Prevalence of Age-Related Macular Degeneration in the US in 2019

David B Rein; John S Wittenborn; Zeb Burke-Conte; Rohit Gulia; Toshana Robalik; Joshua R Ehrlich; Elizabeth A Lundeen; Abraham D Flaxman

doi:10.1001/jamaophthalmol.2022.4401

. 2022 Nov 3;140(12):1202–1208. doi: 10.1001/jamaophthalmol.2022.4401

Prevalence of Age-Related Macular Degeneration in the US in 2019

David B Rein ^1,^✉, John S Wittenborn ¹, Zeb Burke-Conte ², Rohit Gulia ², Toshana Robalik ², Joshua R Ehrlich ^3,⁴, Elizabeth A Lundeen ⁵, Abraham D Flaxman ²

¹NORC, University of Chicago, Chicago, Illinois

²Institute for Health Metrics and Evaluation, University of Washington, Seattle

³Department of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor

⁴Institute for Social Research, University of Michigan, Ann Arbor

⁵Vision Health Initiative, Division of Diabetes Translation, US Centers for Disease Control and Prevention, Atlanta, Georgia

Accepted for Publication: September 1, 2022.

Published Online: November 3, 2022. doi:10.1001/jamaophthalmol.2022.4401

^✉

Corresponding Author: David B. Rein, PhD, NORC at the University of Chicago, 1447 Peachtree St, Ste 700, Atlanta, GA 30309 (rein-david@norc.org).

Author Contributions: Dr Flaxman had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Rein, Wittenborn, Burke-Conte, Gulia, Flaxman.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Rein, Burke-Conte, Gulia, Robalik, Flaxman.

Critical revision of the manuscript for important intellectual content: Rein, Wittenborn, Burke-Conte, Gulia, Ehrlich, Lundeen, Flaxman.

Statistical analysis: Rein, Burke-Conte, Gulia, Robalik, Flaxman.

Obtained funding: Rein, Wittenborn.

Administrative, technical, or material support: Rein, Wittenborn, Burke-Conte, Gulia, Lundeen.

Supervision: Rein, Flaxman.

Conflict of Interest Disclosures: Dr Rein reported grants from the US Centers for Disease Control and Prevention’s Vision Health Initiative Cooperative Agreement (U01DP006444) during the conduct of the study. Dr Burke-Conte reported grants from NORC at the University of Chicago during the conduct of the study. Dr Ehrlich reported personal fees from MetLife outside the submitted work. Dr Flaxman reported grants from NORC during the conduct of the study; assistance for analysis and interpretation of licensed data previously produced by Institute for Health Metrics and Evaluation from Janssen, SwissRe, and Sanofi; and advising fees from Agathos outside the submitted work. No other disclosures were reported.

Funding/Support: This article was supported by funding from the US Centers for Disease Control and Prevention’s Vision Health Initiative via cooperative agreement (U01DP006444).

Disclaimer: The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.

^✉

Corresponding author.

PMCID: PMC9634594 PMID: 36326752

This study attempts to produce estimates of early- and late-stage age-related macular degeneration prevalence overall and by age, gender, race and ethnicity, county, and state.

Key Points

Question

What was the prevalence of early- and late-stage age-related macular degeneration (AMD) in the US nationally, and by county and state in 2019?

Findings

In this study, we estimated that 18.34 million individuals in the US 40 years and older (11.64%) were living with early-stage AMD and 1.49 million (0.94%) were living with late-stage AMD in 2019. Rates of early-stage AMD were higher than previously estimated while rates of late-stage AMD were similar; prevalence rates varied substantially by age group, race and ethnicity, and county.

Meaning

The estimated prevalence of all AMD remains high, although vision-threatening late stages appear to be level with past estimates; state and county estimates may be used for public health planning.

Abstract

Importance

Age-related macular degeneration (AMD) is a leading cause of vision loss and blindness. AMD prevalence has not been estimated for the US in over a decade and early-stage AMD prevalence estimates are scarce and inconsistently measured.

Objective

To produce estimates of early- and late-stage AMD prevalence overall and by age, gender, race and ethnicity, county, and state.

