Bridging the gap? Local production of medicines on the national essential medicine lists of Kenya, Tanzania and Uganda
Bibliographic record
Abstract
BACKGROUND: Essential medicines (EMs) are those that satisfy the basic healthcare needs of the population. However, access to EMs remains a global health challenge. The World Health Organization (WHO) and the East African Community (EAC) manufacturing plan 2017-2027 support local production of EMs as a strategy to improve access to medicines. The aim of this study was to determine for each therapeutic class on the national essential medicine lists (NEMLs) of Kenya, Tanzania and Uganda, the number of EMs produced in each country. METHODS: In 2018, we analysed NEMLs and national drug registers (NDRs) in each country to identify local manufacturers and local products by EM status. For each local manufacturer we determined the number of EM products and individual EMs, and analysed EMs in each therapeutic class by registration status and whether produced locally. RESULTS: There were nine companies manufacturing locally in Kenya, four in Tanzania and six in Uganda. Most local medicine products were non-EM products. Of the 946 locally produced products in Kenya, 310 were EM products; of the 97 locally produced products in Tanzania, 39 were EM products; and of the 181 locally produced products in Uganda, 100 were EM products. Many local EM products were duplicate. Only a small proportion of EMs on each NEML were produced locally: 21% (92/430) in Kenya, 5% (24/510) in Tanzania, and 10% (55/526) in Uganda. Kenya, Tanzania and Uganda had no local EM products in 13/32, 17/28 and 15/32 therapeutic classes, respectively. The proportion of EMs that were registered varied across the countries from 327 (76%) in Kenya, 269 (53%) in Tanzania, and 319 (60%) in Uganda. CONCLUSIONS: This study highlights the importance of auditing NDRs and NEMLs for local production to inform regional and national local manufacturing strategies. EMs should be prioritized for local production and drug registration to ensure that production is aligned with local health needs.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".