A109 A COMPARISON OF MULTIMORBIDITY AT DEATH AMONG PERSONS WITH AND WITHOUT INFLAMMATORY BOWEL DISEASE: A POPULATION-BASED STUDY
Bibliographic record
Abstract
Abstract Background With improved care and life-expectancy, people with IBD are living longer and more likely to develop multiple chronic conditions. However, research on co-morbidities in the IBD population has typically focused on co-occurrences of a specific disease alongside IBD and employed a cross-sectional or short-term cohort perspective. To date, no work has taken a life-course perspective, evaluating retrospectively from death, to identify the population-level burden of multimorbidity. Aims To identify the burden of chronic conditions at time of death among people with IBD relative to their non-IBD counterparts. Methods We conducted a retrospective matched cohort study using health administrative data from Ontario, Canada. Individuals with IBD (identified using a validated algorithm) who died between 2010 to 2020 were matched to controls (1:5 ratio) based on sex, age at death, and year of death. We calculated the proportion of decedents with each of 17 different chronic conditions and the number of conditions at death. Conditions were identified using validated algorithms or clusters of diagnostic codes (when validated algorithms were not available). The number of chronic conditions at death among people with and without IBD was modeled using Poisson regression adjusting for Ontario Marginalization Index and accounting for matching. Results At the time of death, people with IBD (n=9728) were more likely than matched controls (n=42,389) to have mood disorders (69.0% vs. 60.2%), asthma (21.4% vs. 16.5%), renal failure (49.6% vs. 38.5%), osteoarthritis (76.8 vs. 67.6%), and osteoporosis (21.2% vs. 12.8%) (Figure 1B). Compared to their controls, people with IBD had were more likely to have ≥5 conditions (77.8% vs. 70.1%) (Figure 1A). People with IBD had significantly more chronic health conditions (RR 1.11, 95% CI 1.10-1.12). Conclusions People with IBD accumulate a greater degree of morbidity compared to those without IBD. Our findings add a novel understanding of multimorbidity among those with IBD from a life-course perspective. Our research highlights the need to provide quality multi-disciplinary care to people with IBD across the life course to address the increased frequency of multimorbidity. Funding Agencies ACG
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".