Advertisement

Preference-based comparison of the quality of life associated with vision loss in Black and White U.S. ophthalmic populations

  • Gary C. Brown
    Correspondence
    Correspondence to Gary C. Brown, MD, Center for Value-Based Medicine, Box 3417, Hilton Head, SC 29928.
    Affiliations
    Center for Value-Based Medicine, Hilton Head, SC

    Wills Eye Hospital, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, Pa

    Department of Ophthalmology, Emory University School of Medicine, Atlanta, Ga
    Search for articles by this author
  • Melissa M. Brown
    Affiliations
    Center for Value-Based Medicine, Hilton Head, SC

    Wills Eye Hospital, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, Pa

    Department of Ophthalmology, Emory University School of Medicine, Atlanta, Ga
    Search for articles by this author
  • Sanjay Sharma
    Affiliations
    Department of Ophthalmology, Hotel Dieu Hospital, Kingston, Ont.
    Search for articles by this author
Published:November 22, 2022DOI:https://doi.org/10.1016/j.jcjo.2022.11.003

      Abstract

      Purpose

      Utilities are preference-based estimates, typically ranging from 1.00 (normal health) to 0.00 (death), that quantify the quality-of-life improvement associated with a health care intervention. In conjunction with length-of-life gain, depending on the intervention, they measure total interventional value gain in quality-adjusted life years that can be integrated with costs in cost-utility analysis. We believed it relevant to ascertain whether race was a differentiating factor confounding utilities related to vision.

      Methods

      An analysis of cross-sectional data obtained from consecutive Black and White ophthalmic outpatients from the Wills Eye Hospital (Philadelphia, Pa.) practices who participated in a long-standing time trade-off (TTO) vision utility study from 1999 to 2016 was undertaken. Each participant was interviewed by a researcher using a previously validated and reliable TTO vision utility acquisition instrument and assigned to 1 of 5 vision categories according to acuity in the best-seeing eye. Utility outcomes were compared using both the 2-sided t test and the Mann–Whitney U test.

      Results

      Eleven hundred and twenty-five consecutive patients able to successfully answer the questions were included. For vision of 20/200–20/800, White/Black mean vision utilities were, respectively, 0.58/0.59 (p = 0.84); for vision of 20/70–20/100, they were, respectively, 0.72/0.70 (p = 0.85); for vision of 20/50–20/60, they were, respectively, 0.78/0.79 (p = 0.86); for vision of 20/25–20/50, they were, respectively, 0.84/0.88 (p = 0.16); and for vision of 20/20, they were, respectively, 0.91/0.90 (p = 0.43).

      Conclusions

      TTO vision utilities in Black and White ophthalmic patient cohorts were alike at various levels of visual acuity. This suggests a similar quality of life and that TTO vision utilities used in cost-utility analysis do not require adjustment for race in Black and White U.S. ophthalmic populations.
      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Canadian Journal of Ophthalmology
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

      1. International Society for Pharmacoeconomics and Outcomes Research. Pharmacoeconomic guidelines around the world [Internet]. Available at: tools.ispor.org/peguidelines/(accessed 26 Apr 2021).

