Prevalence of Visual Acuity Loss or Blindness in the US: A Bayesian Meta-analysis | Ophthalmology | JAMA Ophthalmology | JAMA Network
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Bourne  RRA, Flaxman  SR, Braithwaite  T,  et al; Vision Loss Expert Group.  Magnitude, temporal trends, and projections of the global prevalence of blindness and distance and near vision impairment.   Lancet Glob Health. 2017;5(9):e888-e897. doi:10.1016/S2214-109X(17)30293-0PubMedGoogle ScholarCrossref
Scott  AW, Bressler  NM, Ffolkes  S, Wittenborn  JS, Jorkasky  J.  Public attitudes about eye and vision health.   JAMA Ophthalmol. 2016;134(10):1111-1118. doi:10.1001/jamaophthalmol.2016.2627 PubMedGoogle ScholarCrossref
Prevent Blindness America. Vision problems in the US: prevalence of adult vision impairment and age-related eye disease in America. Published 2012. Accessed September 30, 2019.
Varma  R, Vajaranant  TS, Burkemper  B,  et al.  Visual impairment and blindness in adults in the United States.   JAMA Ophthalmol. 2016;134(7):802-809. doi:10.1001/jamaophthalmol.2016.1284 PubMedGoogle ScholarCrossref
Elam  AR, Lee  PP.  High-risk populations for vision loss and eye care underutilization.   Surv Ophthalmol. 2013;58(4):348-358. doi:10.1016/j.survophthal.2012.07.005 PubMedGoogle ScholarCrossref
Armstrong  RA, Mousavi  M.  Overview of risk factors for age-related macular degeneration (AMD).   J Stem Cells. 2015;10(3):171-191.PubMedGoogle Scholar
Pleet  A, Sulewski  M, Salowe  RJ,  et al.  Risk factors associated with progression to blindness from primary open-angle glaucoma in an African-American population.   Ophthalmic Epidemiol. 2016;23(4):248-256. doi:10.1080/09286586.2016.1193207PubMedGoogle ScholarCrossref
Penman  A, Hancock  H, Papavasileiou  E,  et al.  Risk factors for proliferative diabetic retinopathy in African Americans with type 2 diabetes.   Ophthalmic Epidemiol. 2016;23(2):88-93. doi:10.3109/09286586.2015.1119287PubMedGoogle ScholarCrossref
Centers for Disease Control and Prevention. The Vision and Eye Health Surveillance System. Published 2019. Accessed October 1, 2019.
Flaxman  AD, Vos  DT, Murray  CJ.  An Integrative Metaregression Framework for Descriptive Epidemiology. University of Washington Press; 2015.
Dougherty  M, Wittenborn  J, Phillips  E, Swenor  B. Published examination-based prevalence of major eye disorders. Published 2018. Accessed March 3, 2021.
Friedman  DS, Repka  MX, Katz  J,  et al.  Prevalence of decreased visual acuity among preschool-aged children in an American urban population.   Ophthalmology. 2008;115(10):1786-1795. doi:10.1016/j.ophtha.2008.04.006 PubMedGoogle ScholarCrossref
Varma  R, Kim  JS, Burkemper  BS,  et al; Chinese American Eye Study Group.  Prevalence and causes of visual impairment and blindness in Chinese American adults.   JAMA Ophthalmol. 2016;134(7):785-793. doi:10.1001/jamaophthalmol.2016.1261 PubMedGoogle ScholarCrossref
Congdon  N, O’Colmain  B, Klaver  CC,  et al; Eye Diseases Prevalence Research Group.  Causes and prevalence of visual impairment among adults in the United States.   Arch Ophthalmol. 2004;122(4):477-485. doi:10.1001/archopht.122.4.477PubMedGoogle ScholarCrossref
Varma  R, Ying-Lai  M, Klein  R, Azen  SP; Los Angeles Latino Eye Study Group.  Prevalence and risk indicators of visual impairment and blindness in Latinos.   Ophthalmology. 2004;111(6):1132-1140. doi:10.1016/j.ophtha.2004.02.002 PubMedGoogle ScholarCrossref
Fisher  DE, Shrager  S, Shea  SJ,  et al.  Visual impairment in White, Chinese, Black, and Hispanic participants from the multi-ethnic study of atherosclerosis cohort.   Ophthalmic Epidemiol. 2015;22(5):321-332. doi:10.3109/09286586.2015.1066395PubMedGoogle ScholarCrossref
National Health and Nutrition Examination Survey. 2007-2008 data documentation, codebook, and frequencies. Published 2009. Accessed October 2, 2019.
Raghunathan  TE, Lepkowski  JM, Van Hoewyk  J, Solenberger  P.  A multivariate technique for multiply imputing missing values using a sequence of regression models.   Survey Methodology. 2001;27(1):85-96.Google Scholar
US Census Bureau. National Survey of Children’s Health (NSCH). Published 2019. Accessed October 3, 2019.
US Census Bureau. American Community Survey (ACS). Published 2019. Accessed October 3, 2019.
King  G.  Unifying Political Methodology: the Likelihood Theory of Statistical Inference. University of Michigan Press; 1998. doi:10.3998/mpub.23784
Patil  A, Huard  D, Fonnesbeck  CJ.  PyMC: Bayesian stochastic modelling in Python.   J Stat Softw. 2010;35(4):1-81. doi:10.18637/jss.v035.i04PubMedGoogle ScholarCrossref
Ko  F, Vitale  S, Chou  C-F, Cotch  MF, Saaddine  J, Friedman  DS.  Prevalence of nonrefractive visual impairment in US adults and associated risk factors, 1999-2002 and 2005-2008.   JAMA. 2012;308(22):2361-2368. doi:10.1001/jama.2012.85685PubMedGoogle ScholarCrossref
Tielsch  JM, Javitt  JC, Coleman  A, Katz  J, Sommer  A.  The prevalence of blindness and visual impairment among nursing home residents in Baltimore.   N Engl J Med. 1995;332(18):1205-1209. doi:10.1056/NEJM199505043321806 PubMedGoogle ScholarCrossref
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    Original Investigation
    May 13, 2021

