- Email: [email protected]
Socioeconomic status and diagnosed diabetes incidence
Diabetes Research and Clinical Practice 68 (2005) 230–236 www.elsevier.com/locate/diabres
Socioeconomic status and diagnosed diabetes incidence Jessica M. Robbinsa,b,*, Viola Vaccarinoc,d, Heping Zhangc, Stanislav V. Kaslc a
Albert Einstein Healthcare Network, Center for Urban Health Policy and Research, Philadelphia, PA, USA b Philadelphia Department of Public Health/AHS, 500 South Broad Street, Philadelphia, PA 19146, USA c Department of Epidemiology and Public Health, Yale University, New Haven, CT, USA d Division of Cardiology, Department of Medicine, Emory University School of Medicine, Atlanta, Georgia, GA, USA Received 14 May 2004; received in revised form 7 September 2004; accepted 15 September 2004 Available online 2 November 2004
Abstract Aims: To investigate the association between socioeconomic status (SES) and incidence of diabetes. Methods: We investigated three measures of SES and incidence of diagnosed diabetes among women and men in the NHANES I Epidemiologic Followup Study, 1971–1992, who were free of diagnosed diabetes in 1980. Results: Among women, diabetes incidence was inversely associated with income (measured as percent of the poverty level), education, and occupational status, adjusting for age and race/ethnicity. The hazard ratio (HR) for women with >16 years education was 0.26 (95% confidence interval (CI) 0.13–0.54) relative to those with <9 years of education. Adjustment for potential mediators, including body size variables, diet, physical activity, and alcohol and tobacco use, substantially attenuated the associations with income and education. Among men a trend toward lower diabetes incidence with higher income and higher education was evident (the HR for men with household income >5 times the poverty level was 0.44 (95% CI 0.19–0.98) relative to those under the poverty line), but there was no inverse association of diabetes incidence with occupational status. Conclusions: SES, assessed with any of three common measures, is a risk factor for diagnosed diabetes in women. Among men these associations are less consistent. # 2004 Elsevier Ireland Ltd. All rights reserved. Keywords: Diabetes mellitus; Educational status; Income; Occupational status; Socioeconomic status
1. Introduction There is substantial evidence that socioeconomic status (SES) has a strong, inverse association with type 2 diabetes prevalence among women across several different SES measures and populations [1–4], * Corresponding author. Tel.: +1 215 685 6426; fax: +1 215 685 6848. E-mail address: [email protected] (J.M. Robbins).
although this association is less consistent among men [1,5,6]. This relationship developed during the mid-to-late twentieth century, reversing an earlier pattern of excess diabetes among the wealthy [7]. Information linking SES to incidence of diabetes is more limited and inconsistent [5,8–14]. We examined the association between three measures of socioeconomic status and incidence of diagnosed diabetes in the NHANES I Epidemiologic Followup Study (NHEFS) [5,11,15–17]. Detailed
0168-8227/$ – see front matter # 2004 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.diabres.2004.09.007
J.M. Robbins et al. / Diabetes Research and Clinical Practice 68 (2005) 230–236
information on a number of socioeconomic indicators and other risk factors for diabetes was collected at the cohort inception (1971–1975), and diagnosed diabetes incidence was followed through 1992, providing a unique opportunity to examine this study question. We examined several measures of obesity as well as a number of health-related behaviors as possible mediators of an association between SES and diabetes incidence [18,19].
2. Materials and methods NHEFS includes data on subjects who were age 25–74 when examined between 1971 and 1975. Follow-up surveys were conducted in 1982–1984,
231
1986, 1987, and 1992 [20]. This incidence study was limited to the 11,069 subjects who were documented as not having died or been diagnosed with diabetes prior to 1980, when standardized diagnostic criteria for diabetes were established [21–23]. Mean followup time after 1980 was 10.0 years. Analyses were stratified by gender and adjusted for single year of age and for race/ethnicity, classified as African–American, non-Hispanic white, Hispanic, or other [20]. Race/ ethnicity was classified based on respondent’s selfreport, or, where self-report was not available, interviewer identification or death certificate. Three measures of SES were examined: poverty income ratio (PIR), education, and occupational status, each of which was assessed during the initial interview. PIR was calculated as the ratio of the total
Table 1 Women
Men
No.
Incident diabetes cases
No.
Incident diabetes cases
Total
6825
460
4244
306
Poverty income ratio <1 1–1.999 2–2.999 3–4.999 5+ Income missing
834 1496 1140 1279 448 188
79 125 65 63 20 12
372 769 678 854 363 117
34 67 50 56 19 13
Education <9 years 9–12 years, not grad HS grad 13–15 years 16+ years
1307 1379 2546 832 723
143 110 141 42 20
1167 666 1173 532 673
99 58 77 35 31
Duncan occupational status score <19.7 19.7–27.73 27.74–47.21 47.22+ No occupation
1056 653 836 609 3671
109 39 44 20 248
610 919 700 1060 955
54 71 55 65 61
Race/ethnicity Non-Hispanic White African–American Hispanic Other
5589 954 226 56
309 127 20 4
3550 507 137 50
237 50 14 5
Age in 1980 29–44 45–64 65–84
2476 2531 1818
122 209 129
1204 1603 1437
38 148 120
232
J.M. Robbins et al. / Diabetes Research and Clinical Practice 68 (2005) 230–236
family income to the poverty threshold determined by the census bureau. Subjects who were first interviewed after July 1974 as part of a special ‘‘augmentation sample’’ did not have PIR calculated, and were excluded from the income analyses. PIR categories used were PIR 0–0.999 (below the poverty line), 1– 1.999 (a range often described as ‘‘near poor’’), 2– 2.999, 3–4.999, and >5. Education was categorized as less than 9 years of education, 9–12 years but not a high school graduate, high school graduate, 13–15 years, and 16 or more years. Occupational status was measured based on usual occupation using the Duncan socioeconomic index (SEI) [24] and the scores were categorized into quartiles. Subjects who did not report a usual occupation (23% of male and 54% of female subjects) were excluded from analyses of this variable. Onset of diagnosed diabetes was ascertained through subject or proxy interview or from hospital records. Respondents were asked if they had ever been diagnosed with diabetes by a physician, and the
date of the diagnosis. Hospitals for which overnight stays were reported (either by the respondent or abstracted from death certificates) were contacted for admission and discharge diagnoses [20]. Where these records indicated a diagnosis of diabetes and there was no report of diabetes prior to the hospitalization, the date of admission was taken as date of initial diagnosis. Lifestyle-related variables that have been identified as potentially modifiable health risk factors [25,26] were considered as possible mediators of the SES-type 2 diabetes relationship. These included body size, physical activity, diet, cigarette smoking, and alcohol consumption. Measured body mass index (BMI = weight (kg)/height (m)2) was modeled as sex-specific quintiles. BMI at age 25 (based on self-reported weight [27]) was used as a measure of lifetime weight and subscapular skinfold thickness was used as a measure of central adiposity. Usual and recreational physical activity were assessed by two questions asked
Fig. 1. Hazard ratios for incidence of type 2 diabetes by socioeconomic status and sex, adjusted for age and race/ethnicity.
