Published online before print April 3, 2008, doi: 10.1161/STROKEAHA.107.507053
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
(Stroke. 2008;39:1675.)
© 2008 American Heart Association, Inc.
Original Contributions |
Regional Differences in Diabetes as a Possible Contributor to the Geographic Disparity in Stroke Mortality
The REasons for Geographic And Racial Differences in Stroke Study
From Departments of Epidemiology (J.V., R.G. J.R.) and Biostatistics (L.M.), School of Public Health, University of Alabama at Birmingham, Birmingham, Ala; Department of Public Health Sciences (R.P.), Wake Forest University School of Medicine, Winston Salem, NC; Department of Medicine (M.C.), University of Vermont College of Medicine, Burlington, Vt; and Department of Neurology, University of Cincinnati (B.K.), Cincinnati, Ohio.
Correspondence to Jenifer Voeks, RPHB 230J, 1530 3rd Ave S, Birmingham, AL 35294-0022; E-mail jvoeks{at}uab.edu
 |
Abstract |
|---|
 Top Abstract IntroductionMethodsResultsDiscussionReferences |
|---|
Background and Purpose— Diabetes and hypertension impart approximately the same increased relative risk for stroke, although hypertension has a larger population-attributable risk because of its higher population prevalence. With a growing epidemic of obesity and associated increasing prevalence of diabetes that disproportionately impacts the southeastern Stroke Belt states, any potential contribution of diabetes to the geographic disparity in stroke mortality will only increase.
Methods— Racial and geographic differences in diabetes prevalence and diabetes awareness, treatment, and control were assessed in the REasons for Geographic And Racial Differences in Stroke study, a national population-based cohort of black and white participants older than 45 years of age. At the time of this report, 21 959 had been enrolled.
Results— The odds of diabetes were significantly increased in both white and black residents of the stroke buckle (OR, 1.26; [1.10, 1.44]; OR, 1.45 [1.26, 1.66], respectively) and Stroke Belt (OR, 1.22; [1.09, 1.36]; OR, 1.13 [1.02, 1.26]) compared to the rest of the United States. In the buckle, regional differences were not fully mediated and remained significant when controlling for socioeconomic status and risk factors. Addition of hypertension to the models did not reduce the magnitude of the associations. There were no significant differences by region with regard to awareness, treatment, or control for either race.
Conclusions— These analyses support a possible role of regional variation in the prevalence of diabetes as, in part, an explanation for the regional variation in stroke mortality but fail to support the potential for a contribution of regional differences in diabetes management.
Key Words: diabetes • geography • racial differences
|
Introduction |
|---|
|
TopAbstractIntroductionMethodsResultsDiscussionReferences |
|---|
The prevalence of diabetes in the United States has increased substantially in the past several decades,1–7 corresponding to dramatic increases in obesity rates.8 Prevalence of diabetes varies by race, with higher rates in both male and female non-Hispanic blacks than non-Hispanic whites.9,10 Diabetes is an independent risk factor for stroke and has been shown to increase the risk of stroke with a relative risk ranging from 1.8 to >8-fold, with larger increased risk in younger age groups.11,12
Stroke mortality varies by race and by geographic region in the United States, with the highest rates in the "buckle" of the Stroke Belt (coastal plain region of North Carolina, South Carolina, and Georgia) and the Stroke Belt states (remainder of North Carolina, South Carolina, and Georgia, plus Alabama, Mississippi, Tennessee, Arkansas, and Louisiana). Although the variation in stroke mortality by race and region is well-described, the underlying causes are not understood.13,14
Hypertension and diabetes impart approximately the same increase in the relative risk for stroke;15 however, hypertension has a larger population-attributable risk because of its higher prevalence.16,17 There is evidence of an increasing prevalence of diabetes that is likely associated with the increasing obesity epidemic and, as such, the importance of diabetes as a stroke risk factor is likely to grow and the impact may be larger in states with a higher prevalence of obesity. The Behavioral Risk Factor Surveillance System reports diabetes and obesity by state on the basis of self-reported disease status and body weight.18 Based on these data the prevalence rate of diabetes (defined as having been told by a physician that you have diabetes) was calculated for adults aged 20 and older, the median prevalence rate was 6.6%, and all 8 Stroke Belt states (North Carolina, South Carolina, Georgia, Tennessee, Alabama, Mississippi, Arkansas, and Louisiana) reported rates higher than this median rate. Likewise, the median percent obese was 21.6, with all of the "Stroke Belt states" reporting rates substantially higher than this average. Hence, with hypertension and diabetes imparting approximately the same relative risk, and with a growing epidemic of obesity that is disproportionately impacting the southeastern Stroke Belt states, the higher-than-expected rate of diabetes in the southeastern Stroke Belt states suggest that diabetes may be a major contributor to the geographic disparity in stroke mortality.
