Nearly 24 million people in the United States—approximately 8 percent of the population—have diabetes 1 , a serious disease that can result in blindness, kidney failure, peripheral neuropathy and arterial disease, cognitive impairment, and death 2 . An estimated one-fourth of all diabetes cases are undiagnosed 1 . Ninety to 95 percent of diagnosed cases are the non-insulin-dependent type 2 form of the disease, characterized by insulin resistance, or the inability of cells to effectively use insulin; the next largest group of diabetics have the type 1 or juvenile-onset form of the disease, characterized by the body's inability to make insulin 1 2 . Older persons are particularly at risk for diabetes. Indeed, the prevalence of diagnosed diabetes in persons 65 years and older is more than 12 times the rate of that among persons 44 years and younger 3 . Diabetes prevalence in younger persons, however, is on the rise. Among individuals aged 30 to 39 years, for instance, the prevalence of diabetes rose from 1.4 percent in 1991 to 3.7 percent in 2001. By 2021, 5.0 percent of individuals in this age group are expected to have the disease 4 . Increasing diabetes prevalence among younger adults suggests that the burden of this disease on population health, and on the nation's health care system, will only worsen in years to come.

In addition to age, known individual-level risk factors for diabetes include genetic predisposition, race/ethnicity (diabetes prevalence is approximately 50 percent higher in African American adults relative to White adults), increased body mass index (BMI), and physical inactivity 1 5 6 . Moreover, a growing body of evidence suggests an association between diabetes, diabetes-related conditions (e.g., cardiovascular disease and obesity) and characteristics of the socioeconomic and built environment. Studies have found, for example, a higher incidence of diabetes among African American women in low socioeconomic status (SES) versus higher SES neighborhoods 7 , greater risk of coronary heart disease in socioeconomically disadvantaged versus more affluent census block groups 8 , increased BMI among women in areas of high unemployment relative to areas of low unemployment 9 , and higher rates of obesity in socioeconomically deprived neighborhoods compared to more affluent neighborhoods 10 (readers are directed to the individual studies cited for detailed information on the definition of small area socioeconomic position used in each respective study). Other investigations indicate higher rates of diabetes 11 12 and obesity 11 13 14 in rural areas relative to urban centers. Poorer health status in socioeconomically deprived and rural environments may reflect, in part, the inaccessibility of such built environmental features as public pools, recreation centers, physical fitness facilities, parks, sidewalks, and streetlights 15 16 17 . The availability and quality of local food establishments also may be associated with health status. For example, lower rates of obesity have been noted among persons living in census tracts with large chain supermarkets (which sell a wide assortment of food products, including fresh fruits and vegetables), while higher rates of obesity have been found among residents of census tracts with convenience stores (which typically offer a limited selection of foodstuffs and little, if any, fresh produce) 18 .

Efforts to stem the diabetes epidemic in the Unites States 2 19 20 must take into account a complex array of individual, social, economic, and built environmental factors. In an age characterized by ever mounting volumes of data, scientists increasingly use information visualization tools to "make sense" of data inputs, to discover critical patterns and associations, restructure problems, and achieve insight through new perspectives 21 . Spatial visualization tools—typically maps—are widely used in the communication of geographic data. The presentation of multivariate data in maps, however, can present numerous cartographic challenges related to classification, symbolization, legibility, and interpretation. For representation of three or more attributes, small multiples—a set of small maps depicting related attributes—can be used to facilitate the evaluation of complex, multivariate spatial patterns 22 . Although small multiples aid in the visualization of patterns, the ability to interpret and integrate information across small multiple maps can prove difficult 23 , particularly if an individual is attempting comparisons between specific locations or between complex attributes 24 .

More recently, ring map visualization has been explored as a means of depicting spatially referenced, multivariate data in a single information graphic. A ring map depicts multiple attribute data sets as separate rings of information surrounding a base map of a particular geographic region of interest 25 26 . Multivariate ring maps have been used to evaluate spatio-temporal patterns of human activity 26 , track pandemic H1N1 infection 27 , and assess potential small area associations between socioeconomic disadvantage and HIV/AIDS 28 .

