- Research article
- Open Access
- Open Peer Review
Awareness and management of chronic disease, insurance status, and health professional shortage areas in the REasons for Geographic And Racial Differences in Stroke (REGARDS): a cross-sectional study
© Durant et al.; licensee BioMed Central Ltd. 2012
- Received: 10 November 2011
- Accepted: 4 July 2012
- Published: 20 July 2012
Limited financial and geographic access to primary care can adversely influence chronic disease outcomes. We examined variation in awareness, treatment, and control of hypertension, diabetes, and hyperlipidemia according to both geographic and financial access to care.
We analyzed data on 17,458 participants in the REasons for Geographic And Racial Differences in Stroke (REGARDS) study with either hypertension, hyperlipidemia, or diabetes and living in either complete Health Professional Shortage Area (HPSA) counties or non-HPSA counties in the U.S. All analyses were stratified by insurance status and adjusted for sociodemographics and health behaviors.
2,261 residents lived in HPSA counties and 15,197 in non-HPSA counties. Among the uninsured, HPSA residents had higher awareness of both hypertension (adjusted OR 2.30, 95% CI 1.08, 4.89) and hyperlipidemia (adjusted OR 1.50, 95% CI 1.01, 2.22) compared to non-HPSA residents. Also among the uninsured, HPSA residents with hypertension had lower blood pressure control (adjusted OR 0.45, 95% CI 0.29, 0.71) compared with non-HPSA residents. Similar differences in awareness and control according to HPSA residence were absent among the insured.
Despite similar or higher awareness of some chronic diseases, uninsured HPSA residents may achieve control of hypertension at lower rates compared to uninsured non-HPSA residents. Federal allocations in HPSAs should target improved quality of care as well as increasing the number of available physicians.
- Underserved Area
- Common Chronic Disease
- Geographic Access
- Primary Medical Care
- Financial Access
The diagnosis and treatment of chronic diseases often takes place in primary care settings . Therefore, patient awareness and management of chronic diseases may vary according to geographic access to primary care. However, traditional “rural” vs. “urban” designations, often based primarily on population density, may be inadequate to capture actual differences in geographic access to primary care . Traditional rural and urban designations often do not account for the availability of health care providers which can vary in geographic areas, independently of the population size . In contrast, federally-designated geographic primary care Health Professional Shortage Areas (HPSA), based on both population size and primary care physician availability, may be better markers for limited geographic access to primary care .
The Health Resource and Services Administration (HRSA) designates geographic primary care HPSAs based on 3 criteria . First, a HPSA must be a rational area (e.g. a county, neighborhood, or grouping of demographically similar townships or census tracts) for the delivery of primary medical care services. Secondly, a HPSA must have a population to full-time-equivalent primary care physician ratio of at least 3,500:1. Alternately, a qualifying HPSA may have a population to full-time equivalent primary care physician ratio of less than 3,500:1 but greater than 3,000:1 and have unusually high needs for primary care services or insufficient capacity of existing primary care providers. Finally, HPSAs must demonstrate that primary medical care professionals in contiguous areas are overutilized, excessively distant, or inaccessible to the population under consideration. HPSA designation guides the allocation of federal resources to the underserved areas to increase the number of practicing primary care physicians and increase Medicare reimbursement among physicians practicing in these areas. Residence in a HPSA has been previously associated with lower health care utilization and poor health outcomes in limited populations [5–7]. Yet, less is known about whether these physician shortages are associated with the diagnoses and management of common chronic diseases typically encountered in primary care settings in underserved populations residing in these areas .
Though geographic primary care HPSAs are based on geographic access to care, inadequate health insurance still potentially looms as an additional barrier to primary care in underserved communities already beset by geographic barriers to care. Individuals living in less densely populated areas are not only more likely to have lower geographic access to primary care physicians, but also less likely to be insured . Furthermore, several studies have demonstrated that lack of insurance often contributes to poorer outcomes in common chronic diseases that are typically managed in primary care settings [10, 11]. Despite federal efforts to overcome geographic barriers to access by increasing primary care physician availability, it is unclear whether lack of insurance remains a financial barrier to access even in the presence of more primary care providers in these underserved areas. Due to multiple types of barriers to access to primary care, residents of medically underserved areas may be particularly vulnerable to both underdiagnosis and suboptimal management of chronic diseases . Though increasing physician availability may improve geographic access to primary care, it may not be sufficient to address persistent financial barriers associated with a lack of insurance. Therefore we sought to examine the influence of physician shortages on awareness, treatment, and control of hypertension, diabetes, and hyperlipidemia among persons both with and without health insurance.
