Impact and cost-effectiveness of measles vaccination through microarray patches in 70 low- and middle-income countries: a modelling study
Bibliographic record
Abstract
Abstract Background Microarray patches are a promising technology being developed to reduce barriers to vaccine delivery based on needles and syringes. To address the evidence gap on the public health value of applying this potential technology to immunisation programmes, we evaluated the health impact on measles burden and resulting cost-effectiveness of introducing measles-rubella microarray patches (MR-MAPs) in 70 low- and middle-income countries (LMICs). Methods We used an age-structured dynamic model of measles transmission and vaccination to project measles cases, deaths, and disability-adjusted life years during 2030⍰2040. Compared to the baseline scenarios with continuing current needle-based immunisation practice, we evaluated the introduction of MR-MAPs under different assumptions on measles vaccine coverage projections and MR-MAP introduction strategies. Costs were calculated based on the ingredients approach, including direct cost of measles treatment, vaccine procurement, and vaccine delivery. Model-based burden and cost estimates were derived for individual countries and country income groups. We compared the incremental cost-effectiveness ratios of introducing MR-MAPs to health opportunity costs. Results MR-MAPs introduction could prevent 27%⍰37% of measles burden between 2030⍰2040 in 70 LMICs. The largest health impact could be achieved under lower coverage projection and accelerated introduction strategy, with 39 million measles cases averted. Cost of measles treatment is a key driver of the net cost of introduction. In LMICs with a relatively higher income, introducing MR-MAPs could be a cost-saving intervention due to reduction in measles treatment costs. Compared to country-specific health opportunity costs, introducing MR-MAPs would be cost-effective in 16⍰81% of LMICs, depending on the MR-MAPs procurement price and vaccine coverage projections. Conclusions Introducing MR-MAPs in LMICs can be a cost-effective strategy to revitalise measles immunisation programmes with stagnant uptake and reach under-vaccinated children. Sustainable introduction and uptake of MR-MAPs has the potential to improve vaccine equity within and between countries and accelerate progress towards measles elimination.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".