prevalence of glaucoma
DESCRIPTION
Prevalence of glaucomaTRANSCRIPT
Regional Variations and Trends in thePrevalence of Diagnosed Glaucoma in theMedicare Population
Sandra D. Cassard, ScD,1 Harry A. Quigley, MD,1 Emily W. Gower, PhD,1,3
David S. Friedman, MD, PhD,1,2 Pradeep Y. Ramulu, MD, PhD,1 Henry D. Jampel, MD, MHS1
Purpose: To determine the prevalence of diagnosed glaucoma in the Medicare population and to assessregional variations and trends.
Design: Retrospective, cross-sectional study.Participants: A 5% random sample of Medicare beneficiaries aged �65 years, excluding those in health
maintenance organizations.Methods: All claims with a glaucoma diagnosis code submitted by ophthalmologists, optometrists, or
ambulatory surgery centers were used to estimate prevalence of the diagnosis of glaucoma for each year from2002 to 2008. Regional variation in diagnosed glaucoma was examined in 9 large geographic regions and in 179smaller subregions, controlling for patient characteristics and provider supply.
Main Outcome Measures: The prevalence of diagnosed open-angle glaucoma suspect (OAG-s), open-angle glaucoma (OAG), angle-closure glaucoma suspect (ACG-s), and angle-closure glaucoma (ACG), trendsover time, and regional variations in prevalence.
Results: The overall prevalence increased from 10.4% in 2002 to 11.9% by 2008, largely owing to increasein diagnosed OAG-s (from 3.2% to 4.5%; P�0.001). The relative prevalence of diagnosed OAG compared withdiagnosed ACG was 32:1. In 2008, multivariable models showed that the New England and Mid-Atlantic regionshad 1.7 times more diagnosed OAG-s than the reference region (East South Central; New England: odds ratio[OR], 1.66; 95% confidence interval [CI], 1.58–1.75; Mid-Atlantic: OR, 1.66; 95% CI, 1.59–1.73). The odds ofdiagnosed OAG was 36% higher in New England (OR, 1.36; 95% CI, 1.30–1.42) and 31% higher in theMid-Atlantic (OR, 1.31; 95% CI, 1.26–1.36) than in the reference region. The New England and Mid-Atlanticregions had the highest odds of diagnosed ACG-s and the Mid-Atlantic region had the highest odds of diagnosedACG. Among 179 subregions, the New York area had high diagnosis rates of all glaucoma types.
Conclusions: The relative prevalence of diagnosed ACG compared with diagnosed OAG was lower thanexpected from population-based data, possibly owing to failure to perform gonioscopy. Substantial regionaldifferences in diagnosed rates existed for all types of glaucoma, even after adjusting for patient characteristicsand provider concentration, suggesting possible overdiagnosis in some areas and/or underdiagnosis in otherareas. Regionally higher diagnosis rates in the New York area deserve further study.
Financial Disclosure(s): The authors have no proprietary or commercial interest in any of the materialsdiscussed in this article. Ophthalmology 2012;119:1342–1351 © 2012 by the American Academy of Ophthalmology.
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Glaucoma is the second leading cause of world blindnessand the third most common age-related eye disease in theUnited States.1,2 Open-angle glaucoma (OAG), the mostcommon form of glaucoma, disproportionately affectsBlack Americans and Latinos, and prevalence increaseswith age across all racial/ethnic groups.2�6 Angle-closureglaucoma (ACG) is more prevalent among women andAsian Americans in population-based prevalence sur-veys.1,2,7 Projected aging of the American population andlikely demographic shifts will result in significant increasesin the prevalence of glaucoma and associated increases inthe cost of related health care in the Medicare and Medicaid
populations. P1342 © 2012 by the American Academy of OphthalmologyPublished by Elsevier Inc.
Geographic variations in medical and surgical care haveeen investigated over the past 3 decades, largely buildingn seminal work in the United States and internationally byennberg et al.8�14 Early analyses were motivated by
ost-containment initiatives to identify and potentially toeduce inappropriate hospital-based surgical procedures andospitalizations for medical conditions that could be man-ged in the outpatient setting. The “practice-style” hypoth-sis was generated based on the assumption that physiciansace substantial uncertainty in diagnosing disease and pre-cribing treatment and, as such, adopt their own “practicetyle” for the management of medical conditions.13
ractice-style differences potentially contribute to the vari-
ISSN 0161-6420/12/$–see front matterdoi:10.1016/j.ophtha.2012.01.032
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ation in utilization rates that cannot be explained by differ-ences in population characteristics.12 A more recent analysisof Medicare data examining billing for gonioscopy beforeglaucoma surgical or laser procedures found regional vari-ations in the billing for this procedure.15 These analysesfocused on regional variations in rates of procedures and useof physician services, and did not evaluate variations indiagnosis. Such variability might reflect differences in ac-cess to care, or it can uncover possible under- or overdiag-nosis of medical conditions. If there is no reason to believethat there are regional variations in true prevalences ofdiagnosed disease after adjustment for patient demographicvariables and provider supply variables, then differencesin diagnostic coding between regions suggest potentialpractice-style differences.
Medicare billing data can be used to estimate the prev-alence of diagnosed diseases among the American elderlypopulation. It has the strength that it includes very largenumbers of patients who are representative of the generalpopulation of the American elderly. Such data must beassessed with the knowledge that they do not necessarilyrepresent the true population prevalence of disease, subjectas they are to factors such as access to care and patient andphysician behavior, especially the process of diagnosis andcoding by physicians. We used this dataset to examine theprevalence of diagnosed glaucoma in the Medicare popula-tion, assess trends between 2002 and 2008, and evaluatepotential cross-sectional, regional variation.14 We were par-ticularly interested in investigating whether there were re-gional variations in diagnosed glaucoma and how suchvariations might relate to provider supply and differentialutilization of eye care services across geographic regions.
Methods
Data Source
The Privacy Board at the Centers for Medicare and MedicaidServices approved the research protocol, and The Johns HopkinsMedicine Institutional Review Board determined that this studyqualified for an exemption from Institutional Review Board reviewand oversight. Statistical analyses were performed using SASsoftware, version 9.2 of the SAS System for Unix (SAS Inc., Cary,NC).
