TY - JOUR
T1 - The value of correctly diagnosing axial spondyloarthritis for patients and society
AU - Webers, Casper
AU - Grimm, Sabine
AU - van Tubergen, Astrid
AU - van Gaalen, Floris
AU - van der Heijde, Désirée
AU - Joore, Manuela
AU - Boonen, Annelies
N1 - Funding Information:
Not applicable. All data relevant to this study are published in the article or in the supplementary files. The model developed for this study is available for collaborative purposes, upon reasonable request. Proposals should be directed to the corresponding author. The authors would like to thank Miranda van Lunteren (Rheumatology, Leiden University Medical Centre, Leiden, the Netherlands) for her assistance with data management of SPACE, Stephen Palmer (Centre for Health Economics, University of York, York, United Kingdom) for consultation on the York model and providing the research team access to the York model [47] and Fariz Yahya (Royal National Hospital for Rheumatic Diseases, Bath, United Kingdom and University of Malaya, Kuala Lumpur, Malaysia) for providing additional data on bDMARD drug survival in the BRITSpA study. [48] Some of the supplementary figures were inspired by the work of McAllister et al. (NICE Guideline 65, [43] in particular Appendix H: Full health economics report). Finally, the authors would like to thank prof. Robert Landewé for his helpful comments on a previous version of this manuscript.
Publisher Copyright:
© 2023 The Authors
PY - 2023/10/1
Y1 - 2023/10/1
N2 - Objective: To demonstrate the value of diagnosing axSpA, by comparing health and costs associated with available diagnostic algorithms and perfect diagnosis. Methods: Using data from SPACE and other cohorts, a model was developed to estimate health (quality-adjusted life-years, QALYs) and costs (healthcare consumption and work productivity losses) of different diagnostic algorithms for axSpA amongst patients with low back pain referred to a rheumatologist, over a 60-year horizon. The model combined a decision-tree (diagnosis) with a state-transition model (treatment). The three algorithms (Berlin [BER, highest specificity], Modification 1 [M1; less strict inflammatory back pain (IBP) criterion] and Modification 2 [M2; IBP not mandatory as entry criterion, highest sensitivity]) were compared. Changes in sensitivity/specificity were explored and the value of perfect diagnosis was investigated. Results: For each correctly diagnosed axSpA patient, up to 4.7 QALYs and €60,000 could be gained/saved, considering a societal perspective. Algorithm M2 resulted in more health and lower costs per patient (24.23 QALYs; €157,469), compared to BER (23.96 QALYs; €159,423) and M1 (24.15 QALYs; €158,417). Hypothetical improvements in M2 sensitivity resulted in slightly more value compared to improvements in specificity. Perfect diagnosis can cost €7,500 per patient and still provide enough value. Conclusion: Correct diagnosis of axSpA results in substantial health and cost benefits for patients and society. Not requiring IBP as mandatory for diagnosis of axSpA (algorithm M2) provides more value and would be preferable. A considerably more expensive diagnostic algorithm with better accuracy than M2 would still be considered good value for money.
AB - Objective: To demonstrate the value of diagnosing axSpA, by comparing health and costs associated with available diagnostic algorithms and perfect diagnosis. Methods: Using data from SPACE and other cohorts, a model was developed to estimate health (quality-adjusted life-years, QALYs) and costs (healthcare consumption and work productivity losses) of different diagnostic algorithms for axSpA amongst patients with low back pain referred to a rheumatologist, over a 60-year horizon. The model combined a decision-tree (diagnosis) with a state-transition model (treatment). The three algorithms (Berlin [BER, highest specificity], Modification 1 [M1; less strict inflammatory back pain (IBP) criterion] and Modification 2 [M2; IBP not mandatory as entry criterion, highest sensitivity]) were compared. Changes in sensitivity/specificity were explored and the value of perfect diagnosis was investigated. Results: For each correctly diagnosed axSpA patient, up to 4.7 QALYs and €60,000 could be gained/saved, considering a societal perspective. Algorithm M2 resulted in more health and lower costs per patient (24.23 QALYs; €157,469), compared to BER (23.96 QALYs; €159,423) and M1 (24.15 QALYs; €158,417). Hypothetical improvements in M2 sensitivity resulted in slightly more value compared to improvements in specificity. Perfect diagnosis can cost €7,500 per patient and still provide enough value. Conclusion: Correct diagnosis of axSpA results in substantial health and cost benefits for patients and society. Not requiring IBP as mandatory for diagnosis of axSpA (algorithm M2) provides more value and would be preferable. A considerably more expensive diagnostic algorithm with better accuracy than M2 would still be considered good value for money.
KW - Axial spondyloarthritis
KW - Cost-effectiveness
KW - Diagnosis
KW - Health economic evaluation
KW - Quality of life
KW - Value
U2 - 10.1016/j.semarthrit.2023.152242
DO - 10.1016/j.semarthrit.2023.152242
M3 - Article
C2 - 37451047
SN - 0049-0172
VL - 62
JO - Seminars in Arthritis and Rheumatism
JF - Seminars in Arthritis and Rheumatism
IS - 1
M1 - 152242
ER -