J.M. Robbins et al. / Diabetes Research and Clinical Practice 68 (2005) 230–236
at baseline. Total kilocalories consumed, total fats as a percentage of kilocalories consumed, and saturated fats as a percentage of kilocalories consumed were assessed with a 24-h food recall. Smoking was assessed during the first followup survey, but dates of smoking initiation and cessation were used to calculate smoking status at baseline. Smoking variables included whether the subject was a past or current smoker and lifetime pack-years of smoking at baseline. Frequency and quantity of alcohol use were modeled as continuous variables along with a dichotomous variable for any use of alcohol at baseline. Proportional hazards models were run using each of the three SES variables individually. Each model was run adjusting first for age and race/ethnicity, and then for all of the potential mediators described. SUDAAN 8.01 was used to adjust for the complex sampling design [28]. Tests for trend were conducted by modeling SES as ordinal variables. Diagnostic tests were conducted to confirm that the proportional hazards assumption was satisfied.
233
3. Results Baseline characteristics of the study sample are shown in Table 1. There were 460 incident cases of diagnosed diabetes among 6825 women and 306 among 4024 men in the NHEFS cohort. Among women, increasing PIR was associated with decreasing hazard ratios (HR) for diabetes incidence after adjustment for age and race/ethnicity (Fig. 1). The HR for women with incomes at least five times the poverty level relative to those below the poverty line was 0.60 (95% CI 0.32–1.15). Education and occupational status were also strongly associated with diabetes incidence in women. In men, there was a significant inverse association between PIR and diabetes incidence, and a significant (but not entirely monotonic) association with education among men in NHEFS. Occupational status was not associated with diabetes incidence among men. Adjustment for other covariables (Fig. 2) essentially eliminated the association between PIR and diabetes incidence in women and attenuated the
Fig. 2. Hazard ratios for incidence of type 2 diabetes by socioeconomic status and sex, adjusted for age, race/ethnicity, and potential mediators.
234
J.M. Robbins et al. / Diabetes Research and Clinical Practice 68 (2005) 230–236
association with education, but did not substantially alter the association between occupational status and diabetes incidence. In the adjusted model, a Duncan SEI score greater than 47.22 (e.g., a teacher or purchasing manager) showed an HR of 0.39 (0.19– 0.82) for diabetes incidence relative to an SEI score of <19.7 (e.g., a childcare worker or waitress). Among men, the associations between diabetes incidence and the SES variables were not substantially altered by controlling for other risk factors. Low PIR remained a significant predictor of diabetes risk among men.
4. Conclusions Our results indicate that low socioeconomic status, assessed using three different measures, is associated with risk of diabetes. This supports the conclusion that the inverse association between SES and diabetes prevalence described in the United States and other developed countries [4,12,29–31] is not solely a result of the impact of diabetes on SES (for example, lower ability to work) or longer survival among diabetes patients of higher SES, but reflects an increased risk of developing diabetes in persons with lower SES. The association of SES with diabetes risk raises the question of what mechanisms link SES with diabetes onset. Controlling for some potential mediators that had been ascertained for the NHEFS cohort, including body size, physical activity, diet, smoking, and alcohol use [32–36], did substantially reduce most of the SESdiabetes associations. The fact that these factors did not account entirely for the association between SES and diabetes incidence may be due to the difficulty of measuring behavioral characteristics such as diet and physical activity, therefore leading to an underestimation of the effect of these factors [37]. However, variables not measured in NHEFS, including chronic stress, may also be implicated in the association between SES and diabetes [38–42]. Gender differences consistent with those we identified have been identified in the associations between SES and major risk factors for diabetes, including body size, diet, and physical activity [33,43– 45], as well as responses to stressors [46,47]. A more unexpected finding was that occupational status was
associated with diabetes risk among women but not men, and that this risk was not explained by the mediating variables we examined. A similar finding that occupational status was associated with obesity among women but not men in the 1996 Health Survey of England [43] suggests that this may reflect a real gender difference in how SES affects the lifestyles and experiences that determine diabetes risks. These results highlight the importance of a multi-dimensional definition of SES and underline the potential impact of work-related stressors on the health of women. Several limitations of this analysis should be recognized. Income, education and occupation may not fully capture the impact of social and economic inequalities over the life-course [48]. We excluded subjects without a reported ‘‘usual occupation,’’ which led to the exclusion of a large proportion of the cohort from analyses of occupational status, reducing the precision of the estimates obtained [49]. Although 93% of the NHEFS cohort members eligible for this analysis were successfully followed at least once for diabetes incidence, and there were no substantial differences in follow-up according to SES in our sample, we cannot exclude the possibility of bias from loss to follow-up. Finally, NHEFS collected data only on physician-diagnosed diabetes. If persons with low SES were less likely to be diagnosed, the true association between SES and diabetes risk would be greater than indicated by our analyses. The prevalence of diabetes continues to increase, exceeding 30% in African–American women over age 60 [50]. Although it has been demonstrated that behavioral and chemotherapeutic interventions can delay or prevent type 2 diabetes [51], these interventions have not been successfully implemented in largescale clinical or population-based programs [52]. While most clinicians who treat patients with diabetes in the United States are aware that type 2 diabetes occurs more frequently among racial and ethnic minorities, many are not aware that it is also more likely to appear among patients with low SES, regardless of race or ethnicity [6]. Our findings suggest that effective, population-based interventions to decrease obesity and improve health behaviors may reduce, but probably not eliminate, SES disparities in diabetes incidence. Future studies should address the mechanisms underlying the effects of SES on diabetes
J.M. Robbins et al. / Diabetes Research and Clinical Practice 68 (2005) 230–236
risk. This knowledge will help guide efforts towards reversing the alarming increases in type 2 diabetes.
