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J Gen Intern Med. Oct 2008; 23(10): 1634–1641.
Published online Jul 31, 2008. doi:  10.1007/s11606-008-0727-5
PMCID: PMC2533376

Socioeconomic Position and Cardiovascular Disease in Adults with and Without Diabetes: United States Trends, 1997–2005

Rosemary Dray-Spira, MD, PhD,corresponding author1,2,3,5 Tiffany L. Gary, PhD, MHS,1 and Frederick L. Brancati, MD, MHS1,4

Abstract

Background

Diabetes and its cardiovascular complications are more common in adults of low socioeconomic position (SEP). In the US, the past decade has seen the establishment of many programs to reduce cardiovascular risk in persons with diabetes, but their effect on socioeconomic disparities is uncertain.

Objective

We sought to investigate recent time trends in socioeconomic disparities in cardiovascular disease (CVD) among persons with and without diabetes.

Participants and Design

Two hundred fifty-five thousand nine hundred sixty-six individuals aged 25 years or older included in the National Health Interview Survey between 1997 and 2005.

Measurements

Educational attainment was used as a marker for SEP and self-reported history of CVD as the main outcome. Educational disparities were measured using prevalence rate ratios (PRR) and the relative index of inequalities (RII).

Main Results

Among adults with diabetes, CVD prevalence was persistently higher in those who did not complete high school (HS) than in college graduates (adjusted PRR [aPRR] 1.20, 95% confidence interval [95%CI] 1.05–1.38 in 1997–1999, and aPRR 1.12, 95% CI 1.00–1.25 in 2003–2005). However, the HS vs. college graduates disparity in CVD declined from 1997–1999 (aPRR 1.20, 95% CI 1.04–1.37) to 2003–2005 (aPRR 1.01, 95% CI 0.90–1.12). Among adults without diabetes educational disparities in CVD widened markedly over time.

Conclusions

Concurrently with improvements in diabetes management, the widening of socioeconomic health disparities has remained limited in the diabetic population during the past decade. This provides evidence for the potential impact of improvements in disparities in health care access and process, such as experienced among persons with diabetes, in limiting socioeconomic health disparities.

KEY WORDS: diabetes mellitus, cardiovascular diseases, socioeconomic factors, health disparities

INTRODUCTION

In the US, over 20 million adults have diabetes and the prevalence is expected to rise substantially in the coming decades.1,2 Diabetic complications impose an enormous burden on public health: compared to their non-diabetic counterparts, adults with diabetes are two to four times more likely to develop cardiovascular disease (CVD), and their age-adjusted mortality rate is approximately twice higher.3

The public health burden of diabetes is unevenly distributed across socioeconomic strata. First, diabetes is more common in ethnic minorities and persons of low education and income level.4 Second, among people with diabetes, low socioeconomic position (SEP) appears to increase risk of morbidity and mortality. Such disparities have been reported in Europe.518 However, in the US only a few studies have focused on SEP-related disparities, and then only in selected subpopulations.1922

Over the past decades, socioeconomic health disparities have widened in the general populations of the US and Europe.23,24 For persons with diabetes, major advances in disease process of care and control have occurred during the past decade.2527 Data from clinical trials suggest that programs of diabetes management may have the greatest benefit in people with low education or low literacy.28,29 However, the extent to which such improvements have benefited equally to all patients regardless of their SEP at the population level has not been studied. The present study aimed at investigating the time trends in socioeconomic health disparities among persons with and without diagnosed diabetes in the US over the past decade. We hypothesized that, concurrently with improvements in diabetes process of care and control, such disparities have increased to a lesser extent among persons with diabetes than among those without.