Diabetes prevalence rates in South Carolina are among the highest in the nation 29 . In this study, ring maps were used to evaluate diabetes prevalence among adult South Carolina Medicaid recipients. In particular, county-level ring maps were used to highlight disparities in diabetes prevalence among adult African Americans and Whites and to explore potential county-level associations between diabetes prevalence among adult African Americans and five measures of the socioeconomic and built environment—persistent poverty, unemployment, rurality, number of fast food restaurants per capita, and number of convenience stores per capita. Although Medicaid pays for the health care of approximately 15 percent of all diabetics 30 , few studies have examined diabetes in adult Medicaid recipients at the county level. The present study thus addresses a critical information gap, while illustrating the utility of ring maps in multivariate investigations of population health and the socioeconomic and built environment.

The diabetes prevalence rate among South Carolina Medicaid recipients aged 18 and older was 11.6 percent. Among subjects younger than 45 years, the rate was 3.9 percent, versus 21.5 percent among those 60 years of age and older. County-level diabetes prevalence rates ranged from 9.0 percent to 19.1 percent (mean = 13.1, SD = 3.0). Relatively high rates of diabetes existed in inland counties, especially along a transect extending from Allendale in the southwestern portion of the state to Marlboro in the northeast (Figure 1). The diabetes prevalence rate was 13.8 percent among adult African American Medicaid recipients, versus 9.6 percent among Whites. County-level rates ranged from 10.6 percent to 22.2 percent for African Americans (mean = 14.9, SD = 2.9) and from 6.8 percent to 16.1 percent for Whites (mean = 10.9, SD = 2.4). County-level rates for African Americans were higher than those for Whites in 45 of the state's 46 counties. Figure 1 shows that in 16 counties diabetes rates among African Americans were two or more class intervals higher than rates among Whites (contrast inner and outer rings). In three counties, diabetes prevalence among African Americans exceeded 19.1 percent (Figure 1).

County-level age-adjusted rates for adult African American Medicaid recipients ranged from 11.3 percent to 18.8 percent. Ninety-five percent confidence intervals associated with age-adjusted prevalence estimates reflected relative population size, with the widest intervals occurring in counties with the smallest number of adult African American Medicaid participants (Figure 2).

A visual association was apparent between diabetes prevalence among adult African Americans and the relative rurality of counties (Figure 3). Five counties in the highest diabetes prevalence quartile (dark gray in the figure) also were in the highest rurality quartile (dark blue in the figure) and none were in the lowest rurality quartile (very light green in the figure). Conversely, five counties in the lowest diabetes prevalence quartile (white in the figure) also were in the lowest rurality quartile; one, however, was in the highest rurality quartile. A visual association also existed between African American diabetes prevalence and persistent poverty. Four counties in the highest diabetes prevalence quartile were persistent poverty counties (purple in the figure), while none of the counties in the lowest diabetes prevalence quartile was persistently impoverished. Less visual correlation was apparent between African American diabetes prevalence and unemployment, number of fast food stores per capita, and number of convenience stores per capita (Figure 3).

Bivariate statistical analyses indicated a strong positive association between rurality and diabetes prevalence (odds ratio = 3.6, p-value = 0.005). Living in a persistent poverty county also was positively associated with diabetes prevalence (odds ratio = 3.9, p-value = 0.040). In a multivariate generalized ordered logistic regression model, rurality was significantly positively associated with diabetes prevalence among adult African American Medicaid recipients (odds ratio = 3.1, p-value = 0.018); however, after adjusting for rurality, living in a persistent poverty county was no longer significantly associated with diabetes prevalence (Table 1).

Medicaid claims data are collected for administrative rather than research purposes. The use of administrative claims codes, which reflect both covered services and reimbursement practices, may result in under- or over-estimates of the prevalence of diabetes in the study population. A broad range of diabetes diagnostic codes encompassing both type 1 and type 2 diabetes was used to define the eligible population. The contextual associations found in this study may vary by diabetes diagnosis. Future research would benefit from the use of clinical records to define diabetes by type and to account for different type-specific physical severity levels.

