Our study population included ≈56 million Medicare beneficiaries for 1995–1996 and ≈55 million for 2005–2006 (Table 1). The national age-adjusted stroke hospitalization rate was 18 per 1000 during 1995–1996 and decreased to 13.6 per 1000 during 2005–2006 (Supplemental I). Rates varied by age, sex, and race or ethnicity, with the highest rates in both periods observed among those aged ≥85, blacks, and men. The global Moran I statistic for 1995–1996 was 0.491 (P=0.001) and 0.506 (P=0.001) for 2005–2006, confirming that spatial clustering was present.

The LISA map for the total population in 2005–2006 (Figure 1) shows statistically significant clusters of both high-rate (22% of all counties) and low-rate counties (24% of all counties). The high-rate clusters were located in southern Appalachia and the Southeast, including much of Alabama, Mississippi, and Louisiana. The low-rate clusters were located in New England, the West, and the West North Central regions. Similar geographic patterns were observed for 1995–1996 (Supplemental Figures 1, 2, and 3).

LISA maps by sex for 2005–2006 (Figure 2) show very similar patterns of geographic clustering of stroke hospitalization rates for women and men, with the overall pattern of high-rate clusters in the Southeast maintained. Slight variations in the cluster locations are found in Texas and the Midwest. In addition, we calculated cluster statistics by race; these LISA maps (Supplemental Figures 1, 2, and 3)show a very similar pattern to Figure 1, with high-rate clusters located primarily in the Southeast and Pennsylvania.

Comparison of LISA estimates between 1995–1996 and 2005–2006 shows that 75% of counties located in a high-rate cluster in 1995–1996 remained in a high-rate cluster for 2005–2006 (n=435). These persistently high-rate counties were found primarily in Appalachia, Alabama, Mississippi, Louisiana, and the coastal regions of North Carolina and South Carolina (Figure 3). Similarly, 76% of counties that were in a low-rate cluster during 1995–1996 remained in a low-rate cluster during 2005–2006 (n=533). These persistently low-rate counties were located primarily in the Mountain States, sections of the West North Central region, New England, and central Virginia. Approximately 25% of counties that were in a high-rate cluster during 1995–1996 (n=148) were no longer part of a high-rate cluster during 2005–2006. These transitional counties that exhibited improvement in stroke hospitalization rates were located in the coastal regions of Georgia and northern Florida, middle Tennessee, parts of the Mississippi Delta region, and near Portland, Oregon. Counties that had not been part of a high-rate cluster during 1995–1996, but became part of a high-rate cluster during 2005–2006 (n=243; 10%) were located primarily in Texas, southern Oklahoma, and western Georgia.

Comparisons of socioeconomic and healthcare variables in persistently high-rate counties compared with persistently low-rate counties (Table 2) demonstrate that persistently high-rate counties had lower median income ($32 475 vs $40 698), higher proportions of people without a high school diploma (30.5% vs 17.2%), and higher proportions of people living in poverty (21.1% vs 12.9%). Furthermore, persistently high-rate counties had higher percentages of counties that were categorized as persistently poor (35.2% vs 4.7%), were more likely to be metropolitan (8.1% vs 5.1%), and were less likely to be designated as retirement destinations (6.7% vs 15.6%). In addition, persistently high-rate counties had fewer hospitals per 10 000 population than persistently low-rate counties (3.9 vs 8.0), but had a higher number of hospital admissions (10 146 vs 7009) and emergency room visits (43 836 vs 31 754) per 100 000, and fewer office-based general medicine physicians per 100 000 (23.3 vs 36.4).

In general, socioeconomic and healthcare profiles of counties that transitioned out of high-rate clusters were better than corresponding profiles for persistently high-rate counties (Table 2). When compared with persistently high-rate counties, counties that transitioned out of high-rate clusters had higher median incomes ($36 057 vs $32 475), lower proportions of people living in poverty (18.9% vs 21.1%), fewer short-term hospital admissions per 100 000 population (8274 vs 10 146), higher population growth (16.3% vs 5.3%), greater percentage of counties designated as retirement destinations (21.6% vs 6.7%), and were more likely to be located in a large metropolitan area (18.9% vs 8.1%). The profiles for counties that transitioned into high-rate clusters during 2005–2006 were similar to those that transitioned out, with socioeconomic conditions that were worse than the counties in persistently low-rate clusters, but better than counties in persistently high-rate clusters. The urban/rural characteristics of these counties more closely matched those of the high-rate cluster counties with more counties located in small metropolitan areas.

In this study, we found that clusters of counties with high- and low-stroke hospitalization rates remained largely intact from 1995–1996 to 2005–2006. Clusters of persistently high-rate counties were located predominantly in the Southeast and Appalachia. In contrast, clusters of persistently low-rate counties were located predominantly in New England and the West. However, changes in cluster status also occurred; 243 counties transitioned into high-rate clusters and 148 counties transitioned out of high-rate clusters. In general, socioeconomic and healthcare profiles were most favorable for persistently low-rate counties, followed by counties that transitioned into or out of high-rate clusters, and least favorable for persistently high-rate counties.

