1518 Diabetes Care Volume 38, August 2015

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1 1518 Diabetes Care Volume 38, August 2015 Impact of Socioeconomic Status on Cardiovascular Disease and Mortality in 24,947 Individuals With Type 1 Diabetes Diabetes Care 2015;38: DOI: /dc Araz Rawshani, 1,2 Ann-Marie Svensson, 2,3 Annika Rosengren, 1,2 Björn Eliasson, 1,2 and Soffia Gudbjörnsdottir 2,3 CARDIOVASCULAR AND METABOLIC RISK OBJECTIVE Socioeconomic status (SES) is a powerful predictor of cardiovascular disease (CVD) and death. We examined the association in a large cohort of patients with type 1 diabetes. RESEARCH DESIGN AND METHODS Clinical data from the Swedish National Diabetes Register were linked to national registers, whereby information on income, education, marital status, country of birth, comorbidities, and events was obtained. Patients were followed until a first incident event, death, or end of follow-up. The association between socioeconomic variables and the outcomes was modeled using Cox regression, with rigorous covariate adjustment. RESULTS We included 24,947 patients. Mean (SD) age and follow-up was 39.1 (13.9) and 6.0 (1.0) years. Death and fatal/nonfatal CVD occurred in 926 and 1378 individuals. Compared with being single, being married was associated with 50% lower risk of death, cardiovascular (CV) death, and diabetes-related death. Individuals in the two lowest quintiles had twice as great a risk of fatal/nonfatal CVD, coronary heart disease, and stroke and roughly three times as great a risk of death, diabetesrelated death, and CV death as individuals in the highest income quintile. Compared with having 9 years of education, individuals with a college/university degree had 33% lower risk of fatal/nonfatal stroke. Immigrants had 19%, 33%, and 45% lower risk of fatal/nonfatal CVD, all-cause death, and diabetes-related death, respectively, compared with Swedes. Men had 44%, 63%, and 29% greater risk of all-cause death, CV death, and diabetes-related death. CONCLUSIONS Low SES increases the risk of CVD and death by a factor of 2 3intype1diabetes. Socioeconomic status (SES) is a complex construct, often conceptualized as the social standing or class of an individual. It is commonly measured as a combination of education, income, and occupation but may include age, sex, ethnicity, and marital status (1). SES may profoundly affect health. It is a powerful predictor of cardiovascular disease (CVD) and death (2 5). The impact of SES on CVD and mortality in type 2 diabetes has been examined (6), but studies in type 1 diabetes are scarce. Available data, which are hampered by 1 Sahlgrenska University Hospital, Gothenburg, Sweden 2 Department of Molecular and Clinical Medicine, University of Gothenburg, Gothenburg, Sweden 3 National Diabetes Register, Centre of Registers, Gothenburg, Sweden Corresponding author: Araz Rawshani, araz. rawshani@gu.se. Received 20 January 2015 and accepted 28 March This article contains Supplementary Data online at suppl/doi: /dc /-/dc1. A slide set summarizing this article is available online by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered.

