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⚠️ Synthetic dataset — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference.
Anti-TB Medicine Quality in Sub-Saharan Africa
Abstract
Synthetic dataset modelling anti-TB medicine quality, rifampicin degradation, treatment outcomes, and MDR-TB emergence across DOTS programs, MDR-TB units, and informal markets in SSA. ~70% of quality reports detected falsified/substandard TB medicines; substandard rifampicin drives treatment failure and MDR-TB.
Parameterization Evidence
| Parameter | Value | Source | Year |
|---|---|---|---|
| ~70% reports detected SF TB/HIV medicines | Prevalence | IntechOpen | 2024 |
| Rifampicin most commonly SF anti-TB drug | Quality | WHO/Lancet ID | 2020 |
| SF rifampicin → treatment failure, MDR-TB | Resistance | Literature | 2022 |
| GDF procurement improves quality assurance | Supply | Stop TB Partnership | 2023 |
Validation
Usage
from datasets import load_dataset
ds = load_dataset("electricsheepafrica/anti-tb-medicine-quality", "dots_program_public")
References
- IntechOpen. Medicines quality in LMICs. 2024.
- WHO. TB drug quality and DOTS programmes. 2020.
- Stop TB Partnership/GDF. Quality-assured TB medicines. 2023.
Citation
@dataset{electricsheepafrica_anti_tb_medicine_quality_2025,
title={Anti-TB Medicine Quality in Sub-Saharan Africa},
author={Electric Sheep Africa},
year={2025},
publisher={HuggingFace},
url={https://huggingface.co/datasets/electricsheepafrica/anti-tb-medicine-quality}
}
License
CC-BY-4.0
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