Cost-effectiveness of an adherence-enhancing intervention for gout based on real-world data


Abstract

Aim: Medication non-adherence influences outcomes of therapies for chronic diseases. Allopurinol is a cornerstone therapy for patients with gout; however, non-adherence to allopurinol is prevalent in Singapore and limits its effectiveness. Between 2008-2010, an adherence-enhancing program was implemented at the rheumatology division of a public tertiary hospital. The cost-effectiveness of this program has not been fully evaluated. With healthcare resources being finite, the value of investing in adherence-enhancing interventions should be ascertained. This study aims to evaluate the cost-effectiveness of this adherence-enhancing program to inform optimal resource allocation toward better gout management.

Method: Adopting a real-world data approach, we utilized patient clinical and financial records generated in their course of routine care. Intervention and control groups were identified in a standing database and matched on nine risk factors through propensity score matching. Cost and effect data were followed through 1-2 years. A decision tree was developed in TreeAge using a societal perspective. Deterministic and probabilistic sensitivity analyses were performed to assess parameter uncertainty.

Results: At an assumed willingness-to-pay threshold of $50 000 USD ($70 000 SGD) per quality-adjusted life year (QALY), the intervention had an 85% probability of being cost-effective compared to routine care. The incremental cost-effectiveness ratio was $12 866 USD per QALY for the base case and ranged from $4 139 to $21 593 USD per QALY in sensitivity analyses.

Conclusion: The intervention is cost-effective in the short-term, although its long-term cost-effectiveness remains to be evaluated.

Keywords: allopurinol; cost-effectiveness analysis; electronic medical records; gout; medication adherence; quality-adjusted life year.

Conflict of interest statement

The author(s) declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1
Figure 1
Non‐adherence framework adapted from Bae et al20
Figure 2
Figure 2
Decision tree model structure

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