Evaluating the impact of population-based and cohort-based models in cost-effectiveness analysis: a case study of pneumococcal conjugate vaccines in infants in Germany
Bibliographic record
Abstract
OBJECTIVE: The objective of this analysis is to evaluate the impact of model choice (closed single-cohort versus population-based) in cost-effectiveness analysis (CEA) using pneumococcal conjugate vaccines (PCVs) in infants in Germany as a case study. METHODS: Two Markov models were developed: one with a closed single-cohort model and one with a population-based model. Except for the design of the modelled population/cohort, all other inputs and characteristics were kept identical between the models. Comparators included PCV20 under a 3 + 1 vaccination schedule versus PCV13 and PCV15 under a 2 + 1 vaccination schedule. Health and economic outcomes were compared between the two models. RESULTS: The population-based model demonstrated that PCV20 was cost-saving and provided better health outcomes compared to both PCV13 and PCV15, indicating PCV20 as the dominant strategy with negative ICERs per QALY. In contrast, the closed single-cohort model showed PCV20 was associated with higher total costs compared to PCV13 and PCV15. CONCLUSION: This analysis highlights the importance of accurately identifying the relevant population when conducting CEAs of vaccines. This is particularly crucial when a vaccine produces indirect effects in individuals who are not directly vaccinated, as this otherwise leads to an underestimation of cost-effectiveness.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".