Slowing the spread of treatment failure to artemisinin-based combination therapies in Uganda
Bibliographic record
Abstract
Abstract Background The multiple emergences and continuing spread of partially artemisinin-resistant Plasmodium falciparum in Africa, where about 95% of malaria occurs, is a health challenge that requires urgent attention. The World Health Organization has developed a resistance response strategy that centers on enhancing surveillance, reducing drug pressure, and evaluating novel tools to slow resistance evolution which includes the deployment of multiple first-line therapies (MFT). Developing a specific resistance response is critical for Uganda, where four pfkelch13 mutations are at local allele frequencies >0.20. Methods Using a previously validated Uganda-calibrated individual-based mathematical model of P. falciparum transmission and evolution, we evaluated 53 public-sector deployment strategies for artemisinin-based combination therapies (ACTs) aimed at reducing treatment failure and slowing the spread of pfkelch13 alleles from 2025 to 2031. We assume that artemether-lumefantrine (AL) will continue to be used in the private sector. Results A change of first-line therapy from AL to artesunate-amodiaquine (ASAQ) is projected to reduce treatment failures by 34.7% to 38.3% (90% range of simulation outcomes) over six years, while a change to dihydroartemisinin-piperaquine (DHA-PPQ) is projected to reduce treatment failures over the same period by 10.0% to 12.9%. This pessimistic projection for DHA-PPQ deployment rests on a model assumption – supported by clinical data from SE Asia – that piperaquine resistance evolution will lead to high rates of treatment failure. Optimal MFT deployments and cycling approaches are projected to reduce treatment failure counts by ∼36% when compared to status quo AL use, an outcome similar to country-wide ASAQ deployment. MFT and cycling approaches are predicted to work best when ASAQ is recommended for a majority of malaria cases and DHA-PPQ for a smaller proportion of cases. Deployment of the triple ACT artemether-lumefantrine-amodiaquine has the potential to reduce treatment failures by ∼42% if enacted immediately. Conclusions Increased adoption of and coverage with ASAQ is projected to play a large role in reducing malaria treatment failure counts in Uganda over the next six years. With continued AL use in the private sector, ASAQ and DHA-PPQ deployment in the public sector creates a public-private MFT mix of antimalarial use. DHA-PPQ deployment should be accompanied by real-time molecular surveillance for piperaquine-resistant genotypes.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".