The 2002 through 2008 5% Medicare research identifiable fileswere obtained from the Centers for Medicare and Medicaid Ser-vices Research Data Distribution Center. These files contain in-formation on payment for health care services provided to Medi-care beneficiaries in the fee-for-service (FFS) sector. The 5% filescontain a random sample of all Medicare beneficiaries. Files ob-tained included the carrier (formerly physician/supplier Part B file)and denominator/beneficiary summary files for each year. Claimsfiles contain information on dates and place of service, Interna-tional Classification of Diseases, 9th Revision Clinical Modifica-tion (ICD-9-CM) diagnosis and Current Procedural Terminology(CPT) (procedure codes, provider identifier numbers for the pro-vider submitting the claim, provider type (e.g., ophthalmologist,optometrist), billing codes, and associated charges and payments.In addition to individual provider claims, the carrier file also
contains facility claims from ambulatory surgery centers (ASCs).a
enominators for Prevalence Estimateshe 2002 through 2008 denominator files provide eligibility, de-ographic, and residence information for all Medicare beneficia-
ies in the 5% sample. Each yearly denominator file was limited toeneficiaries from 1 of the 50 states or the District of Columbiaged �65 years with continuous Part B eligibility and no healthaintenance organization (HMO) enrollment. Because the major-
ty of glaucoma care is provided in the outpatient setting, contin-ous Part A (hospital) Medicare eligibility was not a requirement,lthough approximately 99% of Medicare beneficiaries with Part Boverage also have Part A benefits.16
dentification of Beneficiaries with Diagnosedlaucoma and Calculation of Prevalencell claims with a glaucoma ICD-9-CM diagnosis code (365.XX),
bsolute glaucoma (360.42), glaucomatous atrophy [cupping] ofptic disc (377.14), or buphthalmos (743.2, 743.2X) were selectedrom the carrier files. Claims were limited to those allowed by
edicare and submitted by ophthalmologists, optometrists, orSCs.
One of 7 glaucoma diagnoses was categorized for each claimTable 1; available at http://aaojournal.org). Claims were nexteparated into those representing visits to ophthalmologists orptometrists. Because we assigned only 1 glaucoma diagnosis pereneficiary in a given year, rules to deal with those assigned �1laucoma code in a year are found in Table 2. The annual preva-ence of various types of diagnosed glaucoma was calculated byividing the number of beneficiaries with diagnosed glaucoma byhe total number of beneficiaries eligible for each year from 2002hrough 2008. A generalized linear model with a linear test forrend was used to compare mean prevalence of each type ofiagnosed glaucoma over time.
egionegional variation in diagnosed glaucoma was examined in both
arger and smaller regional units. The larger divisions represented
Table 2. Rules for Classifying Glaucoma Type
Combinations of Diagnoses in aGiven Year
Diagnosis Used inAnalysis
irst OAG-s, then OAG OAGirst OAG, then OAG-s OAG-sirst ACG-s, then ACG ACGirst ACG, then ACG-s ACG-sAG and ACG-s OAGCG and OAG-s ACGAG (OAG-s) and uncharacterizedglaucoma
OAG (OAG-s)
CG (ACG-s) and uncharacterizedglaucoma
ACG (ACG-s)
econdary or childhood glaucomawith or without any other type ofglaucoma
Other/multiple types ofglaucoma
AG and ACG Other/multiple types ofglaucoma
AG-s and ACG-s Other/multiple types ofglaucoma
ncharacterized glaucoma only Other/multiple types ofglaucoma
CG � angle-closure glaucoma; ACG-s � ACG suspect; OAG � open-
ngle glaucoma; OAG-s � OAG suspect.1343
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Ophthalmology Volume 119, Number 7, July 2012
9 regions: New England (Connecticut, Maine, Massachusetts, NewHampshire, Rhode Island, Vermont), Mid-Atlantic (New Jersey,New York, Pennsylvania), South Atlantic (Delaware, District ofColumbia, Florida, Georgia, Maryland, North Carolina, SouthCarolina, Virginia, West Virginia), East North Central (Illinois,Indiana, Michigan, Ohio, Wisconsin), East South Central (Ala-bama, Kentucky, Mississippi, Tennessee), West North Central(Iowa, Kansas, Minnesota, Missouri, North Dakota, Nebraska,South Dakota), West South Central (Arkansas, Louisiana, Okla-homa, Texas), Mountain (Arizona, Colorado, Idaho, Montana,New Mexico, Nevada, Utah, Wyoming) and Pacific (Alaska, Cal-ifornia, Hawaii, Oregon, Washington).17 The mean prevalence ofeach type of diagnosed glaucoma was compared among these 9regions for each year using a generalized linear model. In addition,the 9 regions were used as predictors in person-level models of thelikelihood of diagnosed glaucoma.
A second, more localized regional analysis was conductedbased on 179 subregions centered on metropolitan areas and theirsurrounding counties, called Bureau of Economic Analysis Eco-nomic Areas (BEAEAs).18
Beneficiary-Level VariablesBeneficiary age, gender, and race/ethnicity were obtained from thedenominator files. Race/ethnicity was self-reported upon enroll-ment with the Social Security Administration. This race variablewas employed for comparisons across years. Starting in 2006,ethnicity was assigned by an algorithm developed by the ResearchTriangle Institute (RTI), which incorporates surname, residence,language preference, and first name with the intent better to clas-sify Hispanics and Asians/Pacific Islanders.19 Because varioustypes of diagnosed glaucoma are known to be more prevalentamong these racial/ethnic groups, regional comparisons adjustingfor race/ethnicity utilized 2008 data and the RTI race assignment.North American Natives were grouped with “other” race for allanalyses.
In addition to demographic variables, 2 variables served asproxies for socioeconomic status: Dual eligibility for Medicaidinsurance (from the denominator file) and income estimated by themedian year 2000 household income in the home zip code.20
To estimate eye care utilization, we calculated total eye carevisits from the 2008 carrier file. Claims for an eye examinationoffice visit, or consultation (CPT codes 92001-5, 92011-5,99201-5, 99211-5, and 99241-5) submitted by ophthalmologistsand optometrists were selected, and counts of total eye care officevisits for each beneficiary, for each BEAEA and for the 9 geo-graphic regions were computed.
