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Publication date: 
July 7, 2022


Interventions to reduce disparities in vision loss are often implemented at the local level. Data on local geographic variations are needed to understand the distribution of vision loss in the US and develop effective interventions and policies to address this public health problem. Current information on severe vision loss prevalence below the state level is based on older single-source data.1 We previously published meta-analytic multisource estimates of visual acuity loss and blindness prevalence by US state.2 Here, we provide estimates for US counties.


We used bayesian metaregression to combine multiple data sets to produce county-level prevalence estimates for visual acuity loss or blindness using previously described methods.2 We followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline. Data from 5 population-based studies were used to calculate the combined prevalence of visual acuity loss (best-corrected visual acuity 0.3 logMAR or greater [20/40 or worse Snellen score in the better-seeing eye] as measured by an eye care professional) or blindness (1.0 logMAR or greater [20/200 or worse Snellen score in the better-seeing eye]) by age, sex, race, and ethnicity. Identical calculations were performed on data from the National Health and Nutrition Examination Survey (1999 to 2008) for persons in whom autorefraction was used to correct presenting visual acuity. Self-reported vision impairment and blindness data from the 2016 National Survey of Children’s Health and 2017 American Community Survey were incorporated to provide data on children younger than 12 years and residents of institutional group quarters. Integrative systems modeling, an extension of negative binomial regression, was used to generate composite, county-level prevalence estimates. County-level fixed effects accounted for age, sex, race, and ethnicity distribution using US 2010 Census data and prevalence of vision impairment and blindness at the county level using American Community Survey data. Crude and age-, sex-, race-, and ethnicity-standardized county-level prevalence estimates are presented for visual acuity loss or blindness. We calculated the Pearson correlation coefficient for the linear trend between the standardized county-level prevalence of visual acuity loss or blindness and the percentage of the county’s population living below the federal poverty level, derived from 2019 5-year American Community Survey estimates.


We found considerable geographic variation in county-level prevalence of visual acuity loss or blindness. Crude prevalence ranged from 0.75% (95% uncertainty interval [UI], 0.47-1.09) in Douglas County, Colorado, to 13.16% (95% UI, 7.18-18.41) in Kalawao County, Hawaii. Standardized prevalence ranged from 0.99% (95% UI, 0.53-1.50) in Cumberland County, Maine, to 10.88% (95% UI, 5.18-16.72) in Clay County, Kentucky (Figure). County-level standardized visual acuity loss or blindness prevalence was positively correlated with the percentage of the county’s population living below the poverty level (r, 0.40).


Data identifying geographic variation in the prevalence of visual acuity loss can be used to guide interventions to improve eye care services, as counties with a higher prevalence of visual acuity loss may have less access to and use of eye care services. We found a positive county-level correlation between poverty and visual acuity loss or blindness prevalence, which has been confirmed previously.1 Previous research found the odds of receiving an eye examination in the past year were lower in counties with the highest prevalence of persons with poor vision and eye health.3 A county-level analysis on availability of eye care professionals found that 71.1% of counties in the South were in 1 of the lower 2 quartiles of ophthalmologist and optometrist availability; the South had the lowest number of providers per capita.4 Nonmetropolitan counties have a lower density of ophthalmologists (2.19 per 100 000 persons) than metropolitan counties (6.29 per 100 000 persons).5 The proportion of counties that have no ophthalmologists is higher in nonmetropolitan counties (67.0%) compared with metropolitan counties (35.3%).5 Estimates presented herein are available from the US Centers for Disease Control and Prevention’s Vision and Eye Health Surveillance System.6 In addition to the previously discussed limitations of this analytic approach,2 our results are limited by the lack of county-level measurement of the relative prevalence of visual acuity loss vs blindness.


Lundeen EA, Flaxman AD, Wittenborn JS, et al. County-level variation in the prevalence of visual acuity loss or blindness in the US. JAMA Ophthalmology. 7 July 2022. doi:10.1001/jamaophthalmol.2022.2405.