Baseline data were analyzed from the REasons for Geographic And Racial Differences in Stroke (REGARDS) cohort. REGARDS is a national, population-based longitudinal cohort study of 30,183 self-identified blacks and whites aged 45 and older, living in the community, and balanced on black race and sex by design. The sampling scheme and methods for REGARDS have been described in detail previously . Briefly, the study cohort was recruited with an oversampling of both blacks and persons living in either the Stroke Buckle (coastal plains region of North Carolina, South Carolina, and Georgia) or the Stroke Belt (remainder of North Carolina, South Carolina, and Georgia, plus Alabama, Mississippi, Tennessee, Arkansas, and Louisiana). Written informed consent was obtained from each participant. At baseline, participants underwent computer-assisted telephone interviews (CATI) assessing demographic information, medical history, and functional status. Following the CATI, trained research staff conducted an in-home examination that included blood pressure assessments (average of two measurements taken during same baseline in-home visit) and the collection of blood and urine samples. During the in-home visit, self-administered questionnaires were left with participants to gather information on additional demographic and risk factor characteristics. These questionnaires were completed by the participant after the home visit and were returned by self-addressed, prepaid envelopes. Participants are followed by telephone at 6-month intervals for surveillance of cardiovascular events such as stroke and myocardial infarction. The study methods were reviewed and approved by the University of Alabama at Birmingham Institutional Review Board as well as institutional review boards at the collaborating institutions. This study focuses on the baseline data collected from the CATI, self-administered questionnaires and in-home visits.
The 9 outcomes for the study include the baseline awareness, treatment and control among those with hypertension, diabetes or hyperlipidemia upon study entry. Hypertension was defined by self-reported disease by the participant, or if blood pressure was ≥140/90 mmHg, or if the person had diabetes or renal disease (GFR < 60 mL/min/1.73 m2) and blood pressure ≥130/80 . Diabetes was defined as self-reported disease, treatment with a diabetes medication on pill-bottle review, or a single fasting glucose ≥126 mg/dL or random glucose ≥200 mg/dL . Hyperlipidemia was defined as self-reported disease, treatment with a lipid medication on pill-bottle review, or low density lipoprotein cholesterol ≥130 mg/dL .
Awareness of disease was self-reported in each of the 3 disease groups (e.g. “Has a doctor or other health care professional ever told you that you had diabetes?”). Treatment of disease was assessed by either self-report (“Are you currently taking diabetes pills or insulin?”) or identification of a medication for the target disease (e.g. oral hypoglycemics or insulin for diabetes) on the medication inventory performed during the baseline in-home visit [17, 18]. Control for each disease group was defined by disease-specific targets of therapy. For diabetes, control was defined as a fasting blood sugar <140 mg/dL; for hypertension as a BP <140/90 mmHg (or, for diabetes or CKD, <130/80); for hyperlipidemia as LDL cholesterol <130 mg/dL.
The primary independent variables were residence in a federally-designated geographic primary care HPSA and insurance status at baseline. We used county-level HRSA designations to define all the U.S. counties containing REGARDS participants as complete (n = 2,545), partial (n = 10,236), or non-HPSA counties (n = 17,427). Complete HPSA counties are entirely comprised of HPSAs while non-HPSA counties do not include any HPSAs. Partial HPSA counties are comprised of both HPSAs suffering from limited physician availability and non-HPSA areas without physician shortages. Similar to previous studies using county-level HPSA data [6, 19–21], residents of partial HPSA counties were excluded from the analyses due to an inability to determine if REGARDS participants in these counties resided in the HPSA or non-HPSA portion of the county. Only the 17,458 REGARDS participants falling into at least one of the 3 defined disease groups and residing in either complete HPSA or non-HPSA counties were included in these analyses. Baseline insurance status (insured vs. uninsured) was based on self-report (“Do you have healthcare coverage such as health insurance, an HMO or a government plan like Medicare or Medicaid?”).
