Can Ireland’s colorectal screening programme save more lives, save money and live within existing colonoscopy capacity limits? Findings from the MISCAN microsimulation model

McFerran, E. (Advisor), James F. O'Mahony (Contributor), McVicar, D. (Advisor), Iris Lansdorp_Vogelaar (Advisor), Kee, F. (Advisor)

Activity: Talk or presentation typesInvited talk


OBJECTIVES: Ireland’s colorectal cancer screening programme, BowelScreen, offers biennial faecal immunochemical testing (FIT) for 60-69 year-olds. Screening sensitivity and specificity of FIT are adjusted by varying the test positivity threshold. BowelScreen uses a FIT cut-off of 225ng/ml of haemoglobin. Existing literature indicates that a lower cut-off of 50ng/ml would cost less and be more effective, but require more colonoscopies for positive screen findings, which is a key capacity constraint. The objective of this study was to determine if a more effective, less costly screening strategy exists within BowelScreen’s current colonoscopy capacity requirements.
METHODS: The MISCAN cancer screening model was used to simulate 144 strategies of varying screening intervals, age ranges and FIT cut-offs. Outputs estimated were net costs, quality-adjusted life-years (QALYs) and number of colonoscopies required. RESULTS: A combination of a reduction in the FIT cut-off to 50ng/ml, an extended screening interval of 3 years and a reduced screening start age of 55 saves 20% more QALYs, reduces costs by 7%, and yields a 17% reduction in colonoscopy requirements.
CONCLUSIONS: Simple changes to BowelScreen could save lives, reduce costs and relieve pressure on colonoscopy capacity.
Period11 Jan 2018
Event title SPHeRE (Structured Population and Health-services Research Education) Network 4th Annual Conference, 2018: The Value of Patient and Public Involvement in Research, Healthcare and Health Planning
Event typeConference
LocationDublin, Ireland
Degree of RecognitionInternational


  • Cancer
  • Screening
  • Colonoscopy
  • Capacity Planning
  • Microsimulation
  • Health Economics
  • Optimisation
  • Modelling
  • health services
  • Health Outcomes
  • Health Policy
  • Cancer Prevention