DOP41 Temporal Trends in the epidemiology of Inflammatory Bowel Diseases in the public healthcare system in Brazil: A large population-based study
Bibliographic record
Abstract
Abstract Background Background: Population-based data on incidence and prevalence of Inflammatory Bowel Diseases (IBD) in newly industrialized countries such as Brazil are scarce. This study aims to define temporal trends of estimated incidence and prevalence rates of Crohn’s disease (CD) and ulcerative colitis (UC) in Brazil using unique public healthcare datasets. Methods Methods: All IBD patients (UC and CD) from the unique public healthcare national system (DATASUS) were included from January 2012 to December 2020 and identified according to ICD codes, medication use or IBD-relates procedures. Data extraction was performed with the platform “TT Disease Explorer” (Techtrials Healthcare Data Science, Brazil) and checked by 2 independent reviewers. The platform collects publicly available data from the ministry of health via electronic algorithms (ETLs and Webcrawlers) with automatic updates. The population of Brazil was calculated according to the national Brazilian Geographics and Statistics Institute (IBGE). Average Annual Percent Change (AAPC) and 95% confidence intervals (CI) were calculated using poisson (or negative binomial) regression for incidence and log binomial regression for prevalence. Results Results: A total of 212,026 IBD patients (UC: n=140,705; CD: n=92,326) were included, There was a higher proportion of females as opposed to males, and age at health system entry was similar to developed countries (figure 1). Estimated incidence rates of IBD were 9.41 per 100,000 in 2012 and 9.57 per 100,000 in 2020 (AAPC=0.80%; CI -0.37–1.99; p=0.18); for UC, incidence increased from 5.69 per 100,000 to 6.89 per 100,000 (AAPC=3.04; CI 1.51–4.58; p<0.001) and for CD incidence dropped from 3.71 per 100,000 to 2.68 per 100,000 (AAPC=-3.24%; CI -4.45- -2.02; p<0.001) in the same time period (figure 2). Estimated prevalence rates of IBD increased significantly from 30.01 per 100,000 in 2012 to 100.13 per 100,000 in 2020 (AAPC=14.87%; CI 14.78–14.95; p<0.001); For UC, from 17.4 per 100,000 to 66.45 per 100,000 (AAPC=16.51%; CI 16.41–16.62; p<0.001) and for CD from 14.24 per 100,000 to 43.6 per 100,000 (AAPC=13.49%; CI13.37-13.61; p<0.001) in the same time period (figure 3). Conclusion Conclusions: Estimated incidence rates of IBD have remained stable from 2012–2020. Incidence of CD is significantly decreasing whereas of UC is significantly increasing. There was a significant increase in estimated prevalence rates of CD and UC. This massive rise in prevalence can support planning for future strategies for public healthcare providers in our country towards better IBD care. This is the largest IBD epidemiological study from newly industrialized countries to date.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| 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) | 0.002 | 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".