Global Eradication of Measles: An Epidemiologic and Economic Evaluation
Bibliographic record
Abstract
BACKGROUND: Measles remains an important cause of morbidity and mortality in children in developing countries. Due to the success of the measles mortality reduction and elimination efforts thus far, the WHO has raised the question of whether global eradication of measles is economically feasible. METHODS: The cost-effectiveness of various measles mortality reduction and eradication scenarios was evaluated vis-à-vis the current mortality reduction goal in six countries and globally. Data collection on costs of measles vaccination were conducted in six countries in four regions: Bangladesh, Brazil, Colombia, Ethiopia, Tajikistan, and Uganda. The number of measles cases and deaths were projected from 2010 to 2050 using a dynamic, age-structured compartmental model. The incremental cost-effectiveness ratios were then calculated for each scenario vis a vis the baseline. RESULTS: Measles eradication by 2020 was the found to be the most cost-effective scenario, both in the six countries and globally. Eradicating measles by 2020 is projected to cost an additional discounted $7.8 billion and avert a discounted 346 million DALYs between 2010 and 2050. CONCLUSIONS: In conclusion, the study found that, compared to the baseline, reaching measles eradication by 2020 would be the most cost-effective measles mortality reduction scenario, both for the six countries and on a global basis.
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 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".