Trends in the Diagnosed Prevalence and Incidence of Major Eye Diseases in Medicare Part B Fee-for-Service Beneficiaries 68 Years of Age or Older.

PURPOSE: To study contemporary trends in the diagnosed prevalence and incidence of age-related eye diseases among Medicare Fee-for-Service (FFS) beneficiaries. DESIGN: Analysis of Medicare administrative claims data. PARTICIPANTS: Medicare FFS beneficiaries 68 years of age and older from 2005 through 2020 who were enrolled continuously in both Part A and Part B for 3 years, including the index year and a 2-year lookback period. METHODS: Annual cross-sectional diagnosed prevalence and incidence rates were calculated. Age standardization was performed using the direct standardization method to account for changes in the age structure of the study population. Rates stratified by demographics (age, sex, race, and ethnicity) also were calculated. MAIN OUTCOME MEASURES: Annual prevalence and incidence of diagnosed age-related macular degeneration (AMD), diabetic retinopathy (DR) (among those with diabetes), and glaucoma. RESULTS: At baseline, in 2005, 60% of included beneficiaries were female, 20% were 85 years of age or older, 86% were non-Hispanic White, and one-quarter had a diagnosis of diabetes. From 2005 through 2019, the prevalence of a diagnosis of any of the conditions studied increased from 16.4% (n = 3 628 996) to 17.9% (n = 3 731 281). Diagnosed incidence decreased over this period from 4.9% (n = 954 878) in 2005 to 4.2% in 2019 (n = 757 696). The diagnosed prevalence of AMD increased from 6.8% (n = 1 504 770) to 9.4% (n = 1 965 176); the diagnosed prevalence of any DR among those with diabetes decreased from 9.3% (n = 504 135) to 9.0% (n = 532 859), although the diagnosed prevalence of vision-threatening DR increased from 2.0% to 3.4%; and the diagnosed prevalence of any diagnosed glaucoma decreased from 8.8% (n = 1 951 141) to 8.1% (n = 1 692 837). In 2020, the diagnosed prevalence and incidence of all diagnoses decreased. During the study period, we detected demographic differences in the prevalence and incidence of diagnosis of each condition. CONCLUSIONS: This study presents updated data on the prevalence and incidence of diagnosed major chronic, age-related eye diseases among Medicare FFS beneficiaries. Compared with older epidemiologic estimates, we found that the diagnosed prevalence of each condition studied was higher in more recent years. These findings may inform public health and policy planning and resource allocation to address the eye health of an increasingly older United States population. FINANCIAL DISCLOSURE(S): The author(s) have no proprietary or commercial interest in any materials discussed in this article.

Prevalence of Diabetic Retinopathy in the US in 2021.

Importance: Diabetic retinopathy (DR) is a common microvascular complication of diabetes and a leading cause of blindness among working-age adults in the US. Objective: To update estimates of DR and vision-threatening diabetic retinopathy (VTDR) prevalence by demographic factors and US county and state. Data Sources: The study team included data from the National Health and Nutrition Examination Survey (2005 to 2008 and 2017 to March 2020), Medicare fee-for-service claims (2018), IBM MarketScan commercial insurance claims (2016), population-based studies of adult eye disease (2001 to 2016), 2 studies of diabetes in youth (2021 and 2023), and a previously published analysis of diabetes by county (2012). The study team used population estimates from the US Census Bureau. Study Selection: The study team included relevant data from the US Centers for Disease Control and Prevention's Vision and Eye Health Surveillance System. Data Extraction and Synthesis: Using bayesian meta-regression methods, the study team estimated the prevalence of DR and VTDR stratified by age, a nondifferentiated sex and gender measure, race, ethnicity, and US county and state. Main Outcomes and Measures: The study team defined individuals with diabetes as those who had a hemoglobin A1c level at 6.5% or more, took insulin, or reported ever having been told by a physician or health care professional that they have diabetes. The study team defined DR as any retinopathy in the presence of diabetes, including nonproliferative retinopathy (mild, moderate, or severe), proliferative retinopathy, or macular edema. The study team defined VTDR as having, in the presence of diabetes, severe nonproliferative retinopathy, proliferative retinopathy, panretinal photocoagulation scars, or macular edema. Results: This study used data from nationally representative and local population-based studies that represent the populations in which they were conducted. For 2021, the study team estimated 9.60 million people (95% uncertainty interval [UI], 7.90-11.55) living with DR, corresponding to a prevalence rate of 26.43% (95% UI, 21.95-31.60) among people with diabetes. The study team estimated 1.84 million people (95% UI, 1.41-2.40) living with VTDR, corresponding to a prevalence rate of 5.06% (95% UI, 3.90-6.57) among people with diabetes. Prevalence of DR and VTDR varied by demographic characteristics and geography. Conclusions and Relevance: US prevalence of diabetes-related eye disease remains high. These updated estimates on the burden and geographic distribution of diabetes-related eye disease can be used to inform the allocation of public health resources and interventions to communities and populations at highest risk.