Acknowledgments This study was supported by US Public Health Service grant 5-T32MH 1435 from the National Institute of Mental Health. An earlier version was published in abstract form as ‘‘Education, Income, and Occupational Status as Predictors of Diabetes Diagnosis among Women in the NHANES I Epidemiologic Followup Study,’’ Diabetes 50 Supplement 2: A216– A217, June 2001.
References [1] M.P. Stern, M. Rosenthal, S.M. Haffner, H.P. Hazuda, L.J. Franco, Sex differences in the effects of sociocultural status on diabetes and cardiovascular risk factors in Mexican Americans, Am. J. Epidemiol. 120 (1984) 834–851. [2] M.G. Marmot, G. Davey Smith, S. Stansfeld, C. Patel, F. North, J. Head, et al. Health inequalities among British civil servants: the Whitehall II study, Lancet 337 (1991) 1387–1393. [3] E.J. Brunner, M.G. Marmot, K. Nanchahal, M.J. Shipley, S.A. Stansfeld, M. Juneja, et al. Social inequality in coronary risk: central obesity and the metabolic syndrome. Evidence from the Whitehall II study, Diabetologia 40 (1997) 1341–1349. [4] M.A. Winkleby, H.C. Kraemer, D.K. Ahn, A.N. Varady, Ethnic and socioeconomic differences in cardiovascular disease risk factors. Findings for women from the Third National Health and Nutrition Examination Survey, 1988–1994, JAMA 280 (1998) 356–362. [5] H.E. Resnick, P. Valsania, J.B. Halter, X. Lin, Differential effects of BMI on diabetes risk among black and white Americans, Diabetes Care 21 (1998) 1828–1835. [6] J.M. Robbins, V. Vaccarino, H. Zhang, S.V. Kasl, Socioeconomic status and type 2 diabetes in African-American and nonHispanic white women and men: evidence from the third National Health and Nutrition Examination Survey, Am. J. Public Health 91 (2001) 76–83. [7] W.C. Knowler, D.R. McCance, D.K. Nagi, D.J. Pettitt, Epidemiological studies of the causes of non-insulin-dependent diabetes mellitus, in: R.D.G. Leslie (Ed.), Causes of Diabetes: Genetic and Environmental Factors, Wiley, Chichester, 1993, pp. 187–218. [8] D.J.P. Barker, M.J. Gardner, C. Power, Incidence of diabetes amongst people aged 18–50 years in nine British towns: a collaborative study, Diabetologia 22 (1982) 421–425. [9] S.M. Haffner, H.P. Hazuda, B.D. Mitchell, J.K. Patterson, M.P. Stern, Increased incidence of type II diabetes mellitus in Mexican Americans, Diabetes Care 14 (1991) 102–108.
235
[10] R.M. Paris, S.A. Bedno, M.R. Krauss, L.W. Keep, M.V. Rubertone, Weighing in on type 2 diabetes in the military: characteristics of U.S. military personnel at entry who develop type 2 diabetes, Diabetes Care 24 (2001) 1894–1898. [11] W.J. Butler, L.D. Ostrander, W.J. Carman, D.E. Lamphiear, Diabetes mellitus in Tecumseh, Michigan. Prevalence, incidence, and associated conditions, Am. J. Epidemiol. 116 (1982) 971–980. [12] R.B. Lipton, Y. Liao, G. Cau, R.S. Cooper, D. McGee, Determinants of incident non-insulin-dependent diabetes mellitus among blacks and whites in a national sample: the NHANES I Epidemiologic Follow-up Study, Am. J. Epidemiol. 138 (1993) 826–839. [13] A.E. Monterrosa, S.M. Haffner, M.P. Stern, H.P. Hazuda, Sex differences in lifestyle factors predictive of diabetes in Mexican-Americans, Diabetes Care 18 (1995) 448–456. [14] Y. Morikawa, H. Nakagawa, M. Ishizaki, M. Tabata, M. Nishijo, K. Miura, et al. Ten-year follow-up study on the relation between the development of non-insulin-dependent diabetes mellitus and occupation, Am. J. Ind. Med. 31 (1997) 80–84. [15] E.S. Ford, Vitamin supplement use and diabetes mellitus incidence among adults in the United States, Am. J. Epidemiol. 153 (2001) 892–897. [16] E.S. Ford, A.H. Mokdad, Fruit and vegetable consumption and diabetes mellitus incidence among US adults, Prev. Med. 32 (2001) 33–39. [17] H.E. Resnick, P. Valsania, J.B. Halter, X. Lin, Relation of weight gain and weight loss on subsequent diabetes risk in overweight adults, J. Epidemiol. Community Health 54 (2000) 596–602. [18] B.G. Link, J. Phelan, Social conditions as fundamental causes of disease, J. Health Soc. Behav. (extra issue) (1995) 80–94. [19] K. West, Epidemiology of Diabetes And Its Vascular Lesions, Elsevier, New York, 1978. [20] National Center for Health Statistics, Plan and Operation of the NHANES I Epidemiologic Followup Study 1992, Hyattsville, MD, National Center for Health Statistics; 1997 (Vital and Health Statistics, Ser. 1, No. 35). [21] National Diabetes Data Group, Classification and diagnosis of diabetes mellitus and other categories of glucose intolerance, Diabetes 28 (1979) 1039–1057. [22] World Health Organization, Report of the WHO Expert Committee on Diabetes Mellitus, WHO, Geneva, 1980. [23] P.H. Bennett, M.J. Rewers, W.C. Knowler, Epidemiology of diabetes mellitus, in: D. Porte, R.S. Sherwin (Eds.), Ellenberg and Rifkin’s Diabetes Mellitus, fifth ed. Appleton & Lange, Stamford, 1997, pp. 373–400. [24] G. Stevens, D.L. Featherman, A revised socioeconomic index of occupational status, Soc. Sci. Res. 10 (1981) 364–395. [25] R.R. Wing, M.G. Goldstein, K.J. Acton, L.L. Birch, J.M. Jakicic, J.F. Sallis Jr., et al. Behavioral science research in diabetes: lifestyle changes related to obesity, eating behavior, and physical activity, Diabetes Care 24 (2001) 117–123. [26] U.M. Kujala, J. Kaprio, M. Koskenvuo, Modifiable risk factors as predictors of all-cause mortality: the roles of genetics and childhood environment, Am. J. Epidemiology 156 (2002) 985– 993.