Design, Setting, and Participants

The study team conducted a bayesian meta-regression analysis of relevant data sources containing information on the prevalence of AMD among different population groups in the US.

Data Sources

We included data from the American Community Survey (2019), the National Health and Nutrition Examination Survey (2005-2008), US Centers for Medicare & Medicaid Services claims for fee-for-service beneficiaries (2018), and population-based studies (2004-2016).

Study Selection

We included all relevant data from the US Centers for Disease Control and Prevention’s Vision and Eye Health Surveillance System.

Data Extraction and Synthesis

The prevalence of early- and late-stage AMD was estimated and stratified when possible by factors including county, age group, gender, and race and ethnicity. Data analysis occurred from June 2021 to April 2022.

Main Outcomes or Measures

The prevalence of early- (defined as retinal pigment epithelium abnormalities or the presence of drusen 125 or more microns in diameter in either eye) and late-stage (defined as choroidal neovascularization and/or geographic atrophy in either eye) manifestations of AMD.

Results

This study used data from nationally representative and local population-based studies that represent the populations in which they were conducted. For 2019, we estimated that there were 18.34 million people 40 years and older (95% uncertainty interval [UI], 15.30-22.03) living with early-stage AMD, corresponding to a crude prevalence rate of 11.64% (95% UI, 9.71-13.98). We estimated there were 1.49 million people 40 years and older (95% UI, 0.97-2.15) living with late-stage AMD, corresponding to a crude prevalence rate of 0.94% (95% UI, 0.62-1.36). Prevalence rates of early- and late-stage AMD varied by demographic characteristics and geography.

Conclusions and Relevance

We estimated a higher prevalence of early-stage AMD and a similar prevalence of late-stage AMD as compared with earlier studies. State-level and county-level AMD estimates may help guide public health practice.

Introduction

Age-related macular degeneration (AMD) is historically one of the leading causes of blindness in the US and the leading cause among White individuals.^1,2 AMD is a disorder of the macula characterized in its early stages by the presence of intermediate-sized drusen and retinal pigment epithelium (RPE) abnormalities and in its later vision-threatening stages by geographic atrophy of the RPE and/or choroidal neovascularization.³ For people 50 years and older, AMD prevalence has been estimated between 9.9% and 19.5% for early stages and 1.1% and 3.9% for late-stage AMD.⁴

Treatment of choroidal neovascularization has expanded dramatically since the use of anti–vascular endothelial growth factor intravitreal injection in 2004. Annual costs among Medicare fee-for-service (FFS) beneficiaries increased from $261 per patient in 2004 to $1290 per patient in 2018.⁵ Increased numbers of individuals older than 40 years due to aging of the Baby Boomer generation may have led to increased AMD prevalence. However, reductions in smoking and improvements in blood pressure control may have decreased prevalence.⁶ The Beaver Dam Eye Study⁷ estimated lower AMD incidence among recent birth cohorts compared with earlier cohorts.

The US Center for Disease Control and Prevention’s (CDC) Vision and Eye Health Surveillance System (VEHSS) provides estimates of evaluated AMD prevalence from the National Health and Nutrition Examination Survey (NHANES), diagnosed AMD observed in private insurance and Medicare FFS data, and self-reported estimates of AMD diagnosis from NHANES and the National Health Interview Survey.⁸ However, these individual data sources are insufficient to estimate contemporary AMD prevalence at the national, state, and county levels. In this study, we apply meta-regression methods to primary VEHSS data to estimate the national, state, and county prevalence of early- and late-stage AMD by age group, gender, and race and ethnicity for the year 2019. Demographically stratified, county-level estimates may be helpful to state and local health authorities in understanding local AMD burden.

Methods

Strategy

We used bayesian meta-regression methods⁹ to estimate the point and interval prevalence of early- and late-stage AMD, stratified by age group, gender, race and ethnicity, and county for 2019 and created state estimates using aggregated county counts. These research activities were determined to be not human subjects research by the institutional review board of NORC at the University of Chicago because they are based on secondary analysis of deidentified data sources. The study adhered to the Meta-analysis of Observational Studies in Epidemiology (MOOSE) checklist after peer review.

