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January 2005, Vol 95, No. 1 | American Journal of Public Health 75-77
© 2005 American Public Health Association
DOI: 10.2105/AJPH.2003.031385

RESEARCH AND PRACTICE

Geographic Variation in the Prevalence of Macular Disease Among Elderly Medicare Beneficiaries in Kansas

Carol Ann Holcomb, PhD, CHES and Mu-Chuan Lin, PhD, MPH

Carol Ann Holcomb is with the Department of Human Nutrition and the Galichia Center on Aging at Kansas State University, Manhattan. Mu-Chuan Lin is with the School of Family Studies and Human Services at Kansas State University, Manhattan.

Correspondence: Requests for reprints should be sent to Carol Ann Holcomb, PhD, CHES, Department of Human Nutrition, Kansas State University, 210 Justin Hall, Manhattan, KS 66506-1407 (e-mail: carolann{at}ksu.edu).


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 References
 

This study used Medicare Part B claims and enrollment data to estimate the prevalence of macular disease in Kansas at county and area levels. Spatial analysis by aggregated county clusters was assessed with standardized prevalence ratios and 95% confidence intervals, and a thematic map was produced to illustrate geographic distribution. A total of 17888 unduplicated claims were identified among 335132 beneficiaries older than age 64 years. Compared with the state prevalence of 5.34%, the central agricultural area showed a disproportionately high macular disease prevalence.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
METHODS
 RESULTS
 DISCUSSION
 References
 
Previous disease mapping focused primarily on infectious diseases, cancer, and heart disease.1 From a public health perspective,2 spatial analysis of the prevalence of macular disease in an elderly population may be fruitful for several reasons. Age-related maculopathy is the leading cause of irreversible blindness,3,4 with vast psychosocial effect and economic cost5,6; prevalence is likely to increase in the absence of a preventive strategy7,8; and data in Kansas and other states are limited.9–12

The Centers for Medicare and Medicaid Services maintains a computerized database of claims for physician services (Part B).13 Approximately 96% of the population aged 64 and older in Kansas is covered by Part B insurance.14 Although Medicare data have been used to map several diseases and conditions in older adults,15–17 the use of Medicare data as a source for spatial analysis of macular disease has not been explored.


   METHODS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
RESULTS
 DISCUSSION
 References
 
The study population was constructed by identifying beneficiaries aged 64 and older who had claims for physician services in 1999 with an International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM), code of 362.50 to 362.57, inclusive.18 Selecting the first claim for each physician visit produced unduplicated counts for each county. County cases constituted the numerator, and the county beneficiary enrollment data served as the denominator for calculating prevalence. Prevalence was age adjusted via the direct method with 2000 census data.19

County-specific standardized prevalence ratios and 95% confidence intervals were calculated.20 The number of expected claims was determined by multiplying the number of beneficiaries in each county by the current state prevalence of 5.34%. Because prevalence values in the sparsely populated counties may be unstable, counties were aggregated into larger geographic areas to provide a more consistent analysis. State economic areas in Kansas, originally delineated for the US Census,21 were selected as the aggregate. These county clusters have similar characteristics that provide relatively homogeneous geographic areas for spatial analysis. Areas of this type are especially well suited for analyses when county data are sparse. A map displaying macular disease prevalence was produced with GIS ArcView, Version 8.0, software (ESRI, Redlands, Calif).


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
DISCUSSION
 References
 
During the study period, a total of 17 888 unduplicated claims for macular disease were identified from 335 132 Medicare beneficiaries. The state prevalence was 5.34% and varied by county—from 2.08% in Chase County to 11.52% in Harvey County (data available from authors on request). Table 1Go includes the number of Medicare beneficiaries, number of macular disease claims, and standardized prevalence ratios for each county cluster. The highest prevalence (6.85%) is 28% higher than the state prevalence, whereas the lowest prevalence (4.69%) is 12% lower.