236
J.M. Robbins et al. / Diabetes Research and Clinical Practice 68 (2005) 230–236
[27] National Center for Health Statistics. Plan and Operation of the NHANES I Epidemiologic Followup Study 1982–84, Hyattsville, MD, National Center for Health Statistics, 1987, (Vital and Health Statistics, Ser. 1, No. 22). [28] B.V. Shah, B.G. Barnwell, G.S. Bieler, SUDAAN User’s Manual, Release 7.5, Research Triangle Park, Research Triangle Inst., NC, 1997. [29] T.F. Drury, K.M. Danchik, M.I. Harris, Sociodemographic characteristics of adult diabetics. In: National Diabetes Data Group (Eds.), Diabetes in America: Diabetes Data Compiled 1984, (NIH publ. No. 85–1468), 1985, VII1–VII29. [30] J.A. Marshall, R.F. Hamman, J. Baxter, E.J. Mayer, D.L. Fulton, M. Orleans, et al. Ethnic differences in risk factors associated with the prevalence of non-insulin-dependent diabetes mellitus: the San Luis Valley Diabetes Study, Am. J. Epidemiol. 137 (1993) 706–718. [31] V. Connolly, N. Unwin, P. Sherriff, R. Bilous, W. Kelly, Diabetes prevalence and socioeconomic status: a population based study showing increased prevalence of type 2 diabetes mellitus in deprived areas, J. Epidemiol. Community Health 54 (2000) 173–177. [32] D.E. Bild, D.R. Jacobs, S. Sidney, W.L. Haskell, N. Anderssen, A. Oberman, Physical activity in young black and white women: the CARDIA Study, Ann. Epidemiol. 3 (1993) 636–644. [33] R. Lowry, L. Kann, J.L. Collins, L.J. Kolbe, The effect of socioeconomic status on chronic disease risk behaviors among US adolescents, JAMA 276 (1996) 792–797. [34] M. Tang, Y. Chen, D. Krewski, Gender-related differences in the association between socioeconomic status and selfreported diabetes, Int. J. Epidemiol. 32 (2003) 381–385. [35] J.C. Will, D.A. Galuska, E.S. Ford, A. Mokdad, E.E. Calle, Cigarette smoking and diabetes mellitus: evidence of a positive association from a large prospective cohort study, Int. J. Epidemiol. 30 (2001) 540–546. [36] F. de Vegt, J.M. Dekker, W.J. Groeneveld, G. Nijpels, C.D. Stehouwer, L.M. Bouter, et al. Moderate alcohol consumption is associated with lower risk for incident diabetes and mortality: the Hoorn Study, Diabetes Res. Clin. Pract. 57 (2002) 53–60. [37] S. Paeratakul, B.M. Popkin, L. Kohlmeier, I. Hertz-Picciotto, X. Guo, L.J. Edwards, Measurement error in dietary data: implications for the epidemiologic study of the diet–disease relationship, Eur. J. Clin. Nutr. 52 (1998) 722–727. [38] R.S. Surwit, M.S. Schneider, M.N. Feinglos, Stress and diabetes mellitus, Diabetes Care 15 (1992) 1413–1422.
[39] V.L. Goetsch, B. VanDorsten, L.A. Pbert, I.H. Ullrich, R.A. Yeater, Acute effects of laboratory stress on blood glucose in noninsulin-dependent diabetes, Psychosom. Med. 55 (1993) 492–496. [40] J.K. Wales, Can psychological stress cause diabetes? Diabetic Med. 12 (1995) 109–112. [41] R.S. Surwit, P.G. Williams, Animal models provide insight into psychosomatic factors in diabetes, Psychosom. Med. 58 (1996) 582–589. [42] A. Inui, H. Kitaoka, M. Majima, S. Takamiya, M. Uemoto, C. Yonenaga, et al. Effect of the Kobe earthquake on stress and glycemic control in patients with diabetes mellitus, Arch. Intern. Med. 158 (1998) 274–278. [43] J. Wardle, J. Waller, M.J. Jarvis, Sex differences in the association of socioeconomic status with obesity, Am. J. Public Health 92 (2002) 1299–1304. [44] H.P. Hazuda, S.M. Haffner, M.P. Stern, C.W. Eifler, Effects of acculturation and socioeconomic status on obesity and diabetes in Mexican Americans: the San Antonio Heart Study, Am. J. Epidemiol. 128 (1988) 1289–1301. [45] M.B. Livingstone, P.J. Robson, S. McCarthy, M. Kiely, K. Harrington, P. Browne, et al. Physical activity patterns in a nationally representative sample of adults in Ireland, Public Health Nutr. 4 (2001) 1107–1116. [46] N. Krieger, S. Sidney, Racial discrimination and blood pressure: the CARDIA Study of Young Black and White Adults, Am. J. Public Health 86 (1996) 1370–1378. [47] M.B. MacDonald, G.P. Laing, M.P. Wilson, T.W. Wilson, Prevalence and predictors of white-coat response in patients with treated hypertension, Can. Med. Assoc. J. 161 (1999) 265–269. [48] D.J. Barker, J.G. Eriksson, T. Forsen, C. Osmond, Fetal origins of adult disease: strength of effects and biological basis, Int. J. Epidemiol. 31 (2002) 1235–1239. [49] P.D. Allison, Missing Data, Sage Publications, Thousand Oaks, CA, 2002. [50] M.I. Harris, K.M. Flegal, C.C. Cowie, M.S. Eberhardt, D.E. Goldstein, R.R. Little, et al. 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 21 (1998) 518–524. [51] Diabetes Prevention Program Research Group, Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin, N. Engl. J. Med. 346 (2002) 393–403. [52] P. Zimmet, J. Shaw, K.G. Alberti, Preventing type 2 diabetes and the dysmetabolic syndrome in the real world: a realistic view, Diabetic Med. 20 (2003) 693–702.