Data

Our model used 4 data sources: (1) abstracted published results of population-based studies (PBS), (2) NHANES data collected from 2005 to 2008 (the only years for which retinal imaging data were available), (3) Medicare 2018 FFS claims, and (4) 2019 American Community Survey (ACS) data. For PBS, we searched for publications after 1991 that were representative of the target population, presented primary results or meta-analysis of primary data, and reported age and/or race and ethnicity and/or location-specific prevalence estimates. We identified 5 studies for inclusion: (1) the Chinese American Eye Study,¹⁰ (2) the Los Angeles Latino Eye Study Prevalence and Risk Indicators of Visual Impairment and Blindness in Latinos,¹¹ (3) Prevalence of Age-Related Macular Degeneration in a Population-Based Sample of Hispanic People,¹² (4) the Visual Impairment in White, Chinese, Black, and Hispanic Participants from the Multi-Ethnic Study of Atherosclerosis Cohort,¹³ and (5) a meta-analysis conducted by the Eye Diseases Prevalence Research Group, Causes and Prevalence of Visual Impairment Among Adults in the United States.¹ We abstracted the estimated prevalence rate of early- and late-stage AMD and sample size information from each study by age group, gender, and race and ethnicity.

For NHANES participants 40 years or older, we used data collected as part of a retinal imaging module fielded among a nationally representative sample of community-dwelling individuals in the US during 2005 to 2008. We used NHANES variable OPDUARM (which defined late-stage AMD as exudative age-related maculopathy or any geographic atrophy including geographic atrophy that did not include the macula in the worse-affected eye) to indicate late-stage AMD, which we mapped to 3 values (0, 1, and missing).

For early-stage AMD, we used the current clinical definition of intermediate AMD defined as any RPE abnormalities or drusen 125 μm or more in the worse-affected eye,¹⁴ measured using NHANES variables on drusen and RPE abnormalities. We defined individuals as having early-stage AMD if they had drusen 125 μm or more or RPE abnormalities in either eye, and if they did not have late-stage AMD. We mapped individuals 40 years or older with insufficient drusen and RPE data to determine early-stage AMD status to missing. We imputed missing categorical indicators of early- and late-stage AMD (eAppendix 1 in the Supplement) for individuals with missing early-stage AMD and late-stage AMD (9.0% and 7.0% of the sample, respectively).

We estimated priors on county-level random effects using Part B Medicare FFS data measuring the prevalence of diagnosed AMD stratified by age, gender, and race and ethnicity, but not jointly stratified by these 3 factors at the county level (eAppendix 2 in the Supplement). To address this lack of joint stratification, we used a random-effects logistic regression model with 5% trimming to avoid being unduly influenced by outliers.¹⁵ Outliers may be related to causes other than epidemiology, such as fraudulent billing or local practice patterns that lead to overdiagnosis or underdiagnosis. We controlled for ophthalmologists per capita (which could affect access) using 2017 data from the 2018 to 2019 Area Health Resource File, and the percent of Medicare beneficiaries enrolled in Medicare Advantage in June of 2018 (both measured at the county level).^16,17 This approach assumes that the prevalence of all AMD (diagnosed and undiagnosed) varies at the county level approximately in the same way as the prevalence of diagnosed AMD in the Medicare FFS population, after adjusting for county-level differences in demographics, ophthalmologists per capita, and Medicare Advantage penetration. This assumption is imperfect but reasonable because virtually all late-stage AMD occurs in people 60 years and older, the prevalence of early-stage AMD is approximately 4 times higher among individuals 60 years and older compared with those 40 to 59 years,¹⁸ and approximately 96% of individuals in the US 65 years and older have Medicare insurance.¹⁹

We used population estimates, stratified by age, gender, and race and ethnicity, from the ACS collected in 2019 to estimate prevalence counts of early- and late-stage AMD. The ACS is an annual, national, state, and county representative survey conducted by the US Census Bureau among the US population.²⁰ We validated the model by comparing the association of modeling steps with the complete case estimates derived from NHANES (eAppendix 3 in the Supplement).