Other baseline independent variables included sociodemographics (race, age, gender, and education). Body mass index, medication adherence, and tobacco and alcohol use, and frequency of exercise were also included in the analyses. The rurality of the county of residence was based on criteria set by the U.S. Census Bureau . We defined rural counties as those that are 25% or less urban; urban as those that are 75% or more urban; and mixed counties as 25%-75% urban. Models for each target disease category (hypertension, diabetes, hyperlipidemia) were also adjusted for the presence of one or both of the other target diseases.
Unadjusted logistic regression models were constructed to examine the crude relationship between HPSA status and each of the 9 chronic disease outcomes (awareness, treatment or control for the 3 conditions). Interaction terms (insurance status*HPSA residence) were included in each model, with 4 of 9 demonstrating a significant interaction (P < 0.10) between insurance status and HPSA residence (other interaction terms such as race*HPSA and age*HPSA were not significant). Therefore, all analyses were stratified by insurance status to examine the relationship between HPSA status and the 9 chronic disease outcomes separately among the insured and uninsured. Multivariable logistic regression models stratified by insurance status were then created with all the covariates included to determine the independent relationship of HPSA status on all of the 9 chronic disease outcomes.
Characteristics of study population
Characteristics of study population
Sociodemographic and health related behaviors
n = 2060
n = 14247
n = 201
n = 950
Urban Group (%)
< High school
High school grad
College grad and above
$25,000 to $50,000
Medication Adherence∞ (%)
Tobacco Use (%)
Current Alcohol (%)
Body Mass Index (%)
25 to 29.9 kg/m2
Frequency of Exercise (%)
1 to 3 times per week
4 or more per week
Awareness, treatment and control
Unadjusted prevalence, awareness, treatment and disease control by HPSA residence and insurance status
HPSA n = 2060
Non-HPSA n = 14247
HPSA n = 201
Non-HPSA n = 950
Presence of any of 3 target conditions* (%)
Crude and adjusted odds ratios and 95% confidence intervals for the association between HPSA residence (reference: non-HPSA residence) and awareness, treatment or control of hypertension, diabetes, and hyperlipidemia, stratified by insurance status
95% Confidence interval
95% Confidence interval
95% Confidence interval
95% Confidence interval
HYPERTENSION‡ (n = 12736)
DIABETESβ (n = 4884)
HYPERLIPIDEMIAλ (n = 14053)
Our results indicate few differences in awareness, treatment and control among both insured and uninsured persons living in HPSA and non-HPSA counties. Despite higher awareness and similar treatment among uninsured HPSA residents compared to uninsured non-HPSA residents with hypertension, the control of hypertension was significantly lower for those uninsured persons living in HPSA counties compared to non-HPSA counties.
Similar to previous studies focused on awareness, treatment, and control of hypertension, hyperlipidemia, and diabetes [12, 23–34], our results suggest that rates of awareness and treatment of these chronic conditions are generally higher than corresponding rates of control. National data on hypertension, diabetes, and hyperlipidemia have revealed large proportions of persons, treated for these chronic diseases and still not well-controlled [25, 28]. Separate analyses of National Health and Examination Nutrition Examination Survey (NHANES) data revealed awareness, treatment and control rates of 76%, 65%, and 57% among hypertensives and 71%, 51% and 51% among persons with diabetes [28, 31]. Compared to hypertension and diabetes, NHANES data reveals lower awareness, treatment and control rates for hyperlipidemia at 35%, 12% and 5% . While these and other data demonstrate that awareness, treatment, and control rates vary among these 3 common chronic diseases, significant proportions of persons with diabetes, hypertension, and hyperlipidemia who receive treatment are not well-controlled [12, 23]. Though our results reveal similar trends toward inadequate control of all 3 common chronic diseases, our study differs from others because we were also able to demonstrate some variation in these measures according to geographic access to care and insurance status.