Socioeconomic status and diagnosed diabetes incidence Jessica M. Robbinsa,b,*, Viola Vaccarinoc,d, Heping Zhangc, Stanislav V. Kaslc a
Albert Einstein Healthcare Network, Center for Urban Health Policy and Research, Philadelphia, PA, USA b Philadelphia Department of Public Health/AHS, 500 South Broad Street, Philadelphia, PA 19146, USA c Department of Epidemiology and Public Health, Yale University, New Haven, CT, USA d Division of Cardiology, Department of Medicine, Emory University School of Medicine, Atlanta, Georgia, GA, USA Received 14 May 2004; received in revised form 7 September 2004; accepted 15 September 2004 Available online 2 November 2004
Abstract Aims: To investigate the association between socioeconomic status (SES) and incidence of diabetes. Methods: We investigated three measures of SES and incidence of diagnosed diabetes among women and men in the NHANES I Epidemiologic Followup Study, 1971–1992, who were free of diagnosed diabetes in 1980. Results: Among women, diabetes incidence was inversely associated with income (measured as percent of the poverty level), education, and occupational status, adjusting for age and race/ethnicity. The hazard ratio (HR) for women with >16 years education was 0.26 (95% confidence interval (CI) 0.13–0.54) relative to those with <9 years of education. Adjustment for potential mediators, including body size variables, diet, physical activity, and alcohol and tobacco use, substantially attenuated the associations with income and education. Among men a trend toward lower diabetes incidence with higher income and higher education was evident (the HR for men with household income >5 times the poverty level was 0.44 (95% CI 0.19–0.98) relative to those under the poverty line), but there was no inverse association of diabetes incidence with occupational status. Conclusions: SES, assessed with any of three common measures, is a risk factor for diagnosed diabetes in women. Among men these associations are less consistent. # 2004 Elsevier Ireland Ltd. All rights reserved. Keywords: Diabetes mellitus; Educational status; Income; Occupational status; Socioeconomic status
1. Introduction There is substantial evidence that socioeconomic status (SES) has a strong, inverse association with type 2 diabetes prevalence among women across several different SES measures and populations [1–4], * Corresponding author. Tel.: +1 215 685 6426; fax: +1 215 685 6848. E-mail address: [email protected] (J.M. Robbins).
although this association is less consistent among men [1,5,6]. This relationship developed during the mid-to-late twentieth century, reversing an earlier pattern of excess diabetes among the wealthy [7]. Information linking SES to incidence of diabetes is more limited and inconsistent [5,8–14]. We examined the association between three measures of socioeconomic status and incidence of diagnosed diabetes in the NHANES I Epidemiologic Followup Study (NHEFS) [5,11,15–17]. Detailed
0168-8227/$ – see front matter # 2004 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.diabres.2004.09.007
J.M. Robbins et al. / Diabetes Research and Clinical Practice 68 (2005) 230–236
information on a number of socioeconomic indicators and other risk factors for diabetes was collected at the cohort inception (1971–1975), and diagnosed diabetes incidence was followed through 1992, providing a unique opportunity to examine this study question. We examined several measures of obesity as well as a number of health-related behaviors as possible mediators of an association between SES and diabetes incidence [18,19].
2. Materials and methods NHEFS includes data on subjects who were age 25–74 when examined between 1971 and 1975. Follow-up surveys were conducted in 1982–1984,
231
1986, 1987, and 1992 [20]. This incidence study was limited to the 11,069 subjects who were documented as not having died or been diagnosed with diabetes prior to 1980, when standardized diagnostic criteria for diabetes were established [21–23]. Mean followup time after 1980 was 10.0 years. Analyses were stratified by gender and adjusted for single year of age and for race/ethnicity, classified as African–American, non-Hispanic white, Hispanic, or other [20]. Race/ ethnicity was classified based on respondent’s selfreport, or, where self-report was not available, interviewer identification or death certificate. Three measures of SES were examined: poverty income ratio (PIR), education, and occupational status, each of which was assessed during the initial interview. PIR was calculated as the ratio of the total
Table 1 Women
Men
No.
Incident diabetes cases
No.
Incident diabetes cases
Total
6825
460
4244
306
Poverty income ratio <1 1–1.999 2–2.999 3–4.999 5+ Income missing
834 1496 1140 1279 448 188
79 125 65 63 20 12
372 769 678 854 363 117
34 67 50 56 19 13
Education <9 years 9–12 years, not grad HS grad 13–15 years 16+ years
1307 1379 2546 832 723
143 110 141 42 20
1167 666 1173 532 673
99 58 77 35 31
Duncan occupational status score <19.7 19.7–27.73 27.74–47.21 47.22+ No occupation
1056 653 836 609 3671
109 39 44 20 248
610 919 700 1060 955
54 71 55 65 61
Race/ethnicity Non-Hispanic White African–American Hispanic Other
5589 954 226 56
309 127 20 4
3550 507 137 50
237 50 14 5
Age in 1980 29–44 45–64 65–84
2476 2531 1818
122 209 129
1204 1603 1437
38 148 120
232
J.M. Robbins et al. / Diabetes Research and Clinical Practice 68 (2005) 230–236
family income to the poverty threshold determined by the census bureau. Subjects who were first interviewed after July 1974 as part of a special ‘‘augmentation sample’’ did not have PIR calculated, and were excluded from the income analyses. PIR categories used were PIR 0–0.999 (below the poverty line), 1– 1.999 (a range often described as ‘‘near poor’’), 2– 2.999, 3–4.999, and >5. Education was categorized as less than 9 years of education, 9–12 years but not a high school graduate, high school graduate, 13–15 years, and 16 or more years. Occupational status was measured based on usual occupation using the Duncan socioeconomic index (SEI) [24] and the scores were categorized into quartiles. Subjects who did not report a usual occupation (23% of male and 54% of female subjects) were excluded from analyses of this variable. Onset of diagnosed diabetes was ascertained through subject or proxy interview or from hospital records. Respondents were asked if they had ever been diagnosed with diabetes by a physician, and the
date of the diagnosis. Hospitals for which overnight stays were reported (either by the respondent or abstracted from death certificates) were contacted for admission and discharge diagnoses [20]. Where these records indicated a diagnosis of diabetes and there was no report of diabetes prior to the hospitalization, the date of admission was taken as date of initial diagnosis. Lifestyle-related variables that have been identified as potentially modifiable health risk factors [25,26] were considered as possible mediators of the SES-type 2 diabetes relationship. These included body size, physical activity, diet, cigarette smoking, and alcohol consumption. Measured body mass index (BMI = weight (kg)/height (m)2) was modeled as sex-specific quintiles. BMI at age 25 (based on self-reported weight [27]) was used as a measure of lifetime weight and subscapular skinfold thickness was used as a measure of central adiposity. Usual and recreational physical activity were assessed by two questions asked
Fig. 1. Hazard ratios for incidence of type 2 diabetes by socioeconomic status and sex, adjusted for age and race/ethnicity.
