Cost-effectiveness of introducing the pneumococcal conjugate vaccine for children under 5 years in the Islamic Republic of Iran
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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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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 it