Economic burden of microscopic colitis in relation to disease activity: a nationwide cost-of-illness study
Bibliographic record
Abstract
BACKGROUND AND AIMS: Microscopic colitis (MC) is a chronic gastrointestinal disease with disabling symptoms and associated comorbidities. Yet, the economic impact of MC has not been studied. In this cost-of-illness study, we estimated the economic burden of MC. METHODS: We used histopathology reports from all of Sweden's 28 pathology departments to identify 11 517 adult patients with biopsy-proven MC as of January 1, 2017. Each patient was compared to up to five general-population comparators matched on sex, age, and county of residence. Mean costs for the calendar year of 2016 were calculated based on nationwide register data encompassing healthcare use, dispensed medications, and work loss derived from sick leave and disability leave. The number of budesonide treatments following MC diagnosis was used as a proxy for disease activity. Mean differences were further adjusted for education level. RESULTS: Compared with the general population, patients with MC had an annual mean excess cost of $4805 (USD; adjusted mean difference [95% CI], $4974 [$4650; $5298]), corresponding to a cost ratio of 1.84 (95% CI, 1.74; 1.95). Based on an estimated disease prevalence of ∼0.1%, the economic burden of MC was $1.2 million per 100 000 inhabitants. No significant cost differences were seen based on subtype or sociodemographic factors. However, a high disease activity was associated with higher costs driven by excess work loss. CONCLUSION: Compared with the general population, patients with MC had almost twice as high annual mean costs. Excess costs were particularly high in patients with a high disease activity at onset, mainly driven by work loss.
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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 | 0.001 | 0.001 |
| 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".