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  • Title: Identifying predictors of low adherence in patients with inflammatory bowel disease.
    Author: Coenen S, Weyts E, Ballet V, Noman M, Van Assche G, Vermeire S, Van Emelen J, Ferrante M.
    Journal: Eur J Gastroenterol Hepatol; 2016 May; 28(5):503-7. PubMed ID: 26760588.
    Abstract:
    BACKGROUND AND AIM: Inflammatory bowel diseases (IBDs) are chronic gastrointestinal conditions requiring medical therapy to maintain clinical remission. Low adherence to therapy leads to poorer disease outcome. Therefore, we aimed to identify predictors of low adherence in the Belgian IBD population. PATIENTS AND METHODS: Between November 2013 and March 2014, 570 ambulatory patients (471 patients with IBD and 99 non-IBD controls) visiting a tertiary IBD-referral centre were requested to complete the Morisky 8-Item Medication Adherence Scale (MMAS-8) questionnaire as well as a survey of sociodemographic data (smoking, educational level, marital status and occupation). On the basis of the self-reported MMAS questionnaire, adherence was categorized as low (MMAS-8 score: >2), medium (MMAS-8 score: 1-2) or high (MMAS-8 score: 0). RESULTS: The response rate in the IBD population was as high as 99%. Low adherence was reported less frequently in the IBD population than in the non-IBD controls (36 vs. 49%, P=0.021). In multivariate analysis, factors associated independently with low adherence in the IBD population were age younger than 40 [odds ratio: 1.589 (95% confidence interval: 1.057-2.389), P=0.026], higher educational level [1.961 (1.305-2.946), P=0.001], being single [1.641 (1.020-2.639), P=0.041] and the use of mesalamine [1.591 (1.018-2.487), P=0.041]. Self-employment was identified as a protective factor for low adherence [0.397 (0.167-0.946), P=0.041]. CONCLUSION: Approximately one-third of the IBD patients were low adherers. Predictors of low adherence were aged younger than 40 years, higher educational level, being single and mesalamine use, whereas being self-employed was a protective factor. On the basis of these data, personalized algorithms may be developed to improve patient education, empowerment and follow-up.
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