Geographical barriers to surgical treatment for cataract among California Medicare beneficiaries

In California’s Medicare population, rural residence is associated with longer travel time and distance for cataract surgery. This suggests geographic barriers may contribute to disparities in vision care access.

Abstract

Purpose:

To examine geographic factors associated with the likelihood of receiving cataract surgery.

Setting:

Administrative claims data from 2017 California Medicare Database.

Design:

Cross-sectional.

Methods:

Beneficiaries were included if they were 65 years or older, had a valid postal code, active coverage with Medicare parts A and B, and cataract diagnosis defined by International Classification of Diseases, 10th Revision, Clinic Modification codes. Cataract surgery was defined using billing codes. The association between rural residence and distance and time traveled for cataract surgery was assessed using linear regression models. The odds of receiving cataract surgery were estimated using logistic regression models.

Results:

Among 445 164 beneficiaries with cataract, 127 314 (28.6%) reported rural residency. Rural residence was associated with increased travel time and distance for cataract surgery (+0.16 hours [95% CI 0.15-0.17]; +9.7 miles [95% CI 9.3-10.2]). For every additional 100 miles traveled, odds of cataract surgery decreased by 14% (adjusted odds ratio [aOR] 0.86, 95% CI 0.84-0.87). Stratified analysis demonstrated that Black beneficiaries had the greatest reduced odds of cataract surgery per each 100 additional miles traveled (aOR 0.38, 95% CI 0.31-0.45), followed by Hispanic/Latino beneficiaries (aOR 0.67, 95% CI 0.63-0.72).

Conclusions:

Beneficiaries living in rural California had increased travel distance and time for cataract surgery. Black beneficiaries had the strongest association between increased travel distance and decreased odds of cataract surgery. These findings suggest possible disparities in surgical treatment for cataract among rural populations. Further studies are needed to better understand and address geographic disparities to eyecare.

Cataract is the most common reversible cause of vision impairment in the United States and accounts for approximately 60% of Medicare vision costs.1,2 By 2050, the number of people in the United States with cataract will double from 24.4 million to about 50 million.3 Cataract surgeries increased in persons older than 65 years from 1990 to 2010, and Medicare beneficiaries account for approximately 80% of cataract surgeries performed.4,5 Moreover, rural populations are known to face unique barriers in receiving eyecare and have low utilization of eyecare services.6

Studies have suggested that patients in rural areas have decreased access to cataract surgery.7,8 A study using 2012 group-level Medicare data with estimated projections of expected number of cataract surgeries found that increased travel distance to ophthalmic surgical providers potentially explain lower than expected rates of cataract surgery in rural areas.7 Geographic disparities in cataract surgery are also evident internationally. In England, despite a publicly funded healthcare system under the National Health Service (NHS), rural areas report greater patient need. In response, the NHS implemented more local sites for cataract surgery to reduce access to treatment.9,10 In addition, a study from Florida using group-level ambulatory surgery data found that patient-level and community-level factors including being male, Black, Asian, Hispanic, and residing in higher poverty ZIP codes were associated with a higher likelihood of receiving complex cataract surgery.11

California (CA), the most populous U.S. state, was estimated to have a rural population of 2.3 million in 2020.12 Timely cataract surgery improves overall quality of life and reduces significant morbidity and mortality, and thus, analyzing cataract surgical outcomes in a racially and ethnically diverse state such as CA can highlight areas of geographic and other social disparities at the individual level.13,14 Currently, the association between geographic factors and surgical treatment of cataract is not well understood given reliance on group-level rather than individual-level data, and it is unclear if disparities exist in access to ophthalmic surgical care in this large and racially and ethnically diverse state.

Using the entire sample of 2017 CA Medicare beneficiaries, this study aims to compare travel distance and travel time to cataract surgery among beneficiaries residing in rural vs nonrural areas in CA, and to determine whether travel distance and travel time are associated with the occurrence of cataract surgery. In addition, this study investigates whether there was effect measure modification (EMM) of these associations by race and ethnicity.15

METHODS

This cross-sectional study was conducted using the 2017 CA Medicare Master Beneficiary Summary File (MBSF) and Part B Carrier Claim files from the Centers for Medicare & Medicaid Services (CMS). Beneficiaries were included if they resided in CA in 2017 (i.e., had a valid CA Zone Improvement Plan [ZIP] code for their residential address), were 65 years or older, enrolled in Medicare Part A and Part B, had at least 1 Part B claim in 2017 as a proxy for active use of benefits, and had an International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) diagnosis code of cataract (Supplement 1, available at http://links.lww.com/JRS/B575).

