Reaching the London Declaration on Neglected Tropical Diseases Goals for Onchocerciasis: An Economic Evaluation of Increasing the Frequency of Ivermectin Treatment in Africa
Bibliographic record
Abstract
BACKGROUND: Recently, there has been a shift in onchocerciasis control policy, changing from prevention of morbidity toward elimination of infection. Switching from annual to biannual ivermectin distribution may accelerate progress toward the elimination goals. However, the settings where this strategy would be cost effective in Africa have not been described. METHODS: An onchocerciasis transmission framework (EpiOncho) was coupled to a disease model in order to explore the impact on disability-adjusted life years averted, program cost, and program duration of biannual ivermectin treatment in different epidemiological and programmatic scenarios in African savannah. RESULTS: While biannual treatment yields only small additional health gains, its benefit is pronounced in the context of the elimination goals, shortening the time frames for and increasing the feasibility of reaching the proposed operational thresholds for stopping treatment. In settings with high precontrol endemicity (and/or poor coverage and compliance), it may not be possible to reach such thresholds even within 50 years of annual ivermectin, requiring adoption of biannual treatment. Our projections highlight the crucial role played by coverage and compliance in achieving the elimination goals. CONCLUSIONS: Biannual ivermectin treatment improves the chances of reaching the 2020/2025 elimination goals, potentially generating programmatic cost savings in settings with high precontrol endemicity. However, its benefit and cost are highly sensitive to levels of systematic noncompliance and, in many settings, it will lead to an increase in costs. Furthermore, it may not always be feasible to implement biannual treatment, particularly in hard-to-reach populations. This highlights the continued need for a macrofilaricide.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".