Validity of Administrative Claims and Electronic Health Registry Data From a Single Practice for Eye Health Surveillance.

Importance: Diagnostic information from administrative claims and electronic health record (EHR) data may serve as an important resource for surveillance of vision and eye health, but the accuracy and validity of these sources are unknown. Objective: To estimate the accuracy of diagnosis codes in administrative claims and EHRs compared to retrospective medical record review. Design, Setting, and Participants: This cross-sectional study compared the presence and prevalence of eye disorders based on diagnostic codes in EHR and claims records vs clinical medical record review at University of Washington-affiliated ophthalmology or optometry clinics from May 2018 to April 2020. Patients 16 years and older with an eye examination in the previous 2 years were included, oversampled for diagnosed major eye diseases and visual acuity loss. Exposures: Patients were assigned to vision and eye health condition categories based on diagnosis codes present in their billing claims history and EHR using the diagnostic case definitions of the US Centers for Disease Control and Prevention Vision and Eye Health Surveillance System (VEHSS) as well as clinical assessment based on retrospective medical record review. Main Outcome and Measures: Accuracy was measured as area under the receiver operating characteristic curve (AUC) of claims and EHR-based diagnostic coding vs retrospective review of clinical assessments and treatment plans. Results: Among 669 participants (mean [range] age, 66.1 [16-99] years; 357 [53.4%] female), identification of diseases in billing claims and EHR data using VEHSS case definitions was accurate for diabetic retinopathy (claims AUC, 0.94; 95% CI, 0.91-0.98; EHR AUC, 0.97; 95% CI, 0.95-0.99), glaucoma (claims AUC, 0.90; 95% CI, 0.88-0.93; EHR AUC, 0.93; 95% CI, 0.90-0.95), age-related macular degeneration (claims AUC, 0.87; 95% CI, 0.83-0.92; EHR AUC, 0.96; 95% CI, 0.94-0.98), and cataracts (claims AUC, 0.82; 95% CI, 0.79-0.86; EHR AUC, 0.91; 95% CI, 0.89-0.93). However, several condition categories showed low validity with AUCs below 0.7, including diagnosed disorders of refraction and accommodation (claims AUC, 0.54; 95% CI, 0.49-0.60; EHR AUC, 0.61; 95% CI, 0.56-0.67), diagnosed blindness and low vision (claims AUC, 0.56; 95% CI, 0.53-0.58; EHR AUC, 0.57; 95% CI, 0.54-0.59), and orbital and external diseases (claims AUC, 0.63; 95% CI, 0.57-0.69; EHR AUC, 0.65; 95% CI, 0.59-0.70). Conclusion and Relevance: In this cross-sectional study of current and recent ophthalmology patients with high rates of eye disorders and vision loss, identification of major vision-threatening eye disorders based on diagnosis codes in claims and EHR records was accurate. However, vision loss, refractive error, and other broadly defined or lower-risk disorder categories were less accurately identified by diagnosis codes in claims and EHR data.

Prevalence, Progression, and Modifiable Risk Factors for Diabetic Retinopathy in Youth and Young Adults With Youth-Onset Type 1 and Type 2 Diabetes: The SEARCH for Diabetes in Youth Study.