The mechanisms by which area-level socioeconomic disadvantage adversely affects health are complex and incompletely understood. Small-area socioeconomic deprivation, itself, may directly compromise individual health; alternatively, relatively poor health outcomes in socioeconomically disadvantaged areas may reflect reduced access to health care, limited social support, social disorder, exposure to hazardous environmental pollutants, and/or local discriminatory practices 42 43 44 . In this study, both county-level measures of socioeconomic disadvantage—persistent poverty and unemployment—were positively associated with diabetes prevalence among adult African American Medicaid recipients in bivariate models. Of the two measures, however, only persistent poverty was a significant bivariate predictor. The lack of a statistically significant association between persistent poverty and diabetes in the multivariate model may be due in part to correlation of the predictor variables (although model diagnostics indicated no collinearity). Additional studies are needed to clarify the impact of persistent poverty at the small area level on diabetes prevalence.

Rurality was significantly positively associated with diabetes prevalence in both bivariate and multivariate models. Compared to urban counties, primarily or completely rural counties had higher rates of diabetes among adult African American subjects, even after adjusting for local levels of unemployment, persistent poverty, and food establishment availability. These results are consistent with other investigations showing higher rates of diabetes in rural versus urban areas 11 12 . Elevated rates of diabetes in rural regions might reflect diminished access to primary care 45 , a lack of sidewalks or other safe places to walk 16 17 46 , the relative inaccessibility of parks and recreational facilities 17 46 , low social support 17 , and/or regionally-specific rural cultural norms that can undermine health 46 47 . The observed association between diabetes prevalence and rurality has important implications for public health policy creation and health promotion planning. In particular, efforts to reduce the burden of diabetes among adult African Americans must extend beyond city boundaries and address in culturally relevant ways the specific health needs of rural African Americans in South Carolina and across the Southern "black belt 48 ." Notably, the association between rurality and diabetes in this study was specific to adult African American Medicaid recipients and should not be generalized to non-African American or younger Medicaid recipients, or to the broader non-Medicaid population.

Neither measure of the built environment was significantly associated with diabetes prevalence. This result might partly reflect the study's reliance on a single electronic business directory to identify and spatially locate chain fast food restaurants and convenience stores. Like other commercial business directories, the Dunn and Bradstreet product used in this investigation may contain incomplete business listings, outdated information, and/or street address data for corporate headquarters rather than local places of business. In addition, the self-classification of business type in the Dunn and Bradstreet directory may lead to inconsistent classification across listings. Further investigations are needed to evaluate potential small-area associations between diabetes and the relative availability of unhealthy food outlets, the proximity of such food establishments (e.g., distance to the nearest fast food restaurant or convenience store) 49 , and the frequency with which fast food or convenience-type food products are consumed 39 . Future studies also might consider potential contextual associations between diabetes and access to such healthy food outlets as "green" or farmers' markets, roadside fruit and vegetable stands, and large supermarkets with extensive produce sections.

Recognition of the dependent relationship between spatial phenomenon and geographic unit of analysis—the so-called Modifiable Areal Unit Problem—is critical to the understanding of studies that employ spatially aggregated data 50 . In this investigation, diabetes prevalence and environmental data were aggregated at the county level. The results obtained, therefore, reflect only county-level environmental influences on diabetes prevalence among adult African Americans. Associations between diabetes and environmental context may be different at larger (e.g., state or national) and smaller (e.g., census tract or census block group) geographic scales. County-level spatial analyses of health are appropriate when county-level agents (e.g., county health departments, park and recreation departments, regulatory commissions, and councils on aging) play direct roles in disease prevention/intervention and wellness promotion programs. Although county-level investigations may suggest potential associations between health and environment at different geographic scales, such associations must be evaluated separately using appropriate geographic units of analysis.