METHODS

Data Source

The NHIS is a continuous, annual, household survey conducted by the National Center for Health Statistics (NCHS). The survey uses a three-stage stratified cluster probability sampling design to collect information from a representative sample of the civilian, noninstitutionalized US population. A complete description of NHIS procedures is available elsewhere.30 Each year between 1997 and 2005, data from an average number of 32,190 (range: 30,801 to 36,116) adults aged 18 years or older have been collected through a face-to-face interview. The 9-year average response rate was 73.3% (range: 69.0% to 80.4%).

Variables of Interest

The main dependent variable for this study was self-reported medical history of CVD. Respondents were classified as having a prevalent cardiovascular condition if they answered yes to at least one of the questions asking whether “they had ever been told by a doctor or other health professional that they had: coronary heart disease; angina pectoris; myocardial infarction; any other kind of heart disease or condition; or stroke”.

Information on diabetes was collected using the question “Have you ever been told by a doctor or health professional that you have diabetes or sugar diabetes?” Those reporting ‘borderline’ diabetes and women who had diabetes only during pregnancy were categorized as not having diabetes. Comorbidity was defined by the self-reported history of at least one of the following conditions: hypertension, asthma, emphysema, chronic bronchitis, kidney condition or cancer.

Educational attainment was used as an indicator of SEP. Detailed information on the highest level of school completed was collected and the variable was categorized as: “high school (HS) not completed” (less than HS diploma); “HS completed” (HS diploma, general equivalency diploma, some college, vocational or technical school or associate’s degree); and “college completed” (bachelor’s, master’s, or professional degree). Education was preferred to income as the main measure of SEP because unlike income, education is unlikely to be affected by poor health at adult ages.

Covariates included age, gender, race/ethnicity, and poverty status. NHIS participants were asked to self-identify their race/ethnicity as non-Hispanic White, non-Hispanic black, Hispanic or other. Poverty status was based on family income and family size, and categorized using the US Census Bureau’s poverty thresholds for the previous calendar year as follows: poor (below the poverty thresholds); near poor (100–200% of the poverty thresholds); and not poor (≥200% of the poverty thresholds).30 The multiple imputations income files provided by the NCHS were used.31

Study Sample

Because educational attainment is often still evolving in early adulthood, analyses were limited to participants aged 25 years or older. Individuals with missing data for education ( = 2728), diabetes ( = 292) or CVD ( = 407) were excluded. These exclusion criteria yielded to a final sample of 255,966 individuals.

Statistical Analysis

The 1997–2005 overall study period was divided in three 3-year periods (1997–1999; 2000–2002; 2003–2005) to obtain robust estimates for each time period. Analyses were adjusted for age, sex, race/ethnicity, poverty status, comorbidity and survey year. Poisson regression models with robust estimation of variance were used.32

Age-adjusted, sex-adjusted and race-adjusted prevalences of CVD among persons with and without diabetes were calculated for each three-year period, overall and according to educational attainment. Educational disparities in CVD prevalence were measured for each three-year period among persons with and without diabetes. Two estimates were used: first, prevalence rate ratios (PRR) associated with education were computed for each three-year period, using the highest level of education as reference. Whereas PRRs are easy to interpret, comparisons of PRRs over time are complicated by possible changes over time in the distribution of educational level in the population. The use of the relative index of inequality (RII) as a measure of educational inequalities overcomes this problem by providing a continuous measure of inequalities that accounts simultaneously for the size and relative position of educational groups.33 It does so by using a specific measure of individuals’ relative educational position, i.e., the proportion of the overall population that has an educational level higher than his/her own. This is therefore a continuous measure, taking the value 0 for someone at the top of the educational scale and 1 for a person at the bottom. Using the categorical measure of educational attainment described above, we assigned this new socioeconomic indicator to each individual and then obtained the RII by regressing CVD prevalence on this new indicator. The RII can be interpreted as the change in CVD prevalence when moving from the top to the bottom of the educational scale.