Among the uninsured only, residence in HPSA counties was associated with significantly higher awareness of hypertension and hyperlipidemia and similar awareness of diabetes compared to those living in non-HPSA counties. Our results are similar to a recent study based on the association of HPSA residence with the prevalence, awareness, and control of cardiovascular risk factors in the Multi-Ethnic Study of Atherosclerosis (MESA) cohort . Similar to our data, this study showed fairly equal awareness of diabetes regardless of HPSA status. The investigators found no differences in awareness of hypertension or hyperlipidemia, while we found higher awareness among uninsured persons living in HPSA counties in both these disease groups .
Contrary to our results and those of other studies, one might predict that lower geographic access to primary care providers would lead to fewer physician encounters and lower awareness of disease among HPSA residents [5, 20]. However, our seemingly paradoxical findings could be explained if the additional human and financial resources dedicated to HPSAs not only increase geographic access to physicians in underserved geographic areas, but also mitigate some of the financial barriers to access faced by uninsured HPSA residents. Because uninsured non-HPSA residents do not live in geographic areas receiving these additional human and financial resources to increase access to care, they may be no more likely, or in the cases of hyperlipidemia or hypertension, even less likely, to be aware of their disease. For example, federally-funded rural health clinic (RHC) programs are located exclusively in rural HPSAs and allow providers to receive higher reimbursements from Medicare and Medicaid . Though RHCs are not obligated to provide care for the uninsured, they often serve as a major safety net providers in these areas [36, 37].
Among the insured, the absence of disparities in awareness of disease based on HPSA residence may also be attributable to common financial access to a wider array of health care choices likely available to both insured non-HPSA and insured HPSA residents. Those with insurance typically have higher access to primary care physicians and medical specialists even when controlling for place of residence . Therefore, the increased financial access to care may buffer some geographic barriers to care even among insured HPSA residents.
Despite the relatively higher odds of awareness and similar odds for treatment for hypertension associated with HPSA residence among the uninsured, the odds of control of hypertension were lower for uninsured HPSA residents compared to uninsured non-HPSA residents. Uninsured HPSA residents did not suffer worse control in the diabetes and hyperlipidemia disease groups. Previous studies have demonstrated that hypertension, compared to other cardiovascular diseases, may be particularly vulnerable to variation in control according to access to care . The lower rates of hypertension control among uninsured HPSA residents compared to uninsured non-HPSA residents may also seem paradoxical juxtaposed to similar rates of treatment of hypertension in the two groups.
While resource allocation aimed at increasing geographic access to care among HPSA residents may have positively impacted rates of chronic disease awareness and treatment among those living in underserved geographic areas, similar ameliorative effects do not seem to have been realized with actual blood pressure control. This dichotomy between treatment and blood pressure control may be due to poorer quality of care among uninsured HPSA residents. Though patients are receiving treatment for hypertension, it is unclear whether providers adhere to guidelines in their treatment of uninsured HPSA residents. Adherence to treatment guidelines, such as the first-line use of thiazide diuretics for stage I hypertension (≥140/80 mmHg) and dual antihypertensive therapy for stage II hypertension (≥160/100 mm Mg) , could be added to considerations for renewal of HPSA designations to ensure that continued federal funding is allocated toward increasing quality of care as well as provider availability. Current efforts, such as federal monetary incentives , to increase use of electronic health records would facilitate more feasible assessments of outpatient quality measures in HPSAs in the future.
In addition to physician-level factors, uninsured HPSA residents may be more complex patients due to more prevalent sociodemographic and clinical barriers to optimal health. Compared to uninsured non-HPSA residents, uninsured HPSA residents more frequently report fair or poor health and higher prevalences of chronic diseases such as arthritis, obesity, and serious psychological distress . Uninsured patients residing in HPSAs are also more likely than uninsured non-HPSA residents to report family incomes < 200% of the federal poverty level, to be unemployed and to have no high school diploma . All of these characteristics, found more frequently among uninsured HPSA residents, may increase the complexity of such patients, potentially making disease control targets even more elusive . Some observers have advocated including sociodemographic and clinical characteristics among HPSA residents as additional considerations to increase funds allocated to HPSAs to account for the care of groups, such as the uninsured, with potentially higher-than-average health care needs .