Few studies have described geographic variations in the prevalence of diabetes in the United States, and most of those available have relied on self-reported diabetes status.9,19,20 We previously reported higher prevalence of diabetes in the Stroke Belt than in the rest of the United States based on measured diabetes status in a large national sample of blacks and whites.21 In this report we investigate these differences in greater detail and additionally assess if there are regional differences in diabetes management. Also, there are well-known geographic differences in the prevalence of hypertension, with a higher prevalence in the Stroke Belt region.22,23 Because hypertension and diabetes frequently cluster in the same individuals, these regional differences in hypertension could introduce regional differences in diabetes. As such, in the final modeling step we also establish if the regional differences in diabetes are independent of regional differences in hypertension, that is, whether there is a higher prevalence of diabetes in the southeastern United States that is not only a corollary of the known higher prevalence of hypertension.
|
Methods |
|---|
|
TopAbstractIntroductionMethodsResultsDiscussionReferences |
|---|
Data from the REasons for Geographic And Racial Differences in Stroke (REGARDS) study were used to estimate the prevalence and management rates for diabetes in the United States population of blacks and whites. REGARDS is a national population-based longitudinal cohort study designed to examine the causes for geographic and racial differences in stroke mortality with a recruitment goal of 30 000 individuals older than 45 years of age, with equal representation of whites and blacks and men and women.24 Twenty percent of the sample is selected from the Stroke Buckle, 30% from the Stroke Belt states, and the remaining 50% from the other 40 contiguous states. Individuals are recruited from commercially available lists of residents using a combination of mail and telephone contact. For those agreeing to participate, demographic information, medical history, and measures of cognitive function and quality of life are obtained by computer-assisted telephone interview. Approximately 1 month after the telephone interview, physical measures including height, weight, blood pressure, and fasting morning blood samples were collected at an in-home examination. After centrifugation, blood samples were shipped overnight on ice packs to a central laboratory for analysis. Glucose was measured in serum using colorimetric reflectance spectrophotometry on the Ortho Vitros 950 IRC Clinical Analyzer (Johnson & Johnson Clinical Diagnostics) with a coefficient of variation of 1%.
The study methods were reviewed and approved by the Institutional Review Boards at the collaborating institutions. Additional methodological details have been provided previously.24 Enrollment began in February 2003 and at the time of this analysis, baseline and in-home examinations were complete for the first 21 959 participants. Of these participants, 1053 (4.8%) were excluded because of missing data regarding fasting status (n=125) or glucose measurements (n=928) for a study cohort of 20 906 participants.
Definitions
Diabetes status was ascertained using data from the baseline telephone interview and the in-home examination. On the baseline interview, participants were asked if they had ever been told by a doctor that they had diabetes or high blood sugar. If female they were asked if this was only during pregnancy. All people who responded "yes" to the initial question regarding diabetes outside of pregnancy were then asked if they were using diabetes medication and specifically if they were using diabetes pills or insulin. Diabetes was then defined as the blood measurement fasting glucose >126 mg/dL, nonfasting glucose >200 mg/dL, or current diabetes treatment with pills or insulin.
Among persons with diabetes, awareness was defined as having responded positively to "have you ever been told by a doctor that you had diabetes or high blood sugar?" Among individuals who were "aware" of their diabetes status, treatment was then defined as self-report of taking diabetes pills or insulin. Finally, among those being treated for diabetes, control was defined as having fasting glucose levels within the bounds of 
126 mg/dL or nonfasting glucose 200 mg/dL.
Blood pressure was the average of 2 measurements taken after the participant was seated for 5 minutes, measured by a trained technician using a standard protocol and regularly tested aneroid sphygmomanometer. Hypertension was defined as SBP 
140 mm Hg, or DBP 90 mm Hg, or self-report of current use of hypertension medication (a positive response to "has a doctor or other health professional ever told you that you have high blood pressure?", and to "Are you currently taking medication for high blood pressure?").