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  		TABLE 1—— Number of Beneficiaries, Number of Macular Disease Claims, County Cluster Prevalence, and Standardized Prevalence Ratio (SPR) in an Elderly Medicare Population: Kansas, 1999
 
Geographic distribution of prevalence by county clusters is shown in the thematic map (Figure 1Go). The central 13 counties in a north to south band are in the highest quintile range, bordered by a cluster of counties to the east in the lowest quintile range. The southwest area is also in the lowest quintile range.



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  		FIGURE 1—— Standardized prevalence ratio for macular disease among elderly Medicare beneficiaries, by county clusters: Kansas, 1999.

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
References
 
Prevalence of macular disease among elderly Medicare beneficiaries in Kansas varies considerably by geographic area as assessed by standardized prevalence ratios. Clearly, the central agricultural area of the state has a significant excess compared with the state as a whole. Further research is needed to identify the risk factors that may be unique to the central region of Kansas.

Limitations in the use of Medicare data files are inherent. First, the accuracy of claims can be questioned because the data are collected for billing purposes and not for surveillance. Second, no studies have been published to test the sensitivity of Medicare claims for a diagnosis of macular disease. Third, unlike data collected by research ophthalmologists, the data lack graded retinal photographs for confirmation of a diagnosis of macular disease. Thus, Medicare patients with suspected, but not proven, disease may have been included in the claims data.

Our study, on the contrary, had several important strengths. The use of Medicare claims to measure the prevalence of a less common but significantly debilitating condition among older adults offers several potential advantages. In a state like Kansas, with ophthalmologists in underserved counties and elders scattered over large geographic areas, it is expensive, time-consuming, and practically impossible to conduct primary studies. Thus, the use of secondary data is less costly. Unlike telephone and mail surveys that tend to oversample people with telephones and who are literate, Medicare claims cut across all socioeconomic strata, thus eliminating some bias. Therefore, claims data provide a relatively inexpensive alternative method of examining the spatial distribution of macular disease in a defined geographic area.

Future ecological studies of the data presented in this brief cannot substitute for clinically based research, but they may be worthwhile,22–24 especially when their limitations are acknowledged and bias is minimized. Analysis of the current data is under way to determine the association of multiple factors with the prevalence of macular disease in this elderly Medicare population.25


   Acknowledgments
 
Support for this project was provided by the Kansas Agricultural Experiment Station, Manhattan (Contribution 04-038-J).

The authors acknowledge the valuable contribution of the following people: Jay Alloway, for data transfer from the mainframe computer; Max Lu, for consultation on spatial analysis; and Vasanth Kumar Tatipalli, for preparation of the thematic map.

Human Participant Protection
The Centers for Medicare and Medicaid Services (CMS) approved the use of the Medicare denominator and carrier data files (Data Use Agreement 10964). The institutional review board for research on human subjects at Kansas State University reviewed and approved the study protocol (Protocol 2086). Stephen Stroka at CMS also reviewed and approved the use of Medicare data presented in the table and figure accompanying this brief.


   Footnotes
 
Contributors
C. A. Holcomb originated the design of the study, supervised its implementation, interpreted the results, and wrote the brief. M.-C. Lin conducted the data extraction and analysis from the Medicare files, reviewed the draft manuscript, and provided comments for the brief.

Peer Reviewed

Accepted for publication February 19, 2004.


   References
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 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 References
 
1. Walter SD. Disease mapping: a historical perspective. In: Elliott P, Wakefield JC, Best NG, Briggs DJ, eds. Spatial Epidemiology: Methods and Applications. New York, NY: Oxford University Press; 2000:223–239.

2. Richards TB, Croner CM, Rushton G, Brown CK, Littleton F. Geographic information systems and public health: mapping the future. Public Health Rep. 1999; 114:359–360.[CrossRef][Web of Science][Medline]

3. Hyman L, Neborsky R. Risk factors for age-related macular degeneration: an update. Curr Opin Ophthalmol. 2002;13:171–175.[CrossRef][Medline]

4. Smith W, Assink J, Klein R, et al. Risk factors for age-related macular degeneration: pooled findings from three continents. Ophthalmology. 2001;108:697–704.[CrossRef][Web of Science][Medline]

5. Williams RA, Brody BL, Thomas RG, Kaplan RM, Brown SI. The psychosocial impact of macular degeneration. Arch Ophthalmol. 1998;116:514–520.[Abstract/Free Full Text]

6. Perry DP. ARMD is robbing older people blind—and stealing their independence, too. J Am Optom Assoc. 1999;70:7–9.[Medline]

7. National Advisory Eye Council. Vision Research, a National Plan, 1999–2003. Bethesda, Md: US Dept of Health and Human Services; 1998.

