Cost of illness: An international comparison Australia, Canada, France, Germany and The Netherlands
Bibliographic record
Abstract
Objectives To assess international comparability of general cost of illness (COI) studies and to examine the extent to which COI estimates differ and why.Methods Five general COI studies were examined. COI estimates were classified by health provider using the system of health accounts (SHA). Provider groups fully included in all studies and matching SHA estimates were selected to create a common data set. In order to explain cost differences descriptive analyses were carried out on a number of determinants.Results In general similar COI patterns emerged for these countries, despite their health care system differences. In addition to these similarities, certain significant disease-specific differences were found. Comparisons of nursing and residential care expenditure by disease showed major variation. Epidemiological explanations of differences were hardly found, whereas demographic differences were influential. Significant treatment variation appeared from hospital data.Conclusions A systematic analysis of COI data from different countries may assist in comparing health expenditure internationally. All cost data dimensions shed greater light on the effects of health care system differences within various aspects of health care. Still, the study's objectives can only be reached by a further improvement of the SHA, by international use of the SHA in COI studies and by a standardized methodology.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".