This study has a few limitations. REGARDS is a nationwide community-based cohort, but it may not be representative of the entire U.S. population. The cohort is a relatively older population comprised exclusively of African Americans and whites so results may be less generalizable to younger populations or other races or ethnicities. We used HPSA designations from 2006 though these designations may change depending on population shifts, changes in the physician workforce, or other local factors . Therefore, we were unable to determine how changes in HPSA designations over time may have influenced our findings. Our data are cross-sectional, without the ability to establish causality. We also used county-level HPSA designations and excluded partial-county HPSAs. This conservative approach [6, 19–21] ensured that we did not include persons living in portions of partial-county HPSAs that were not underserved. However, we also miss some participants residing in underserved portions of partial-HPSA counties. Though we measured geographic and financial access to primary care using HPSA and insurance status, respectively, we were unable to measure health care utilization to determine, directly, how each factor might impact our outcomes. However, previous studies have demonstrated that both the uninsured and HPSA residents tend to have lower health care utilization across multiple chronic diseases . In addition to the absence of health care utilization data, we were also unable to determine if HPSA residents accessed care within or outside their counties of residence. However, even if participants were able to access care outside the HPSA-designated area, the impact of longer travel distances for outpatient care has been shown to lead to lower health care utilization for multiple medical conditions independent of HPSA status [44–47].
In summary, uninsured HPSA residents were similarly or more likely to be aware of chronic disease compared to uninsured non-HPSA residents. However, disease control rates for hypertension are lower for uninsured HPSA residents compared to non-HPSA residents who are uninsured. Federal HPSA designations may have made a positive impact on the identification of chronic disease among the most vulnerable HPSA residents, but the management of these chronic conditions has not been optimized to the same extent. Consequently, in addition to increasing the number of primary care physicians, policymakers should consider other strategies both to improve the quality of care and to address the needs of particularly vulnerable HPSA residents, such as the uninsured.
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 Service. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Neurological Disorders and Stroke or the National Institutes of Health. Representatives of the funding agency have been involved in the review of the manuscript but not directly involved in the collection, management, analysis or interpretation of the data. The authors thank the other investigators, the staff, and the participants of the REGARDS study for their valuable contributions. A full list of participating REGARDS investigators and institutions can be found at http://www.regardsstudy.org. Additional funding was provided by an investigator-initiated grant R01 HL80477 and a supplemental grant R01 HL080477-02 S1 from the National Heart, Lung and Blood Institute.
- Bodenheimer T, Wagner EH, Grumbach K: Improving Primary Care for Patients With Chronic Illness. J Am Med Assoc. 2002, 288: 1775-1779. 10.1001/jama.288.14.1775.View ArticleGoogle Scholar
- Probst JC, Moore CG, Glover SH, Samuels ME: Person and Place: The Compounding Effects of Race/Ethnicity and Rurality on Health. Am J Public Health. 2004, 94: 1695-1703. 10.2105/AJPH.94.10.1695.View ArticlePubMedPubMed CentralGoogle Scholar
