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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".