    Prevalence of Visual Acuity Loss or Blindness in the US: A Bayesian Meta-analysis

    Author Affiliations
    • 1Institute for Health Metrics and Evaluation, University of Washington, Seattle
    • 2NORC at the University of Chicago, Chicago, Illinois
    • 3Applied Statistical Consulting LLC, Atlanta, Georgia
    • 4Division of Diabetes Translation, Vision Health Initiative Centers for Disease Control and Prevention, Atlanta, Georgia
    JAMA Ophthalmol. Published online May 13, 2021. doi:10.1001/jamaophthalmol.2021.0527
    Key Points

    Question  How many people in the US are living with uncorrectable visual acuity loss or blindness?

    Findings  This bayesian meta-analysis generated an estimate that, in 2017, there were 7.08 million people living with visual acuity loss, of whom 1.08 million were living with blindness.

    Meaning  Per this study, uncorrectable visual acuity loss and blindness are even larger drivers of health burden in the US than was previously known.


    Importance  Globally, more than 250 million people live with visual acuity loss or blindness, and people in the US fear losing vision more than memory, hearing, or speech. But it appears there are no recent empirical estimates of visual acuity loss or blindness for the US.

    Objective  To produce estimates of visual acuity loss and blindness by age, sex, race/ethnicity, and US state.

    Data Sources  Data from the American Community Survey (2017), National Health and Nutrition Examination Survey (1999-2008), and National Survey of Children’s Health (2017), as well as population-based studies (2000-2013), were included.

    Study Selection  All relevant data from the US Centers for Disease Control and Prevention’s Vision and Eye Health Surveillance System were included.

    Data Extraction and Synthesis  The prevalence of visual acuity loss or blindness was estimated, stratified when possible by factors including US state, age group, sex, race/ethnicity, and community-dwelling or group-quarters status. Data analysis occurred from March 2018 to March 2020.

    Main Outcomes or Measures  The prevalence of visual acuity loss (defined as a best-corrected visual acuity greater than or equal to 0.3 logMAR) and blindness (defined as a logMAR of 1.0 or greater) in the better-seeing eye.

    Results  For 2017, this meta-analysis generated an estimated US prevalence of 7.08 (95% uncertainty interval, 6.32-7.89) million people living with visual acuity loss, of whom 1.08 (95% uncertainty interval, 0.82-1.30) million people were living with blindness. Of this, 1.62 (95% uncertainty interval, 1.32-1.92) million persons with visual acuity loss are younger than 40 years, and 141 000 (95% uncertainty interval, 95 000-187 000) persons with blindness are younger than 40 years.

    Conclusions and Relevance  This analysis of all available data with modern methods produced estimates substantially higher than those previously published.