Cost-Effectiveness of Hydroxyurea for Sickle Cell Anemia in a Low-Income African Setting: A Model-Based Evaluation of Two Dosing Regimens
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: The disease burden of sickle cell anemia (SCA) in sub-Saharan African (SSA) countries is substantial, with many children dying without an established diagnosis or proper treatment. The global burden of SCA is increasing each year, making therapeutic intervention a high priority. Hydroxyurea is the only disease-modifying therapy with proven feasibility and efficacy suitable for SSA; however, no one has quantified the health economic implications of its use. Therefore, from the perspective of the health care provider, we estimated the incremental cost-effectiveness of hydroxyurea as a fixed-dose regimen or maximum tolerated dose (MTD) regimen, versus SCA care without hydroxyurea. METHODS: We estimated the cost of providing outpatient treatment at a pediatric sickle cell clinic in Kampala, Uganda. These estimates were used in a discrete-event simulation model to project mean costs (2021 US$), disability-adjusted life years (DALYs), and consumption of blood products per patient (450 mL units), for patients between 9 months and 18 years of age. We calculated cost-effectiveness as the ratio of incremental costs over incremental DALYs averted, discounted at 3% annually. To test the robustness of our findings, and the impact of uncertainty, we conducted probabilistic and one-way sensitivity analyses, scenario analysis, and price threshold analyses. RESULTS: Hydroxyurea treatment averted an expected 1.37 DALYs and saved US$ 191 per patient if administered at the MTD, compared with SCA care without hydroxyurea. In comparison, hydroxyurea at a fixed dose averted 0.80 DALYs per patient at an incremental cost of US$ 2. The MTD strategy saved 11.2 (95% CI 11.1-11.4) units of blood per patient, compared with 9.1 (95% CI 9.0-9.2) units of blood per patient at the fixed-dose alternative. CONCLUSIONS: Hydroxyurea at MTD is likely to improve quality of life and reduce the consumption of blood products for children with SCA living in Uganda. Compared with a fixed dose regimen, treatment dosing at MTD is likely to be a cost-effective treatment for SCA, using realistic ranges of hydroxyurea costs that are relevant across SSA. Compared with no use of the drug, hydroxyurea could lead to substantial net savings per patient, while reducing the disease morbidity and mortality and increasing quality of life.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".