Beneficiaries with a diagnosis of cataract comprised the primary study population. Key variables that were assessed included (1) nonrural or rural beneficiary residence, (2) estimated patient-to-cataract surgery provider travel distance and travel time, and (3) occurrence of cataract surgery. Beneficiary residential address ZIP codes were categorized into nonrural or rural designations using the Health Resources & Services Administration’s 2010 List of Rural Counties and Designated Eligible Census Tracts in Metropolitan Counties.16 The 2017 Social Security Administration (SSA) to Federal Information Processing Standard Core-Based Statistical Area and Metropolitan and Micropolitan Statistical Area County Crosswalk file was used to find the corresponding SSA county code to the county name, and the fourth Quarter 2017 U.S. Department of Housing and Urban Development U.S. Postal Service ZIP Code Crosswalk file was used to identify the census tracts within ZIP codes.17,18 In general, ZIP codes are much larger areas and include an average of thousands of houses compared with postcode areas in the United Kingdom that include a few hundred addresses.19 Travel distance was calculated as miles traveled, and travel time was defined in hours traveled. ZIP codes for beneficiary residence and cataract surgery provider location were linked to the centroid of a ZIP Code Tabulation Area for geo-referencing and spatial analysis in ArcGIS Pro (v. 2.9, Environmental Systems Research Institute).20 ZIP Code Tabulation Area centroids may not accurately represent the patient’s or provider’s exact location. Travel time and travel distance analysis from patient ZIP code to provider ZIP code was performed using ArcGIS Pro’s route analysis function.21,22 To estimate driving times, ArcGIS Pro models the movement of motor vehicles and calculates dynamic travel speeds based on typical traffic conditions based on Global Positioning System data provided by HERE Technologies.23 The occurrence of surgical treatment for cataract was defined using Current Procedural Terminology (CPT) codes for cataract surgery (Supplement 2, available at http://links.lww.com/JRS/B576). CPT codes are unique to the U.S. Medicare billing system and differ from ICD-10 codes; generally, CPT codes are used to bill for procedures, while ICD-10 codes identify diagnoses during medical visits. SNOMED codes were not used because of differences in granularity and organizing principles compared with CPT codes.24 CPT codes are more reliable in assessing the occurrence of procedures in the Medicare database.

Covariates included age, sex, race and ethnicity, and systemic disease burden estimated by the Charlson Comorbidity Index (CCI) score.25 Patient sex was coded as male or female based on the CMS Common Medicare Environment; race and ethnicity was coded as White, Black, Asian, Hispanic/Latino, or Other based on the Research Triangle Institute race code; and age was categorized as 65 to 69, 70 to 74, 75 to 79, 80 to 84, 85 to 89, and 90 years and older. The CCI score was categorized as no comorbidities (CCI score of 0), mild (CCI scores of 1-2), moderate (CCI scores of 3-4), and severe (CCI scores ≥5).26,27

Descriptive statistics for categorical and continuous variables were calculated for beneficiaries with cataract diagnosis, comparing those who received cataract surgery and those who did not receive cataract surgery. Chi-squared tests were performed to determine the differences in categorical variables (age, sex, race and ethnicity; CCI score; and nonrural/rural residence) between subgroups who received cataract surgery vs those who did not. Given the skewed distribution of the travel distance and travel time variables, Kruskal-Wallis tests were performed to determine the differences in travel distance and travel time between subgroups who received cataract surgery vs those who did not.

Within each population of beneficiaries with cataract, 4 associations were examined: (1) the association between rural vs nonrural residence and total estimated travel distance to cataract surgery among patients with cataract surgery, (2) the association between rural vs nonrural residence and total estimated travel time to cataract surgery among patient with cataract surgery, (3) the association between travel distance and odds of cataract surgery among all patients with cataract, and (4) the association between travel time and odds cataract surgery among all patients with cataract. Covariates included in models of associations between rural vs nonrural residence and travel distance and time for cataract surgery included age, sex, race and ethnicity, and CCI. Covariates included in models of associations between travel distance and time and odds of cataract surgery included age, sex, race and ethnicity, CCI, and rural/nonrural residence. Unadjusted and adjusted estimates of mean difference in travel distance and mean difference in travel time by rural vs nonrural residence were assessed using linear regression. Unadjusted and adjusted odds of cataract surgery by travel distance and travel time in beneficiaries with cataract were estimated using separate logistic regression models. For the analysis of effect modification of travel distance and time by race and ethnicity, 2 multivariate logistic regression models were constructed with cataract surgery as the outcome and travel distance as the exposure, adjusting for age, sex, race and ethnicity, CCI score, and rural/nonrural residence, with an interaction term between travel distance and race and ethnicity. If the (travel distance) × (race and ethnicity) interaction term was found to be statistically significant in the model, then separate multivariate models with cataract surgery as the outcome and travel distance as the exposure were performed, stratified by each racial and ethnic group. All statistical analyses were performed using SAS v. 9.4 (SAS Institute, Inc.). The study was approved by the Institutional Review Board of the University of California, Los Angeles (IRB# 17-000914). This study followed the Strengthening the Reporting of Observational Studies in Epidemiology reporting guidelines.

