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Examining Treatment Decision-Making Among Patients With Axial Spondyloarthritis: Insights From a Conjoint Analysis Survey

Authors
  • Joo, Woojin
  • Almario, Christopher V.
  • Ishimori, Mariko
  • Park, Yujin
  • Jusufagic, Alma
  • Noah, Benjamin
  • Gensler, Lianne S.
  • Venuturupalli, R. Swamy
  • Kay, Jonathan
  • Weisman, Michael H.
  • Spiegel, Brennan M. R.
Publication Date
Jun 09, 2020
Source
[email protected]
Keywords
License
Green
External links

Abstract

OBJECTIVE: The number of therapies for axial spondyloarthritis (axSpA) is increasing. Thus, it has become more challenging for patients and physicians to navigate the risk-benefit profiles of the various treatment options. In this study, we used conjoint analysis-a form of trade-off analysis that elucidates how people make complex decisions by balancing competing factors-to examine patient decision-making surrounding medication options for axSpA. METHODS: We conducted an adaptive choice-based conjoint analysis survey for patients with axSpA to assess the relative importance of medication attributes (eg, chance of symptom improvement, risk of side effects, route of administration, etc) in their decision-making. We also performed logistic regression to explore whether patient demographics and disease characteristics predicted decision-making. RESULTS: Overall, 397 patients with axSpA completed the conjoint analysis survey. Patients prioritized medication efficacy (importance score 26.8%), cost (26.3%), and route of administration (13.9%) as most important in their decision-making. These were followed by risk of lymphoma (9.5%), dosing frequency (7.2%), risk of serious infection (6.0%), tolerability of side effects (5.3%), and clinic visit and laboratory test frequency (4.8%). In regression analyses, there were few significant associations between patients' treatment preferences and sociodemographic and axSpA characteristics. CONCLUSIONS: Treatment decision-making in axSpA is highly individualized, and demographics and baseline disease characteristics are poor predictors of individual preferences. This calls for the development of online shared decision-making tools for patients and providers, with the goal of selecting a treatment that is consistent with patients' preferences.

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