Variables that could potentially confound the relationship between region and diabetes were categorized into 3 groups: demographic factors, including age (categorized as 45 to 54, 55 to 64, 65 to 74, and 85 and older), gender and race (white or black); socioeconomic factors (SES), including years of education (less than high school, high school graduate, some college or college graduate), income status (<$25 000, $25 000 to $50 000, >$50 000), and whether the participant had health insurance (answered "yes" to "Do you have any kind of healthcare coverage such as health insurance, an HMO, or a government plan like Medicare or Medicaid?"), and risk factors of interest, including cigarette smoking (categorized as never, past, or current), alcohol use (never, current or past), body mass index (categorized as normal 24.9 kg/m2, overweight 25 to 29.9 kg/m2, or obese 30 kg/m2), and frequency of exercise (categorized by the question "How many times per week do you engage in intense physical activity, enough to work up a sweat?" into never, <5 times per week, and 5 times per week).
Statistical Analysis
The cohort was described using frequencies and percents for categorical variables. Univariate logistic regression was used to assess the relationship between diabetes and region of residence. Multivariable logistic regression was then performed to estimate the relation between region and diabetes, after adjustment for groups of potential confounders. These analyses were performed using a series of models that progressed from considering region alone to adding demographic factors, then adding socioeconomic factors, and then adding the risk factors of interest. Finally, hypertension was added to the model to determine if any observed regional differences were independent of hypertension status. Differences in diabetes awareness and treatment and glucose control by region were also assessed using an incremental series of models that first looked at region alone, then added in demographic factors, and finally added in SES factors. Because race varies by region, all analyses were stratified by race.
|
Results |
|---|
|
TopAbstractIntroductionMethodsResultsDiscussionReferences |
|---|
Of the 20 906 eligible participants, 4500 (21.5%) had diabetes. The distribution of demographic and risk factor characteristics and the prevalence of diabetes within each characteristic are given in Table 1
. Statistically significant (P<0.0001) univariate differences in diabetes rates existed for all the characteristics in Table 1 with the exception of region and insurance status. The factors that had the largest impact on the likelihood of prevalent diabetes were race (30% in blacks vs 16% whites), income status (28% in lowest vs 15% in highest bracket), BMI category (10% normal vs 33% obese), physical activity status (26% never vs 19% any days of intense exercise), and hypertension status (28% hypertensive vs 12% nonhypertensive). Also of note is that prevalence of diabetes increased with increasing age category until age 74, then decreased in the 2 highest age categories, possibly indicative of higher mortality for those with diabetes.
|
|
Racial and Regional Variation in Diabetes
In white participants, the results of multivariable modeling adjusting for demographic factors revealed that residency in the Belt (OR, 1.22; 95% CI, 1.09, 1.36) and Buckle (OR, 1.26; 95% CI, 1.10, 1.44) was associated with increased risk for diabetes when compared to residency in the remainder of the United States (Table 2). Adjustment for SES resulted in only a slight reduction in the magnitude of this risk in both the Buckle and the Belt. Additional adjustment for risk factors further reduced this risk in the Belt (OR, 1.13; 95% CI, 1.00, 1.28) and Buckle (OR, 1.18; 95% CI, 1.01, 1.37). The addition of hypertension to the models had little effect on the magnitude of the associations, although for whites in the Buckle the difference no longer reached statistical significance (Table 2).
|
In black participants, after adjusting for demographic factors, residency in the Belt (OR, 1.13; 95% CI, 1.02,1.26) and Buckle (OR, 1.45; 95% CI, 1.26, 1.66) was associated with increased risk for diabetes when compared to residency in the remainder of the United States (Table 2). Adjusting for SES reduced the odds among residents of the Belt to only 5% higher (OR, 1.05; 95% CI, 0.93, 1.17), a difference that was not statistically significant. In residents of the Buckle, adjusting for SES had minimal effect on the risk of diabetes, which remained a significant 38% greater than for residents in the remainder of the United States (OR, 1.38; 95% CI, 1.19, .59). These estimates changed minimally after additional adjustment for risk factors (OR, 1.34; 95% CI, 1.15,1.57). The addition of hypertension to the model had no effect on the magnitude of the association in the Buckle (Table 2).