RESULTS

There were 445 164 beneficiaries with cataract in the 2017 CA Medicare population 65 years and older with a valid CA ZIP code, Part A and Part B coverage, and at least 1 Part B claim. A total of 130 beneficiaries were removed from the study sample because of having ZIP codes that could not be geocoded on ArcGIS Pro. Table 1 describes baseline characteristics of the study population. Of the beneficiaries with cataracts, 93 460 (21.0%) received cataract surgery. There were statistically significant differences by race and ethnicity in the prevalence of cataract surgery; of those who received surgery, 61 069 (63.3%) were White, 3013 (3.2%) were Black, 11 966 (12.8%) were Asian, 13 496 (14.9%) were Hispanic/Latino, and 3466 (3.7%) were Other race and ethnicity (P < .001). In addition, 27 587 (29.5%) of beneficiaries who received cataract surgery resided in rural CA. For beneficiaries with cataract, there were statistically significant differences in age (P < .0001) and race and ethnicity (P < .0001) in those who received vs did not receive surgery. Furthermore, in those with cataract, there were statistically significant differences in sex (P = .03) and CCI score (P < .0001) for those who received vs did not receive surgery.

Table 1.

Characteristics of 2017 California Medicare beneficiaries with cataract and surgery

Characteristic
All beneficiaries with cataracts (N = 445 164)

Cataract surgery (n = 93 460)
No cataract surgery (n = 351 704)

P value

Age (y), n (%)

 65-69
19 610 (21.0)
85 172 (24.2)
<.0001

 70-74
26 280 (28.1)
101 807 (28.9)

 75-79
23 354 (25.0)
77 211 (22.0)

 80-84
14 837 (15.9)
47 115 (13.4)

 85-89
7046 (7.5)
25 587 (7.3)

 90+
2333 (2.5)
14 812 (4.2)

Sex, n (%)

 Male
38 022 (40.7)
141 739 (40.3)
.03

 Female
55 438 (59.3)
209 965 (59.7)

Race and ethnicity, n (%)

 White
61 069 (65.3)
241 494 (68.7)
<.0001

 Black
3013 (3.2)
13 227 (3.8)

 Asian
11 966 (12.8)
42 011 (11.9)

 Hispanic/Latino
13 946 (14.9)
41 635 (11.8)

 Othera

3466 (3.7)
13 337 (3.8)

CCI score, n (%)

 0
26 208 (28.0)
110 639 (31.5)
<.0001

 1-2
34 945 (37.4)
125 863 (35.8)

 3-4
18 926 (20.3)
66 003 (18.8)

 5+
13 381 (14.3)
49 199 (14.0)

Type of residence, n (%)

 Nonrural
65 873 (70.5)
251 977 (71.6)
<.0001

 Rural
27 587 (29.5)
99 727 (28.4)

Travel time and distance, mean ± SD

 Time (h)
0.43 ± 0.60
0.46 ± 0.79
<.0001

 Distance (miles)
17 ± 33
19 ± 45
<.0001

Beneficiaries living in nonrural ZIP codes traveled an average of 14 miles for cataract surgery (SD 29 miles); however, beneficiaries living in rural ZIP codes traveled an average of 24 miles (SD 40 miles) for cataract surgery. An unadjusted linear regression model comparing rural and nonrural residence and travel distance found this difference to be statistically significant (P < .0001). Beneficiaries living in nonrural ZIP codes traveled an average of 0.39 hours for cataract surgery (SD 0.53 hours); however, beneficiaries living in rural ZIP codes traveled an average of 0.55 hours (SD 0.73 hours) for cataract surgery. An adjusted linear regression model comparing rural and nonrural residence and travel time found this difference to be statistically significant (P < .0001). In adjusted linear regression analyses, beneficiaries with rural residence traveled 9.7 additional miles (β = 9.7 miles, 95% CI 9.3-10.2 miles) and 0.16 additional hours (β = 0.16 hours, 95% CI 0.15-0.17 hours) to those living in nonrural settings (Table 2).

Table 2.

