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estimating the uk population cost of crohn's disease and ulcerative colitis using a ... - Springer Nature
This study quantifies the large NHS and societal costs of Crohn’s disease and ulcerative colitis in the UK and highlights that ongoing management and maintaining remission are major drivers of expense.
That matters for patients, services, and policy planning because interventions that reduce flares or long-term complications could lower both health and productivity costs.
Adult patients with Crohn’s disease or ulcerative colitis, caregivers, clinicians, health-service planners, policy makers, and health-economics researchers.
What To Know
What to know This open-access study presents a flexible cost-of-illness model estimating the UK population-level economic burden of Crohn’s disease and ulcerative colitis, using public datasets and the 2023 IBD UK patient survey.
The authors estimate annual direct NHS costs of around £3 billion and total healthcare plus productivity costs of about £3.8 billion, and highlight that ongoing management—especially treatments during active disease and maintaining remission—drives most spending.
The paper describes model inputs (prevalence, diagnosis, management, complications, mortality) aligned with ideal patient pathways and IBD standards, and reports separate cost components for flare-ups, remission, and indirect societal costs (lost productivity).
The analysis was funded by Crohn’s & Colitis UK with additional grants disclosed; the article is published in BMC Health Services Research and is open access.
What this means for people with IBD The study suggests that better control of inflammation and maintaining remission could reduce downstream costs from hospitalisations, surgery, disability, and lost work. The model is intended as a practical tool that can be updated as more real-world data become available.
Practical note This is a modelling and health-economics analysis rather than a clinical trial or new treatment study. It relies on survey and published data and includes assumptions about an “optimal patient journey,” so absolute cost estimates may change with more complete population data.
As a model-based analysis using survey and public data, results depend on underlying assumptions and available datasets; authors note population costs are likely underestimated and the model is designed to be updated with more robust real-world data.