8. Evans J, Wormald R. Is the incidence of registrable age-related macular degeneration increasing? Br J Ophthalmol. 1996;80:9–14.[Abstract/Free Full Text]

9. Leibowitz HM, Krueger DE, Maunder LR, et al. The Framingham Eye Study monograph: an opthalmological and epidemiological study of cataract, glaucoma, diabetic retinopathy, macular degeneration, and visual acuity in a general population of 2631 adults, 1973–1975. Surv Ophthalmol. 1980;24(suppl):335–610.[CrossRef][Medline]

10. Klein R, Klein BEK, Linton KL. Prevalence of age-related maculopathy. The Beaver Dam Eye Study. Ophthalmology. 1992;99:933–943.[Web of Science][Medline]

11. Cruickshanks KJ, Hamman RF, Klein R, Nondahl DM, Shetterly SM. The prevalence of age-related maculopathy by geographic region and ethnicity. The Colorado-Wisconsin Study of Age-Related Maculopathy. Arch Ophthalmol. 1997;115:242–250.[Abstract/Free Full Text]

12. Friedman DS, Katz J, Bressler NM, et al. Racial differences in the prevalence of age-related macular degeneration: the Baltimore Eye Survey. Ophthalmology. 1999;106:1049–1055.[CrossRef][Web of Science][Medline]

13. Moon M. Medicare Now and in the Future. Washington, DC: The Urban Institute Press; 1993.

14. Fisher ES, Baron JA, Malenka DJ, Barrett J, Bubolz TA. Overcoming potential pitfalls in the use of Medicare data for epidemiologic research. Am J Public Health. 1990;80:1487–1490.[Abstract/Free Full Text]

15. Lu-YaoGL, McLerran D, Wasson J, Wennberg JE, Prostate Patient Outcomes Research Team. An assessment of radical prostatectomy: time trends, geographic variation, and outcomes. JAMA. 1993;269:2633–2636.[Abstract/Free Full Text]

16. Johanson JF. Geographic distribution of constipation in the United States. Am J Gastroenterol. 1998;93: 188–191.[CrossRef][Web of Science][Medline]

17. Wrobel JS, Mayfield JA, Reiber GE. Geographic variation of lower-extremity major amputation in individuals with and without diabetes in the Medicare population. Diabetes Care. 2001;24:860–864.[Abstract/Free Full Text]

18. International Classification of Diseases, Ninth Revision, Clinical Modification. Hyattsville, Md: National Center for Health Statistics; 1980. DHHS publication PHS 80–1260.

19. Kahn HA, Sempos CT. Statistical Methods in Epidemiology. New York, NY: Oxford University Press; 1989: 87–95.

20. Abramson JH, Gahlinger PM. Computer Programs for Epidemiologists: PEPI, Version 4.0. Salt Lake City, Utah: Sagebrush Press; 2001:43–52.

21. State Economic Areas. Washington, DC: Bureau of the Census; 1951.

22. Susser M. The logic inecological, I: the logic of analysis. Am J Public Health. 1994;84:825–829.[Abstract/Free Full Text]

23. Susser M. The logic inecological, II: the logic of design. Am J Public Health. 1994;84:830–835.[Abstract/Free Full Text]

24. Koopman JS, Longini IM Jr. The ecological effects of individual exposures and nonlinear disease dynamics in populations. Am J Public Health. 1994;84:836–842.[Abstract/Free Full Text]

25. Poole C. Ecological analysis as anoutlook and method. Am J Public Health. 1994;84:715–716.[Free Full Text]




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This Article
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