Reimbursement of Dialysis: A Comparison of Seven Countries
Bibliographic record
Abstract
Reimbursement for chronic dialysis consumes a substantial portion of healthcare costs for a relatively small proportion of the total population. Each country has a unique reimbursement system that attempts to control rising costs. Thus, comparing the reimbursement systems between countries might be helpful to find solutions to minimize costs to society without jeopardizing quality of treatment and outcomes. We conducted a survey of seven countries to compare crude reimbursement for various dialysis modalities and evaluated additional factors, such as inclusion of drugs or physician payments in the reimbursement package, adjustment in rates for specific patient subgroups, and pay for performance therapeutic thresholds. The comparison examines the United States, the province of Ontario in Canada, and five European countries (Belgium, France, Germany, The Netherlands, and the United Kingdom). Important differences between countries exist, resulting in as much as a 3.3-fold difference between highest and lowest reimbursement rates for chronic hemodialysis. Differences persist even when our data were adjusted for per capita gross domestic product. Reimbursement for peritoneal dialysis is lower in most countries except Germany and the United States. The United Kingdom is the only country that has implemented an incentive if patients use an arteriovenous fistula. Although home hemodialysis (prolonged or daily dialysis) allows greater flexibility and better patient outcomes, reimbursement is only incentivized in The Netherlands. Unfortunately, it is not yet clear that such differences save money or improve quality of care. Future research should focus on directly testing both outcomes.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".