Provider Supply VariablesTo quantify the regional distribution of optometrists and ophthal-mologists, we used databases that identify each specialist’s loca-tion. For optometry, the 2011 Blue Book of Optometrists listsoptometrists by zip code,21 and these data were used to identify theBEAEA location of each optometrist. For ophthalmologists, county-level estimates of the number of ophthalmologists in 2007 wereobtained from the 2008 Area Resource File.22 The county-levelestimates were aggregated to the BEAEA level. The number ofproviders per 10 000 beneficiaries was calculated after estimatingthe number of beneficiaries as 20 times the number present in the5% subsample.
Prevalence of Diagnosed Glaucoma in 2008Rates of each type of diagnosed glaucoma by 9 geographic re-
gions, adjusted for beneficiary demographic and SES variables and A1344
he number of providers per 10 000 beneficiaries, were computedsing generalized linear models. Both regional and person-levelnalyses were employed to explore variations in diagnosed glau-oma prevalence. The prevalence of OAG-s and OAG at theEAEA level was analyzed using weighted least-squares regres-
ion. The weight variable in each regression was the inverse of theariance of the sampling error of each prevalence rate.
Multivariable logistic regression was used for the person-levelnalyses to examine regional differences in the likelihood of eachype of diagnosed glaucoma after controlling for beneficiary- androvider-level variables. A negative binomial model was used toxamine predictors of the number of eye care office visits made byeneficiaries in 2008. This type of model is the preferred alter-ative to a Poisson model when the variance is greater than theean.23 Person-level models were then limited to the subset ofedicare beneficiaries who made �1 eye care office visit to
urther explore regional variations in diagnosed glaucomarevalence.
esults
atabase Features
he number of beneficiaries included in the 5% Medicare FFSample declined from 1 417 189 persons in 2002 to 1 361 403ersons in 2008. The decrease over the 7 years reflects increasinghoice of a managed care Medicare plan over FFS plans byeneficiaries during this time. Sixteen percent of Medicare bene-ciaries were enrolled in an HMO in 2002 compared with 24% by008. Beneficiaries receiving care through a Medicare HMO wereimilar in age and gender to those in the FFS sector. However,on-whites represented a greater proportion of the HMO group,nd the difference grew across years. Although the proportion ofon-whites grew within both the FFS and HMO sectors, thencrease was larger in the HMO sector. The FFS sector experi-nced a 0.7% increase in non-whites (from 13.4% in 2002 to4.7% in 2008), whereas the HMO sector experienced a 2.2%ncrease (from 15.2% to 17.4%).
verall Prevalence from 2002 to 2008
he crude prevalence of any form of diagnosed glaucoma in-reased from 10.4% in 2002 to 11.9% by 2008. This increase wasrimarily associated with an increased prevalence of OAG sus-ects (OAG-s): 3.2% in 2002, rising to 4.5% by 2008 (P�0.001),hereas the prevalence of other glaucoma types remained constant
Fig 1). Increased diagnosed OAG-s across the 7 years was evidentn both optometry and ophthalmology coding (data not shown).or each year, the prevalence of diagnosed OAG was higher than
he prevalence of diagnosed OAG-s (Fig 1). The prevalence ofiagnosed ACG suspects (ACG-s; 0.3%) and diagnosed ACG0.2%) were substantially lower than for OAG. Categories oflaucoma listed as “other” or multiple types were diagnosed in.5% of beneficiaries.
revalence (2008) by Patient Demographics
he prevalence of each type of diagnosed glaucoma in 2008 wasigher among women. Prevalence tended to increase with age untilge 79 and then declined among those �80 years. The exceptiono this decline was OAG, which continued to increase with age.lacks and Asians had the highest rates of OAG-s, Blacks andispanics had the highest rates of OAG, whereas Hispanics and
sians had the highest rates of ACG-s and ACG (Table 3).
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Cassard et al � Regional Variations in Glaucoma in Medicare Population
Prevalence (2008) by Income and Provider
Persons estimated to have lower income by 2 measures differedfrom those with higher estimated income in some diagnosed glau-coma rate categories. Patients with both Medicaid and Medicarecoverage had higher rates of diagnosed ACG-s and ACG, similarrates of diagnosed OAG and lower rates of diagnosed OAG-s,compared with those with Medicare-only coverage (Table 3).Residing in a zip code where the median household income was inthe lowest quartile for this sample of beneficiaries (�$33 050 peryear) was associated with lower rates for all types of diagnosedglaucoma, except for OAG.
Rates of each type of glaucoma were higher for beneficiariesresiding in BEAEAs with higher concentrations of ophthalmolo-gists, increasing from lowest concentration to highest in a dose–response relationship (Table 3). However, there was no apparentassociation between the concentration of optometrists in a BEAEAand the prevalence of any type of glaucoma.
Prevalence (2008) by Geographic Area
Among the 9 geographic regions, New England and the Mid-Atlantic had the highest rates of diagnosed OAG-s, OAG, andACG-s and the East South Central and Mountain regions had thelowest rates (Table 3). Although overall prevalence of ACG-s wasonly 0.3%, rates varied substantially by region, ranging from0.12% in the East South Central region to 0.67% in the Mid-Atlantic region. Less regional variation was found in the preva-lence of ACG. Regional variation in prevalence of diagnosedglaucoma remained significant after adjustment for age, gender,race/ethnicity, Medicaid coverage, median income, concentrationof ophthalmologists, and concentration of optometrists (Fig 2).Adjusted rates of OAG-s, OAG, and ACG-s remained highest inthe Mid-Atlantic and New England regions and lowest in theMountain and East South Central regions. The Mid-Atlantic regionalso had the highest adjusted rate of ACG. Regional differenceswere similar in analyses of 2002 through 2007 data (data notshown).
In multivariable models examining regional variations in thelikelihood of each type of diagnosed glaucoma in 2008, benefi-
Figure 1. The 2002 through 2008 unadjusted prevalence of diagnosedglaucoma among aged Medicare beneficiaries in the fee-for-service sector.ACG � angle-closure glaucoma; ACG-s � ACG suspect; OAG � open-angle glaucoma; OAG-s � OAG suspect; Other/Multiple � other ormultiple types of glaucoma.
ciaries in the New England or Mid-Atlantic regions were 1.7 times A
ore likely to have an OAG-s diagnosis (New England: odds ratioOR], 1.66; 95% confidence interval [CI], 1.58–1.75; Mid-Atlantic:R, 1.66; 95% CI, 1.59–1.73) than beneficiaries in the East Southentral region (Table 4; available at http://aaojournal.org). Inddition, OAG diagnosis was 31% more likely in the Mid-Atlanticegion (OR, 1.31; 95% CI, 1.26–1.36) and 36% more likely inew England (OR, 1.36; 95% CI, 1.30–1.42) compared with theast South Central region. Beneficiaries in these 2 regions also had
he highest likelihood of an ACG-s diagnosis, and the Mid-Atlanticegion had the highest likelihood of an ACG diagnosis (Table 5;vailable at http://aaojournal.org). For each type of diagnosedlaucoma, a higher concentration of ophthalmologists was associ-ted with a greater likelihood of diagnosis and a higher concen-ration of optometrists was associated with a lower likelihood ofiagnosis.
