Temporal trends in the epidemiology of inflammatory bowel diseases in the public healthcare system in Brazil: A large population-based study
Bibliographic record
Abstract
Background: Population-based data on epidemiology of Inflammatory Bowel Diseases (IBD) in Brazil are scarce. This study aims to define temporal trends of incidence and prevalence rates of Crohn's disease (CD) and ulcerative colitis (UC) in Brazil. Methods: All IBD patients from the public healthcare national system were included from January 2012 to December 2020. Average Annual Percent Change (AAPC) and 95% confidence intervals (CI) were calculated using log-linear regression for incidence and binomial regression for prevalence. Moran's I autocorrelation index was used to analyse clustering of cities by level of prevalence. Findings: A total of 212,026 IBD patients were included. Incidence of IBD rose from 9.4 in 2012 to 9.6 per 100,000 in 2020 (AAPC=0.8%; 95% CI -0.37, 1.99); for UC, incidence increased from 5.7 to 6.9 per 100,000 (AAPC=3.0%; 95% CI 1.51, 4.58) and for CD incidence decreased from 3.7 to 2.7 per 100,000 (AAPC=-3.2%; 95% CI -4.45, -2.02). Prevalence of IBD increased from 30.0 in 2012 to 100.1 per 100,000 in 2020 (AAPC=14.8%; CI 14.78-14.95); for UC, from 15.7 to 56.5 per 100,000 (AAPC=16.0%; CI 15.94, 16.17); for CD from 12.6 to 33.7 per 100,000 (AAPC=12.1% CI 11.95, 12.02). A south-north gradient was observed in 2020 prevalence rates of IBD [I=0.40 (p<0.0001)], CD [I=0.22 (p<0.0001)] and UC [I=0.42 (p<0.0001)]. Interpretation: Incidence of CD is decreasing whereas of UC is increasing, leading to stabilization in the incidence of IBD from 2012 to 2020 in Brazil. Prevalence of IBD has been climbing with 0.1% of Brazilians living with IBD in 2020. Funding: None.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".