Linear regression models for the difference in travel time and travel distance to cataract surgery

Variable
Model
Cataract surgery (n = 93 460)
β (95% CI)

Travel time (h)

 Rural residency (ref = nonrural)
Unadjusted
0.16 (0.15-0.17)

Adjusteda

0.16 (0.15-0.17)

Travel distance (miles)

 Rural residency (ref = nonrural)
Unadjusted
10.0 (9.5-10.5)

Adjusteda

9.7 (9.3-10.2)

Table 3 summarizes the results of the logistic regressions assessing associations between travel time and travel distance and odds of cataract surgery among beneficiaries with cataract, controlling for rural/nonrural residence (Supplement 3, available at http://links.lww.com/JRS/B577). Among beneficiaries with cataracts (n = 445 164), every additional hour of travel time was associated with 6% decreased adjusted odds of cataract surgery in adjusted analyses (adjusted odds ratio [aOR] 0.94, 95% CI 0.93-0.95). Every additional 100 miles of travel distance was associated with 14% decreased adjusted odds of cataract surgery (aOR 0.86, 95% CI 0.84-0.87).

Table 3.

Logistic regressions assessing associations between travel barriers and odds of cataract surgery

Variable
Interval
Model
Cataract surgery (n = 445 164)
OR (95% CI)

Travel time
1 additional hour
Unadjusted
0.95 (0.94-0.96)

Adjusteda

0.94 (0.93-0.95)

Travel distance
100 additional miles
Unadjusted
0.87 (0.85-0.88)

Adjusteda

0.86 (0.84-0.87)

Table 4 summarizes the results from analyses of the association between travel distance and cataract surgery, stratified by race and ethnicity. There was statistically significant interaction between travel distance and race and ethnicity on the outcome of cataract surgery (P < .0001). For every 100 additional miles of travel required for cataract surgery, White beneficiaries had 8% decreased adjusted odds (aOR 0.92, 95% CI 0.90-0.94), Black beneficiaries had 62% decreased odds (aOR 0.38, 95% CI 0.31-0.45), Hispanic/Latino beneficiaries had 33% decreased adjusted odds (aOR 0.67, 95% CI 0.63-0.72), and Asian beneficiaries had 19% decreased adjusted odds (aOR 0.81, 95% CI 0.76-0.87) of receiving cataract surgery.

Table 4.

Association between travel distance and cataract surgery, stratified by racial and ethnic group

Travel per 100 miles
OR of cataract surgery (95% CI)

Non-Hispanic White
0.92 (0.90-0.94)

Asian
0.81 (0.76-0.87)

Black
0.38 (0.31-0.45)

Hispanic/Latino
0.67 (0.63-0.72)

DISCUSSION

In this study of 2017 CA Medicare beneficiaries with cataract, we quantified individual-level geographic barriers to receiving cataract surgery by comparing the distance and time traveled for cataract surgery between rural and nonrural residents and then examining the association between increased travel distance and travel time and occurrence of cataract surgery, independent of rural/nonrural residence. Compared with beneficiaries in nonrural areas, those in rural areas had increased travel distance and time for cataract surgery. In addition, increased travel distance and time were independently associated with reduced likelihood of cataract surgery, especially for Black and Hispanic/Latino beneficiaries. Given that cataract surgery has been linked to a lower risk of serious injuries such as hip fractures, these findings underscore the importance of improving access to timely surgical care for vulnerable populations.13,28

Previous studies have identified rural geography, distance, and travel time as important factors in accessing ophthalmic care and ophthalmology clinical trials.7,29–35 Internationally, the NHS in England reported the greatest patient need and longest waiting lists for cataract surgery in rural areas.10 When comparing travel time to a main hospital vs a newly established outreach site at a community hospital, they found that rural patients traveling to the main hospital for cataract surgery experienced, on average, 13 minutes longer travel time and incurred 2.4 times greater travel-related costs.9 Lee et al. evaluated access to eyecare in the U.S. Medicare population by calculating driving time to optometrists and ophthalmologists.29 Using group-level regional data on extrapolated estimates, they found that more than 90% of the U.S. Medicare beneficiary population lived within a 30-minute drive of an ophthalmologist, but that patients who live in rural areas must travel farther to seek care from ophthalmologists, resulting in lower rates of cataract surgery.7 Moreover, in a study that identified a total of 8480 cataract surgeons from the Medicare database, only 9.1% were located in nonmetropolitan areas.36 Thus, it is possible that beneficiaries with cataract with greater travel burden could have delayed surgery until they experienced a greater degree of visual impairment. Our analysis similarly found that rural residence and increased travel distance were associated with decreased likelihood of cataract surgery and builds on previous findings by demonstrating consistency of results in the large and diverse CA population using empiric individual-level data.29