J.M. Robbins et al. / Diabetes Research and Clinical Practice 68 (2005) 230–236
at baseline. Total kilocalories consumed, total fats as a percentage of kilocalories consumed, and saturated fats as a percentage of kilocalories consumed were assessed with a 24-h food recall. Smoking was assessed during the first followup survey, but dates of smoking initiation and cessation were used to calculate smoking status at baseline. Smoking variables included whether the subject was a past or current smoker and lifetime pack-years of smoking at baseline. Frequency and quantity of alcohol use were modeled as continuous variables along with a dichotomous variable for any use of alcohol at baseline. Proportional hazards models were run using each of the three SES variables individually. Each model was run adjusting first for age and race/ethnicity, and then for all of the potential mediators described. SUDAAN 8.01 was used to adjust for the complex sampling design [28]. Tests for trend were conducted by modeling SES as ordinal variables. Diagnostic tests were conducted to confirm that the proportional hazards assumption was satisfied.
233
3. Results Baseline characteristics of the study sample are shown in Table 1. There were 460 incident cases of diagnosed diabetes among 6825 women and 306 among 4024 men in the NHEFS cohort. Among women, increasing PIR was associated with decreasing hazard ratios (HR) for diabetes incidence after adjustment for age and race/ethnicity (Fig. 1). The HR for women with incomes at least five times the poverty level relative to those below the poverty line was 0.60 (95% CI 0.32–1.15). Education and occupational status were also strongly associated with diabetes incidence in women. In men, there was a significant inverse association between PIR and diabetes incidence, and a significant (but not entirely monotonic) association with education among men in NHEFS. Occupational status was not associated with diabetes incidence among men. Adjustment for other covariables (Fig. 2) essentially eliminated the association between PIR and diabetes incidence in women and attenuated the
Fig. 2. Hazard ratios for incidence of type 2 diabetes by socioeconomic status and sex, adjusted for age, race/ethnicity, and potential mediators.
234
J.M. Robbins et al. / Diabetes Research and Clinical Practice 68 (2005) 230–236
association with education, but did not substantially alter the association between occupational status and diabetes incidence. In the adjusted model, a Duncan SEI score greater than 47.22 (e.g., a teacher or purchasing manager) showed an HR of 0.39 (0.19– 0.82) for diabetes incidence relative to an SEI score of <19.7 (e.g., a childcare worker or waitress). Among men, the associations between diabetes incidence and the SES variables were not substantially altered by controlling for other risk factors. Low PIR remained a significant predictor of diabetes risk among men.
4. Conclusions Our results indicate that low socioeconomic status, assessed using three different measures, is associated with risk of diabetes. This supports the conclusion that the inverse association between SES and diabetes prevalence described in the United States and other developed countries [4,12,29–31] is not solely a result of the impact of diabetes on SES (for example, lower ability to work) or longer survival among diabetes patients of higher SES, but reflects an increased risk of developing diabetes in persons with lower SES. The association of SES with diabetes risk raises the question of what mechanisms link SES with diabetes onset. Controlling for some potential mediators that had been ascertained for the NHEFS cohort, including body size, physical activity, diet, smoking, and alcohol use [32–36], did substantially reduce most of the SESdiabetes associations. The fact that these factors did not account entirely for the association between SES and diabetes incidence may be due to the difficulty of measuring behavioral characteristics such as diet and physical activity, therefore leading to an underestimation of the effect of these factors [37]. However, variables not measured in NHEFS, including chronic stress, may also be implicated in the association between SES and diabetes [38–42]. Gender differences consistent with those we identified have been identified in the associations between SES and major risk factors for diabetes, including body size, diet, and physical activity [33,43– 45], as well as responses to stressors [46,47]. A more unexpected finding was that occupational status was
associated with diabetes risk among women but not men, and that this risk was not explained by the mediating variables we examined. A similar finding that occupational status was associated with obesity among women but not men in the 1996 Health Survey of England [43] suggests that this may reflect a real gender difference in how SES affects the lifestyles and experiences that determine diabetes risks. These results highlight the importance of a multi-dimensional definition of SES and underline the potential impact of work-related stressors on the health of women. Several limitations of this analysis should be recognized. Income, education and occupation may not fully capture the impact of social and economic inequalities over the life-course [48]. We excluded subjects without a reported ‘‘usual occupation,’’ which led to the exclusion of a large proportion of the cohort from analyses of occupational status, reducing the precision of the estimates obtained [49]. Although 93% of the NHEFS cohort members eligible for this analysis were successfully followed at least once for diabetes incidence, and there were no substantial differences in follow-up according to SES in our sample, we cannot exclude the possibility of bias from loss to follow-up. Finally, NHEFS collected data only on physician-diagnosed diabetes. If persons with low SES were less likely to be diagnosed, the true association between SES and diabetes risk would be greater than indicated by our analyses. The prevalence of diabetes continues to increase, exceeding 30% in African–American women over age 60 [50]. Although it has been demonstrated that behavioral and chemotherapeutic interventions can delay or prevent type 2 diabetes [51], these interventions have not been successfully implemented in largescale clinical or population-based programs [52]. While most clinicians who treat patients with diabetes in the United States are aware that type 2 diabetes occurs more frequently among racial and ethnic minorities, many are not aware that it is also more likely to appear among patients with low SES, regardless of race or ethnicity [6]. Our findings suggest that effective, population-based interventions to decrease obesity and improve health behaviors may reduce, but probably not eliminate, SES disparities in diabetes incidence. Future studies should address the mechanisms underlying the effects of SES on diabetes
J.M. Robbins et al. / Diabetes Research and Clinical Practice 68 (2005) 230–236
risk. This knowledge will help guide efforts towards reversing the alarming increases in type 2 diabetes.
