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Cost-effectiveness of polygenic risk profiling for primary open-angle glaucoma in the United Kingdom and Australia

Eye volume 37, pages 2335–2343 (2023)Cite this article

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Abstract

Background

Primary open-angle glaucoma (POAG) is the most common subtype of glaucoma. We evaluate the cost-effectiveness of polygenic risk score (PRS) profiling as a screening tool for POAG.

Methods

We used a Markov cohort model to evaluate the cost-effectiveness of implementing PRS screening in the UK and Australia, conducted from the healthcare payer’s perspective. We used published data to calculate prevalence, transition probabilities, utility, cost and other parameters in the model. Our main outcome measure was the incremental cost-effectiveness ratio (ICER) and secondary outcomes were years of blindness avoided and a ‘Blindness ICER’. We did one-way as well as two-way deterministic and probabilistic sensitivity analyses.

Results

The proposed screening programme for POAG in the UK is predicted to result in ICER of £24,783 (95% CI: £13,373–66,960) and would avoid 1 year of blindness at ICER of £10,095 (95% CI: £5513–27,656). In Australia, it is predicted to result in ICER of AU$34,252 (95% CI: AU$21,324–95,497) and would avoid 1 year of blindness at ICER of AU$13,359 (95% CI: AU$8143–37,448). Using the willingness to pay thresholds of $54,808 and £30,000, the proposed screening model is 79.2% likely to be cost-effective in Australia and is 60.2% likely to be cost-effective in the UK, respectively.

Conclusion

We describe and model the cost-efficacy of incorporating a polygenic risk score for POAG screening in Australia and the UK for the first time and results indicated this is a promising cost-effectiveness strategy.

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Fig. 1: Overview of POAG-screening pathways and economical models.
Fig. 2: Cost effectiveness and sensitivity analysis for PRS-based POAG screening.
Fig. 3: Two-way sensitivity analysis of the cost-effectiveness simultaneously varying key parameters over large ranges.

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Data availability

The majority of the data generated or analysed during this study are included in this published article (and its supplementary information files) - e.g., every variable used to generate the model is available in the supplementary tables, with references to the original source. The wide range of output datasets generated during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

DAM, SM, JEC, LS and AWH are supported by the Australian National Health and Medical Research Council (NHMRC) Fellowships. XH is supported by the University of Queensland Research Training Scholarship and QIMR Berghofer Medical Research Institute PhD Top Up Scholarship. We are grateful for funding from a NHMRC Programme grant (1150144), Partnership grant (1132454) and a Centre for Research Excellence grant (1116360).

Funding

We had funding from a NHMRC Programme grant (1150144), Partnership grant (1132454) and a Centre for Research Excellence grant (1116360).

Author information

Authors and Affiliations

  1. Menzies Institute for Medical Research, School of Medicine, University of Tasmania, Hobart, TAS, Australia

    Qinqin Liu, John Davis, David A. Mackey & Alex W. Hewitt

  2. Centre for Eye Research Australia, University of Melbourne, Melbourne, VIC, Australia

    Qinqin Liu, John Davis & Alex W. Hewitt

  3. QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia

    Xikun Han & Stuart MacGregor

  4. Centre for Ophthalmology and Visual Science, Lions Eye Institute, University of Western Australia, Perth, WA, Australia

    David A. Mackey

  5. Department of Ophthalmology, Flinders University, Flinders Medical Centre, Adelaide, SA, Australia

    Jamie E. Craig

  6. School of Health Sciences, Western Sydney University, Campbelltown, NSW, Australia

    Lei Si

  7. The George Institute for Global Health, University of New South Wales, Kensington, NSW, Australia

    Lei Si

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Contributions

The contribution each author made to the manuscript is listed as following: (1) Conceptualisation: AWH, LS, JD, QL (2) Data curation: JD, QL (3) Formal Analysis: JD, LS (4) Funding acquisition: AWH, LS, DAM, SM, JEC (5) Investigation: QL, JD (6) Methodology: AWH, LS, JD, QL (7) Project administration: AWH (8) Resources: N/A. (9) Software: AWH (10) Supervision: LS, AWH (11) Validation: LS, JD (12) Visualisation: JD (13) Writing- original draft: 80% by QL, 20% by JD (14) Writing- review and editing: AWH, LS, XH, DAM, SM, JEC.

Corresponding authors

Correspondence to Lei Si or Alex W. Hewitt.

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Competing interests

SM, JEC, and AWH are listed as co-inventors on a patent application (WO2019241844A1) for the use of genetic risk scores to determine risk and guide treatment for glaucoma.

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Liu, Q., Davis, J., Han, X. et al. Cost-effectiveness of polygenic risk profiling for primary open-angle glaucoma in the United Kingdom and Australia. Eye 37, 2335–2343 (2023). https://doi.org/10.1038/s41433-022-02346-2

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