The observed geographic patterns of stroke hospitalization rates among Medicare beneficiaries are consistent with previously observed patterns of stroke incidence,^9,19 prevalence,²⁰ and death.^1–6 The consistency of these geographic patterns supports the hypothesis that place-based characteristics play an important role in the occurrence of stroke and its risk factors.^21–25 We examined 2 sets of place-based characteristics: socioeconomic resources and healthcare resources.

Our finding that counties with persistently high rates of stroke hospitalizations had the least favorable socioeconomic resources is consistent with other community-level studies of socioeconomic resources and stroke.^26,27 In France, neighborhoods with the least favorable socioeconomic resources (eg, higher levels of unemployment, percentage renting main residence, percentage without a car, income inequality) had the highest levels of stroke incidence.²⁷ A New Zealand study found community socioeconomic status (measured by average household income) to be inversely associated with the incidence of stroke, even after adjusting for individual income and individual-level risk factors for stroke.²⁶ In the United States, the Brain Attack Surveillance in Corpus Christi Project reported an inverse association between neighborhood-level socioeconomic status and risk for stroke. The association was reduced when individual-level socioeconomic status was included; however, individual-level socioeconomic status was not available for ≈50% of the stroke cases.²⁸ Many other studies have documented the association between community-level socioeconomic resources and other cardiovascular conditions, including coronary heart disease,²¹ atherosclerosis,^22,25 hypertension,^23,24 and cardiovascular disease death.^29,30 Socioeconomic resources at the community level are widely understood to influence opportunities for residents to engage in healthy lifestyles that influence their risk for stroke (eg, diet and exercise,³¹ low stress levels,³² access to health care³³).

Our findings that counties with persistently high-stroke hospitalization rates also had less access to primary health-care services (eg, fewer general practice physicians and more emergency room visits) are consistent with other studies that found higher rates of stroke death in communities with lower ratios of primary care physicians to the population.^34,35 Stroke is preventable. Access to quality preventive care is particularly important for stroke prevention because the main risk factors for stroke (ie, hypertension, high cholesterol, diabetes mellitus) are preventable, modifiable, and should be assessed in the primary care setting.^36,37

The observation that persistently high-rate counties had the fewest hospitals per population suggests that additional secondary treatment resources are needed in those counties. Increasingly, efforts are being made to provide telemedicine resources at hospitals located in rural areas to meet the need for specialized stroke treatment.³⁷

The finding that, in general, counties transitioning into and out of high-rate clusters for stroke hospitalizations had socioeconomic and healthcare profiles that were more favorable than counties in persistently high-rate clusters, but not as favorable as counties in low-rate clusters, supports the hypothesis that improvements in socioeconomic and health-care resources can reduce the burden of stroke hospitalizations. Additional spatiotemporal studies are needed that address the effect of changing community-level resources on the burden of stroke.

The limitations in this study are restricted primarily to those inherent in using administrative databases for surveillance studies. The Centers for Medicare and Medicaid Services’ Medicare Provider Analysis and Review file does not include detailed nonbilling information and is subject to variation in coding practices. However, studies have found that International Classification of Diseases codes in administrative databases do accurately identify strokes.^38–40 Our study population, traditional fee-for-service Medicare beneficiaries aged ≥65, represents ≈85% of the Medicare population. Although 70% of all stroke hospitalizations occur among people aged ≥65, blacks tend to have strokes at younger ages than whites;^41,42 therefore, our rates for blacks may be underestimates. In addition, Hispanic ethnicity is not coded as a mutually exclusive group in the race/ethnicity collected in the Medicare data, and Hispanics also tend to have strokes at younger ages than whites, leading to underestimates in those rates.^42,43 Last, Centers for Medicare and Medicaid Services’ Medicare Provider Analysis and Review Part A files do not distinguish between repeat hospitalizations and first-time hospitalizations; therefore, our data may include multiple hospitalizations for the same patient. Past studies have found a 1-year recurrence rate for stroke of <14%.^44–46

The strengths of our study include the use of spatial statistics to identify and monitor county clusters of stroke hospitalization rates, the identification of transitional counties in addition to persistently high- and persistently low-rate clusters, and the inclusion of community-level healthcare profiles and socioeconomic profiles. The use of Medicare data enabled nationwide analysis of geographic patterns in stroke hospitalizations.

Age-adjusted stroke hospitalization rates among Medicare beneficiaries exhibited statistically significant and persistent spatial clustering in the United States. High-rate clusters were located predominantly in the Southeast and had the least favorable socioeconomic and healthcare profiles. In general, counties transitioning into and out of high-rate clusters for stroke hospitalizations had socioeconomic and healthcare profiles that were more favorable than counties in persistently high-rate clusters, but not as favorable as counties in low-rate clusters. These findings add a new dimension to understanding the role that social determinants play on geographic disparities and the burden of stroke.