Acknowledgments This study was supported by US Public Health Service grant 5-T32MH 1435 from the National Institute of Mental Health. An earlier version was published in abstract form as ‘‘Education, Income, and Occupational Status as Predictors of Diabetes Diagnosis among Women in the NHANES I Epidemiologic Followup Study,’’ Diabetes 50 Supplement 2: A216– A217, June 2001.
References [1] M.P. Stern, M. Rosenthal, S.M. Haffner, H.P. Hazuda, L.J. Franco, Sex differences in the effects of sociocultural status on diabetes and cardiovascular risk factors in Mexican Americans, Am. J. Epidemiol. 120 (1984) 834–851. [2] M.G. Marmot, G. Davey Smith, S. Stansfeld, C. Patel, F. North, J. Head, et al. Health inequalities among British civil servants: the Whitehall II study, Lancet 337 (1991) 1387–1393. [3] E.J. Brunner, M.G. Marmot, K. Nanchahal, M.J. Shipley, S.A. Stansfeld, M. Juneja, et al. Social inequality in coronary risk: central obesity and the metabolic syndrome. Evidence from the Whitehall II study, Diabetologia 40 (1997) 1341–1349. [4] M.A. Winkleby, H.C. Kraemer, D.K. Ahn, A.N. Varady, Ethnic and socioeconomic differences in cardiovascular disease risk factors. Findings for women from the Third National Health and Nutrition Examination Survey, 1988–1994, JAMA 280 (1998) 356–362. [5] H.E. Resnick, P. Valsania, J.B. Halter, X. Lin, Differential effects of BMI on diabetes risk among black and white Americans, Diabetes Care 21 (1998) 1828–1835. [6] J.M. Robbins, V. Vaccarino, H. Zhang, S.V. Kasl, Socioeconomic status and type 2 diabetes in African-American and nonHispanic white women and men: evidence from the third National Health and Nutrition Examination Survey, Am. J. Public Health 91 (2001) 76–83. [7] W.C. Knowler, D.R. McCance, D.K. Nagi, D.J. Pettitt, Epidemiological studies of the causes of non-insulin-dependent diabetes mellitus, in: R.D.G. Leslie (Ed.), Causes of Diabetes: Genetic and Environmental Factors, Wiley, Chichester, 1993, pp. 187–218. [8] D.J.P. Barker, M.J. Gardner, C. Power, Incidence of diabetes amongst people aged 18–50 years in nine British towns: a collaborative study, Diabetologia 22 (1982) 421–425. [9] S.M. Haffner, H.P. Hazuda, B.D. Mitchell, J.K. Patterson, M.P. Stern, Increased incidence of type II diabetes mellitus in Mexican Americans, Diabetes Care 14 (1991) 102–108.
235
[10] R.M. Paris, S.A. Bedno, M.R. Krauss, L.W. Keep, M.V. Rubertone, Weighing in on type 2 diabetes in the military: characteristics of U.S. military personnel at entry who develop type 2 diabetes, Diabetes Care 24 (2001) 1894–1898. [11] W.J. Butler, L.D. Ostrander, W.J. Carman, D.E. Lamphiear, Diabetes mellitus in Tecumseh, Michigan. Prevalence, incidence, and associated conditions, Am. J. Epidemiol. 116 (1982) 971–980. [12] R.B. Lipton, Y. Liao, G. Cau, R.S. Cooper, D. McGee, Determinants of incident non-insulin-dependent diabetes mellitus among blacks and whites in a national sample: the NHANES I Epidemiologic Follow-up Study, Am. J. Epidemiol. 138 (1993) 826–839. [13] A.E. Monterrosa, S.M. Haffner, M.P. Stern, H.P. Hazuda, Sex differences in lifestyle factors predictive of diabetes in Mexican-Americans, Diabetes Care 18 (1995) 448–456. [14] Y. Morikawa, H. Nakagawa, M. Ishizaki, M. Tabata, M. Nishijo, K. Miura, et al. Ten-year follow-up study on the relation between the development of non-insulin-dependent diabetes mellitus and occupation, Am. J. Ind. Med. 31 (1997) 80–84. [15] E.S. Ford, Vitamin supplement use and diabetes mellitus incidence among adults in the United States, Am. J. Epidemiol. 153 (2001) 892–897. [16] E.S. Ford, A.H. Mokdad, Fruit and vegetable consumption and diabetes mellitus incidence among US adults, Prev. Med. 32 (2001) 33–39. [17] H.E. Resnick, P. Valsania, J.B. Halter, X. Lin, Relation of weight gain and weight loss on subsequent diabetes risk in overweight adults, J. Epidemiol. Community Health 54 (2000) 596–602. [18] B.G. Link, J. Phelan, Social conditions as fundamental causes of disease, J. Health Soc. Behav. (extra issue) (1995) 80–94. [19] K. West, Epidemiology of Diabetes And Its Vascular Lesions, Elsevier, New York, 1978. [20] National Center for Health Statistics, Plan and Operation of the NHANES I Epidemiologic Followup Study 1992, Hyattsville, MD, National Center for Health Statistics; 1997 (Vital and Health Statistics, Ser. 1, No. 35). [21] National Diabetes Data Group, Classification and diagnosis of diabetes mellitus and other categories of glucose intolerance, Diabetes 28 (1979) 1039–1057. [22] World Health Organization, Report of the WHO Expert Committee on Diabetes Mellitus, WHO, Geneva, 1980. [23] P.H. Bennett, M.J. Rewers, W.C. Knowler, Epidemiology of diabetes mellitus, in: D. Porte, R.S. Sherwin (Eds.), Ellenberg and Rifkin’s Diabetes Mellitus, fifth ed. Appleton & Lange, Stamford, 1997, pp. 373–400. [24] G. Stevens, D.L. Featherman, A revised socioeconomic index of occupational status, Soc. Sci. Res. 10 (1981) 364–395. [25] R.R. Wing, M.G. Goldstein, K.J. Acton, L.L. Birch, J.M. Jakicic, J.F. Sallis Jr., et al. Behavioral science research in diabetes: lifestyle changes related to obesity, eating behavior, and physical activity, Diabetes Care 24 (2001) 117–123. [26] U.M. Kujala, J. Kaprio, M. Koskenvuo, Modifiable risk factors as predictors of all-cause mortality: the roles of genetics and childhood environment, Am. J. Epidemiology 156 (2002) 985– 993.