Additional analyses were conducted to measure educational disparities in CVD prevalence over time among persons with and without diabetes i) separately according to age, gender, race/ethnicity and poverty status, and ii) restricting the outcome to either coronary heart disease events (CHD, as defined by self-reported coronary heart disease, angina pectoris or myocardial infarction) or stroke. Lastly, for each time period, PRRs associated with diabetes were calculated separately by educational level. Changes over time in these diabetes-associated PRRs were compared across categories of educational level. Because the risk of CVD associated with diabetes differs according to gender,34 these analyses were performed separately in men and women. Changes over time in the magnitude of the various associations of interest were measured using terms of interaction with time period treated as a continuous variable in the models.

All statistical analyses were performed using Stata 8.0® (Stata Corporation, College Station, TX) and accounted for the complex sampling design and data weighting of NHIS so that estimates are representative of the US population.

RESULTS

Sample Characteristics

The proportion of persons reporting diabetes increased from 5.9% to 7.9% over the period 1997–2005. As shown in Table 1, as compared to the non-diabetic population persons with diabetes were older (median age: 60 vs. 45 years), more frequently of non-Hispanic black or Hispanic race/ethnicity, less educated and poorer. Adults with diabetes more frequently reported a medical history of CVD (more than one third vs. only 12%) and comorbidity.

Table 1
Characteristics* of US Adults with and Without Diabetes by Time Period—NHIS 1997–2005 (N = 255,966)

The distribution of educational level showed substantial changes over the period 1997–2005. Regardless of diabetes status, the proportion of persons who had not completed HS significantly decreased over time (from 18.1% to 16.0% overall), whereas the proportion of those who had completed college increased (from 24.7% to 27.4%).

CVD Prevalence

As shown in Figure Figure1,1, throughout the study period the overall age, sex and race-adjusted prevalence of CVD was substantially higher among persons with vs. without diabetes. The adjusted CVD prevalence decreased from 24.0% to 20.5% ( = 0.01) between 1997 and 2005 among diabetic adults, while no significant trend was observed among those non-diabetic. However, such time trends were not homogeneous across categories of educational attainment: among persons with diabetes, CVD prevalence significantly decreased among HS graduates (from 24.1% to 19.9%;  = 0.002) but remained stable in the other educational groups. In contrast, in non-diabetic adults, CVD prevalence decreased in college graduates, increased in HS graduates, and remained stable in those who did not complete HS.

Fig. 1
Age, sex, and race-adjusted prevalence of CVD over time among adults with diabetes (plain lines) and without diabetes (dotted lines), overall and by education. Legends: * Time trend:  < 0.05. HS: high school.

Socioeconomic Disparities in CVD Prevalence

Table 2 shows the various measures of educational disparities in CVD prevalence over time, according to diabetes status. Among persons with diabetes, the RII was significantly greater than one in 1997–1999 and 2003–2005, suggesting that CVD prevalence was higher when moving from the top to the bottom of the educational scale. These disparities remained stable between 1997 and 2005 ( for time trend: 0.58). The PRRs suggested that CVD prevalence was persistently higher in adults who did not complete HS than in college graduates (adjusted PRR [aPRR] 1.21, 95% CI [1.06–1.38], 1.06 [0.94–1.21], and 1.14 [1.02–1.27], respectively, for periods 1997–1999, 2000–2002 and 2003–2005; for time trend: 0.64). In contrast, the PRR for HS graduates decreased significantly over time ( for time trend: 0.03). Consequently, the HS vs. college graduates disparity in CVD prevalence resolved between 1997–1999 (aPRR 1.22, 95% CI [1.07–1.40]) and 2003–2005 (aPRR 1.01, 95% CI [0.91–1.13]).

Table 2
Educational Disparities in the Prevalence of Self-reported CVD over Time among US Adults with and Without Diabetes

In adults without diabetes, socioeconomic disparities in CVD prevalence as measured by RII and aPRR increased markedly over time (Table 2). The magnitude of these disparities did not differ between adults with and without diabetes in 1997–1999. However, during the subsequent time periods the RII and the aPRRs for those who did not complete HS and for HS graduates became significantly higher among non-diabetic than among diabetic adults.