Awareness and Treatment of Diabetes and Control of Glucose Levels
Of the 4500 people with diabetes, 88.2% were aware that they had the disease, 95.7% of those aware were being treated, but only 56.1% of those being treated had their diabetes under control based on glucose level (Table 3). Multivariable analyses revealed that there were no significant differences by region with regard to awareness and treatment of diabetes, or control of glucose levels, for either whites or blacks adjusting for demographic and SES factors (data not shown).
|
|
Discussion |
|---|
|
TopAbstractIntroductionMethodsResultsDiscussionReferences |
|---|
This study confirms previous findings of both racial and geographic variation in the prevalence of diabetes with higher prevalence among blacks and southerners, suggesting that diabetes is a substantial contributor to the racial and geographic disparities in stroke mortality. Diabetes is significantly increased in both whites and blacks in the Buckle and the Belt compared to residents in the remainder of the United States. In black residents of the Belt, variations in SES, particularly education and income, appeared to explain much of the regional variations in diabetes. Whether there are other SES measures that are associated with prevalent diabetes and that might account for more of the association needs further exploration. Among black and white residents in the Buckle and whites in the Belt, regional differences were not fully mediated and remained significant when controlling for relevant risk factors. Addition of hypertension to these models did not explain this variation, suggesting that the increased risk in the region is not attributable to a single subset of people having an increased "cluster" of risk factors including both diabetes and hypertension (that is, the increased risk in diabetes is "independent" of the increased risk in hypertension). The observed geographic variation in diabetes was also not explained by variation in awareness, treatment, or control of diabetes. As such, the reasons for this variation remain unclear, because it is unlikely that selective survival of those with diabetes in the regions where stroke mortality is highest explains the regional differences.
Whereas there are at least 10 hypothesized reasons for the existence of the Stroke Belt, perhaps the reason with the most national acceptance is a hypothesized excess of hypertension in the southeastern United States, which would contribute to a higher incidence of stroke.14,23,25–27 The acceptance of this hypothesis has contributed to the funding of studies that are attempting to "eliminate" the Stroke Belt primarily through improved blood pressure control in the region.28 These analyses have shown that the observed regional variation in diabetes exists regardless of whether hypertension was included. Thus, it appears that regional variations in diabetes do follow a pattern similar to that observed for stroke mortality and that this variation is independent of hypertension status. These findings suggest that efforts directed at reducing diabetes in the Belt, and especially the Buckle, may be an important additional interventional strategy to reduce the disparity in stroke mortality associated with the Stroke Belt, especially because the combination of diabetes and hypertension may be more potent with regard to stroke risk and stroke mortality as compared to hypertension alone.
These findings also suggest that the prevalence of diabetes, but not the control of diabetes, is likely contributing to the geographic disparity in stroke mortality. Whereas this finding is currently true, the higher prevalence of obesity in the southern Stroke Belt region would seem to suggest that the geographic disparities in diabetes are likely to further increase, further fueling the geographic disparity in stroke. This adds further importance to reducing the geographic disparities in obesity.
The strengths and limitations of this study need to be mentioned. A limitation of these analyses is their cross-sectional nature and that they are not linked to risk of stroke. As with all epidemiological studies, participation rates also need to be considered. Because it is difficult to determine the eligibility of nonparticipants, the proportion of eligible subjects who participate is difficult to estimate. It has been conservatively estimated that 
40% of the eligible participants contacted have participated in the telephone interview,29 which compares well with similar epidemiological studies. As is true in National Health and Nutrition Examination Survey (NHANES), this population is limited to noninstitutionalized individuals. The REGARDS cohort was not designed to be representative of the United States population because of over-sampling of blacks and residents of the Stroke Belt. Participants in REGARDS were required to have a telephone, which may further restrict the representativeness of the cohort; however, the recent report of Blumberg et al30 estimates that among the target age group of REGARDS, 96% of individuals aged 45 to 64 and 98% of those older than age 65 have a land-line phone. Another possible limitation, which has been documented elsewhere,24 is that the data at the in-home visit was collected by any of 6000 employees of Examination Management, Services, Inc. Although substantial training and visit standardization were attempted, those efforts cannot replicate the quality of a smaller clinic-based staff. This limitation is partially offset by advantages of a true nationally based large sample in which participants were assessed in their homes. In addition, because this report is based on a partial sample (21 959 of 30 000 participants) there could be a potential for temporal change; however, because the participants are being randomly selected (from 12 strata; 3 regions x2 races x2 sexes) there is no reason to assume that results from this partial sample will differ from the entire cohort. There was no evidence of temporal change in the age-race-sex adjusted rates of diabetes or hypertension.
