IDDF2018-ABS-0034 High age-specific prevalence of inflammatory bowel disease amongst the elderly in the city of canada bay area, sydney: a metropolitan, population-based study
Bibliographic record
Abstract
Background Knowledge of Inflammatory Bowel Disease (IBD) prevalence allows health care administrators to understand disease burden and appropriately plan for research and medical care. Young IBD subjects often migrate from rural to urban areas for education and work opportunities, necessitating metropolitan prevalence studies to reduce under-representation. Also, the impact of urbanisation on IBD prevalence requires further exploration. Unlike IBD incidence, where young age-groups dominate, we hypothesised that the elderly age-groups would have the highest IBD prevalence given mortality rates being equivalent to the general population. We aimed to determine the first IBD prevalence rates for New South Wales, Australia. Methods This was an observational, population-based epidemiological study which captured disease information of people living with IBD within the metropolitan City of Canada Bay Local Government Area on the 1st of January 2016. The diagnosis was according to the Copenhagen Criteria. Age-standardisation was according to the WHO Standard Population. Results We identified 330 cases of IBD (49.1% male, median age 47, IQR=27, crude point prevalence rate of 371.5 per 100,000). Full diagnostic confirmation was achieved in 100%. The age-standardised point prevalence rate was 359.2 per 1 00 000. The crude point prevalence rates were 167.8, 158.8 and 45.0 per 1 00 000 for Crohn’s disease (CD), ulcerative colitis (UC) and IBD Unspecified (IBDU), respectively. The age-standardised rates were 171.6, 148.1 and 39.5 per 1 00 000 for CD, UC, and IBDU respectively. IBD prevalence steadily increased with age, peaking at 1061 per 1 00 000 in patients older than 85 years. A trend was observed between prevalence and socioeconomic status between suburbs. Conclusions Sydney exhibited the highest prevalence of IBD in Australasia. The extrapolated estimate for Australia was 89 000 people with IBD. Higher socioeconomic status and urbanisation may be contributing factors. The ageing IBD population accounts for the highest prevalence, peaking at greater than 1000 per 1 00 000. Safer therapies, cancer screening strategies and greater attention towards comorbidities are therefore of increasing importance in managing IBD patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".