Health care without medicine: the impact of war on Sudan’s pharmaceutical manufacturing and supply
Bibliographic record
Abstract
BACKGROUND: Armed conflict in Sudan since April 2023 has led to the widespread disruption of health care services, the destruction of public and private health care infrastructure nationwide, and the targeting of health care personnel. This study examines the impact of the conflict on a less publicized sector of Sudan's health system: the national pharmaceutical manufacturing and supply chain. METHODS: Research interviews were conducted with representatives of 4 companies engaged in pharmaceutical manufacturing and/or supply in Sudan, in addition to a review of primary information from medical regulators and industry associations. Based on these interviews, 3 case studies are presented; insights from the fourth interview inform the study overall. RESULTS: All 4 companies reported significant impacts on their operations, as well as on other companies in the pharmaceutical sector. Domestic pharmaceutical manufacturing has effectively come to a halt, leading to the non-availability of certain drugs in parts of the country. Manufacturers have shifted course to focus on imports, but heavy losses of domestically stored inventory and an inability to distribute medicines to large parts of the country have had a significant impact on pharmaceutical supply to retail pharmacies, public and private sector health facilities, and the National Medical Supplies Fund (NMSF). CONCLUSIONS: Sudan's pharmaceutical manufacturing industry has been devasted. Although less visible than hospitals and medical personnel, the effects on the pharmaceutical sector are consequential for Sudan's health sector, particularly for chronic conditions.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".