A previous study combined national cataract prevalence rates and the Florida State Ambulatory Surgery database to determine the estimated cases of cataract and number of cataract procedures performed in Florida in 2010.37 They segmented counties based on racial composition and excluded counties with large Hispanic/Latino populations because their primary objective was to analyze racial disparities and not possible ethnic disparities, and Hispanic refers to an ethnicity per the United States Office of Management and Budget standards. The study found that Black patients were less likely than their White counterparts to receive necessary cataract surgery, indicating county-level racial disparities in cataract surgical treatment. This study builds on these findings by examining patient-level data and the association between travel distance and time and cataract surgery within a racially and ethnically diverse population that included White, Black, Hispanic/Latino, and Asian racial and ethnic groups. Moreover, geospatial analysis was not restricted to county-level patterns, but rather quantification of distance and residence ZIP code of the individual beneficiary. This showed that increased travel distance and time were associated with decreased odds of cataract surgery. Although a causal mechanism cannot be inferred from our analyses, we hypothesize that this finding is likely due to the accessibility to cataract surgeons or lack thereof. Although a recent systematic review on cataract surgery access found consistently lower utilization among rural populations due to increased travel burdens, no patient-reported outcome data were included. Thus, although travel distance and time are suspected barriers to cataract surgical treatment among minoritized communities, incorporating patient reported data would strengthen this evidence.34,38

In analysis of EMM by race and ethnicity on the association between travel distance and cataract surgery, Black and Hispanic/Latino beneficiaries were found to have increased geographic barriers to cataract surgery in comparison with other racial and ethnic groups. Importantly, race is a social construct and not inherently genetic or reflective of biologic differences.23 A recent study found that Black, Hispanic/Latino, and Asian patients were more likely to demonstrate worse visual acuity resulting from cataracts at the time of cataract surgery.39 In this context, our finding of a potential increase in geographic barriers to cataract surgery for racially and ethnically minoritized individuals suggests a critical need to further investigate barriers to care for these individuals and the role of individual-level and structural-level social determinants of health in contributing to these barriers.40 More specifically, structural-level barriers may include the geographic location of ophthalmologists in urban and high-income areas that lead to increased transportation time for care.38,41 These barriers often intersect with underinvestment in minoritized communities and safety-net health systems in low-income areas.42 To address barriers to care at the local level, it would be beneficial to streamline preoperative testing and medical clearance processes within the same appointment as the cataract diagnosis. In addition, implementing patient navigator programs that proactively identify patient needs and assist with transportation or logistical challenges can further support successful surgical treatment.43,44 Soares et al. and Mallem et al. used ArcGIS Pro’s service area and driving time functions to identify geographic and socioeconomic variables associated with residential proximity to phase 3 ophthalmology clinical trials and travel time to uveitis specialists, respectively.35,45 Both analyses led to the conclusion that more providers are needed in rural areas because patients residing in rural areas are more likely to live under the poverty line, have lower education level, and have higher levels of self-reported visual impairment. However, data used by Mallem et al. was limited to geographic location of uveitis specialists specifically and exclusively examined White and African American racial groups. Soares et al. found geographic barriers for rural resident participation in clinical trials, with analysis based on census tract-level sociodemographic factors and driving time from census tract to clinical trial site. Moreover, in a recent study on cataract surgery utilization within an academic medical health system, Black patients had the lowest rates of cataract surgery utilization and the poorest preoperative visual acuities.46 This study builds on the findings from Mallem and Soares by investigation of additional eye conditions and their treatments with incorporation of the specific location of each individual based on ZIP code. In addition, our study corroborates the findings in Cho et al. but also expands on these findings by incorporating the factor of travel distance and using a large, statewide dataset with individual-level data. Furthermore, the EMM analysis is a first step to incorporate the intersectionality of minoritized communities residing in rural area to eventually develop more inclusive interventions to expand access to ophthalmic care.47,48