- Hendryx M: Mental health professional shortage areas in rural Appalachia. J Rural Health. 2008, 24: 179-182. 10.1111/j.1748-0361.2008.00155.x.View ArticlePubMedGoogle Scholar
- U S. Health Resources and Services Administration: Primary Care HPSA Designation Overview. [http://bhpr.hrsa.gov/shortage/hpsas/designationcriteria/primarycarehpsaoverview.html]
- Asch SM, Sloss EM, Hogan C, Brook RH, Kravitz RL: Measuring Underuse of Necessary Care Among Elderly Medicare Beneficiaries Using Inpatient and Outpatient Claims. J Am Med Assoc. 2000, 284: 2325-2333. 10.1001/jama.284.18.2325.View ArticleGoogle Scholar
- Kohrs FP, Mainous AG: The Relationship of Health Professional Shortage Areas to Health Status: Implications for Health Manpower Policy. Arch Fam Med. 1995, 4: 681-685. 10.1001/archfami.4.8.681.View ArticlePubMedGoogle Scholar
- Parchman ML, Culler SD: Preventable Hospitalizations in Primary Care Shortage Areas: An Analysis of Vulnerable Medicare Beneficiaries. Arch Fam Med. 1999, 8: 487-491. 10.1001/archfami.8.6.487.View ArticlePubMedGoogle Scholar
- Allen NB, Diez-Roux A, Liu K, Bertoni AG, Szklo M, Daviglus M: Association of Health Professional Shortage Areas and Cardiovascular Risk Factor Prevalence, Awareness, and Control in the Multi-Ethnic Study of Atherosclerosis (MESA). Circ Cardiovasc Qual Outcomes. 2011, 4: 565-572. 10.1161/CIRCOUTCOMES.111.960922.View ArticlePubMedGoogle Scholar
- Larson SL, Hill SC: Rural–urban differences in employment-related health insurance. J Rural Health. 2005, 21: 21-30. 10.1111/j.1748-0361.2005.tb00058.x.View ArticlePubMedGoogle Scholar
- Brooks EL, Preis SR, Hwang S-J, Murabito JM, Benjamin EJ, Kelly-Hayes M, Sorlie P, Levy D: Health Insurance and Cardiovascular Disease Risk Factors. Am J Med. 2010, 123: 741-747. 10.1016/j.amjmed.2010.02.013.View ArticlePubMedPubMed CentralGoogle Scholar
- McWilliams JM: Health Consequences of Uninsurance among Adults in the United States: Recent Evidence and Implications. Milbank Q. 2009, 87: 443-494. 10.1111/j.1468-0009.2009.00564.x.View ArticlePubMedPubMed CentralGoogle Scholar
- Mainous AG, King DE, Garr DR, Pearson WS: Race, rural residence, and control of diabetes and hypertension. Ann Fam Med. 2004, 2: 563-568. 10.1370/afm.119.View ArticlePubMedPubMed CentralGoogle Scholar
- 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. 10.1159/000086678.View ArticlePubMedGoogle Scholar
- Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo JL, Jones DW, Materson BJ, Oparil S, Wright JT, Roccella EJ: The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: the JNC 7 report. JAMA. 2003, 289: 2560-2572. 10.1001/jama.289.19.2560.View ArticlePubMedGoogle Scholar
- American Diabetes Association: Diagnosis and Classification of Diabetes Mellitus. Diabetes Care. 2011, 34: S62-S69.View ArticlePubMed CentralGoogle Scholar
- Grundy SM, Cleeman JI, Merz CNB, Brewer HB, Clark LT, Hunninghake DB, Pasternak RC, Smith SC, Stone NJ: Program ftCCotNCE, et al: Implications of Recent Clinical Trials for the National Cholesterol Education Program Adult Treatment Panel III Guidelines. Circulation. 2004, 110: 227-239. 10.1161/01.CIR.0000133317.49796.0E.View ArticlePubMedGoogle Scholar
- 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. 10.1161/01.STR.0000217222.09978.ce.View ArticlePubMedGoogle Scholar
- Ostchega Y, Dillon CF, Hughes JP, Carroll M, Yoon S: Trends in hypertension prevalence, awareness, treatment, and control in older U.S. adults: data from the National Health and Nutrition Examination Survey 1988 to 2004. J Am Geriatr Soc. 2007, 55: 1056-1065. 10.1111/j.1532-5415.2007.01215.x.View ArticlePubMedGoogle Scholar
- Fink KS, Phillips RL, Fryer GE, Koehn N: International Medical Graduates And The Primary Care Workforce For Rural Underserved Areas. Health Aff. 2003, 22: 255-262.View ArticleGoogle Scholar