As shown in Figure Figure2,2, regardless of diabetes status time trends in educational disparities in CVD prevalence as measured by RII were consistent across gender, race/ethnicity and poverty status. However, such trends differed according to age and diabetes status: among adults aged 25–64 years, a significant increase in educational disparities in CVD prevalence was observed in those non-diabetic ( for time trend: 0.004) but not in those with diabetes ( for time trend: 0.18); among older adults, no significant time trend was observed regardless of diabetes status. Additional analyses showed similar trends in educational disparities by diabetes status whether the health outcome was restricted to either CHD or stroke.

Fig. 2
Relative index of inequality (and 95% confidence intervals) over time among adults with and without diabetes, by gender, age, race/ethnicity and poverty status. Legends: Adjusted for age, race/ethnicity, poverty status, comorbidity ...

Relative Prevalence of CVD in Adults with vs. Without Diabetes

Overall, the age and race-adjusted prevalence of CVD was approximately 2.5 times higher in adults with vs. without diabetes, regardless of sex (aPRR 2.44, 95% CI [2.18–2.73] and 2.47 [2.20–2.77], respectively, in men and women). As shown in Figure Figure3,3, this association was not homogeneous across time and educational attainment, even after adjustment for potential confounders including poverty status and comorbidity. Among men, the adjusted PRR associated with diabetes overall significantly decreased over time between 1997 and 2005, from 2.11 (95% CI [1.89–2.36]) in 1997–1999 to 1.81 (95% CI [1.63–2.01]) in 2003–2005 ( for time trend: 0.03). However, this overall trend reflected different patterns according to education: whereas in HS graduate men the aPRR significantly decreased over time, from 2.23 (95% CI [1.97–2.52]) in 1997–1999 to 1.73 (95% CI [1.55–1.94]) in 2003–2005 ( for time trend: <0.001), no significant trend was observed in the other educational groups. Among women, no significant change over time was observed in the aPRR associated with diabetes overall as well as among those who did not complete HS or college graduates. However, in HS graduate women the aPRR significantly decreased over time, from 2.16 (95% CI [1.91–2.45]) in 1997–1999 to 1.70 (95% CI [1.51–1.92]) in 2003–2005 ( for time trend: 0.01).

Fig. 3
Adjusted prevalence rate ratios of CVD associated with diabetes (and 95% confidence intervals) over time, by gender and educational attainment. Legends: Adjusted for age, race/ethnicity, poverty status, comorbidity and survey year. ...

DISCUSSION

Our results provide evidence for the existence of educational disparities in CVD prevalence among persons with diagnosed diabetes in the US, suggesting that socioeconomic health disparities reported among persons with diabetes in Europe occur as well in this population. Furthermore, our results suggest that the increase in educational health disparities reported among the US general population has not occurred among the population diagnosed with diabetes over the past decade, a period during which major advances in diabetes process of care and control have occurred. Indeed, while educational health inequalities remained stable in adults with diabetes, they widened markedly in the non-diabetic population, a feature particularly salient in middle-aged adults.

SEP may influence major determinants of the health of persons with diabetes including health behaviors, access to and process of care.35 Underlying pathways probably involve a large range of factors including individual (e.g., social support, mental health, comorbidity, health literacy), provider, communities, and health care system characteristics.35 Socioeconomic health disparities have been reported to be attenuated in adults with diabetes as compared to those without in Finland36 and in Italy.37 Our findings of lower educational disparities in CVD prevalence in adults with vs. without diabetes beginning in 2000–2002 suggest that such a salutary role of diabetes management may have occurred as well in the US context as diabetes process of care and control have improved. In addition, the age heterogeneity we found suggests that this salutary effect may be particularly salient in adults aged below 65 years, i.e., those who don’t benefit from universal Medicare health coverage and thus encounter the highest health care disparities in the US.38 This suggests that such a salutary effect may be related to the leveling off of socioeconomic disparities in health care as a result of diabetes management. Thereby our results provide evidence for the potential impact of improvements in disparities in health care access and process, such as experienced among persons with diabetes in the past decade, in limiting socioeconomic health disparities.