Among the 179 BEAEA regions, unadjusted OAG-s ratesanged from 1.5% in the Abilene, Texas, area to 7.9% in theangor, Maine, area. Unadjusted OAG rates ranged from 3.2% in
he Johnson City–Kingsport–Bristol (Tennessee–Virginia) area to.7% in the Mason City, Iowa, area. Rates of OAG-s and OAG forach BEAEA, adjusted for beneficiary age, gender and race/thnicity are displayed in Table 6 (available at http://aaojournal.rg). Sixteen BEAEAs had no diagnosed ACG-s, many of whichere in the Mountain or East South Central regions. Another 45EAEAs had only 1 to 3 cases of ACG-s. The highest rates ofCG-s were found in the Burlington–South Burlington, Vermont,
rea (0.9%) and the New York–Newark–Bridgeport (New Jersey–ew York–Connecticut–Pennsylvania) area (0.9%). Twenty-twoEAEAs had no diagnosed ACG, all in areas outside of the eastern
eaboard regions, and another 54 BEAEAs had only 1 to 3 casesf ACG. The 2 areas with the highest rates of ACG were Honolulu,awaii (0.7%), and Billings, Montana (0.8%). Because of the low
requency of diagnosed ACG-s and ACG at the BEAEA level,EAEA-level regression analyses were limited to diagnosedAG-s and OAG.
The BEAEA weighted least-squares regression models foundhat patient characteristics and provider concentration explained7% of the variance in diagnosed OAG-s and 66% of the variancen diagnosed OAG. Greater proportions of blacks and Hispanics in
BEAEA area were predictive of higher rates of OAG, andEAEAs with a large proportion of beneficiaries residing in a zipode with a low median household income (�$33 050) had aignificantly lower rate of diagnosed OAG-s (coefficient �0.019; P � 0.0004) and OAG (coefficient � �0.016; P �
.0001). The BEAEA areas with more optometrists per populationad fewer cases of diagnosed OAG-s (coefficient � �0.187;�0.0001) and OAG (coefficient � �0.897; P � 0.005). Con-ersely, areas with more ophthalmologists per population hadignificantly more diagnosed OAG-s (coefficient � 0.196; P �.004). However, when BEAEA 118, the New York–Newark–ridgeport (New Jersey–New York–Connecticut–Pennsylvania)rea, was removed from the analyses, ophthalmologist concen-ration was no longer a positive predictor of diagnosed OAG-scoefficient � 0.106; P � 0.160). This BEAEA is a heavilyopulated area with the highest per capita income of all 179EAEAs. It has a high concentration of ophthalmologists (7.3/0 000 versus the median of 3.4/10 000) and very precisestimates of each type of diagnosed glaucoma, resulting in aarge weight in each analysis. Beneficiaries from this areaepresent 7.7% of the denominator in the present analyses, but1.2% of the diagnosed OAG-s, 9.5% of the diagnosed OAG,3.2% of the diagnosed ACG-s, and 17.6% of the diagnosed
CG in this sample.1345
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Ophthalmology Volume 119, Number 7, July 2012
Eye Care Visits and Regional Differences
Across all race/ethnicity groups, men were less likely to have aneye examination than women, and the proportion of beneficiarieswith �1 eye examination was lower in age groups with lowerdiagnosis rates (Table 7). Furthermore, Asians, blacks, and His-panics were less likely to have an eye care visit than non-Hispanicwhites (Table 7). A negative binomial regression analysis of thefrequency of eye care visits made by each beneficiary in 2008,controlling for patient characteristics and provider concentration,
Table 3. 2008 Unadjusted Rate* (95% CI) of Diagnosed Glauc
OAG-s
Overall 4.5 (4.5–4.5)Patient characteristics
Age group (yrs)65–69 3.9 (3.8–4.0)70–74 5.1 (5.0–5.2)75–79 5.2 (5.1–5.2)80–84 4.9 (4.8–4.9)85–89 4.0 (3.9–4.1)�90 2.7 (2.5–2.8)
GenderMale 4.0 (3.9–4.0)Female 4.9 (4.9–4.9)
Race/ethnicity†
Asian/Pacific Islander 5.1 (4.8–5.3)Black (or African American) 4.8 (4.6–4.9)Hispanic 4.0 (3.8–4.1)Other 3.8 (3.5–4.1)Non-Hispanic White 4.5 (4.5–4.5)
Medicaid coverageYes 3.5 (3.4–3.6)No 4.7 (4.6–4.7)
Median income �$33 050‡
Yes 3.8 (3.7–3.9)No 4.7 (4.7–4.8)
Provider concentration§
OphthalmologistsHigh (�5.4/10 000) 5.3 (5.3–5.4)Moderate (�4.3–5.4/10 000) 4.4 (4.4–4.5)Low (�3.4–4.3/10 000) 4.2 (4.1–4.3)Very low (�3.4/10 000) 4.1 (4.0–4.2)
OptometristsHigh (�8.3/10 000) 4.4 (4.3–4.5)Moderate (�6.9–8.3/10 000) 4.0 (3.9–4.0)Low (�5.7–6.9/10 000) 4.9 (4.8–5.0)Very low (�5.7/10 000) 4.7 (4.6–4.7)
RegionNew England 5.5 (5.4–5.7)Mid-Atlantic 5.7 (5.6–5.8)South Atlantic 5.1 (5.0–5.2)East North Central 4.2 (4.1–4.3)East South Central 3.2 (3.1–3.4)West North Central 4.1 (4.0–4.2)West South Central 3.5 (3.4–3.6)Mountain 3.5 (3.4–3.7)Pacific 4.4 (4.3–4.5)
ACG � angle-closure glaucoma; ACG-s � ACG suspect; CI � confiden*Rates expressed per 100 beneficiaries.†Race was unknown for 3044 (0.2%) beneficiaries.‡Census 2000 median household income at the zip code level; one-fourth oincome below $33 050; income data were missing for 48 750 (3.6%) bene§Concentration per 10 000 aged Medicare beneficiaries within a Bureau o
showed regional differences (data not shown). Beneficiaries in f
1346
ew England and the Mid-Atlantic region had the highest rates ofye care visits, whereas East South Central and Mountain regioneneficiaries had the lowest rates.