OBJECTIVE: To determine the prevalence, progression, and modifiable risk factors associated with the development of diabetic retinopathy (DR) in a population-based cohort of youth-onset diabetes. RESEARCH DESIGN AND METHODS: We conducted a multicenter, population-based prospective cohort study (2002-2019) of youth and young adults with youth-onset type 1 diabetes (n = 2,519) and type 2 diabetes (n = 447). Modifiable factors included baseline and change from baseline to follow-up in BMI z score, waist/height ratio, systolic and diastolic blood pressure z score, and A1C. DR included evidence of mild or moderate nonproliferative DR or proliferative retinopathy. Prevalence estimates were standardized to estimate the burden of DR, and inverse probability weighting for censoring was applied for estimating risk factors for DR at two points of follow-up. RESULTS: DR in youth-onset type 1 and type 2 diabetes is highly prevalent, with 52% of those with type 1 diabetes and 56% of those with type 2 diabetes demonstrating retinal changes at follow-up (mean [SD] 12.5 [2.2] years from diagnosis). Higher baseline A1C, increase in A1C across follow-up, and increase in diastolic and systolic blood pressure were associated with the observation of DR at follow-up for both diabetes types. Increase in A1C across follow-up was associated with retinopathy progression. BMI z score and waist/height ratio were inconsistently associated, with both positive and inverse associations noted. CONCLUSIONS: Extrapolated to all youth-onset diabetes in the U.S., we estimate 110,051 cases of DR developing within ∼12 years postdiagnosis. Tight glucose and blood pressure management may offer the opportunity to mitigate development and progression of DR in youth-onset diabetes.

Association Between Social Determinants of Health and Examination-Based Vision Loss vs Self-reported Vision Measures.

Importance: Recent evidence suggests that social determinants of health (SDOH) affect vision loss, but it is unclear whether estimated associations differ between clinically evaluated and self-reported vision loss. Objective: To identify associations between SDOH and evaluated vision impairment and to assess whether these associations hold when examining self-reported vision loss. Design, Setting, and Participants: This population-based cross-sectional comparison included participants 12 years and older in the 2005 to 2008 National Health and Nutrition Examination Survey (NHANES), participants of all ages (infants and older) in the 2019 American Community Survey (ACS), and adults 18 years and older in the 2019 Behavioral Risk Factor Surveillance System (BRFSS). Exposures: Five domains of SDOH that are based on Healthy People 2030: economic stability, education access and quality, health care access and quality, neighborhood and built environment, and social and community context. Main Outcomes and Measures: Presenting vision impairment of 20/40 or worse in the better-seeing eye (NHANES) and self-reported blindness or serious difficulty seeing, even with glasses (ACS and BRFSS). Results: Of 3 649 085 included participants, 1 873 893 were female (51.1%) and 2 504 206 were White (64.4%). SDOH across domains of economic stability, educational attainment, health care access and quality, neighborhood and built environment, and social context were significant predictors of poor vision. For example, higher income (poverty to income ratio [NHANES]: OR, 0.91; 95% CI, 0.85-0.98; [ACS]: OR, 0.93; 95% CI, 0.93-0.94; categorical income [BRFSS:<$15 000 reference]: $15 000-$24 999; OR, 0.91; 95% CI, 0.91-0.91; $25 000-$34 999: OR, 0.80; 95% CI, 0.80-0.80; $35 000-$49 999: OR, 0.71; 95% CI, 0.71-0.72; ≥$50 000: OR, 0.49; 95% CI, 0.49-0.49), employment (BRFSS: OR, 0.66; 95% CI, 0.66-0.66; ACS: OR, 0.55; 95% CI, 0.54-0.55), and owning a home (NHANES: OR, 0.85; 95% CI, 0.73-1.00; BRFSS: OR, 0.82; 95% CI, 0.82-0.82; ACS: OR, 0.79; 95% CI, 0.79-0.79) were associated with lower odds of vision loss. The study team identified no differences in the general direction of the associations when using either clinically evaluated or self-reported vision measures. Conclusions and Relevance: The study team found evidence that associations between SDOH and vision impairment track together when using either clinically evaluated or self-reported vision loss. These findings support the use of self-reported vision data in a surveillance system to track trends in SDOH and vision health outcomes within subnational geographies.