We also found that the gap between persons with and without diagnosed diabetes regarding CVD prevalence has shown heterogeneous time trends across educational groups between 1997 and 2005. Indeed, HS graduates constitute the only group among whom such a gap significantly decreased during this period of improvement in diabetes process of care and control. This is consistent with data from clinical trials showing that the magnitude of improvements in glycemic control following an intensification of diabetes management are not homogeneous across the subgroups of patients, those with high levels of education or literacy benefiting to a lesser extent than the others.28,29 Our findings additionally suggest that, while improvements in diabetes management may have been beneficial to HS graduates, this is not true for the lowest educated groups of the population who may face additional barriers that are probably independent of the offer of care.

To our knowledge, this is one of few studies to provide an overview of educational health disparities among persons with diagnosed diabetes at the population level in the US. The NHIS, thanks to its large nationally representative sample and stable design and questionnaire since 1997, allowed us to estimate the magnitude and time trends of such disparities among various subgroups of the population, controlling for potential confounders. Diabetes diagnosis criteria have remained constant since 1997, suggesting that the time trends we show are unlikely to result from changes in disease classification. Furthermore, the use of the RII as a measure of health disparities allowed us to account for the changing socioeconomic composition of the population over time.

Nonetheless, the interpretation of our findings must remain cautious due to several limitations. First, NHIS data on diabetes and CVD are based entirely on self-report; therefore, they should not be interpreted as estimates of the true burden of disease in the population. However, agreement between self-report and medical record has been reported to be substantial for these conditions;3942 furthermore, our estimates of diagnosed diabetes prevalence, time trend in CVD among diabetic adults and diabetes-associated CVD risk are consistent with reports using other sources of data including NHANES,43 the National Hospital Discharge Survey44 and the Framingham Heart Study.45

Reliable estimates of educational disparities in self-reported diabetes require two conditions: 1) adequate self-report of diabetes if it has been diagnosed, regardless of educational level; and 2) comparable diagnosed/undiagnosed ratio across educational groups. Although the accuracy of diabetes self-report may improve with educational level,39,42 the SEP gradient in diabetes frequency has been shown to appear with equal force when using either self-reported or biological (i.e., glycosylated hemoglobin A1C) data.46 Moreover, approximately one third of US adults with diabetes are estimated to be undiagnosed.43 The extent to which individuals with undiagnosed diabetes differ from those diagnosed with regard to education is not clear.47,48 In complementary analyses, we found that our estimation of educational disparities among persons with diabetes remained unchanged when analyses were restricted to the most advanced cases of diabetes, i.e., those treated or those diagnosed prior to 1997—that is, previous to the lowering of the diagnostic fasting plasma glucose level from 7.8 to 7.0 mmol/L. This suggests that differences in diabetes detection are unlikely to explain the disparities we found.

Second, the health indicator we used, i.e., prevalent CVD, is difficult to interpret because it reflects both CVD incidence and survival following a diagnosis of CVD. Our data do not allow us to disentangle the contribution of each of these two phenomena in explaining the time trends we report. Moreover, a cross-sectional study of prevalent CVD excludes the most severe cases, i.e., those fatal. Because education is inversely associated with CVD case-fatality,49 educational disparities in CVD may have been underestimated in our study.

Because diabetes is common in all populations in industrialized nations and affects persons at all levels of the society, tackling socioeconomic health disparities among persons with diabetes is likely to have a major public health impact. Our study suggests that improvements in diabetes management over the past decade may have resulted in limiting the widening of socioeconomic health disparities in this population, probably through the leveling off of disparities in health care access and process. This provides evidence for the potential beneficial impact of programs of universal health insurance on socioeconomic health disparities in the US. However, health disparities are probably only partially explained by differential access to health care; a wider understanding of underlying pathways should guide any attempt to their reduction.

