International comparison of cost ofillness
Bibliographic record
Abstract
All Western countries spent every year a lot of money on health care. Cost of illness (COI) studies describe how health care costs are related to epidemiological and demographic variables. This report compares COI-studies for some European and OECD countries as the Netherlands, Germany, France, Canada and Australia. It is demonstrated that COI-studies can help to explain international differences in health expenditure. It is also shown that acute care costs for major disease groups are more or less the same in the different countries. Comparisons of long term care expenditure were hampered by country specific definitions and provisions. This report argues that cost of illness studies can be useful: 1) to identify cross-national differences in health expenditure; 2) to monitor the cost development between countries; 3) to investigate the effect of health care reforms from the perspective of disease, age and gender. The availability of appropriate data is a critical condition here. International standardization of data, classifications and methods is important, as well as for expenditure data as with regard to utilization data and the allocation of costs to disease, age and gender. A common approach will result in better cost of illness figures that serve the national and international debate on health and health expenditure with a deeper understanding of the interrelationships between demand and supply of health care.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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; both teacher heads 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".