Race and ethnicity information was obtained using self-reported values. The NHANES categorizes race or ethnicity as Mexican American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black, and other race (includes Asian individuals of any origin, American Indian individuals, Alaskan Native individuals, Pacific Islander individuals, multiracial individuals, and other groups not otherwise classified). Medicare uses respondent self-reported values and algorithmic adjustments for missing responses. ACS uses self-reported data. We based race and ethnicity stratification on NHANES categories after collapsing Mexican American and other Hispanic into a single Hispanic category.

Estimation

Using an integrative structural modeling approach developed by the Global Burden of Disease Study,⁹ we estimated 2 statistical models to predict (1) the prevalence rate of early-stage AMD and (2) the prevalence rate of late-stage AMD with both models stratified by age group, gender, race and ethnicity, and county. The model estimated observed prevalence in each stratum as a negative binomially distributed function of the stratum sample size, gender, age, race and ethnicity (non-Hispanic Black, non-Hispanic White, Hispanic, and all other race and ethnicities), and data source. Our integrative systems modeling reduces to an extension of negative binomial regression, with a piecewise linear spline to represent the nonlinear age pattern, and an age-standardizing likelihood to account for the heterogeneous reporting of age groups in PBS data.⁹ This allowed us to include data from PBS and NHANES in the likelihood during estimation. We used DisMod-MR 1.1.1, which implements the model in Python using PyMC 2 and fit the model with 400 000 iterations of Markov chain Monte Carlo using an adaptive Metropolis step method.⁹ The model assumes that the age-stratified prevalence rate has not changed since it was measured in the NHANES and PBS study data. We derived county-level estimates by applying the county-specific priors described above. We estimated standardized rates of early- and late-stage AMD by gender and by race and ethnicity, as the expected prevalence for each strata assuming the national average distribution for age, and gender, or race and ethnicity.

Results

Early-Stage AMD

We estimated that there are 18.34 million people (95% uncertainty level [UI], 15.30-22.03) living with early-stage AMD in the US in 2019, corresponding to a prevalence rate of 11.64% (95% UI, 9.71-13.98) among individuals 40 years or older (Table). Prevalence of early-stage AMD increased with age, from 2.00% (95% UI, 1.32-2.87) among people 40 to 44 years (Figure 1) to 35.24% (95% UI, 21.91-53.91) among people 85 years or older. The race and ethnicity– and age-standardized rate of early-stage AMD for men 40 years or older was higher at 12.72% (95% UI, 9.96-15.99) than for women 40 years or older at 10.73% (95% UI, 8.42-13.46), although this difference was highly uncertain. Gender- and age-standardized rates of early-stage AMD among non-Hispanic White individuals and Hispanic individuals were nearly identical: 12.30% for non-Hispanic White individuals 40 years or older (95% UI, 9.81-15.41) and 12.17% for Hispanic individuals 40 years or older (95% UI, 9.44-15.63). The gender- and age-standardized rate for non-Hispanic Black individuals was lower at 7.16% (95% UI, 5.44-9.24) than for non-Hispanic White individuals and Hispanic individuals. The gender- and age-standardized rate for individuals of all other race and ethnicities 40 years or older was also lower at 10.47% (95% UI, 7.23-14.64) than for non-Hispanic White individuals, but this result was highly uncertain. High uncertainty refers to overlapping UIs.

Table. Estimated Prevalence of People Living With Early and Late Age-Related Macular Degeneration (AMD), Stratified by Gender and Race and Ethnicity.