Acknowledgments

All data used in this study were collected by the National Center for Health Statistics (NCHS), Centers for Disease Control and Prevention. There was no other external funding for this work.

Conflict of interest None disclosed.

References

1. Engelgau MM, Geiss LS, Saaddine JB, et al. The evolving diabetes burden in the United States. Ann Intern Med. 2004;140(11):945–50. [PubMed]
2. Wild S, Roglic G, Green A, Sicree R, King H. Global prevalence of diabetes: estimates for the year 2000 and projections for 2030. Diabetes Care. 2004;27(5):1047–53. [PubMed]
3. Diabetes Atlas. 3rd ed: International Diabetes Federation; 2006.
4. Cowie C, Eberhardt M. Sociodemographic characteristics of persons with diabetes. In: Harris M, Cowie C, Stern M, et al, eds. Diabetes in America, 2nd ed. Bethesda, MD: National Institute of Health; 1995:85–116.
5. Bachmann MO, Eachus J, Hopper CD, et al. Socio-economic inequalities in diabetes complications, control, attitudes and health service use: a cross-sectional study. Diabet Med. 2003;20(11):921–9. [PubMed]
6. Bihan H, Laurent S, Sass C, et al. Association among individual deprivation, glycemic control, and diabetes complications: the EPICES score. Diabetes Care. 2005;28(11):2680–85. [PubMed]
7. Chaturvedi N, Jarrett J, Shipley MJ, Fuller JH. Socioeconomic gradient in morbidity and mortality in people with diabetes: cohort study findings from the Whitehall Study and the WHO Multinational Study of Vascular Disease in Diabetes. BMJ. 1998;316(7125):100–5. [PMC free article] [PubMed]
8. Forssas E, Keskimaki I, Reunanen A, Koskinen S. Widening socioeconomic mortality disparity among diabetic people in Finland. Eur J Public Health. 2003;13(1):38–43. [PubMed]
9. Kelly WF, Mahmood R, Kelly MJ, Turner S, Elliott K. Influence of social deprivation on illness in diabetic patients. BMJ. 1993;307(6912):1115–6. [PMC free article] [PubMed]
10. Larranaga I, Arteagoitia JM, Rodriguez JL, Gonzalez F, Esnaola S, Pinies JA. Socio-economic inequalities in the prevalence of Type 2 diabetes, cardiovascular risk factors and chronic diabetic complications in the Basque Country, Spain. Diabet Med. 2005;22(8):1047–53. [PubMed]
11. Lawlor DA, Patel R, Fraser A, Smith GD, Ebrahim S. The association of life course socio-economic position with diagnosis, treatment, control and survival of women with diabetes: findings from the British Women’s Heart and Health Study. Diabet Med. 2007;24(8):892–900. [PubMed]
12. Mielck A, Reisig V, Rathmann W. Health inequalities among persons with type 2 diabetes: the example of intermittent claudication. Gesundheitswesen. 2005;67(Suppl 1):S137–43. [PubMed]
13. Nilsson PM, Johansson SE, Sundquist J. Low educational status is a risk factor for mortality among diabetic people. Diabet Med. 1998;15(3):213–9. [PubMed]
14. Robinson N, Lloyd CE, Stevens LK. Social deprivation and mortality in adults with diabetes mellitus. Diabet Med. 1998;15(3):205–12. [PubMed]
15. Roper NA, Bilous RW, Kelly WF, Unwin NC, Connolly VM. Excess mortality in a population with diabetes and the impact of material deprivation: longitudinal, population based study. BMJ. 2001;322(7299):1389–93. [PMC free article] [PubMed]
16. Unwin N, Binns D, Elliott K, Kelly WF. The relationships between cardiovascular risk factors and socio-economic status in people with diabetes. Diabet Med. 1996;13(1):72–9. [PubMed]