The limitations of this study are mainly related to its cross-sectional nature and use of claims data. In addition, the racial and ethnic distribution of our sample does not fully reflect the demographic make-up of the older adult population in California. Selection bias may exist given that Black and Hispanic/Latino Medicare beneficiaries under-enroll in Medicare and underutilize its benefits.49,50 Moreover, we are unable to determine whether there were more complications after cataract surgery among rural vs nonrural beneficiaries. In a post hoc analysis, we found that rural beneficiaries were less likely to receive complex cataract surgery compared with nonrural beneficiaries. However, it is difficult to draw conclusions from this. It may be because rural ophthalmologists may not have the appropriate resources or differences in billing in smaller practices because the use of billing codes to assess diagnoses and procedures creates the possibility of misclassification. Given that claims data have no clinical parameters, it is unknown which beneficiaries met clinical criteria for requiring cataract surgery. By using billing codes, we are unable to differentiate whether the provider location reflects the location of the surgical treatment or the location of where the surgery was billed. Furthermore, there is a possibility of unmeasured confounding. More specifically, we cannot control for factors that influence beneficiaries’ preference. As this study was performed with CA Medicare administrative claims data, the results may not be generalizable to individuals who are younger than 65 years old, have access to private insurance or to those who are uninsured. It would be of benefit to look at databases that include patients referred from federally qualified health centers and other types of insurance that include all minoritized groups. An additional limitation when working with spatial units is the modifiable areal unit problem. The smallest areal unit available on the Medicare MBSF and Part B Carrier Claim files is ZIP code, but Health Resources & Services Administration’s 2010 List of Rural Counties and Designated Eligible Census Tracts in Metropolitan Counties is listed by county code and census tracts which may affect the accuracy of measurements.51,52 More specifically, using ZIP-based centroids to measure distance from patient to provider may underestimate the distance and time traveled if surgical treatment was received within the same ZIP code. However, the U.S. Department of Housing and Urban Development has established that the U.S. Department of Housing and Urban Development ZIP Code crosswalk files are one of the best available datasets for allocating locations from census tracts, SSA county codes to ZIP codes, or other geographies.53

In conclusion, our study demonstrated that Medicare beneficiaries residing in rural areas traveled longer distances for cataract surgical treatment. Moreover, longer travel time and distance were associated with decreased odds of cataract surgery, independent of rural/nonrural residence. This association may be exacerbated among Black and Hispanic/Latino communities in the CA Medicare population and suggests the possibility of geographic disparities in the surgical treatment of cataract, which may compound existing racial and ethnic disparities.37,54,55 Further studies are required to analyze the intersection of geographic residence, race and ethnicity, and socioeconomic status when accessing surgical ophthalmic care. Looking ahead, system-level interventions are essential to improving access to surgical treatment for cataract modeled after successful implementation in other regions in the United States and globally. It would be of benefit to include subsidized travel, expansion of teleophthalmology triage sites, and investing in targeted community outreach to engage beneficiaries living in underresourced areas. By increasing ophthalmic resources and investing in strengthening community hospitals in rural and medically underresourced areas, a foundation could be provided to strive toward equitable access to vital vision care.

WHAT WAS KNOWN

WHAT THIS PAPER ADDS

• Provides patient-level quantification of travel distance and travel time burden for cataract surgery in one of the largest states.

• Demonstrates that rural disparities in cataract surgery are increased among Black and Latino Medicare beneficiaries.

Footnotes

This study was supported by unrestricted grant from Research to Prevent Blindness to the UCLA Department of Ophthalmology. K. Murillo has funding from the 2023 Research to Prevent Blindness Medical Student Eye Research Fellowship.

Presented in part as a poster at the Association for Research in Vision and Ophthalmology Annual Meeting, Denver, Colorado, May 2022; and presented in part as an oral presentation at the NMA-Ophthalmology Section Meeting, Atlanta, Georgia, July 2022.

Disclosures: V.L. Tseng has research funding from the Research to Prevent Blindness Career Development Award and the Research to Prevent Blindness and American Academy of Ophthalmology Award for IRIS Registry Research. None of the other authors have any financial or proprietary interest in any material or method mentioned..

First author:

Karla Murillo, MPH

Program in Medical Education – Leadership & Advocacy (PRIME-LA), David Geffen School of Medicine at UCLA, Los Angeles, California

Contributor Information

Karla Murillo, Email: KMurillo@mednet.ucla.edu.

Ken Kitayama, Email: kkitayama@mednet.ucla.edu.

Fei Yu, Email: fyu@ucla.edu.

Victoria L. Tseng, Email: vtseng@mednet.ucla.edu.