- Liu JJ: Health professional shortage and health status and health care access. J Health Care Poor Underserved. 2007, 18: 590-598. 10.1353/hpu.2007.0062.View ArticlePubMedGoogle Scholar
- Pathman DE, Fryer GE, Green LA, Phillips RL: Changes in Age-Adjusted Mortality Rates and Disparities for Rural Physician Shortage Areas Staffed by the National Health Service Corps: 1984–1998. J Rural Health. 2005, 21: 214-220. 10.1111/j.1748-0361.2005.tb00085.x.View ArticlePubMedGoogle Scholar
- U.S Census bureau: Census. 2000, [http://www.census.gov/geo/www/ua/ua_2k.html], Urban and Rural ClassificationGoogle Scholar
- Carroll MD, Lacher DA, Sorlie PD, Cleeman JI, Gordon DJ, Wolz M, Grundy SM, Johnson CL: Trends in Serum Lipids and Lipoproteins of Adults, 1960–2002. JAMA. 2005, 294: 1773-1781. 10.1001/jama.294.14.1773.View ArticlePubMedGoogle Scholar
- Egan BM, Zhao Y, Axon RN: US Trends in Prevalence, Awareness, Treatment, and Control of Hypertension, 1988–2008. JAMA. 2010, 303: 2043-2050. 10.1001/jama.2010.650.View ArticlePubMedGoogle Scholar
- Ford ES, Mokdad AH, Giles WH, Mensah GA: Serum Total Cholesterol Concentrations and Awareness, Treatment, and Control of Hypercholesterolemia Among US Adults: Findings From the National Health and Nutrition Examination Survey, 1999 to 2000. Circulation. 2003, 107: 2185-2189. 10.1161/01.CIR.0000066320.27195.B4.View ArticlePubMedGoogle Scholar
- Gomes J, Wu C-K, Schauble B, Winter MR, Chamlian V, Allen NC, Babikian VL: Diagnosis of hyperlipidemia and treatment with a statin in the outpatient stroke clinic. J of Stroke Cerebrovasc Dis. 2001, 10: 132-134. 10.1053/jscd.2001.25465.View ArticleGoogle Scholar
- Kirk JK, D'Agostino RB, Bell RA, Passmore LV, Bonds DE, Karter AJ, Narayan KM: Disparities in HbA1c levels between African-American and non-Hispanic white adults with diabetes: a meta-analysis. Diabetes Care. 2006, 29: 2130-2136. 10.2337/dc05-1973.View ArticlePubMedPubMed CentralGoogle Scholar
- McDonald M, Hertz RP, Unger AN, Lustik MB: Prevalence, Awareness, and Management of Hypertension, Dyslipidemia, and Diabetes Among United States Adults Aged 65 and Older. J Gerontol A: Biol Sci Med Sci. 2009, 64A: 256-263. 10.1093/gerona/gln016.View ArticleGoogle Scholar
- McWilliams JM, Meara E, Zaslavsky AM, Ayanian JZ: Differences in control of cardiovascular disease and diabetes by race, ethnicity, and education: U.S. trends from 1999 to 2006 and effects of medicare coverage. Ann Intern Med. 2009, 150: 505-515.View ArticlePubMedGoogle Scholar
- Muntner P, DeSalvo KB, Wildman RP, Raggi P, He J, Whelton PK: Trends in the Prevalence, Awareness, Treatment, and Control of Cardiovascular Disease Risk Factors among Noninstitutionalized Patients with a History of Myocardial Infarction and Stroke. Am J Epidemiol. 2006, 163: 913-920. 10.1093/aje/kwj124.View ArticlePubMedGoogle Scholar
- Ong KL, Cheung BM, Man YB, Lau CP, Lam KS: Prevalence, awareness, treatment, and control of hypertension among United States adults 1999–2004. Hypertension. 2007, 49: 69-75.View ArticlePubMedGoogle Scholar
- Ostchega Y, Hughes JP, Wright JD, McDowell MA, Louis T: Are demographic characteristics, health care access and utilization, and comorbid conditions associated with hypertension among US adults?. Am J Hypertens. 2008, 21: 159-165. 10.1038/ajh.2007.32.View ArticlePubMedGoogle Scholar
- Shirani S, Kelishadi R, Sarrafzadegan N, Khosravi A, Sadri G, Amani A, Heidari S, Ramezani MA: Awareness, treatment and control of hypertension, dyslipidaemia and diabetes mellitus in an Iranian population: the IHHP study. Eastern Mediterranean Health Journal = La Revue De Santé De La Méditerranée Orientale = Al-Majallah Al-OṢiḥḥīyah Li-Sharq Al-Mutawassiṭ. 2009, 15: 1455-1463.Google Scholar