When multivariable logistic regression analysis was limited toatients who made �1 eye care visit in 2008, and controlled for theumber of eye care visits made and the type of provider(s) seen,he same regional differences noted previously were still signifi-ant, although smaller in magnitude (Table 8). Of note, males werelightly more likely to be diagnosed with OAG compared with
by Patient Characteristics, Provider Concentration, and Region
OAG ACG-s ACG
(6.3–6.4) 0.29 (0.28–0.30) 0.20 (0.20–0.21)
(3.0–3.1) 0.27 (0.25–0.28) 0.15 (0.14–0.17)(5.0–5.2) 0.34 (0.32–0.36) 0.23 (0.22–0.25)(6.9–7.1) 0.35 (0.33–0.37) 0.24 (0.22–0.26)(8.8–9.1) 0.28 (0.26–0.30) 0.23 (0.21–0.25)(10.1–10.3) 0.24 (0.21–0.26) 0.20 (0.18–0.23)(10.2–10.5) 0.18 (0.14–0.22) 0.15 (0.12–0.18)
(5.6–5.8) 0.21 (0.20–0.23) 0.15 (0.14–0.16)(6.8–6.9) 0.35 (0.34–0.36) 0.24 (0.23–0.25)
(5.7–6.2) 0.57 (0.52–0.63) 0.67 (0.62–0.72)(11.0–11.3) 0.19 (0.15–0.22) 0.20 (0.17–0.23)(6.2–6.6) 0.39 (0.35–0.43) 0.34 (0.30–0.37)(4.5–5.2) 0.27 (0.19–0.35) 0.22 (0.15–0.29)(6.0–6.0) 0.29 (0.28–0.30) 0.18 (0.17–0.19)
(6.4–6.6) 0.34 (0.31–0.36) 0.30 (0.28–0.32)(6.3–6.4) 0.28 (0.27–0.29) 0.19 (0.18–0.19)
(6.2–6.4) 0.21 (0.19–0.23) 0.17 (0.15–0.18)(6.4–6.5) 0.32 (0.31–0.33) 0.22 (0.21–0.22)
(7.0–7.2) 0.49 (0.47–0.51) 0.30 (0.28–0.31)(6.7–6.9) 0.25 (0.23–0.27) 0.22 (0.20–0.23)(5.7–5.9) 0.23 (0.22–0.25) 0.16 (0.15–0.18)(5.7–5.9) 0.21 (0.19–0.23) 0.14 (0.12–0.15)
(6.3–6.4) 0.30 (0.28–0.32) 0.22 (0.20–0.23)(5.9–6.1) 0.20 (0.18–0.22) 0.16 (0.14–0.18)(6.6–6.7) 0.41 (0.39–0.43) 0.25 (0.23–0.26)(6.4–6.5) 0.24 (0.22–0.26) 0.18 (0.16–0.19)
(7.1–7.5) 0.44 (0.40–0.48) 0.19 (0.16–0.22)(7.4–7.6) 0.67 (0.64–0.69) 0.35 (0.33–0.38)(6.6–6.8) 0.27 (0.25–0.29) 0.20 (0.19–0.22)(6.2–6.4) 0.24 (0.22–0.26) 0.17 (0.16–0.19)(5.2–5.5) 0.12 (0.08–0.15) 0.12 (0.09–0.15)(5.9–6.2) 0.18 (0.14–0.21) 0.13 (0.10–0.15)(5.8–6.0) 0.20 (0.17–0.22) 0.15 (0.13–0.17)(4.9–5.2) 0.13 (0.10–0.17) 0.14 (0.11–0.17)(6.0–6.3) 0.27 (0.24–0.30) 0.26 (0.24–0.28)
terval; OAG � open-angle glaucoma; OAG-s � OAG suspect.
beneficiaries in this analysis resided in zip codes with a median householdies.nomic Analysis Economic Area (BEAEA), categorized into quartiles.
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Discussion
The overall prevalence of most forms of diagnosed glaucomaremained stable from 2002 to 2008. Only the prevalence ofdiagnosed OAG-s increased significantly, from 3.2% to 4.5%,over this 7-year period. This increase coincides with the periodafter publication of the Ocular Hypertension Treatment Trial(2002), which demonstrated that treatment of OAG-s withtopical ocular hypotensive medications can delay the onset ofOAG.24 By contrast, the prevalence of diagnosed OAG and theforms of ACG did not increase during this time period. Oneexplanation for the unique increase in billing of OAG-s isincreased recognition of early OAG brought about by thisimportant trial. We will investigate other aspects of this findingin subsequent reports that will detail the rates of diagnostictesting and treatments for glaucoma. The rate of diagnosedOAG was 1.4 times higher than that for diagnosed OAG-s in2008. This ratio is lower than the �2-fold difference reportedfrom Medicare data by Ellwein and Urato a decade earlier.25
The apparent difference derives from the higher rate of OAG-sin the current analysis rather than a fall in the OAG rate.