Other limitations relate to establishing diabetes status. First, it was not possible to separate type 1 from type 2 diabetes. Second, we use only 1 assessment of glucose to establish diabetes status compared to the standard clinical definition that requires 2 assessments and as such this could contribute to "over-diagnosis" (by the clinical definition) of diabetes in this article; however, the definition used here is quite standard in epidemiological studies, mainly because it is impractical to ask volunteers to return to a clinic visit (or in the case of REGARDS to perform 2 in-home assessments). Third, glucose measurements are missing on 928 participants. These data were missing for 2 reasons: (1) delays in blood shipment so that the glucose assay would be biased and (2) a minor number were not available because the assays were currently being processed in the central laboratory and results were not transferred to the data center. The likelihood that either of these causes would occur is not related to diabetes status or region of the country. Any missing data are "missing at random" and the presence/absence of individual data items should not affect results.
A final limitation relates to definitions of diabetes treatment and control. As is common in other studies, we defined "treatment" of diabetes as the self-reported use of medications. It is possible that some participants with diabetes were on nondrug interventions (diet, weight loss, exercise, or the like) and would not be classified as having diabetes. With respect to diabetes control, hemoglobin A1C values, the preferred method for estimating diabetes control, were not available. As such, estimates of control had to be made using blood glucose levels which are known to be less precise.
A strength of the study is that determination of disease status did not solely depend on self-report. Previous studies, such as the Behavioral Risk Factor Surveillance System, used self-reported diabetes alone, which can result in substantial misclassification and which could differ by region and race/ethnicity, leading to spurious differences by these factors. An additional strength of REGARDS is that the current analyses consisting of >20 000 participants is almost 4-times the size of the 5448 available in NHANES III,23 thus yielding more precise estimates of rates of diabetes and diabetes awareness, treatment, and control. For instance, if we compare diabetes prevalence and diabetes awareness (using the same definition of awareness) in non-Hispanic white participants aged 60 and older in REGARDS to the same race and age group in NHANES, we find higher prevalence of diabetes (22.8% REGARDS vs 16.5% NHANES) and diabetes awareness (87.0% REGARDS vs 65.3% NHANES) in the REGARDS cohort, suggesting that NHANES may be underestimating the prevalence of diabetes and the associated magnitude of its effect on geographic disparities in stroke mortality.31
In summary, these analyses support a possible role of regional variation in diabetes as in part an explanation for the regional variation in stroke mortality, and more importantly suggest that there may be unique factors present in the Buckle and Belt that lead to an increased risk of diabetes more than that which can be explained by regional variation in the standard risk factors for diabetes. With the obesity epidemic particularly in states in the southeastern United States, the contribution of diabetes to the geographic disparity in stroke mortality will likely grow. Without intervention to reduce the impact of diabetes, unfortunately, these findings may portend an increasing disparity of stroke in the southeastern United States.
|
Acknowledgments |
|---|
The authors acknowledge the participating investigators and institutions for their valuable contributions: University of Alabama at Birmingham, Birmingham, Alabama (Study PI, Data Coordinating Center, Survey Research Unit): George Howard, Leslie McClure, Virginia Howard, Virginia Wadley, Rodney Go; University of Vermont (Central Laboratory): Mary Cushman; Wake Forest University Medical Center (ECG Reading Center): Ron Prineas; Alabama Neurological Institute (Stroke Validation Center, Medical Monitoring): Camilo Gomez; University of Arkansas for Medical Sciences (Survey Research): LeaVonne Pulley; University of Cincinnati: Brett Kissela, Dawn Kleindorfer; Examination Management Services Incorporated (In-Home Visits): Andra Graham; and National Institute of Neurological Disorders and Stroke, National Institutes of Health (funding agency): Claudia Moy.
Sources of Funding
This research project is supported by a cooperative agreement U01 NS041588 from the National Institute of Neurological Disorders and Stroke, National Institutes of Health, Department of Health and Human Services.
Disclosures
None.
Received October 16, 2007; revision received November 13, 2007; accepted November 14, 2007.
|
References |
|---|
|
TopAbstractIntroductionMethodsResultsDiscussionReferences |
|---|
1. Burke JP, Williams K, Gaskill SP, Hazuda HP, Haffner SM, Stern MP. Rapid rise in the incidence of type 2 diabetes from 1987 to 1996: results from the San Antonio Heart Study. Arch Intern Med. 1999; 159: 1450–1456.
