Africa's largest long-lasting insecticide-treated net producer: lessons from A to Z Textiles
Bibliographic record
Abstract
BACKGROUND: Field trials have demonstrated the efficacy of insecticide-treated nets, and the WHO has recently endorsed a shift toward Long-Lasting Insecticide Treated nets (LLINs) due to factors such as reduced distribution costs. However, the need for LLINs poses several challenges. Is it possible to manufacture LLINs in large quantities in the African continent, where malaria is most endemic? When production is located in low-income countries, what role is played by local funding and employment, scaling up manufacturing, and partnerships? What factors influence availability and pricing? DISCUSSION: A case study of A to Z Textiles was undertaken to answer the question of how large-scale production of LLINs can occur in a low income setting. One of the largest sources of bed nets for Africa, A to Z Textiles is Africa-based, and its Tanzanian operations have a production capacity of 30 million LLINs per year, along with full WHO recommendation for its nets. Our analysis is based on semi-structured interviews with key informants familiar with A to Z, site visits in Tanzania, and literature reviews.This paper discusses the history and current status of A to Z Textiles, identifies the factors that led to its success, and suggests policy considerations that could support similar initiatives in the future. Local funding, scaling up manufacturing, technology transfer, and partnerships all played important roles in A to Z's ascent, as did perceived benefits of local employment and capacity-building. Regulatory issues and procurement rules acted as barriers. A to Z cost-effectively manufactures high-quality LLINs where malaria is most endemic. SUMMARY: With a production capacity of 30 million LLINs per year, and full WHOPES (WHO Pesticide Evaluation Scheme) certification, A to Z Textiles demonstrates how key health goods can be successfully produced in the low-income countries that use them. Its example may be instructive and of high interest to readers in the malaria community, especially in developing countries, and to those who wish to support or partner with efforts by developing countries to build their health innovation capacity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".