General population versus disease-specific event rate and cost estimates: potential bias for economic appraisals
Bibliographic record
Abstract
Economic appraisals are increasingly being used for reimbursement decision making. Differences exist in the population data sources used in different studies and these differences may result in errors or biased estimates. A review of the literature suggests that very little has been written on this topic and guidelines and good practice documents are silent on the issue. Using illustrative examples, it was found that the population chosen for event/complication costing did not have a large impact on a cost-effectiveness analysis; however, the choice of population did have a large impact for cost-of-illness (COI) estimation. It was found that not controlling for event/complication rates in a nondiseased population resulted in a 15% inflated COI estimate and using event costs from the general population underestimated COI by 20-32%. Our analysis suggests that using event costs from the general population instead of a diseased population may not have a significant impact on cost-effectiveness estimates; however, COI studies should only use excess event/complication rates and should also only use event costs from populations with the disease.
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 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 | 0.436 | 0.712 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.008 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".