• Josef S Smolen, Dafna Gladman, H Patrick McNeil, Philip J Mease, Joachim Sieper, Maja Hojnik, Pascal Nurwakagari, and John Weinman.
• Division of Rheumatology, Department of Medicine III, Medical University of Vienna and Hietzing Hospital, Vienna, Austria.
• RMD Open. 2019 Jan 1; 5 (1): e000585.

ObjectiveThis analysis explored the association of treatment adherence with beliefs about medication, patient demographic and disease characteristics and medication types in rheumatoid arthritis (RA), psoriatic arthritis (PsA) or ankylosing spondylitis (AS) to develop adherence prediction models.MethodsThe population was a subset from ALIGN, a multicountry, cross-sectional, self-administered survey study in adult patients (n=7328) with six immune-mediated inflammatory diseases who were routinely receiving systemic therapy. Instruments included Beliefs about Medicines Questionnaire (BMQ) and 4-item Morisky Medication Adherence Scale (MMAS-4©), which was used to define adherence.ResultsA total of 3390 rheumatological patients were analysed (RA, n=1943; PsA, n=635; AS, n=812). Based on the strongest significant associations, the adherence prediction models included type of treatment, age, race (RA and AS) or disease duration (PsA) and medication beliefs (RA and PsA, BMQ-General Harm score; AS, BMQ-Specific Concerns score). The models had cross-validated areas under the receiver operating characteristic curve of 0.637 (RA), 0.641 (PsA) and 0.724 (AS). Predicted probabilities of full adherence (MMAS-4©=4) ranged from 5% to 96%. Adherence was highest for tumour necrosis factor inhibitors versus other treatments, older patients and those with low treatment harm beliefs or concerns. Adherence was higher in white patients with RA and AS and in patients with PsA with duration of disease <9 years.ConclusionsFor the first time, simple medication adherence prediction models for patients with RA, PsA and AS are available, which may help identify patients at high risk of non-adherence to systemic therapies.Trial Registration NumberACTRN12612000977875.

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