1443PD - Analysis of compliance factors for colorectal cancer screening using a Bayesian network

Date 09 September 2017
Event ESMO 2017 Congress
Session Public health policy and health economics
Topics Cancer in Adolescents
Cancer Aetiology, Epidemiology, Prevention
Colon Cancer
Rectal Cancer
Presenter François Eisinger
Citation Annals of Oncology (2017) 28 (suppl_5): v511-v520. 10.1093/annonc/mdx385
Authors F. Eisinger1, J. Viguier2, J. Blay3, A. Cortot4, C. Touboul5, C. Lhomel6, L. Greillier7, S. Couraud8, J. Morere9
  • 1Aix Marseille Univ, Inserm, Sesstim, Institute Paoli Calmettes, 1300674 - Marseille/FR
  • 2Medical Oncology, CHRU Bretonneau, 37044 - Tours/FR
  • 3Medical Oncology, Centre Leon Berard, 69008 - Lyon/FR
  • 4Pneumology-oncology, Hospital Albert Calmette, 59000 - Lille/FR
  • 5Statistics, Kantarhealth, 75014 - Paris/FR
  • 6Medical, Roche - France, 9265000 - Boulogne-Billancourt/FR
  • 7Multidisciplinary Oncology And Therapeutic Innovations, Assistance Publique - Hopitaux de Marseille, 13915 - Marseille/FR
  • 8Respiratory Diseases And Thoracic Oncology, Centre Hospitalier Lyon Sud, 69495 - Pierre Bénite/FR
  • 9Medical Oncology, Hopital Paul Brousse, 94804 - Villejuif/FR

Abstract

Background

Compared to standard explanatory analyses based on multivariate regressions, Bayesian network analyses enable multiple hypotheses and clear graphical representations of complex interactions. They provide visual descriptions of causal pathways to distinguish between direct/indirect factors. We compared multivariate regression and a Bayesian network to assess factors associated with colorectal cancer (CRC) screening.

Methods

The 5th French observational survey, EDIFICE 5, was conducted (Nov 22-Dec 7, 2016) by phone interviews of a representative sample of 1501 individuals (age, 50-75 y). The present analysis focuses on 1299 individuals with no history of cancer (50-74 y). Bayesian analysis was performed with the bnlearn R Package. Parameters of the Bayesian analysis were based on the literature and our own data (logistic regression). “Blacklist/whitelist”-type restrictions were used to reset current understanding of the correlations between variables. We also analyzed the network topology.

Results

In our sample, 36% (N = 469) declared never having undergone CRC screening (colonoscopy, fecal occult blood test) in their lifetime. The Bayesian model revealed 5 direct correlating factors: age, smoking status, social vulnerability, psychological reassurance in the screening test (PRST), and confidence in the efficacy of the test. The latter 2 account for 43% of the observed sum of the mutual informations. Other relevant factors typically seen in the literature and regression analysis had an indirect impact: level of education, self-perception of own risk of CRC, gender, temporal perspective, confidence in their physician and fear of the disease. Multiple regression analysis identified PRST (OR = 0.84, 95% CI 0.80-0.88, P 

Conclusions

We showed that Bayesian network analysis provides a novel representation of factors associated with CRC screening, and may explain why interventions focusing on indirect factors might be ineffective if the next step of the causal pathway remains unchanged. We suggest that Bayesian networks should be used more often to drive timely interventions (short term vs long term).

Clinical trial identification

Legal entity responsible for the study

Kantar Health

Funding

Roche

Disclosure

F. Eisinger, J-Y. Blay, A. Cortot, L. Greillier, S. Couraud, J-F. Morere: Honorarium fees from Roche Edifice surveys were funded by Roche S.A. C. Lhomel: Employee of Roche. All other authors have declared no conflicts of interest.