Decolonizing global health: Africa’s pursuit of pharmaceutical sovereignty
Bibliographic record
Abstract
BACKGROUND: Africa's continued reliance on imported medicines, vaccines, and active pharmaceutical ingredients is the direct legacy of colonial extraction, intensified by the structural-adjustment era's dismantling of state-owned drug plants and cemented by intellectual-property regimes that keep critical know-how offshore. The COVID-19 vaccine scramble exposed the full cost of this vulnerability and has triggered a continent-wide push for pharmaceutical sovereignty-an explicit, decolonizing agenda to localize research, regulation, and production. This review distils the latest evidence on the barriers that still block that transition and maps the regulatory, financial, technological, and civic opportunities most likely to accelerate it. METHODOLOGY: A critical narrative literature review was conducted. Six databases (PubMed, Scopus, ProQuest, Google Scholar, BMJ Global Health and the Institute for Economic Justice repository) were searched for English-language records published January 2000-May 2025 using the terms Africa AND (pharmaceutic OR vaccine OR API) AND (sovereign OR manufactur OR decoloni). Grey literature from AU agencies, Africa CDC, WHO and UNIDO was added. Forty-five documents met inclusion criteria and were included in the article. Reflexive thematic analysis identified recurrent barriers and enabling pathways; intercoder reliability was ensured through independent coding and consensus meetings. RESULTS: Four structurally reinforcing barriers dominate the evidence base: (i) TRIPS-based patent exclusivities that restrict technology transfer; (ii) fragmented and immature regulatory capacity (iii) chronic under-investment; and (iv) import-biased procurement. The countervailing opportunities center on (i) AMA-led regulatory harmonization, (ii) pooled-demand instruments (iii) technology-transfer partnerships and (iv) civic-sector mobilization. CONCLUSION: Africa now possesses the regulatory blueprint, pooled-demand incentives, and emerging technology platforms to localize production of medicines and vaccines. However, its realization is dependent on synchronizing these levers by easing IP constraints, completing AMA-led regulatory convergence, mobilizing concessional finance for API and bulk-drug capacity, and reforming procurement to reward local value. If pursued in concert, these steps can convert pharmaceutical sovereignty from a political slogan into a resilient, continent-wide industrial reality- anchoring Africa's wider agenda to decolonize global health.
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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 | 0.026 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| 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".