Does using vision utilities acquired from surrogate (nonpatient) individuals and/or vision utility gains limited by systemic comorbidity utility affect preference-based comparative effectiveness and cost-effectiveness of ophthalmic interventions?
In this economic evaluation, cost-utility analyses of cataract surgery and neovascular age-related macular degeneration therapy using nonpatient vision utilities and/or vision utility gain limited to the systemic comorbidity utility level decreased preference-based comparative effectiveness and cost-effectiveness, potentially biasing against disabled, elderly, and minority populations.
Bias against ophthalmic interventional comparative effectiveness and cost-effectiveness can theoretically limit advantageous patient interventions, decrease cost-utility analysis acceptance in US public policy, reduce ophthalmic research dollars, diminish interventional reimbursement, and lessen therapeutic advances.
Select research methods in cost-utility analysis (incremental cost-effectiveness analysis) might potentially bias against patient value (quality-adjusted life-year [QALY]) gain and cost-effectiveness associated with common ophthalmic interventions in disabled, elderly, and African American populations.
To ascertain whether using nonpatient vision utilities and/or a maximum limit model constraining vision utility gain to the systemic comorbidity utility level biases against ophthalmic cost-utility outcomes.
Design, Setting, and Participants
This economic evaluation predominantly used data from the Center for Value-Based Medicine database to perform preference-based comparative effectiveness and cost-utility analyses for cataract surgery and intravitreal ranibizumab therapy for neovascular age-related macular degeneration (NVAMD) using vision utilities acquired from patients with ophthalmic disease (ophthalmic patient utilities) and from surrogate individuals (nonophthalmic patient vision utilities) with and without integrating systemic comorbidity utility limits on vision utility gain. Ophthalmic patient data were collected from January 1, 2000, to December 31, 2016, and analyzed from April 1 to July 1, 2020.
Cost-utility analysis with 3% discount rate in 2018 US dollars.
Main Outcomes and Measures
QALY gains and dollars expended per QALY gain (the cost-utility ratio).
A total of 309 participants in the nonophthalmic patient cohort and 505 patients in the ophthalmic patient cohort were included. A reference case of first-eye cataract surgery using ophthalmic patient vision utilities and no systemic comorbidity utility limits yielded a 2.574 (34.2%) QALY gain vs observation. Substituting nonophthalmic patient utilities resulted in a 1.502 (15.5%) QALY gain, whereas using the 0.76 patient systemic comorbidity utility to limit cataract surgery vision utility gain yielded a 1.337 (17.8%) QALY gain. Using both nonophthalmic patient utilities and systemic comorbidity utility limits yielded a 0.839 (8.7%) QALY gain. The substitutions decreased cataract surgery cost-effectiveness by 71.3% (95% CI, 70.6%-72.1%) for nonophthalmic patient utilities, 92.5% (95% CI, 51.9%-133.1%) for patient systemic comorbidity utility, and 206.8% (95% CI, 202.6%-211.2%) for both. The NVAMD ranibizumab therapy reference case yielded a 1.339 (26.1%) QALY gain. Similar substitutions resulted in QALY gains of 1.164 (22.7%) for nonophthalmic patient utilities while reducing cost-effectiveness by 16.4%, 1.001 (19.5%) for systematic-limiting comorbidity utility while reducing cost-effectiveness by 33.8%, and 0.971 (18.9%) for both while reducing cost-effectiveness by 37.9%.
Conclusions and Relevance
Using nonophthalmic patient vision utilities and/or the maximum limit model of limiting patient utility gains to the population systemic comorbidity utility level resulted in large decreases in patient value (QALY) gain and cost-effectiveness for common ophthalmic interventions. Ophthalmologists should realize these phenomena and consider correcting the potential discrimination against disabled, elderly, and African American populations. This negative potential bias could theoretically result in beneficial intervention denial, less research dollars, curbed therapeutic advances, and decreased interventional reimbursement.
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CME Disclosure Statement: Unless noted, all individuals in control of content reported no relevant financial relationships. If applicable, all relevant financial relationships have been mitigated.
Accepted for Publication: November 12, 2020.
Published Online: February 4, 2021. doi:10.1001/jamaophthalmol.2020.6591
Corresponding Author: Gary C. Brown, MD, MBA, Center for Value-Based Medicine, PO Box 3417, Hilton Head, SC 29928 (firstname.lastname@example.org).
Author Contributions: Dr G. C. Brown had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Concept and design: G. C. Brown, M. M. Brown.
Acquisition, analysis, or interpretation of data: All authors.
Drafting of the manuscript: G. C. Brown, M. M. Brown.
Critical revision of the manuscript for important intellectual content: All authors.
Statistical analysis: G. C. Brown.
Administrative, technical, or material support: M. M. Brown.
Supervision: M. M. Brown.
Conflict of Interest Disclosures: Dr G. C. Brown reported being a shareholder in the Center for Value-Based Medicine. Dr M. M. Brown reported being a shareholder in the Center for Value-Based Medicine. No other disclosures were reported.
Additional Contributions: Sharon L. Christ, PhD, MS, Department of Human Development and Family Studies at the College of Health and Human Sciences, Purdue University, calculated the life expectancy associated with different levels of visual loss in the best-seeing eye. Dr Christ received no compensation from the authors for this endeavor.
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