REFERENCES

• 1.Congdon N, O’Colmain B, Klaver CC, Klein R, Muñoz B, Friedman DS, Kempen J, Taylor HR, Mitchell P; 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.122.4.477
  [DOI] [PubMed] [Google Scholar]
• 2.Ellwein LB, Urato CJ. Use of eye care and associated charges among the Medicare population: 1991–1998. Arch Ophthalmol. 2002;120(6):804–811. doi: 10.1001/archopht.120.6.804
  [DOI] [PubMed] [Google Scholar]
• 3.National Eye Institute. NEI charts a clearer future for cataract prevention and treatment. Published June 29, 2017. Accessed December 2, 2022. https://www.nei.nih.gov/about/news-and-events/news/nei-charts-clearer-future-cataract-prevention-and-treatment
• 4.Klein BEK, Howard KP, Lee KE, Klein R. Changing incidence of lens extraction over 20 years: the Beaver Dam eye study. Ophthalmology. 2014;121(1):5–9. doi: 10.1016/j.ophtha.2013.06.006
  [DOI] [PMC free article] [PubMed] [Google Scholar]
• 5.Erie JC, Baratz KH, Hodge DO, Schleck CD, Burke JP. Incidence of cataract surgery from 1980 through 2004: 25-year population-based study. J Cataract Refract Surg. 2007;33(7):1273–1277. doi: 10.1016/j.jcrs.2007.03.053
  [DOI] [PubMed] [Google Scholar]
• 6.Elam AR, Lee PP. High-risk populations for vision loss and eye care underutilization: a review of the literature and ideas on moving forward. Surv Ophthalmol. 2013;58(4):348–358. doi: 10.1016/j.survophthal.2012.07.005
  [DOI] [PubMed] [Google Scholar]
• 7.Lee CS, Su GL, Baughman DM, Wu Y, Lee AY. Disparities in delivery of ophthalmic care; an exploration of public Medicare data. PLoS One. 2017;12(8):e0182598. doi: 10.1371/journal.pone.0182598
  [DOI] [PMC free article] [PubMed] [Google Scholar]
• 8.Wang KM, Tseng VL, Liu X, Pan D, Yu F, Baker R, Mondino BJ, Coleman AL. Association between geographic distribution of eye care clinicians and visual impairment in California. JAMA Ophthalmol. 2022;140(6):577–584. doi: 10.1001/jamaophthalmol.2022.1001
  [DOI] [PMC free article] [PubMed] [Google Scholar]
• 9.Haynes R, Gale S, Mugford M, Davies P. Cataract surgery in a community hospital outreach clinic: patients’ costs and satisfaction. Soc Sci Med. 2001;53(12):1631–1640. doi: 10.1016/s0277-9536(00)00448-2
  [DOI] [PubMed] [Google Scholar]
• 10.The Royal College of Ophthalmologists . Three steps to sustainable patient care: RCOphth view on the independent sector and the delivery of NHS cataract. 2021. Accessed September 9, 2025. https://www.rcophth.ac.uk/wp-content/uploads/2021/11/RCOphth-Three-Steps-to-sustainable-patient-care.pdf [Google Scholar]
• 11.Moxon NR, Wang A, Margo CE, Greenberg PB, French DD. The association between complex cataract surgery and social determinants of health in Florida. Ophthalmic Epidemiol. 2022;29(3):279–285. doi: 10.1080/09286586.2021.1939888
  [DOI] [PubMed] [Google Scholar]
• 12.U.S. Census Bureau. Urban and Rural. U.S. Census Bureau. Accessed April 22, 2025. https://www.census.gov/programs-surveys/geography/guidance/geo-areas/urban-rural.html [Google Scholar]
• 13.Tseng VL, Yu F, Lum F, Coleman AL. Risk of fractures following cataract surgery in Medicare beneficiaries. JAMA. 2012;308(5):493–501. doi: 10.1001/jama.2012.9014
  [DOI] [PubMed] [Google Scholar]
• 14.Tseng VL, Yu F, Lum F, Coleman AL. Cataract surgery and mortality in the United States Medicare population. Ophthalmology. 2016;123(5):1019–1026. doi: 10.1016/j.ophtha.2015.12.033
  [DOI] [PubMed] [Google Scholar]
• 15.Corraini P, Olsen M, Pedersen L, Dekkers OM, Vandenbroucke JP. Effect modification, interaction and mediation: an overview of theoretical insights for clinical investigators [published correction appears in Clin Epidemiol. 2018;10:223. doi:10.2147/CLEP.S162236; published correction appears in Clin Epidemiol. 2019;11:245. doi:10.2147/CLEP.S198519]. Clin Epidemiol. 2017;9:331–338. doi: 10.2147/CLEP.S129728
  [DOI] [PMC free article] [PubMed] [Google Scholar]
• 16.Health Resources & Services Administration. List of Rural Counties and Designated Eligible Census Tracts in Metropolitan Counties. Health Resources & Services Administration; 2018 [Google Scholar]
• 17.National Bureau of Economic Research. SSA to Federal Information Processing Series (FIPS) Core-Based Statistical Area (CBSA) and Metropolitan and Micropolitan Statistical Area (MSA) County Crosswalk. National Bureau of Economic Research; 2017 [Google Scholar]
