Variability in the incidence of renal replacement therapy over time in Western industrialized countries: A retrospective registry analysis
Bibliographic record
Abstract
INTRODUCTION: A growing number of patients started renal replacement therapy (RRT) in Western industrialized countries between 1980 an early 2000s. Thereafter reports from national and international registries suggest a trend towards stabilization and sometimes a decrease in the incidence rate. AIM: To investigate the differences in overall and age-specific incidence rates between industrialized countries from 1998 until 2013. Secondly, to investigate changes in incidence rates over time and their association with specific age categories. METHOD: We extracted the unadjusted overall incidence of RRT and age-specific incidence rates from renal registry reports in Europe, the United States, Canada, Australia and New Zealand. Time trends in the incidence rate by country and age categories were analyzed by Joinpoint regression analysis. RESULTS: The incidence rate in 2013 ranged from 89 per million population (pmp) in Finland to 363 pmp in the US. Incidence rates in the lower age categories (20-64 year) were similar between countries and remained stable over time. Higher incidence countries were characterized by higher numbers of patients in both the 65-74 and ≥75 year categories starting RRT. Joinpoint analysis confirmed that most countries had significant reductions in the incidence rate at the end of the study period. These reductions were explained by lower numbers of older patients starting RRT and were observed also in countries with lower overall incidence rates. CONCLUSION: This study confirmed different incidence rates of RRT between industrialized countries worldwide. Countries with the highest overall incidence rates also had the highest incidence rates in the oldest age categories. Since the early 2000's the number of older patients starting RRT is either stabilizing or even decreasing in most countries. This reduction is universal and is also observed in countries with previously low incidence rates.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".