Characteristic	Mean (2.5th percentile-97.5th percentile)
Characteristic	Prevalence count, in millions (95% UI)	Prevalence rate, % (95% UI)
Early-stage AMD
Sex
Female	9.21 (7.22-11.51)	10.73 (8.42-13.46)
Male	9.14 (7.19-11.51)	12.72 (9.96-15.99)
Total	18.34 (15.30-22.03)	11.64 (9.71-13.98)
Ethnicity^a
Hispanic	2.06 (1.59-2.61)	12.17 (9.44-15.63)
Non-Hispanic
Race^a
Black	1.14 (0.87-1.47)	7.16 (5.44-9.24)
White	14.03 (11.14-17.65)	12.30 (9.81-15.41)
Other^b	1.11 (0.76-1.54)	10.47 (7.23-14.64)
Late-stage AMD
Sex
Female	0.88 (0.53-1.35)	0.94 (0.57-1.43)
Male	0.60 (0.36-0.91)	0.95 (0.56-1.43)
Total	1.49 (0.97-2.15)	0.94 (0.62-1.36)
Ethnicity^a
Hispanic	0.05 (0.02-0.10)	0.38 (0.16-0.79)
Non-Hispanic
Race^a
Black	0.09 (0.05-0.14)	0.65 (0.37-1.03)
White	1.25 (0.78-1.86)	1.03 (0.65-1.52)
Other^b	0.09 (0.04-0.18)	1.02 (0.44-1.99)

Open in a new tab

Abbreviation: UI, uncertainty interval.

^{^a}

Ethnicity and race were self-reported.

^{^b}

Includes Asian individuals of any origin, American Indian individuals, Alaska Native individuals, Pacific Islander individuals, multiracial individuals, and other groups not otherwise classified.

Crude prevalence rates of early-stage AMD among the population 40 years or older ranged from 5.66% (95% UI, 4.79-6.60) in Washington, DC, to 17.08% (95% UI, 13.98-20.90) in Rhode Island. Age-, gender-, and race and ethnicity– standardized prevalence among people 40 years or older ranged from 7.28% (95% UI, 6.05-8.77) in Washington, DC, to a high of 16.85% (95% UI, 14.03-20.31) in Hawaii (Figure 2; eTable and eFigure 1 in the Supplement). Case counts and crude and standardized prevalence estimates created by this study are reported by strata at the state and county level on the CDC VEHSS website.²⁷

Late-Stage AMD

We estimated that 1.49 million people (95% UI, 0.97-2.15) were living with late-stage AMD in the US in 2019, corresponding to a prevalence rate of 0.94% (95% UI, 0.62-1.36) among the population 40 years or older (Table). Prevalence of late-stage AMD increased with age from 0.02% (95% UI, 0.01-0.05) among those 40 to 44 years (Figure 3) to 11.39% (95% UI, 6.45-18.46) among people 85 years or older. The race and ethnicity– and age-standardized rate of late-stage AMD for women (0.94%; 95% UI, 0.57-1.43) was nearly identical to that for men (0.95%; 95% UI, 0.56-1.43). The gender- and age-standardized rate of late AMD for non-Hispanic White individuals 40 years and older was 1.03% (95% UI, 0.65-1.52). Gender- and age-standardized rates were 0.38% (95% UI, 0.16-0.79) for Hispanic individuals and 0.65% (95% UI, 0.37 to 1.03) for non-Hispanic Black individuals, both lower than that of non-Hispanic White individuals. The gender- and age-standardized rate for people of all other race and ethnicities who were 40 years or older was nearly identical to that of non-Hispanic White individuals at 1.02% (95% UI, 0.44-1.99).

Crude prevalence rates of late-stage AMD among the population 40 years or older ranged from 0.53% (95% UI, 0.35-0.79) in Washington, DC, to 1.49% (95% UI, 0.93-2.22) in North Dakota. Age-, gender-, and race and ethnicity– standardized prevalence for those 40 years or older ranged from a low of 0.61% (95% UI, 0.40-0.88) in Hawaii to a high of 1.16% (95% UI, 0.76-1.69) in North Dakota (Figure 4; eFigure 2 in the Supplement).

Discussion

We estimated that approximately 20 million individuals in the US were living with AMD in 2019, 18.34 million with early-stage AMD (95% UI, 15.30-22.03) and 1.49 million with late-stage AMD (95% UI, 0.97-2.15). Gender- and age-standardized rates of early-stage AMD were lower for non-Hispanic Black individuals than for other race and ethnicity groups and gender- and age-standardized rates of late-stage AMD were lower for non-Hispanic Black and Hispanic individuals as compared with non-Hispanic White or other ethnicity individuals. AMD prevalence varied at the state and county level after adjusting for demographics. State and county estimates were highly uncertain, and many UIs overlapped. Primary contributions of this work as compared with earlier estimates include updating US population demographics, the inclusion of individuals in group quarters, inclusion of newer population-based study data, and estimation of state and county estimates.