17. van der Meer JB, Mackenbach JP. The care and course of diabetes: differences according to level of education. Health Policy. 1999;46(2):127–41. [PubMed]
18. Weng C, Coppini DV, Sonksen PH. Geographic and social factors are related to increased morbidity and mortality rates in diabetic patients. Diabet Med. 2000;17(8):612–7. [PubMed]
19. Haffner SM, Hazuda HP, Stern MP, Patterson JK, Van Heuven WA, Fong D. Effects of socioeconomic status on hyperglycemia and retinopathy levels in Mexican Americans with NIDDM. Diabetes Care. 1989;12(2):128–34. [PubMed]
20. West SK, Munoz B, Klein R, et al. Risk factors for Type II diabetes and diabetic retinopathy in a mexican-american population: Proyecto VER. Am J Ophthalmol. 2002;134(3):390–8. [PubMed]
21. Chin MH, Zhang JX, Merrell K. Diabetes in the African-American Medicare population. Morbidity, quality of care, and resource utilization. Diabetes Care. 1998;21(7):1090–5. [PubMed]
22. McEwen LN, Kim C, Karter AJ, et al. Risk Factors for Mortality Among Patients With Diabetes: The Translating Research Into Action for Diabetes (TRIAD) Study. Diabetes Care. 2007;30(7):1736–41. [PubMed]
23. Singh GK, Siahpush M. Widening socioeconomic inequalities in US life expectancy, 1980–2000. Int J Epidemiol. 2006;35(4):969–79. [PubMed]
24. Mackenbach JP, Bos V, Andersen O, et al. Widening socioeconomic inequalities in mortality in six Western European countries. Int J Epidemiol. 2003;32(5):830–7. [PubMed]
25. Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications Research Group. Retinopathy and nephropathy in patients with type 1 diabetes four years after a trial of intensive therapy. N Engl J Med. 2000;342(6):381–9. [PMC free article] [PubMed]
26. Imperatore G, Cadwell BL, Geiss L, et al. Thirty-year trends in cardiovascular risk factor levels among US adults with diabetes: National Health and Nutrition Examination Surveys, 1971–2000. Am J Epidemiol. 2004;160(6):531–9. [PubMed]
27. Saaddine JB, Cadwell B, Gregg EW, et al. Improvements in diabetes processes of care and intermediate outcomes: United States, 1988–2002. Ann Intern Med. 2006;144(7):465–74. [PubMed]
28. Goldman DP, Smith JP. Can patient self-management help explain the SES health gradient? Proc Natl Acad Sci USA. 2002;99(16):10929–34. [PMC free article] [PubMed]
29. Rothman RL, DeWalt DA, Malone R, et al. Influence of patient literacy on the effectiveness of a primary care-based diabetes disease management program. JAMA. 2004;292(14):1711–6. [PubMed]
30. National Center for Health Statistics, Centers for Disease Control and Prevention. National Health Interview Survey. Hyattsville, MD. Available at: http://www.cdc.gov/nchs/nhis.htm. Accessed June 26, 2008.
31. Schenker N, Raghunathan T, Chiu P, Makuc D, Zhang G, Cohen A. Multiple imputation of family income and personal earnings in the National Health Interview Survey: methods and examples. Hyattville, MD: National Center for Health Statistics; 2006.
32. Barros AJ, Hirakata VN. Alternatives for logistic regression in cross-sectional studies: an empirical comparison of models that directly estimate the prevalence ratio. BMC Med Res Methodol. 2003;3:21. [PMC free article] [PubMed]
33. Mackenbach JP, Kunst AE. Measuring the magnitude of socio-economic inequalities in health: an overview of available measures illustrated with two examples from Europe. Soc Sci Med. 1997;44(6):757–71. [PubMed]
