Trends in the Prevalence and Incidence of Chronic Dialysis in Brazil (2006–2024) With Projections to 2039
Bibliographic record
Abstract
INTRODUCTION: Brazil is the third country in the world with the highest number of dialysis patients. The increasing number of dialysis patients may reflect the state of renal care in the country. We analyzed and reported trends in the prevalence and incidence of chronic dialysis in Brazil since 2006, and projected estimates for 2039. METHODS: We compared trends in the prevalence and incidence rates of the Brazilian Dialysis annual Surveys since 2006. Monotonic trends were evaluated using the Mann-Kendall test, and forecasting trends were performed using classical and exponential smoothing techniques until 2039. FINDINGS: The incidence and prevalence rates of dialysis in Brazil have shown an upward trend over the past 19 years. Prevalence rates have increased across all five regions of the country. Regarding incidence rates, only the northeast region of Brazil did not show a significant upward trend over the period. The projected incidence and prevalence rates for 2039 are 338 patients per million population (95% CI: 306-370) and 1225 per million population (95% CI: 1179-1271), respectively. This represents an increase of 35.8% and 50.9% over 15 years, respectively. DISCUSSION: The projected rise in the incidence and prevalence of chronic dialysis in Brazil over the next decade is worrisome. It underscores the need for more vigorous measures to manage the burden of chronic kidney disease in the country, aiming to mitigate its impact on health care and associated costs.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".