Cost-effectiveness of introducing the pneumococcal conjugate vaccine for children under 5 years in the Islamic Republic of Iran
Bibliographic record
Abstract
BACKGROUND: Pneumococcal disease caused by Streptococcus pneumoniae results in considerable mortality and morbidity. Pneumococcal conjugate vaccines (PCV), such as PCV-13, can prevent invasive pneumococcal disease and avoid disability and death. The cost of introducing PCV-13 in childhood immunization schedules should be assessed against the cost of pneumococcal diseases for each community. AIMS: This study aimed to evaluate the cost-effectiveness of introducing PCV-13 in the national immunization programme for children under 5 years in the Islamic Republic of Iran. METHODS: The TRIVAC decision support model was used to estimate total costs of introducing PCV-13 and the disability- adjusted life years (DALYs) averted. The main pneumococcal diseases were considered-pneumonia, meningitis, acute otitis media, and non-pneumonia, non-meningitis infections-in terms of hospital admissions, outpatient visits and deaths. Local data were used to estimate costs. RESULTS: Pneumococcal disease is estimated to affect 18 713 211 children under 5 years (519 412 pneumonia, 18 148 116 acute otitis media, 6884 meningitis, and 38 799 non-pneumonia, non-meningitis) in 10 years (2014-2023) without use of the vaccine. Introduction of PCV-13 would prevent 4 900 084 cases of pneumococcal disease (190 849 pneumonia, 4 692 450 acute otitis media, 2529 meningitis, and 14 256 non-pneumonia, non-meningitis). Pneumococcal infection would cause 287 950 hospital admissions and 29 399 deaths; vaccination could avert 105 802 hospital admissions and 9997 deaths. The incremental cost-effectiveness was estimated to be US$ 1890 and US$ 1538 per averted DALY for the government and society respectively. CONCLUSION: According to WHO-recommended thresholds for interpreting cost-effectiveness, introduction of PCV-13 for children under 5 years in the Islamic Republic of Iran would be cost-effective.
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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 | 0.002 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".