34. Huxley R, Barzi F, Woodward M. Excess risk of fatal coronary heart disease associated with diabetes in men and women: meta-analysis of 37 prospective cohort studies. BMJ. 2006;332(7533):73–8. [PMC free article] [PubMed]
35. Brown AF, Ettner SL, Piette J, et al. Socioeconomic position and health among persons with diabetes mellitus: a conceptual framework and review of the literature. Epidemiol Rev. 2004;26:63–77. [PubMed]
36. Koskinen SV, Martelin TP, Valkonen T. Socioeconomic differences in mortality among diabetic people in Finland: five year follow up. BMJ. 1996;313(7063):975–8. [PMC free article] [PubMed]
37. Gnavi R, Petrelli A, Demaria M, Spadea T, Carta Q, Costa G. Mortality and educational level among diabetic and non-diabetic population in the Turin Longitudinal Study: a 9-year follow-up. Int J Epidemiol. 2004;33(4):864–71. [PubMed]
38. McWilliams JM, Meara E, Zaslavsky AM, Ayanian JZ. Use of health services by previously uninsured Medicare beneficiaries. N Engl J Med. 2007;357(2):143–53. [PubMed]
39. Bergmann MM, Byers T, Freedman DS, Mokdad A. Validity of self-reported diagnoses leading to hospitalization: a comparison of self-reports with hospital records in a prospective study of American adults. Am J Epidemiol. 1998;147(10):969–77. [PubMed]
40. Edwards WS, Winn DM, Kurlantzick V. Evaluation of National Health Interview Survey diagnostic reporting. Hyattville, MD: National Center for Health Statistics; 1994. Vital Health Stat 2(120). [PubMed]
41. Kehoe R, Wu SY, Leske MC, Chylack LT Jr. Comparing self-reported and physician-reported medical history. Am J Epidemiol. 1994;139(8):813–8. [PubMed]
42. Okura Y, Urban LH, Mahoney DW, Jacobsen SJ, Rodeheffer RJ. Agreement between self-report questionnaires and medical record data was substantial for diabetes, hypertension, myocardial infarction and stroke but not for heart failure. J Clin Epidemiol. 2004;57(10):1096–103. [PubMed]
43. Cowie CC, Rust KF, Byrd-Holt DD, et al. Prevalence of diabetes and impaired fasting glucose in adults in the U.S. population: National Health And Nutrition Examination Survey 1999–2002. Diabetes Care. 2006;29(6):1263–8. [PubMed]
44. Centers for Disease Control and Prevention, National Diabetes Surveillance System. Diabetes Data and Trends. Available at: http://www.cdc.gov/diabetes/statistics/index.htm. Accessed June 26, 2008.
45. Fox CS, Coady S, Sorlie PD, et al. Trends in cardiovascular complications of diabetes. JAMA. 2004;292(20):2495–9. [PubMed]
46. Banks J, Marmot M, Oldfield Z, Smith JP. Disease and disadvantage in the United States and in England. JAMA. 2006;295(17):2037–45. [PubMed]
47. Wilder RP, Majumdar SR, Klarenbach SW, Jacobs P. Socio-economic status and undiagnosed diabetes. Diabetes Res Clin Pract. 2005;70(1):26–30. [PubMed]
48. Smith JP. Economics of Health and Mortality Special Feature: Nature and causes of trends in male diabetes prevalence, undiagnosed diabetes, and the socioeconomic status health gradient. Proc Natl Acad Sci USA. 2007;104(33):13225–31. [PMC free article] [PubMed]
49. Salomaa V, Niemela M, Miettinen H, et al. Relationship of socioeconomic status to the incidence and prehospital, 28-day, and 1-year mortality rates of acute coronary events in the FINMONICA myocardial infarction register study. Circulation. 2000;101(16):1913–8. [PubMed]

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