236
J.M. Robbins et al. / Diabetes Research and Clinical Practice 68 (2005) 230–236
[27] National Center for Health Statistics. Plan and Operation of the NHANES I Epidemiologic Followup Study 1982–84, Hyattsville, MD, National Center for Health Statistics, 1987, (Vital and Health Statistics, Ser. 1, No. 22). [28] B.V. Shah, B.G. Barnwell, G.S. Bieler, SUDAAN User’s Manual, Release 7.5, Research Triangle Park, Research Triangle Inst., NC, 1997. [29] T.F. Drury, K.M. Danchik, M.I. Harris, Sociodemographic characteristics of adult diabetics. In: National Diabetes Data Group (Eds.), Diabetes in America: Diabetes Data Compiled 1984, (NIH publ. No. 85–1468), 1985, VII1–VII29. [30] J.A. Marshall, R.F. Hamman, J. Baxter, E.J. Mayer, D.L. Fulton, M. Orleans, et al. Ethnic differences in risk factors associated with the prevalence of non-insulin-dependent diabetes mellitus: the San Luis Valley Diabetes Study, Am. J. Epidemiol. 137 (1993) 706–718. [31] V. Connolly, N. Unwin, P. Sherriff, R. Bilous, W. Kelly, Diabetes prevalence and socioeconomic status: a population based study showing increased prevalence of type 2 diabetes mellitus in deprived areas, J. Epidemiol. Community Health 54 (2000) 173–177. [32] D.E. Bild, D.R. Jacobs, S. Sidney, W.L. Haskell, N. Anderssen, A. Oberman, Physical activity in young black and white women: the CARDIA Study, Ann. Epidemiol. 3 (1993) 636–644. [33] R. Lowry, L. Kann, J.L. Collins, L.J. Kolbe, The effect of socioeconomic status on chronic disease risk behaviors among US adolescents, JAMA 276 (1996) 792–797. [34] M. Tang, Y. Chen, D. Krewski, Gender-related differences in the association between socioeconomic status and selfreported diabetes, Int. J. Epidemiol. 32 (2003) 381–385. [35] J.C. Will, D.A. Galuska, E.S. Ford, A. Mokdad, E.E. Calle, Cigarette smoking and diabetes mellitus: evidence of a positive association from a large prospective cohort study, Int. J. Epidemiol. 30 (2001) 540–546. [36] F. de Vegt, J.M. Dekker, W.J. Groeneveld, G. Nijpels, C.D. Stehouwer, L.M. Bouter, et al. Moderate alcohol consumption is associated with lower risk for incident diabetes and mortality: the Hoorn Study, Diabetes Res. Clin. Pract. 57 (2002) 53–60. [37] S. Paeratakul, B.M. Popkin, L. Kohlmeier, I. Hertz-Picciotto, X. Guo, L.J. Edwards, Measurement error in dietary data: implications for the epidemiologic study of the diet–disease relationship, Eur. J. Clin. Nutr. 52 (1998) 722–727. [38] R.S. Surwit, M.S. Schneider, M.N. Feinglos, Stress and diabetes mellitus, Diabetes Care 15 (1992) 1413–1422.
[39] V.L. Goetsch, B. VanDorsten, L.A. Pbert, I.H. Ullrich, R.A. Yeater, Acute effects of laboratory stress on blood glucose in noninsulin-dependent diabetes, Psychosom. Med. 55 (1993) 492–496. [40] J.K. Wales, Can psychological stress cause diabetes? Diabetic Med. 12 (1995) 109–112. [41] R.S. Surwit, P.G. Williams, Animal models provide insight into psychosomatic factors in diabetes, Psychosom. Med. 58 (1996) 582–589. [42] A. Inui, H. Kitaoka, M. Majima, S. Takamiya, M. Uemoto, C. Yonenaga, et al. Effect of the Kobe earthquake on stress and glycemic control in patients with diabetes mellitus, Arch. Intern. Med. 158 (1998) 274–278. [43] J. Wardle, J. Waller, M.J. Jarvis, Sex differences in the association of socioeconomic status with obesity, Am. J. Public Health 92 (2002) 1299–1304. [44] H.P. Hazuda, S.M. Haffner, M.P. Stern, C.W. Eifler, Effects of acculturation and socioeconomic status on obesity and diabetes in Mexican Americans: the San Antonio Heart Study, Am. J. Epidemiol. 128 (1988) 1289–1301. [45] M.B. Livingstone, P.J. Robson, S. McCarthy, M. Kiely, K. Harrington, P. Browne, et al. Physical activity patterns in a nationally representative sample of adults in Ireland, Public Health Nutr. 4 (2001) 1107–1116. [46] N. Krieger, S. Sidney, Racial discrimination and blood pressure: the CARDIA Study of Young Black and White Adults, Am. J. Public Health 86 (1996) 1370–1378. [47] M.B. MacDonald, G.P. Laing, M.P. Wilson, T.W. Wilson, Prevalence and predictors of white-coat response in patients with treated hypertension, Can. Med. Assoc. J. 161 (1999) 265–269. [48] D.J. Barker, J.G. Eriksson, T. Forsen, C. Osmond, Fetal origins of adult disease: strength of effects and biological basis, Int. J. Epidemiol. 31 (2002) 1235–1239. [49] P.D. Allison, Missing Data, Sage Publications, Thousand Oaks, CA, 2002. [50] M.I. Harris, K.M. Flegal, C.C. Cowie, M.S. Eberhardt, D.E. Goldstein, R.R. Little, et al. 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 21 (1998) 518–524. [51] Diabetes Prevention Program Research Group, Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin, N. Engl. J. Med. 346 (2002) 393–403. [52] P. Zimmet, J. Shaw, K.G. Alberti, Preventing type 2 diabetes and the dysmetabolic syndrome in the real world: a realistic view, Diabetic Med. 20 (2003) 693–702.