- Victor RG, Leonard D, Hess P, Bhat DG, Jones J, Vaeth PA, Ravenell J, Freeman A, Wilson RP, Haley RW: Factors associated with hypertension awareness, treatment, and control in Dallas County, Texas. Arch Intern Med. 2008, 168: 1285-1293. 10.1001/archinte.168.12.1285.View ArticlePubMedGoogle Scholar
- Probst JC, Laditka JN, Laditka SB: Association between community health center and rural health clinic presence and county-level hospitalization rates for ambulatory care sensitive conditions: an analysis across eight US states. BMC Health Serv Res. 2009, 9: 134-10.1186/1472-6963-9-134.View ArticlePubMedPubMed CentralGoogle Scholar
- U S. Government Accountability Office: Health Professional Shortage Areas: Problems Remain with Primary Care Shortage Area Designation System. Book Health Professional Shortage Areas: Problems Remain with Primary Care Shortage Area Designation System. (Editor ed.^eds.). City [http://www.gao.gov/new.items/d0784.pdf]
- U S. Government Accoutability Office: Health Centers and Rural Clinics. Payments Likely to be Constrained Under Medicaid's New System. Book Health Centers and Rural Clinics. Payments Likely to be Constrained Under Medicaid's New System. (Editor ed.^eds.). City [http://www.gao.gov/assets/240/231681.pdf]
- Ferrer RL: Pursuing equity: contact with primary care and specialist clinicians by demographics, insurance, and health status. Ann Fam Med. 2007, 5: 492-502. 10.1370/afm.746.View ArticlePubMedPubMed CentralGoogle Scholar
- Fowler-Brown A, Corbie-Smith G, Garrett J, Lurie N: Risk of cardiovascular events and death–does insurance matter?. J Gen Intern Med. 2007, 22: 502-507. 10.1007/s11606-007-0127-2.View ArticlePubMedPubMed CentralGoogle Scholar
- Bruen BK, Ku L, Burke MF, Buntin MB: More Than Four In Five Office-Based Physicians Could Qualify For Federal Electronic Health Record Incentives. Health Affairs. 2011, 30: 472-480. 10.1377/hlthaff.2010.0932.View ArticlePubMedGoogle Scholar
- Hoffman C, Damico A, Garfield R: Insurance Coverager and Access to Care in Primary Care Shortage Areas. Book Insurance Coverager and Access to Care in Primary Care Shortage Areas. 2011, City: The Henry J. Kaiser Family Foundation, (Editor ed.^eds.). CityGoogle Scholar
- Safford M, Allison J, Kiefe C: Patient Complexity: More Than Comorbidity.The Vector Model of Complexity. J Gen Intern Med. 2007, 22: 382-390.View ArticlePubMedPubMed CentralGoogle Scholar
- Ricketts TC, Goldsmith LJ, Holmes GM, Randolph RM, Lee R, Taylor DH, Ostermann J: Designating places and populations as medically underserved: a proposal for a new approach. J Health Care Poor Underserved. 2007, 18: 567-589. 10.1353/hpu.2007.0065.View ArticlePubMedGoogle Scholar
- Athas WF, Adams-Cameron M, Hunt WC, Amir-Fazli A, Key CR: Travel Distance to Radiation Therapy and Receipt of Radiotherapy Following Breast-Conserving Surgery. J Natl Cancer Inst. 2000, 92: 269-271. 10.1093/jnci/92.3.269.View ArticlePubMedGoogle Scholar
- Blow FC, Booth BM, Bunn JY, Cook CAL, Fortney JC: The effects of travel barriers and age on the utilization of alcoholism treatment aftercare. Am J Drug and Alcohol Abuse. 1995, 21: 391-10.3109/00952999509002705.View ArticleGoogle Scholar
- Fortney J, Rost K, Zhang M, Warren J: The impact of geographic accessibility on the intensity and quality of depression treatment. Med Care. 1999, 37: 884-893. 10.1097/00005650-199909000-00005.View ArticlePubMedGoogle Scholar
- Piette JD, Moos RH: The influence of distance on ambulatory care use, death, and readmission following a myocardial infarction. Health Serv Res. 1996, 31: 573-591.PubMedPubMed CentralGoogle Scholar
- The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1472-6963/12/208/prepub
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.