this may be shortened to ~35 years. The lowest overall direct cost was for beta thalassemia major ($69k). The majority of direct costs are split between treatment costs and medical procedures. Patient productivity loss is estimated to be >3.5 weeks of work loss/year in the 60% of patients who require bimonthly transfusions. Caregiver burden constitutes 9.2 hours/week of work loss. New therapies are likely to offset mortality costs in the future. Although the direct, indirect, and mortality costs of these 5 disorders are high (average total cost $228k), the burden of cost is higher in all scenarios if treatments did not exist (60% increase in overall cost). As would be expected under the "no treatment" scenarios, the direct costs attributed to each disease decreased, but indirect and mortality costs increased. Value of treatment is demonstrated by decreases in PPPY indirect costs. When no treatments were available, the range for productivity loss was ~$33k to $61k for patients and ~$25k to $61k for caregivers, compared with ~$3k to $22k for patients and ~$4k to $5k for caregivers when treatments were available. The average PPPY costs of MM diseases for which treatments are available, including DVT, are estimated to be between $6k to $27k for direct costs, $10k to $16k for indirect costs, and $3k to $24k for mortality costs. In comparison with MM diseases, including DVT, the 5 rare disorders had average direct costs of ~$169k (a 6.25- to 26-fold increase), indirect costs of ~$9k (a modest decrease), and mortality costs of ~$50k (a 2.1- to 15-fold increase). CONCLUSIONS: These scenario analyses demonstrate that RD therapies generate positive economic value. Further, analysis shows that RD pose a greater social economic burden than MM diseases. This information can be utilized to further efforts by the RD community for increased governmental investment in RD treatment, diagnosis, and access. Disclosures Andreu Perez: Chiesi Global Rare Diseases: Other: PA is a full-time employee IQVIA. The employer of PA received consulting fees from Chiesi Global Rare Diseases for this analysis. Cioffi: Chiesi Global Rare Diseases: Current Employment. Karam: Chiesi Global Rare Diseases: Other: JK is a full-time employee IQVIA. The employer of JK received consulting fees from Chiesi Global Rare Diseases for this analysis. Child: Chiesi Global Rare Diseases: Other: CC is a full-time employee IQVIA. The employer of CC received consulting fees from Chiesi Global Rare Diseases for this analysis. Tricta: Chiesi Canada Corp: Current Employment. Chiesi: Chiesi Farmaceutici SpA: Current Employment.

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)0.032
metaresearch head score (Gemma)0.100
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.043
Threshold uncertainty score0.169

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0320.100
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0030.003
Science and technology studies0.0020.003
Scholarly communication0.0120.007
Open science0.0040.004
Research integrity0.0140.010
Insufficient payload (model declined to judge)0.0110.001

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 head0.025
GPT teacher head0.345
Teacher spread0.319 · 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 designNot applicable
Domainnot available
GenreCommentary

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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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