Comparing Telephone Survey Responses to Best-Corrected Visual Acuity to Estimate the Accuracy of Identifying Vision Loss: Validation Study.

BACKGROUND: Self-reported questions on blindness and vision problems are collected in many national surveys. Recently released surveillance estimates on the prevalence of vision loss used self-reported data to predict variation in the prevalence of objectively measured acuity loss among population groups for whom examination data are not available. However, the validity of self-reported measures to predict prevalence and disparities in visual acuity has not been established. OBJECTIVE: This study aimed to estimate the diagnostic accuracy of self-reported vision loss measures compared to best-corrected visual acuity (BCVA), inform the design and selection of questions for future data collection, and identify the concordance between self-reported vision and measured acuity at the population level to support ongoing surveillance efforts. METHODS: We calculated accuracy and correlation between self-reported visual function versus BCVA at the individual and population level among patients from the University of Washington ophthalmology or optometry clinics with a prior eye examination, randomly oversampled for visual acuity loss or diagnosed eye diseases. Self-reported visual function was collected via telephone survey. BCVA was determined based on retrospective chart review. Diagnostic accuracy of questions at the person level was measured based on the area under the receiver operator curve (AUC), whereas population-level accuracy was determined based on correlation. RESULTS: The survey question, "Are you blind or do you have serious difficulty seeing, even when wearing glasses?" had the highest accuracy for identifying patients with blindness (BCVA ≤20/200; AUC=0.797). The highest accuracy for detecting any vision loss (BCVA <20/40) was achieved by responses of "fair," "poor," or "very poor" to the question, "At the present time, would you say your eyesight, with glasses or contact lenses if you wear them, is excellent, good, fair, poor, or very poor" (AUC=0.716). At the population level, the relative relationship between prevalence based on survey questions and BCVA remained stable for most demographic groups, with the only exceptions being groups with small sample sizes, and these differences were generally not significant. CONCLUSIONS: Although survey questions are not considered to be sufficiently accurate to be used as a diagnostic test at the individual level, we did find relatively high levels of accuracy for some questions. At the population level, we found that the relative prevalence of the 2 most accurate survey questions were highly correlated with the prevalence of measured visual acuity loss among nearly all demographic groups. The results of this study suggest that self-reported vision questions fielded in national surveys are likely to yield an accurate and stable signal of vision loss across different population groups, although the actual measure of prevalence from these questions is not directly analogous to that of BCVA.

Trends in the Prevalence and Treatment of Diabetic Macular Edema and Vision-Threatening Diabetic Retinopathy Among Commercially Insured Adults Aged <65 Years.

OBJECTIVE: Examine the 10-year trend in the prevalence and treatment of diabetic macular edema (DME) and vision-threatening diabetic retinopathy (VTDR) among commercially insured adults with diabetes. RESEARCH DESIGN AND METHODS: We analyzed the 10-year trend (2009-2018) in health care claims for adults aged 18-64 years using the IBM MarketScan Database, a national convenience sample of employer-sponsored health insurance. We included patients continuously enrolled in commercial fee-for-service health insurance for 24 months who had a diabetes ICD-9/10-CM code on one or more inpatient or two or more different-day outpatient claims in the index year or previous calendar year. We used diagnosis and procedure codes to calculate the annual prevalence of patients with one or more claims for 1) any DME, 2) either DME or VTDR, and 3) antivascular endothelial growth factor (anti-VEGF) injections and laser photocoagulation treatment, stratified by any DME, VTDR with DME, and VTDR without DME. We calculated the average annual percent change (AAPC). RESULTS: From 2009 to 2018, there was an increase in the annual prevalence of patients with DME or VTDR (2.1% to 3.4%; AAPC 7.5%; P < 0.001) and any DME (0.7% to 2.6%; AAPC 19.8%; P < 0.001). There were sex differences in the annual prevalence of DME or VTDR and any DME, with men having a higher prevalence than women. Annual claims for anti-VEGF injections increased among patients with any DME (327%) and VTDR with DME (206%); laser photocoagulation decreased among patients with any DME (-68%), VTDR with DME (-54%), and VTDR without DME (-62%). CONCLUSIONS: Annual claims for DME or VTDR and anti-VEGF injections increased whereas those for laser photocoagulation decreased among commercially insured adults with diabetes.

