International comparison of cost of\nillness
Bibliographic record
Abstract
All Western countries spent every year a lot of money on health care. \nCost of illness (COI) studies describe how health care costs are related\nto epidemiological and demographic variables. This report compares\nCOI-studies for some European and OECD countries as the Netherlands,\nGermany, France, Canada and Australia. It is demonstrated that\nCOI-studies can help to explain international differences in health\nexpenditure. It is also shown that acute care costs for major disease\ngroups are more or less the same in the different countries. Comparisons\nof long term care expenditure were hampered by country specific\ndefinitions and provisions. This report argues that cost of illness\nstudies can be useful: 1) to identify cross-national differences in\nhealth expenditure; 2) to monitor the cost development between\ncountries; 3) to investigate the effect of health care reforms from the\nperspective of disease, age and gender. The availability of appropriate\ndata is a critical condition here. International standardization of\ndata, classifications and methods is important, as well as for\nexpenditure data as with regard to utilization data and the allocation of\ncosts to disease, age and gender. A common approach will result in\nbetter cost of illness figures that serve the national and international\ndebate on health and health expenditure with a deeper understanding of\nthe interrelationships between demand and supply of health\ncare.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".