• 18.U.S. Department of Housing and Urban Development. 4th Quarter 2017 HUD USPS ZIP Code Crosswalk Files. 2017 [Google Scholar]
• 19.Postal Geographies. Office for National Statistics . October
  5, 2025. Updated January 3, 2023. Accessed November 11, 2025. https://www.ons.gov.uk/methodology/geography/ukgeographies/postalgeography [Google Scholar]
• 20.Esri. ArcGIS Pro: Version 2.9 [software]. Esri; 2021 [Google Scholar]
• 21.Esri. Route Analysis. Accessed May 8, 2022. https://desktop.arcgis.com/en/arcmap/latest/extensions/network-analyst/route.htm [Google Scholar]
• 22.Esri. Connect Origins to Destinations. Esri. Accessed May 8, 2022. https://doc.arcgis.com/en/arcgis-online/analyze/connect-origins-to-destinations.htm [Google Scholar]
• 23.ArcGIS Online. Create Drive-Time Areas. https://doc.arcgis.com/en/arcgis-online/analyze/create-drive-time-areas.htm Accessed June 28, 2022 [Google Scholar]
• 24.National Library of Medicine. SNOMED CT to ICD-10-CM Map. March
  1, 2021. Accessed October 1, 2025. https://www.nlm.nih.gov/research/umls/mapping_projects/snomedct_to_icd10cm.html [Google Scholar]
• 25.Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373–383. doi: 10.1016/0021-9681(87)90171-8
  [DOI] [PubMed] [Google Scholar]
• 26.Research Data Assistance Center. Research Triangle Institute (RTI) Race Code. Accessed November 22, 2022. https://resdac.org/cms-data/variables/research-triangle-institute-rti-race-code [Google Scholar]
• 27.Research Data Assistance Center. Sex. Accessed November 22, 2022. https://resdac.org/cms-data/variables/sex [Google Scholar]
• 28.Lim JY, Yoo JI, Kim RB, Koo HJ, Kong GM, Ha YC. Comparison of the incidence rates of hip and vertebral fragility fractures according to cataract surgery in elderly population: a nationwide cohort study. Arch Osteoporos. 2022;17(1):30. doi: 10.1007/s11657-021-01018-0
  [DOI] [PubMed] [Google Scholar]
• 29.Lee CS, Morris A, Van Gelder RN, Lee AY. Evaluating access to eye care in the contiguous United States by calculated driving time in the United States Medicare population. Ophthalmology. 2016;123(12):2456–2461. doi: 10.1016/j.ophtha.2016.08.015
  [DOI] [PMC free article] [PubMed] [Google Scholar]
• 30.Ehrlich JR, Anthopolos R, Tootoo J, Andrews CA, Miranda ML, Lee PP, Musch DC, Stein JD. Assessing geographic variation in strabismus diagnosis among children enrolled in Medicaid. Ophthalmology. 2016;123(9):2013–2022. doi: 10.1016/j.ophtha.2016.05.023
  [DOI] [PubMed] [Google Scholar]
• 31.Jani PD, Forbes L, McDaniel P, Viera A, Garg S. Geographic information systems mapping of diabetic retinopathy in an ocular telemedicine network. JAMA Ophthalmol. 2017;135(7):715–721. doi: 10.1001/jamaophthalmol.2017.1153
  [DOI] [PMC free article] [PubMed] [Google Scholar]
• 32.Rothman AL, Stoler JB, Vu DM, Chang TC. A geodemographic service coverage analysis of travel time to glaucoma specialists in Florida. J Glaucoma. 2020;29(12):1147–1151. doi: 10.1097/IJG.0000000000001648
  [DOI] [PubMed] [Google Scholar]
• 33.Vu DM, Stoler J, Rothman AL, Chang TC. A service coverage analysis of primary congenital glaucoma care across the United States. Am J Ophthalmol. 2021;224:112–119. doi: 10.1016/j.ajo.2020.12.009
  [DOI] [PMC free article] [PubMed] [Google Scholar]
• 34.Malwankar J, Son HS, Chang DF, Dun C, Woreta F, Prescott C, Makary M, Srikumaran D. Trends, factors, and outcomes associated with immediate sequential bilateral cataract surgery among medicare beneficiaries. Ophthalmology. 2022;129(5):478–487. doi: 10.1016/j.ophtha.2021.12.015
  [DOI] [PubMed] [Google Scholar]
• 35.Soares RR, Huang C, Sharpe J, Cobbs L, Gopal A, Rao W, Samuelson A, Parikh D, Zhang Q, Bailey R, Dunn JP, Minor J, Moster ML, Penne RB, Shields C, Shukla AG, Syed Z, Wisner D, Haller JA, Yonekawa Y. Geographic and socioeconomic access disparities to phase 3 clinical trials in ophthalmology in the United States. Eye (Lond). 2023;37(9):1822–1828. doi: 10.1038/s41433-022-02244-7
  [DOI] [PMC free article] [PubMed] [Google Scholar]
• 36.–55.References 36–55 are listed in Supplemental Data File 1 (http://links.lww.com/JRS/B587)