Urologic Cancer Drug Costs in Low- and Middle-Income Countries
Bibliographic record
Abstract
All 189 World Bank member countries are classified by their capita gross national income into one of four income groups. In this review, we aim to explore the economic burden and management of urologic oncology conditions in low- and middle-income countries (LMICs), emphasizing disparities and challenges in treatment access. The current World Bank classification system highlights economic stratification, showing significant health outcome disparities, particularly in urologic oncology conditions including kidney, bladder, and prostate cancer. First, this review focuses on the management of advanced prostate cancer in Asian LMICs, revealing higher mortality-to-incidence ratios and a greater prevalence of metastatic disease compared to high-income countries (HICs). The prohibitive costs of novel hormonal therapies (NHTs) like abiraterone and enzalutamide limit their use and exacerbate outcome disparities. Second, we review Wilms tumor treatment with chemotherapy in African countries, noting significant price variations for adapted and non-adapted regimens across different economic settings. The cost of chemotherapy agents, particularly dactinomycin, acts as a primary driver of treatment expenses, underscoring the economic challenges in providing high-quality care. Lastly, bladder cancer treatment costs in Brazil and Middle Eastern countries are examined, highlighting how detrimental the economic burden of intravesical therapies, like mitomycin C and Bacillus Calmette–Guérin (BCG), is on treatment accessibility. Overall, this literature review emphasizes the financial strain on healthcare systems and patients, particularly in regions facing economic instability and drug shortages, and underscores the need for international cooperation and effective resource allocation to address the economic barriers to urologic care in LMICs, aiming to improve health outcomes and ensure equitable access to advanced treatments.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".