As this study shows, ring maps can highlight racial disparities in health, convey epidemiological uncertainty data (e.g., confidence interval data associated with standardized morbidity and mortality rates), and suggest small area-level associations between adverse health outcomes and characteristics of the socioeconomic and built environment. The ring maps presented here only begin to illustrate the potential utility of this visualization method for health geographers. For example, ring maps can depict multiple attributes at the census tract, census block group, ZIP code area, hospital catchment area, or public health service area level, in addition to the county level as shown in the figures 25 27 . In addition, a ring map can be used to depict a single attribute at multiple geographic scales. For instance, a ring map might show diabetes prevalence rates for South Carolina at the census tract level in a base map and prevalence rates at the county level and public health region (multiple county) level in successive rings (in this case, the number of enumeration units in the inner ring would reflect the number of counties, and the number of enumerations units in the outer ring would reflect the number of health regions in the state). Ring maps also can display time-series data for a single variable of interest 25 27 . A map with six rings might show annual incidence rates of cardiovascular disease over a six-year period, for example; alternatively, a ring map might depict the weekly incidence of cases associated with an influenza outbreak. Ring maps thus permit the exploration of relevant distributions, patterns, and associations across both space and time 25 27 . Additional layers of data are easy to add to ring map visualizations, requiring only that new rings be drawn 27 . Although the ring maps shown are circular, elliptical or even non-continuous rings can be drawn to accommodate irregularly shaped geographic regions 25 27 . In short, ring maps provide sufficient flexibility in design and development to permit the visualization—and visual exploration—of spatiotemporal data across a wide range of health applications.

A distinct problem associated with ring map visualization is the loss of complete topology (i.e., information about the spatial relationships of geographic units) in the rings. Although a single county in South Carolina may have as many as nine adjacent neighbors, only two adjacent neighbors (spokes) exist in ring displays. Complete spatial topology is retained in the central base map, though, allowing users to determine adjacent relationships, relative direction, and the relative nearness or farness of geographic units. Another practical limitation is the ability of ring maps to display data for a large number of geographic units. It would be challenging to construct—and difficult to interpret—a ring map depicting the more than 800 census tracts in South Carolina, for instance. Ring maps—as they appear in print—also suffer limitations common to all static map products. They depict a set number of predetermined data layers, using a static data classification method, and an unchanging symbolization scheme.

Most of the graphic limitations associated with static ring map visualization, including the limited representation of spatial topology in rings, might be addressed in a dynamic ring mapping environment. In such an environment, a user could interactively select the geographic area of interest, establish the geographic scale of representation, choose data elements for exploration, assign attributes to rings, modify the classification and symbolization of data, reorder (and even resize and reshape) rings, and "click on" one or more ring attribute elements to see the corresponding geographic unit(s) highlighted on the base map in complete spatial topological context. A dynamic and fully interactive ring mapping application of this sort would represent a powerful information visualization tool with which to integrate and explore diverse data sets, frame questions, generate hypotheses, restructure problems, and achieve insights 21 critical to the protection and promotion of population health.

Further studies are needed to evaluate the relative strengths and weaknesses of ring maps—in both static and dynamic forms—as multivariate data visualization tools. Such studies might explore the optimal number of rings to display for specific data types and data type combinations, the maximum number of aggregation units that can be effectively depicted, and the effect of color symbolization on ring map interpretation. Finally, research is needed to demonstrate the relative effectiveness of ring maps versus small multiple map displays in the visualization of multivariate health data.

The authors declare that they have no competing interests.

JES co-conceived and coordinated the study, helped derive the data, helped develop the ring maps, and drafted the manuscript. SEB co-conceived the study, developed the ring maps, and helped to draft the manuscript. ALD co-conceived the study, provided all Medicaid data, and helped to draft the manuscript. KCR co-conceived the study, performed GIS analyses, helped derive the data, helped develop the ring maps, and helped to draft the manuscript. JWH performed all statistical analyses and helped to draft the manuscript. KMS co-conceived the study and contributed to the manuscript. All authors read and approved the final manuscript.