Medical and Non-medical Costs of Sickle Cell Disease and Treatments from a US Perspective: A Systematic Review and Landscape Analysis
Bibliographic record
Abstract
BACKGROUND: Sickle cell disease (SCD) is a complex genetic disorder that manifests in infancy and progresses throughout life in the form of acute and chronic complications. As the upfront costs of potentially curative, genetic therapies will likely be high, an assessment and comprehensive characterization of the medical and non-medical cost burden will inform future decision making. OBJECTIVE: We sought to systematically summarize the existing literature surrounding SCD medical and non-medical costs. METHODS: We searched MEDLINE and EMBASE (2008-2020) and identified US-based studies that detailed medical or non-medical costs. Eligible studies provided empirical estimates about any aspect of cost or SCD individuals of all ages and their caregivers. Study quality was assessed using the Newcastle-Ottawa Scale, and costs were adjusted to 2019 US$. RESULTS: Search queries returned 479 studies, with 342 from medical burden searches and 137 from non-medical burden searches, respectively. Herein, we report the results of the 40 studies that contained relevant cost information: 39 detailed medical costs and 1 detailed non-medical costs. Costs were higher for SCD patients when compared with non-SCD individuals (cost difference range: $6636-$63,436 annually). The highest medical cost component for SCD patients was inpatient ($11,978-$59,851 annually), followed by outpatient and then pharmacy. No studies characterized the cost burden throughout the lifetime disease trajectory of an SCD individual, and no studies captured caregiver or productivity costs. CONCLUSION: Our results reveal an incomplete characterization of medical and non-medical costs within SCD. A deeper understanding of the medical and non-medical cost burden requires completion of additional studies that capture the burden across the patient's lifetime, in addition to expression of the impact of existing and emergent health technologies on disease trajectory.
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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 | 0.009 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.013 |
| Bibliometrics | 0.026 | 0.031 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".