The stable rate of diagnosed OAG in our data seems tocontradict the fact that the overall Medicare population (both
Figure 2. Adjusted rates of diagnosed open-angle glaucoma suspect (OAangle-closure glaucoma suspect (ACG-s; top right) and angle closure glaurace/ethnicity, Medicaid coverage, median household income at zip code l10 000 aged Medicare beneficiaries within Bureau of Economic Analysis Ec9.0 software used to create maps (available at: http://www.esri.com/software(Jenks GF. The Data Model Concept in Statistical Mapping. Internation
those enumerated in this FFS database and those in HMO w
edicare) from 2002 to 2008 had an increasing proportion offrican-American and Hispanic seniors, whose known popu-
ation-based prevalence of OAG is greater than among Euro-ean-derived Americans. In fact, we found that there was aisproportionate movement of minorities to Medicare HMOoverage, which might have decreased the expected rise iniagnosed prevalence. The rates of diagnosis also can be af-ected by changes in coding practice over time. For example, iflinicians changed to more stringent criteria to bill OAG overhe period of study, using OAG-s instead, the result could beoth increases in OAG-s and a stable OAG diagnosis ratehen an increase was to be expected. Another potential con-
ributing feature for change in coding might be increased use ofiagnostic testing or improved diagnosis methods. We willxamine how the use of imaging, visual field testing, andonioscopy, among other examination methods, are associatedith the present findings in a subsequent report.Our prevalence of diagnosed OAG was substantially higher
han that reported in a metaanalysis of population-based stud-es of OAG prevalence, but this is not surprising, consideringeveral differences in methodology.26 First, those whose glau-oma is undiagnosed do not appear in a billing database,otentially leading to underestimation of prevalence compared
op) and diagnosed open-angle glaucoma (OAG; bottom left) diagnosed(ACG; bottom right). By United States region (adjusted for age, gender,concentration of ophthalmologists and concentration of optometrists peric Area). Rates for Pacific coast states include Alaska and Hawaii. ArcGISis). Categories formed by natural breaks using Jenks Optimization methodarbook of Cartography 1967;7:186�90).
G-s; tcomaevel,onom/arcg
ith population studies that examine every sampled subject,
1347
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Ophthalmology Volume 119, Number 7, July 2012
regardless of previous eye care. This factor would, all otherthings being equal, lead to lower estimated prevalence in adatabase study. However, in a billing database the diagnosticcriteria used by providers may be less stringent (and morevariable) than the strict criteria typical in population-basedsurveys. Most population-based prevalence studies requireboth optic disc damage and visual field defects for a definitiveglaucoma diagnosis.27 This factor is the most probable causefor the higher prevalence of diagnosed OAG in our database.In support of the likelihood of this factor, the GlaucomaAdherence and Persistency Study compared billing code withactual chart data analysis, and determined that 62% of thosewith diagnosis codes for OAG would be classified as OAG-sby standard criteria used in population-based studies.28
The prevalence of diagnosed OAG increased with olderage, as expected. However, prevalence of the remaining 3glaucoma diagnoses increased with age through age group75 through 79 years, then declined in older age groups. Onepossible explanation for lower prevalence of OAG-s amongthose �80 years of age is that the oldest beneficiaries maynot receive eye care at recommended intervals. In fact,among those without any diagnosed glaucoma, those �85were less likely to make an eye care office visit comparedwith those aged 80 to 84 years. Lower rates of diagnosedACG-s and ACG beyond age group 75 to 79 years may beowing to lack of access to eye care or higher rates ofcataract surgery, which would mask the ACG diagnosis byproducing gonioscopically open angles.29
Although the prevalence of glaucoma may differ be-tween analysis of billing data and population-based surveys,the ratio of various glaucoma types would be expected to bemore similar in the 2 approaches. Yet, diagnosed ACG-sand ACG rates were much lower than would be expectedwhen compared with the prevalence of OAG. Models pro-jecting the number of OAG and ACG cases worldwide by2010 predict an 8:1 ratio of OAG to ACG for those ofEuropean descent.1 Our study found a 32:1 ratio of OAG toACG. In a prior study, through chart review of a randomlysampled set of charts from a single insurer, we determinedthat gonioscopy was not documented in at least half of those
Table 7. Proportion with an Eye Care Visit in
Race/Ethnicity* Gender Overall n All Ag
Asian/Pacific Islander Male 13 494 38.6Female 18 940 43.1
Black (or African American) Male 38 762 30.9Female 60 511 43.0
Hispanic Male 28 164 31.8Female 38 082 41.5
Other Male 6523 34.3Female 10 719 36.4
Non-Hispanic White Male 485 905 43.6Female 657 259 51.6
*Race was unknown for 3044 beneficiaries.
with diagnosed OAG.28 Thus, the lower than expected pro- s
1348
ortion of ACG compared with OAG in our study mayndicate a failure to conduct formal angle evaluation asandated by the American Academy of Ophthalmologyreferred Practice Patterns. In fact, Coleman et al15 deter-ined that only 49% of Medicare beneficiaries undergoing
laucoma surgery in 1999 had a claim for gonioscopy in theto 5 years preceding the surgery. In a study by Stein et al,7
sing a managed care insurance billing database, the ACG-snd ACG prevalence rate and ratio of OAG to ACG differedrom our study. However, their beneficiaries were, on av-rage, 20 years younger and the denominator for ratesncluded only those who had �1 eye care visit. Theseethodologic and population differences may contribute to
ifferences in the prevalence rates reported between theirtudy and the present one.
The prevalence of diagnosed OAG among women wasigher compared with men after adjusting for age andace/ethnicity, findings that are similar to those from aounger cohort of a large managed care organization.30 Thisender difference is likely owing to a greater number of eyeare visits made by women rather than a higher prevalencef OAG among women. When analyses were restricted toeneficiaries who made �1 eye care visit in 2008 andontrolled for the number of eye care visits made and theype of provider(s) seen, males were slightly more likely toe diagnosed with OAG.
Regional models at the BEAEA level suggest that low-ncome Medicare beneficiaries have lower rates of diag-osed glaucoma of all types. Person-level models usingogistic regression also showed that residing in a low-ncome zip code predicted a lesser likelihood of diagnosedlaucoma. It may be argued that Medicare/Medicaid cover-ge would minimize any reduction in diagnosed glaucomaate that was owing to lack of access or cost of care. Indeed,e found that those with combined Medicare/Medicaid
overage had equal or higher rates of diagnosed ACG,CG-s, and OAG compared with those with Medicare
overage alone. However, even those covered by Medicareust pay the annual deductible, as well as a 20% copay-ent, and the ongoing cost of eye drops if they are pre-
8 by Race/Ethnicity, Gender, and Age Group
Proportion of Beneficiaries with an Eye Care Visit
Age Group (yrs)
65–69 70–74 75–79 80–84 85–89 �90
28.6 39.0 44.9 46.0 43.2 32.636.7 45.0 48.4 46.4 41.9 35.023.2 33.5 36.1 36.9 36.5 34.035.6 46.5 48.0 47.4 43.9 36.723.3 32.4 38.7 39.9 38.6 28.032.1 44.7 49.0 45.9 42.7 35.625.3 40.5 41.8 42.7 43.9 37.426.1 45.5 51.5 46.1 42.8 34.929.5 43.4 50.8 54.3 52.5 47.040.0 53.0 58.6 58.7 54.8 45.1
200
es
cribed. Those who do not qualify for Medicaid and who
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Cassard et al � Regional Variations in Glaucoma in Medicare Population
cannot afford supplemental insurance may be less willing orable to access the health care system to attend eye careexaminations, or to continue appropriate follow-up.
