Assessing the Cost-Effectiveness of Measles Elimination in Uganda: Local Impact of a Global Eradication Program
Bibliographic record
Abstract
BACKGROUND: Measles control has succeeded worldwide, and many countries have substantially reduced incidence and mortality. This has led to consideration of the feasibility of measles elimination in Uganda within the context of global eradication. Before an elimination program is initiated, it is important to consider its potential economic impact, including its cost-effectiveness. METHODS: Incremental cost-effectiveness ratios (ICERs) were estimated for measles mortality reduction and measles elimination in Uganda. A dynamic age-structured compartmental model of measles transmission was used to simulate scenarios and estimate health outcomes and costs. The main outcome measures were costs, measles cases, measles deaths, disability-adjusted life-years (DALYs), and ICERs measured as cost per DALY averted through either the year 2030 or 2050. RESULTS: Measles elimination by 2020 averted 130,232 measles cases, 3520 measles deaths, and 106,330 DALYs through the year 2030, compared with the next best scenario (95% mortality reduction by 2015), and it was the most cost-effective strategy, with ICERs of $556 per DALY averted (2030 time horizon) and $284 per DALY averted (2050 time horizon). CONCLUSIONS: Measles elimination in Uganda, as part of a global eradication program, is projected to be highly cost-effective and should be considered among the available policy options for dealing with the disease.
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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 | 0.001 | 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) | 0.000 | 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".