The Use of Cost‐Effectiveness Analysis for Pediatric Immunization in Developing Countries
Bibliographic record
Abstract
CONTEXT: Developing countries face critical choices for introducing needed, effective, but expensive new vaccines, especially given the accelerated need to decrease the mortality of children under age five and the increased immunization resources available from international donors. Cost-effectiveness analysis (CEA) is a tool that decision makers can use for efficiently allocating expanding resources. Its use in developing countries, however, lags behind that in industrialized countries. METHODS: We explored how CEA could be made more relevant to immunization policymaking in developing countries by identifying the limitations for using CEA in developing countries and the impact of donor funding on the CEA estimation. We conducted a comprehensive literature search using formal search protocols and hand searching indexed and gray literature sources. We then systematically summarized the application of CEA in industrialized and developing countries through thematic analysis, focusing on pediatric immunization and methodological and contextual issues relevant to developing countries. FINDINGS: Industrialized and developing countries use CEA differently. The use of the Disability-Adjusted Life Year (DALY) outcome measure and an alternative generalized cost-effectiveness analysis approach is restricted to developing countries. In pediatric CEAs, the paucity of evaluations and the lack of attention to overcoming the methodological limitations pertinent to children's cognitive and development distinctiveness, such as discounting and preference characterization, means that pediatric interventions may be systematically understudied and undervalued. The ability to generate high-quality CEA evidence in child health is further threatened by an inadequate consideration of the impact of donor funding (such as GAVI immunization funding) on measurement uncertainty and the determination of opportunity cost. CONCLUSIONS: Greater attention to pediatric interventions and donor funding in the conduct of CEA could lead to better policies and thus more worthwhile and good-value programs to benefit children's health in developing countries.
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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 | 0.012 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".