Substantial regional differences in diagnosed rates per-sisted for all types of glaucoma, even after adjusting forpatient demographics, socioeconomic status, and providerconcentration. Overall, the eastern seaboard regions had thehighest rates of diagnosed glaucoma and the East SouthCentral and Mountain regions had the lowest. Two potential
Table 8. Likelihood of Diagnosed Glaucoma for Those with
OAG-s
RegionNew England 1.42 (1.35–1Mid-Atlantic 1.50 (1.43–1South Atlantic 1.32 (1.26–1East North Central 1.34 (1.28–1East South Central (Referent) 1.00West North Central 1.18 (1.12–1West South Central 1.06 (1.01–1Mountain 1.07 (1.01–1Pacific 1.41 (1.34–1
Patient characteristicsAge group (yrs)
65–69 (referent) 1.0070–74 0.94 (0.92–075–79 0.82 (0.79–080–84 0.72 (0.70–085–89 0.62 (0.60–0�90 0.47 (0.44–0
Male 0.93 (0.91–0Race/ethnicity†
Asian/Pacific Islander 1.29 (1.22–1Black (or African American) 1.31 (1.26–1Hispanic 1.14 (1.09–1Other 1.08 (0.99–1Non-Hispanic White (Referent) 1.00Black male 1.04 (0.97–1
Medicaid 0.82 (0.79–0Median income �$33 050‡ 0.95 (0.93–0Number of eye care visits 1.10 (1.09–1
Provider concentration§
OphthalmologistsHigh (�5.4/10 000) 1.14 (1.11–1Moderate (�4.3–5.4/10 000) 1.05 (1.02–1Low (�3.4–4.3/10 000) 1.01 (0.99–1Very low (�3.4/10 000; Referent) 1.00
OptometristsHigh (�8.3/10 000) 0.87 (0.84–0Moderate (�6.9–8.3/10 000) 0.90 (0.88–0Low (�5.7–6.9/10 000) 0.94 (0.91–0Very low (�5.7/10 000; Referent) 1.00
Provider utilizationOptometrist and ophthalmologist visits in 2008 1.05 (1.01–1Ophthalmologist-only visits in 2008 1.16 (1.13–1Optometrist-only visits in 2008 (Referent) 1.00
ACG � angle-closure glaucoma; ACG-s � ACG suspect; CI � confiden*There were 633 114 with �1 eye examinations.†Race was unknown for 691 (0.1%) beneficiaries.‡Census 2000 median household income at the zip code level; one-fourth oincome below $33 050; income data were missing for 21 512 (3.4%) bene§Concentration per 10 000 aged Medicare beneficiaries within a Bureau o
variables could play a role in these differences. Areas with o
ower prevalence may have poorer access to care. However,he same regional differences in diagnosed glaucoma per-isted even after limiting analyses to those who had �1 eyexamination and also controlling for the number of eye careisits made and the type of provider seen in 2008. Thiseems to reduce the likelihood that access to care explainshe observed regional differences in diagnosis rate. Alter-atively, there may be a greater likelihood that doctors inhe New England/Mid-Atlantic region diagnose glaucoma
ye Examination in 2008,* Adjusted Odds Ratios (95% CI)
OAG ACG-s ACG
1.13 (1.08–1.19) 2.74 (2.16–3.46) 1.04 (0.79–1.37)1.07 (1.03–1.12) 3.88 (3.13–4.80) 1.86 (1.49–2.32)0.97 (0.94–1.01) 1.74 (1.41–2.15) 1.24 (1.00–1.53)1.11 (1.07–1.15) 1.97 (1.58–2.47) 1.29 (1.03–1.63)
1.00 1.00 1.001.11 (1.06–1.16) 1.51 (1.18–1.95) 1.01 (0.77–1.32)1.03 (0.99–1.07) 1.57 (1.25–1.99) 1.00 (0.79–1.28)0.97 (0.92–1.02) 1.05 (0.79–1.41) 1.09 (0.82–1.44)1.00 (0.96–1.05) 1.87 (1.47–2.37) 1.36 (1.07–1.74)
1.00 1.00 1.001.18 (1.15–1.22) 0.89 (0.81–0.98) 1.08 (0.96–1.22)1.41 (1.38–1.45) 0.80 (0.73–0.88) 0.93 (0.82–1.05)1.78 (1.73–1.83) 0.59 (0.53–0.66) 0.83 (0.73–0.94)2.20 (2.14–2.26) 0.50 (0.44–0.57) 0.74 (0.63–0.86)2.86 (2.76–2.96) 0.41 (0.34–0.50) 0.66 (0.54–0.80)1.07 (1.05–1.09) 0.72 (0.67–0.78) 0.74 (0.68–0.81)
1.10 (1.04–1.16) 1.77 (1.49–2.09) 2.72 (2.30–3.22)2.86 (2.77–2.95) 0.74 (0.61–0.88) 1.06 (0.88–1.28)1.44 (1.39–1.50) 1.39 (1.20–1.61) 1.66 (1.41–1.95)1.35 (1.25–1.46) 1.10 (0.81–1.49) 1.43 (1.02–2.01)
1.00 1.00 1.001.19 (1.13–1.25) 0.98 (0.69–1.40) 1.12 (0.79–1.57)1.05 (1.02–1.08) 1.27 (1.15–1.40) 1.54 (1.38–1.72)1.07 (1.05–1.09) 0.89 (0.81–0.97) 0.96 (0.87–1.07)1.45 (1.45–1.46) 1.00 (0.98–1.02) 1.13 (1.12–1.15)
0.96 (0.93–0.99) 1.24 (1.10–1.39) 1.35 (1.17–1.56)1.02 (1.00–1.05) 1.08 (0.97–1.20) 1.26 (1.11–1.43)0.97 (0.95–1.00) 1.07 (0.96–1.20) 1.12 (0.98–1.27)
1.00 1.00 1.00
1.00 (0.97–1.03) 0.93 (0.81–1.06) 0.90 (0.77–1.06)1.02 (0.99–1.05) 0.85 (0.75–0.98) 1.02 (0.87–1.19)1.00 (0.98–1.03) 1.12 (1.00–1.25) 1.04 (0.91–1.19)
1.00 1.00 1.00
0.99 (0.96–1.03) 2.77 (2.42–3.18) 3.62 (3.00–4.37)1.83 (1.79–1.88) 1.95 (1.77–2.15) 3.65 (3.13–4.25)
1.00 1.00 1.00
terval; OAG � open-angle glaucoma; OAG-s � OAG suspect.
beneficiaries in this analysis resided in zip codes with a median householdies.nomic Analysis Economic Area (BEAEA), categorized into quartiles.