2. Geiss LS, Pan L, Cadwell B, Gregg EW, Benjamin SM, Engelgau MM. Changes in incidence of diabetes in US adults, 1997–2003. Am J Prev Med. 2006; 30: 371–377.[CrossRef][Medline] [Order article via Infotrieve]
3. Harris MI. Diabetes in America: epidemiology and scope of the problem. Diabetes Care. 1998; 21 Suppl 3: C11–C14.[Medline] [Order article via Infotrieve]
4. Mokdad AH, Serdula MK, Dietz WH, Bowman BA, Marks JS, Koplan JP. The continuing epidemic of obesity in the United States. JAMA. 2000; 284: 1650–1651.
5. Mokdad AH, Ford ES, Bowman BA, Nelson DE, Engelgau MM, Vinicor F, Marks JS. Diabetes trends in the US: 1990–1998. Diabetes Care. 2000; 23: 1278–1283.
6. Ogden CL, Carroll MD, Curtin LR, McDowell MA, Tabak CJ, Flegal KM. Prevalence of overweight and obesity in the United States, 1999–2004. JAMA. 2006; 295: 1549–1555.
7. Zimmet P, Alberti KG, Shaw J. Global and societal implications of the diabetes epidemic. Nature. 2001; 414: 782–787.[CrossRef][Medline] [Order article via Infotrieve]
8. Flegal KM, Carroll MD, Ogden CL, Johnson CL. Prevalence and trends in obesity among United States adults, 1999–2000. JAMA. 2002; 288: 1723–1727.
9. Harris MI, Flegal KM, Cowie CC, Eberhardt MS, Goldstein DE, Little RR, Wiedmeyer HM, Byrd-Holt DD. Prevalence of diabetes, impaired fasting glucose, and impaired glucose tolerance in US adults. The Third National Health and Nutrition Examination Survey, 1988–1994. Diabetes Care. 1998; 21: 518–524.[Abstract]
10. Cowie CC, Rust KF, Byrd-Holt DD, Eberhardt MS, Flegal KM, Engelgau MM, Saydah SH, Williams DE, Geiss LS, Gregg EW. Prevalence of diabetes and impaired fasting glucose in adults in the US population: National Health And Nutrition Examination Survey 1999–2002. Diabetes Care. 2006; 29: 1263–1268.
11. Goldstein LB, Adams R, Becker K, Furberg CD, Gorelick PB, Hademenos G, Hill M, Howard G, Howard VJ, Jacobs B, Levine SR, Mosca L, Sacco RL, Sherman DG, Wolf PA, del Zoppo GJ. Primary prevention of ischemic stroke: A statement for healthcare professionals from the Stroke Council of the Am Heart Association. Stroke. 2001; 32: 280–299.
12. Kissela BM, Khoury J, Kleindorfer D, Woo D, Schneider A, Alwell K, Miller R, Ewing I, Moomaw CJ, Szaflarski JP, Gebel J, Shukla R, Broderick JP. Epidemiology of ischemic stroke in patients with diabetes: the greater Cincinnati/Northern Kentucky Stroke Study. Diabetes Care. 2005; 28: 355–359.
13. Howard G. Why do we have a stroke belt in the southeastern United States? A review of unlikely and uninvestigated potential causes. Am J Med Sci. 1999; 317: 160–167.[CrossRef][Medline] [Order article via Infotrieve]
14. Perry HM, Roccella EJ. Conference report on stroke mortality in the southeastern United States. Hypertension. 1998; 31: 1206–1215.
15. Wolf PA, D’Agostino RB, Belanger AJ, Kannel WB. Probability of stroke: a risk profile from the Framingham Study. Stroke. 1991; 22: 312–318.
16. Whisnant JP. Modeling of risk factors for ischemic stroke. The Willis Lecture. Stroke. 1997; 28: 1840–1844.
17. Ohira T, Shahar E, Chambless LE, Rosamond WD, Mosley TH Jr, Folsom AR. Risk factors for ischemic stroke subtypes: the Atherosclerosis Risk in Communities study. Stroke. 2006; 37: 2493–2498.
