Health and economic impact of PHiD-CV in Canada and the UK: a Markov modelling exercise
Bibliographic record
Abstract
OBJECTIVE: The spectrum of diseases caused by Streptococcus pneumoniae and non-typeable Haemophilus influenzae (NTHi) represents a large burden on healthcare systems around the world. Meningitis, bacteraemia, community-acquired pneumonia (CAP), and acute otitis media (AOM) are vaccine-preventable infectious diseases that can have severe consequences. The health economic model presented here is intended to estimate the clinical and economic impact of vaccinating birth cohorts in Canada and the UK with the 10-valent, pneumococcal non-typeable Haemophilus influenzae protein D conjugate vaccine (PHiD-CV) compared with the newly licensed 13-valent pneumococcal conjugate vaccine (PCV-13). METHODS: The model described herein is a Markov cohort model built to simulate the epidemiological burden of pneumococcal- and NTHi-related diseases within birth cohorts in the UK and Canada. Base-case assumptions include estimates of vaccine efficacy and NTHi infection rates that are based on published literature. RESULTS: The model predicts that the two vaccines will provide a broadly similar impact on all-cause invasive disease and CAP under base-case assumptions. However, PHiD-CV is expected to provide a substantially greater reduction in AOM compared with PCV-13, offering additional savings of Canadian $9.0 million and £4.9 million in discounted direct medical costs in Canada and the UK, respectively. LIMITATIONS: The main limitations of the study are the difficulties in modelling indirect vaccine effects (herd effect and serotype replacement), the absence of PHiD-CV- and PCV-13-specific efficacy data and a lack of comprehensive NTHi surveillance data. Additional limitations relate to the fact that the transmission dynamics of pneumococcal serotypes have not been modelled, nor has antibiotic resistance been accounted for in this paper. CONCLUSION: This cost-effectiveness analysis suggests that, in Canada and the UK, PHiD-CV's potential to protect against NTHi infections could provide a greater impact on overall disease burden than the additional serotypes contained in PCV-13.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.006 | 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".