Our estimated prevalence rate of late-stage AMD, 0.94% (95% UI, 0.62-1.36) among those aged 40 years or older, is comparable with other studies that used similar definitions of choroidal neovascularization and geographic atrophy. Using data collected from PBS during the 1980s and 1990s in the US and abroad, Friedman et al²¹ estimated a prevalence of late-stage AMD of 1.47% (95% CI, 1.38-1.55) among individuals in the US 40 years or older in the year 2000. Using 2005 to 2008 NHANES data, Klein et al¹⁸ estimated a prevalence rate of late AMD of 0.8% among people 40 years or older, but that estimate excluded adjustments for people living in institutionalized settings or increased aging of the US population from 2008 to 2019. Rudnicka et al²² used studies published between 1982 and 2006 to estimate late-stage AMD prevalence by 5-year age groups.²² Klein at al¹⁸ and Rudnicka et al²² noted declines in prevalence when comparing data from the 2000s with data collected in earlier decades, observations that are consistent with our estimates.

We estimated a 2019 prevalence rate of early-stage AMD of 11.64% (95% UI, 9.71-13.98) for individuals 40 years or older using definitions in concordance with clinical staging definitions of intermediate AMD,¹⁴ as data indicate that these individuals are at elevated risk of progression to late-stage AMD.²³ This definition excludes those with small drusen (less than 63 μm in diameter), few medium drusen (63-124 μm in diameter), and/or minimally detected or no RPE abnormalities in the macula but includes individuals with drusen between 125 and 500 μm or RPE abnormalities in either eye. Applying the same definition of early-stage AMD to the 2005 to 2008 NHANES data results in a prevalence of 12.7% for noninstitutionalized individuals 50 years or older before adjusting for missing data or additional modeling.²⁴ Forecasting estimates predicted a 2020 prevalence of early-stage AMD of 12.0% among persons 50 years or older.⁴ Our current estimates are slightly higher than these earlier projections when accounting for the inclusion of individuals 40 years or older. This difference may have been due to our use of missing data imputation in NHANES, the use of the model to estimate increases in prevalence older than 80 years, use of 2019 population data, which includes more people in the oldest age groups, and the use of PBS, which estimated a higher prevalence for Hispanic people than seen in NHANES (eAppendix 2 in the Supplement).

Limitations

Our study is limited by several factors. First, NHANES data were last collected in 2008 and some of the PBS data were even older. Furthermore, the NHANES data used to estimate early- and late-stage AMD status were missing for 9.01% and 6.98% of respondents, respectively. We used imputation methods to account for missing data, which resulted in 15% higher estimates of early-stage AMD and 40% higher estimates of late-stage AMD, as compared with a complete case analysis (eAppendix 2 in the Supplement). In other research, the strongest predictor of ungradable ophthalmic retinal images was increased age, which is strongly associated with higher incidence of early- and late-stage AMD.²⁵ Research on ophthalmic photographs used to detect diabetic retinopathy found that using better photographic technology to repeat photographs that were not possible to grade using previous technology increased the detection of diabetic retinopathy by 85%.²⁶

Second, we used Medicare Part B FFS data on diagnosed prevalence of AMD to estimate variation by county. Our assumption in using these claims to estimate county variation is that medically diagnosed prevalence is strongly correlated with true prevalence after model adjustments. However, Medicare FFS diagnoses may be influenced by the supply of ophthalmologists. Furthermore, the Medicare FFS population may be sicker than the overall Medicare population due to the exclusion of Medicare Advantage patients. To account for potential biases, our model of county-level random effects adjusted for the number of ophthalmologists per capita and the percentage of Medicare patients enrolled in Medicare Advantage at the county level. We were unable to control for other health system factors that might drive diagnosis, such as differences in practice patterns or pharmaceutical marketing. Medicare FFS data limitations likely drive anomalies in our results, such as the result that Hawaii has the highest standardized rate of early-stage AMD and the lowest standardized rate of late-stage AMD, although the population of Hawaii is also quite different from most other states.