Engagement in the Michigan Screening and Intervention for Glaucoma and Eye Health through Telemedicine Program: Comparing the Effect of Clinic versus Community-Based Recruitment Strategies.

PURPOSE: To determine the effectiveness of adding community-based recruitment to clinic-based recruitment to engage participants in a glaucoma detection program. DESIGN: Prospective cohort study. SUBJECTS: Anyone ≥ 18 years of age who does not meet exclusion criteria. METHODS: The Michigan Screening and Intervention for Glaucoma and Eye Health through Telemedicine (MI-SIGHT) program tests a novel way of improving glaucoma detection in communities with populations at high risk for disease, including people who identify as Black and Hispanic and those living with low socioeconomic status. The MI-SIGHT program is conducted in a free clinic (Ypsilanti, MI) and in a federally qualified health center (FQHC) (Flint, MI). Community engagement methods were used to identify outreach strategies to enhance recruitment. Participants were asked "How did you hear about the MI-SIGHT program?" and responses were summarized overall and by clinic and compared between clinic-based and community-based recruitment strategies. MAIN OUTCOME MEASURES: Proportion recruited by location, within or outside of the clinic. RESULTS: In total, 647 participants were recruited in the first 11 months of the study, 356 (55.0%) at the free clinic over 11 months and 291 (45.0%) at the FQHC over 6 months. Participants were on average 54.4 years old (standard deviation = 14.2); 60.9% identified as female, 45.6% identified as Black, 37.8% identified as White, 9.6% identified as Hispanic, and 10.9% had less than high school education. Participants reported hearing about the MI-SIGHT program from a clinic phone call (n = 168, 26.1%), a friend (n = 112, 17.4%), nonmedical clinic staff (n = 100, 15.5%), a clinic doctor (n = 77, 11.9%), an in-clinic brochure or flyer (n = 51, 7.9%), a community flyer (n = 44, 6.8%), the clinic website or social media (n = 28, 4.3%), or an "other" source (n = 65, 10.1%). Recruiting from the community outside the medical clinics increased participation by 265% at the free clinic and 46% at the FQHC. CONCLUSIONS: The Community Advisory Board recommendation to use community-based recruitment strategies in addition to clinic-based strategies for recruitment resulted in increased program participation. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found after the references.

Health Care Access and Use Among Adults With and Without Vision Impairment: Behavioral Risk Factor Surveillance System, 2018.

INTRODUCTION: Adults with vision impairment may have unique needs when accessing health care to maintain good health. Our study examined the relationship between vision status and access to and use of health care. METHODS: We analyzed data on adults aged 18 years or older who participated in the 2018 Behavioral Risk Factor Surveillance System. Vision impairment was identified by a yes response to the question "Are you blind or do you have serious difficulty seeing, even when wearing glasses?" Survey questions assessed health care access over the past year (having health insurance coverage, a usual health care provider, or unmet health care needs because of cost) and use of health care during that period (routine checkup and dental visit). We estimated age-adjusted prevalence of our outcomes of interest and used bivariate analyses to compare estimates of the outcomes by vision impairment status. RESULTS: The prevalence of self-reported vision impairment was 5.3%. Compared with adults without impaired vision, adults with vision impairment had a lower prevalence of having health insurance coverage (80.6% vs 87.6%), a usual health care provider (71.9% vs 75.7%), or a dental visit in the past year (52.9% vs 67.2%) and a higher prevalence of having an unmet health care need in the past year because of cost (29.2% vs 12.6%). CONCLUSION: Adults with vision impairment reported lower access to and use of health care than those without. Further research can better identify and understand barriers to care to improve access to and use of health care among this population.