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1349
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Ophthalmology Volume 119, Number 7, July 2012
that the association between higher concentrations of oph-thalmologists and higher rates of OAG-s was because ofonly 1 large, affluent area including New York City and thesurrounding counties. Excluding this zone, the rate ofOAG-s was not related to the number of ophthalmologistsper 10 000 beneficiaries. The BEAEA-level analyses ofcataract surgery rates in the mid-1980s also found no asso-ciation with ophthalmologist concentration.14 The currentanalysis also accounted for patient characteristics, so thedistributions of age, gender, and ethnicity were not theexplanation for the higher rates in New York. Althoughpossible, it seems unlikely that diagnostic accuracy isgreater in New York than in Philadelphia, Boston, or Bal-timore in a manner that would identify significantly moreglaucoma. However, Coleman et al15 found that Medicarepatients undergoing glaucoma surgery were much morelikely to have had gonioscopy performed before surgery ifthey lived in New York State or New Jersey compared withother areas of the country. So, higher rates of diagnosedglaucoma in the New York area may be explained by higherrates of testing for glaucoma. For the reasons indicated, ourdata cannot be used to equate “diagnosed glaucoma” with arecognized gold standard diagnostic set of criteria. As aresult, we cannot distinguish 2 possible explanations: (1)New York area doctors are recognizing glaucoma moreappropriately, or (2) they are overdiagnosing it.
Medicare beneficiaries who were dually enrolled in theVeterans Administration health system and received all oftheir care through the Veterans Administration would not beenumerated here. Although we cannot estimate the extent ofthis limitation, a study of Medicare-eligible Veterans Ad-ministration primary care patients found that �60% ofveterans utilized Medicare-reimbursed specialty care from2001 to 2004,31 suggesting that our analysis includes asubstantial proportion of veterans.
Analyses of large administrative databases, such as theMedicare billing databases, provide important, practical infor-mation about the clinical practice of medicine, although theirlimitations must be taken into account during interpretation. Bysummarizing large numbers of outcomes, one can estimate theincidence of relatively uncommon complications, such as en-dophthalmitis or retinal detachment after cataract extrac-tion.32,33 Trends over time in the diagnosis or care deliverymethods for eye disease also can be studied in care that isnationally representative, compared with reports from tertiaryacademic centers. Regional variations in diagnosis of glau-coma, when controlled for patient characteristics and providersupply, may suggest overdiagnosis, underdiagnosis, or both.Potential disparities in glaucoma care by gender or ethnicgroup also can be suggested. Despite careful selection andinterpretation, large database information depends on whichpatients are recorded and how physicians coded their diagnosesand treatments. These issues must be carefully considered inany such analysis, as we have tried to do here, and limit thestrength of some aspects of their conclusions.
In summary, the prevalence of diagnosed OAG in a Medi-care database is substantially higher than expected from pop-ulation-based prevalence data that use random sampling andstandardized criteria. Black beneficiaries had higher rates of
OAG, as expected from population-based data. After adjusting1350
or number of eye care visits, males had a slightly higher ratef OAG than females. In addition, ACG was diagnosed morerequently among Asian and Hispanic beneficiaries than whitesr blacks. The extremely low rate of ACG suggests underdi-gnosis of ACG across all regions, and suggests a failure oflinical care, which needs to be investigated. Low-incomeeneficiaries were diagnosed less often, and this result empha-izes the need for case finding in this population. Regionallyigher diagnosis rates in the Northeast, especially in the Nework area, deserve further study. Future research aimed atetermining disparities in glaucoma care will need to accountor these regional differences in diagnosis.
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1. Liu CF, Chapko M, Bryson CL, et al. Use of outpatient carein Veterans Health Administration and Medicare among vet-erans receiving primary care in community-based and hospitaloutpatient clinics. Health Serv Res 2010;45:1268–86.
2. West ES, Behrens A, McDonnell PJ, et al. The incidence ofendophthalmitis after cataract surgery among the U.S. Medi-care population increased between 1994 and 2001. Ophthal-mology 2005;112:1388–94.
3. Javitt JC, Street DA, Tielsch JM, et al, Cataract Patient Out-comes Research Team. National outcomes of cataractextraction: retinal detachment and endophthalmitis after out-
patient cataract surgery. Ophthalmology 1994;101:100–5.Originally received: August 10, 2011.Final revision: January 15, 2012.Accepted: January 17, 2012.Available online: April 4, 2012. Manuscript no. 2011-1200.1 Wilmer Eye Institute, The Johns Hopkins University School of Medicine,Baltimore, Maryland.2 The Johns Hopkins University Bloomberg School of Public Health,Baltimore, Maryland.3 Department of Epidemiology and Prevention, Wake Forest School ofMedicine, Winston-Salem, North Carolina.
Presented in part at: The Association for Research in Vision and Ophthal-
inancial Disclosure(s):he authors have no proprietary or commercial interest in any of theaterials discussed in this article.
unded by the Centers for Disease Control and Prevention, Atlanta,eorgia, grant no. 1U58DP002653-01. Drs. Gower and Ramulu are
upported by Special Scholars Awards from Research to Prevent Blind-ess. The funding organizations had no role in the design or conduct ofhis research.
orrespondence:
andra D. Cassard, ScD, Wilmer Eye Institute, Johns Hopkins University,00 N. Wolfe Street, Woods Building 167, Baltimore, MD 21287. E-mail:
[email protected].1351