        • Sanders GD
        • Neumann PJ
        • Basu A
        • et al.
        Recommendations for conduct, methodological practices, and reporting of cost-effectiveness analyses: second panel on cost-effectiveness in health and medicine.
        JAMA. 2016; 316: 1093-1103
        • Brown MM
        • Brown GC
        • Sharma S
        Evidence-based to value-based medicine.
        AMA Press, Chicago2005: 151-233
        • Brown GC.
        Vision and quality of life.
        Trans Am Ophthalmol Soc. 1999; 97: 473-512
        • Brown MM
        • Brown GC
        • Sharma S
        • Kistler J
        • Brown H.
        Utility values associated with blindness in an adult population.
        Br J Ophthalmol. 2001; 85: 327-331
        • Brown MM
        • Brown GC
        • Sharma S
        • Busbee B
        • Brown H.
        Quality of life associated with visual loss: a time tradeoff utility analysis comparison with medical health states.
        Ophthalmology. 2003; 110: 1076-1081
        • Brown GC
        • Brown MM.
        Healthcare stakeholder perceptions of vision loss.
        Surv Ophthalmol. 2019; 64: 345-352
        • Brown MM
        • Brown GC
        • Sharma S
        • Landy J.
        Quality of life with visual acuity loss from diabetic retinopathy and age-related macular degeneration.
        Arch Ophthalmol. 2002; 120: 481-484
        • Stein J
        • Brown GC
        • Brown MM
        • Sharma S
        • Hollands H
        • Stein HD.
        The quality of life of patients with hypertension.
        J Clin Hypertens (Greenwich). 2002; 4: 181-188
        • Brown GC
        • Brown MM
        • Sharma S
        • Brown H
        • Gozum M
        • Denton P.
        Quality-of-life associated with diabetes mellitus in an adult population.
        J Diabetes Complications. 2000; 14: 18-24
        • Brown MM
        • Brown GC
        • Sharma S
        • Hollands H.
        Quality-of-life and systemic comorbidities in patients with ophthalmic disease.
        Br J Ophthalmol. 2002; 86: 8-11
        • Real FJ
        • Brown GC
        • Brown HC
        • Brown MM.
        The effect of comorbidities upon ocular and systemic health-related quality of life.
        Br J Ophthalmol. 2008; 92: 770-774
        • Brown GC
        • Brown MM
        • Sharma S
        • Beauchamp G
        • Hollands H.
        The reproducibility of ophthalmic utility values.
        Trans Am Ophthalmol Soc. 2001; 99: 199-203
        • Hollands H
        • Lam M
        • Pater J
        • et al.
        Reliability of the time trade-off technique of utility assessment in patients with retinal disease.
        Can J Ophthalmol. 2001; 36: 202-209
        • Sharma S
        • Brown GC
        • Brown MM
        • Hollands H
        • Robbins R
        • Shah G.
        Validity of the time trade-off and standard gamble methods of utility assessment in retinal patients.
        Br J Ophthalmol. 2002; 86: 493-496
        • Sharma S
        • Oliver A
        • Bakal J
        • Hollands H
        • Brown GC
        • Brown MM.
        Utilities associated with diabetic retinopathy: results from a Canadian sample.
        Br J Ophthalmol. 2003; 87: 259-261
        • DiScala G
        • Brown GC
        • Brown MM.
        Vision utilities in Italy and the United States: are they similar?.
        Eur J Ophthalmol. 2020; 30: 253-257
        • Kobelt G
        • Jonsson B
        • Bergstrom A
        • Chen E
        • Linden C
        • Alm A.
        Cost-effectiveness analysis in glaucoma: what drives utility? Results from a pilot study in Sweden.
        Acta Ophthalmol Scand. 2006; 84: 363-371
        • Yanagi Y
        • Ueta T
        • Obata R
        • et al.
        Utility values in Japanese patients with exudative age-related macular degeneration.
        Jpn J Ophthalmol. 2011; 55: 35-38
        • Zhu M
        • Yu J
        • Zhang J
        • et al.
        Evaluating vision-related quality of life in preoperative age-related cataract patients and analyzing its influencing factors in China: a cross-sectional study.
        BMC Ophthalmol. 2015; 15: 160
        • Zhu X
        • Sun Q
        • Zou H
        • Xu X
        • Zhang X.
        Disparities between ophthalmologists and patients in estimating quality of life associated with diabetic retinopathy.
        PLoS One. 2015; 10e0143678
        • Stein JD
        • Brown MM
        • Brown GC
        • Sharma S
        • Hollands H.
        Quality of life with macular degeneration: perceptions of patients, clinicians, and community members.
        Br J Ophthalmol. 2003; 87: 8-12
        • Souchek J
        • Byrne MM
        • Kelly PA
        • et al.
        Valuation of arthritis health states across ethnic groups and between patients and community members.
        Med Care. 2005; 43: 921-928
        • Brown GC
        • Brown MM
        • Sharma S.
        Difference between ophthalmologist and patient perceptions of quality-of-life associated with age-related macular degeneration.
        Can J Ophthalmol. 2000; 35: 27-32
        • Chaudry I
        • Brown GC
        • Brown MM.
        Medical student perceptions of quality-of-life associated with vision loss.
        Can J Ophthalmol. 2015; 50: 217-224
        • Stevens W
        • Brown GC
        • Brown MM.
        Vision-related quality-of-life estimates in adolescent youths.
        Can J Ophthalmol. 2021; 56: 385-390
        • Johnson D
        • Sacrinty M
        • Mehta H
        • et al.
        Cardiac rehabilitation in African Americans: evidence for poorer outcomes compared with Whites, especially in women and diabetic participants.
        Am Heart J. 2015; 169: 102-107
        • Cunningham WE
        • Hays RD
        • Burton TM
        • Kingston RS.
        Health status measurement performance and health status differences by age, ethnicity, and gender: assessment in the Medical Outcomes Study.
        J Health Care Poor Underserved. 2000; 11: 58-76
        • Brown GC
        • Brown MM
        • Sharma S.
        Ethnicity and diabetic quality-of-life.
        Am J Med Sci. 2019; 358: 121-126
        • Ghazi LJ
        • Lydecker AD
        • Patil SA
        • Rustgi A
        • Cross RK
        • Flasar MH.
        Racial differences in disease activity and quality of life in patients with Crohn's disease.
        Dig Dis Sci. 2014; 59: 2508-2513
        • Ashing-Giwa K
        • Ganz PA
        • Petersen I.
        Quality of life of African-American and White long term breast cancer survivors.
        Cancer. 1999; 85: 732-733
        • Ringsdorf L
        • McGwin G
        • Owsley C.
        Visual field defects and vision-specific health-related quality of life in African Americans and Whites with glaucoma.
        J Glaucoma. 2006; 15: 414-418
      2. 2019 National healthcare quality and disparities report. AHRQ Pub. No. 20(21)-0045-EF. Rockville, MD: Agency for Healthcare Research and Quality; December 2020.