18. Ahluwalia IB, Mack KA, Murphy W, Mokdad AH, Bales VS. State-specific prevalence of selected chronic disease-related characteristics–Behavioral Risk Factor Surveillance System, 2001. MMWR Surveill Summ. 2003; 52: 1–80.[Medline] [Order article via Infotrieve]
19. Ford ES, Mokdad AH, Giles WH, Galuska DA, Serdula MK. Geographic variation in the prevalence of obesity, diabetes, and obesity-related behaviors. Obes Res. 2005; 13: 118–122.[Medline] [Order article via Infotrieve]
20. Hahn RA, Heath GW, Chang MH. Cardiovascular disease risk factors and preventive practices among adults—United States, 1994: a behavioral risk factor atlas. Behavioral Risk Factor Surveillance System State Coordinators. MMWR CDC Surveill Summ. 1998; 47: 35–69.[Medline] [Order article via Infotrieve]
21. Cantrell RA, Howard G, Howard VJ, Prineas RJ, Cushman M, Gomez CR, Moy CS, Temple E. Differences in Predicted 10-Year Risk of Stroke by Region and Race. Circulation. 2005; 111: 220.
22. Burt VL, Whelton P, Roccella EJ, Brown C, Cutler JA, Higgins M, Horan MJ, Labarthe D. Prevalence of hypertension in the United States adult population. Results from the Third National Health and Nutrition Examination Survey, 1988–1991. Hypertension. 1995; 25: 305–313.
23. Obisesan TO, Vargas CM, Gillum RF. Geographic variation in stroke risk in the United States. Region, urbanization, and hypertension in the Third National Health and Nutrition Examination Survey. Stroke. 2000; 31: 19–25.
24. Howard VJ, Cushman M, Pulley L, Gomez CR, Go RC, Prineas RJ, Graham A, Moy CS, Howard G. The reasons for geographic and racial differences in stroke study: objectives and design. Neuroepidemiology. 2005; 25: 135–143.[CrossRef][Medline] [Order article via Infotrieve]
25. Hall WD, Ferrario CM, Moore MA, Hall JE, Flack JM, Cooper W, Simmons JD, Egan BM, Lackland DT, Perry M Jr, Roccella EJ. Hypertension-related morbidity and mortality in the southeastern United States. Am J MedSci. 1997; 313: 195–209.[CrossRef][Medline] [Order article via Infotrieve]
26. Hertz RP, Unger AN, Cornell JA, Saunders E. Racial disparities in hypertension prevalence, awareness, and management. Arch Intern Med. 2005; 165: 2098–2104.
27. Jones D, Basile J, Cushman W, Egan B, Ferrario C, Hill M, Lackland D, Mensah G, Moore M, Ofili E, Roccella EJ, Smith R, Taylor H. Managing hypertension in the southeastern United States: applying the guidelines from the Sixth Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (JNC VI). Am J Med Sci. 1999; 318: 357–364.[CrossRef][Medline] [Order article via Infotrieve]
28. RFA HL 04-007. http://grants.nih.gov/grants/guide/rfa-files/RFFA-HL-04-007.html. NIH-NHLIB. 2003.
29. Howard G, Prineas R, Moy C, Cushman M, Kellum M, Temple E, Graham A, Howard V. Racial and geographic differences in awareness, treatment, and control of hypertension: the REasons for Geographic And Racial Differences in Stroke study. Stroke. 2006; 37: 1171–1178.
30. Blumberg SJ, Luke JV, Cynamon ML. Telephone coverage and health survey estimates: evaluating the need for concern about wireless substitution. Am J Public Health. 2006; 96: 926–931.
31. Hertz RP, Unger AN, Ferrario CM. Diabetes, hypertension, and dyslipidemia in Mexican Americans and non-Hispanic whites. Am J Prev Med. 2006; 30: 103–110.[CrossRef][Medline] [Order article via Infotrieve]
This article has been cited by other articles:
 |
|
 |
|
  Y. Liao, K. J. Greenlund, J. B. Croft, N. L. Keenan, and W. H. Giles Factors Explaining Excess Stroke Prevalence in the US Stroke Belt Stroke, October 1, 2009; 40(10): 3336 - 3341. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 N. R. Cook and M. A. Albert Regarding REGARDS: Does Inflammation Explain Racial and Regional Differences in Cardiovascular Disease Risk? Clin. Chem., September 1, 2009; 55(9): 1603 - 1605. [Full Text] [PDF]
|
|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||