Third, our model assumes that prevalence rates observed in the NHANES noninstitutionalized population are applicable to older adults living in group quarters, such as assisted living or nursing home facilities as no studies have measured AMD prevalence in these settings. This assumption likely biases our estimates downward as older adults in group quarters are generally less healthy than those dwelling in community settings.

Conclusions

We estimate that 19.83 million individuals in the US were living with some form of AMD in 2019: 18.34 million with early-stage AMD (95% UI, 15.30-22.03) and 1.49 million with late-stage (95% UI, 0.97-2.15) AMD. We also estimated substantial variation in the prevalence of both early- and late-stage AMD at the state and county level, even after accounting for differences in demographics. Readers should be aware that the NHANES and PBS data used are old and newer data would improve these estimates. Until such data are available, this research can be used for surveillance and public health planning purposes.

Supplement.

eAppendix 1. Imputation

eAppendix 2. Statistical Model

eAppendix 3. Validation and Verification

eTable. AMD Prevalence Rates (Percent) by U.S. State in 2019

eFigure 1. Age-, Gender-, and Race/Ethnicity–Standardized Early-Stage AMD Prevalence by State in 2019, with 95% Uncertainty Intervals

eFigure 2. Age-, Gender-, and Race/Ethnicity–Standardized Late-Stage AMD Prevalence by State in 2019, with 95% Uncertainty Intervals

eReferences

Click here for additional data file.^{(697.6KB, pdf)}

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26.Silva PS, Horton MB, Clary D, et al. Identification of diabetic retinopathy and ungradable image rate with ultrawide field imaging in a national teleophthalmology program. Ophthalmology. 2016;123(6):1360-1367. doi: 10.1016/j.ophtha.2016.01.043 [DOI] [PubMed] [Google Scholar]
27.US Centers for Disease Control and Prevention . Vision and Eye Health Surveillance System (VEHSS). Accessed October 3, 2022. https://www.cdc.gov/visionhealth/vehss/

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplement.

eAppendix 1. Imputation

eAppendix 2. Statistical Model

eAppendix 3. Validation and Verification

eTable. AMD Prevalence Rates (Percent) by U.S. State in 2019

eFigure 1. Age-, Gender-, and Race/Ethnicity–Standardized Early-Stage AMD Prevalence by State in 2019, with 95% Uncertainty Intervals

eFigure 2. Age-, Gender-, and Race/Ethnicity–Standardized Late-Stage AMD Prevalence by State in 2019, with 95% Uncertainty Intervals

eReferences

Click here for additional data file.^{(697.6KB, pdf)}

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PERMALINK

Prevalence of Age-Related Macular Degeneration in the US in 2019

David B Rein, PhD, MPA

John S Wittenborn, BS

Zeb Burke-Conte, BS

Rohit Gulia, MS

Toshana Robalik, BS

Joshua R Ehrlich, MD, MPH

Elizabeth A Lundeen, PhD

Abraham D Flaxman, PhD

Key Points

Question

Findings

Meaning

Abstract

Importance

Objective

Design, Setting, and Participants

Data Sources

Study Selection

Data Extraction and Synthesis

Main Outcomes or Measures

Results

Conclusions and Relevance

Introduction

Methods

Strategy

Data

Estimation

Results

Early-Stage AMD

Table. Estimated Prevalence of People Living With Early and Late Age-Related Macular Degeneration (AMD), Stratified by Gender and Race and Ethnicity.

Figure 1. Crude Prevalence Rate of Early-Stage Age-Related Macular Degeneration (AMD) by Age for All Racial and Ethnic Groups in 2019.

Figure 2. Age-, Gender-, and Race and Ethnicity–Standardized Early-Stage Age-Related Macular Degeneration Prevalence Rate by County in 2019.

Late-Stage AMD

Figure 3. Crude Prevalence Rate of Late-Stage Age-Related Macular Degeneration (AMD) by Age for All Racial and Ethnic Groups in 2019.

Figure 4. Age-, Gender-, and Race and Ethnicity–Standardized Late-Stage Age-Related Macular Degeneration Prevalence Rate by County in 2019.

Discussion

Limitations

Conclusions

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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