      3. Stangroom J. Mann–Whitney U test calculator [Internet]. Available at: www.socscistatistics.com/tests/mannwhitney (accessed 5 Sep 2021).

      4. ISPOR Patient Preferences Good Practices Task Force. Using patient preferences to inform healthcare decision making [Internet]. Available at: www.ispor.org/docs/default-source/task-forces/ispor-webinar-using-patient-preferences-to-inform-decision-making-nov-20-2020-emd(2).pdf?sfvrsn=8694f849_0 (accessed 13 May 2012).

      5. Nittle NK. Understanding the difference between race and ethnicity: ethnicity can be concealed, but race typically cannot [Internet]. Available at: www.thoughtco.com/difference-between-race-and-ethnicity-2834950#:∼:text=%20Race%20is %20usually%20seen%20as%20biological%2C%20referring,on%20display%2C%20to%20a%20greater %20or%20lesser%20degree (accessed 30 Apr 2022).

      6. Taylor P, Lopez MH, Martinez J, Velasco G II. Identity, pan-ethnicity and race [Internet]. Available at: www.pewresearch.org/hispanic/2012/04/04/ii-identity-pan-ethnicity-and-race/(accessed 1 May 2022).

      7. U.S. Census Bureau. Comparative demographic estimates [Internet]. Available at: data.census.gov/cedsci/table?q=population%20by%20race&tid=ACSCP1Y2019.CP05 (accessed 30 Apr 2021).

        • Han MK
        • Curran-Everett D
        • Dransfield MT.
        Racial differences in quality of life in patients with COPD.
        Chest. 2011; 140: 1169-1176
        • Riegel R
        • Moser DK
        • Rayens MK
        • et al.
        Ethnic differences in quality-of-life in persons with heart failure.
        J Card Fail. 2008; 14: 41-47
        • Lee EW
        • Marien T
        • Laze J
        • et al.
        Comparison of health-related quality-of-life outcomes for African-American and Caucasian-American men after radical prostatectomy.
        BJU Int. 2012; 110: 1129-1133
        • Phipps E
        • Braitman LE
        • Stites S
        • Leighton JC.
        Quality of life and symptom attribution in long-term cancer survivors.
        J Eval Clin Pract. 2008; 14: 254-258
        • Ibrahim SA
        • Burant CJ
        • Siminoff LA
        • et al.
        Self-assessed global quality of life: a comparison between African-American and White older patients with arthritis.
        J Clin Epidemiol. 2002; 55: 512-517
        • Friedenberg FK
        • Kowalczyk M
        • Parkman HK.
        The influence of race on symptom severity and quality of life in gastroparesis.
        J Clin Gastroenterol. 2013; 47: 757-761
        • Minocha A
        • Wigington WC
        • Johnson WD.
        Detailed characterization of epidemiology of uninvestigated dyspepsia and its impact on quality of life among African Americans as compared to Caucasians.
        Am J Gastroenterol. 2006; 101: 336-342
        • Pereira C
        • Palta M
        • Mullahy J
        • Fryback DG.
        Race and preference-based health-related quality of life measures in the United States.
        Qual Life Res. 2011; 20: 969-978
        • Joober R
        • Schmitz N
        • Annable L
        • et al.
        Publication bias: what are the challenges and how can they be overcome?.
        J Psychiatry Neurosci. 2012; 37: 149-152
      8. Artiga S, Orgera K, Damico A, for the Henry J. Kaiser Family Foundation. Changes in health coverage by race and ethnicity since the ACA, 2010–2018 [Internet]. Available at: www.kff.org/racial-equity-and-health-policy/issue-brief/changes-in-health-coverage-by-race-and-ethnicity-since-the-aca-2010-2018/(accessed 4 May 2021).

        • Lumley T
        • Diehr P
        • Emerson Chen L.
        The importance of the normality assumption in large public health data sets.
        Annu Rev Public Health. 2002; 23: 151-169