2 care.diabetesjournals.org Rawshani and Associates 1519 small samples and inadequate adjustment for confounders, suggest either a modest effect or no significant effect of SESondeathandCVD(7 9). Consequently, no reliable studies relating SES to CVD and mortality in type 1 diabetes have been conducted. This might explain why socioeconomic variables have not been considered in recent risk prediction models for type 1 diabetes (10,11). We used the Swedish National Diabetes Register (NDR), which provides almost complete national coverage of type 1 diabetes, to investigate how income, education, marital status, immigrant status, and sex relate to CVD and death (12,13). It is important to examine this relationship, as it may reveal easily accessible risk markers. RESEARCH DESIGN AND METHODS The NDR was initiated in 1996 as a caregiver tool for local quality assurance and as a feedback tool in diabetes care. Roughly 95% of all individuals age 18 years and older with type 1 diabetes in Sweden are included (12). Data provided by nurses and physicians trained in register procedures are obtained at visits in outpatient clinics of hospitals and primary health care centers nationwide. Clinical information and various measurements are updated at least once a year. The study was approved by the regional ethics review board at the University of Gothenburg. All patients give their informed consent before being included in the NDR. Study Cohort We included 24,947 individuals (220,281 appointments) with type 1 diabetes who had at least one listing in the NDR between 1 January 2006 and 31 December This excluded 2,186 individuals with a history of CVD (as defined below), who were listed in the NDR during the same period. Type 1 diabetes was definedonthebasisof epidemiologic data: treatment with insulin and a diagnosis at the age of 30 years or younger. This definition has been validated as accurate in 97% of the cases listed in the register (14). Socioeconomic Data Data on annual income in Swedish kronor, highest educational level, country of birth, marital status, and occupation were obtained from Statistics Sweden. Education was stratified into lower (#9 years, the length of compulsory education in Sweden), intermediate (10 12 years, upper secondary) and higher (college/university). Income was stratified into quintiles (Q1 [lowest] to Q5 [highest]). Immigrant status was defined as immigrant or native Swede depending on country of birth. Marital categories were single (defined as never married and not cohabiting), married/cohabiting, divorced, or widowed. We decided beforehand to omit occupation from the analyses, assuming that income, education, immigrant status, and marital status were sufficient socioeconomic indicators. Socioeconomic variables were updated annually. Clinical Data BMI was calculated as weight in kilograms divided by the square of height in meters. Glycemic control was measured as HbA 1c. Analyses were quality assured nationwide by regular calibration with the high-performance liquid chromatography Mono-S method and then converted to millimoles per mole (IFCC) (22). LDL, HDL, and total cholesterol were measured in millimoles per liter. Microalbuminuria was defined as two positive tests out of three samples taken within a year, with albuminto-creatinine ratio 3 30 mg/mmol or a urinary albumin clearance of mg/min or mg/l, and macroalbuminuria as albumin-to-creatinine ratio.30 mg/mmol or a urinary albumin clearance of.200 mg/min or.300 mg/l. Glomerular filtration rate was estimated(egfr)withthemdrdequation (15). Systolic blood pressure was the mean value of two supine readings (Korotkoff 1 5) with a cuff of appropriate size and after at least 5 min of rest. Method of insulin delivery was defined as either multiple daily injections or use of continuous subcutaneous insulin infusion (CSII). Use of lipid-lowering medications, blood pressure lowering medications, and aspirin was dichotomized. Smoking was coded as present if the patient was a current smoker. Physical activity (for at least 30 min) was rated from 1 to 5: never (level 1), less than once a week (level 2), once or twice a week (level 3), 3 5 times per week (level 4), or daily (level 5). Time-varying variables were updated for each appointment in the NDR. Comorbidities, Events, and Vital Status Comorbidities and events were collected before baseline examination and during follow-up by linking data from the NDR to the Swedish Inpatient Registry (IPR) and the Causes of Death Registry. The IPR was initiated in the 1960s and has nationwide coverage since It includes mandatory information on all principal and secondary hospital discharge diagnoses. The ICD system is used to classify diagnoses in the IPR. Sensitivity and specificity for diagnoses of acute myocardial infarction, coronary heart disease (CHD), hospitalization for heart failure (HF), atrial fibrillation (AF), and stroke have been validated (16,17). The following comorbidities were assessed: HF (ICD-10 code I50, ICD-9 code 428), AF (ICD-10 code I48, ICD-9 code 427D), any cancer (ICD-10 codes C00-C97, ICD-9 codes ), and mental disorders (ICD-10 codes F20 29 and F30 39). The following codes were used for renal dialysis and transplantation: V42A, V45B, V56A, V56 W (ICD-9), and Z94.0, Z49, and Z99.2 (ICD-10). Stage 5 chronic kidney disease was defined as the need for renal dialysis or renal transplantation or as an egfr of,15 ml/min. Nonfatal CHD was defined as nonfatal myocardial infarction (ICD-10 code I21), unstable angina (ICD-10 code I20.0), percutaneous coronary intervention, or coronary artery bypass grafting. Fatal CHD was defined as ICD-10 codes I20-I25. Stroke was defined as nonfatal or fatal cerebral infarction, intracerebral hemorrhage, or unspecified stroke (ICD-10 codes I61 I64). CVD was defined as the composite of CHD or strokedwhichever came first. Cardiovascular (CV) mortality was defined as I00 I99 and diabetesrelated death as E10 E14. All individuals were followed from the baseline examination until a first incident event or death or otherwise until the censor date of 31 December Mean (SD) follow-up was 6.0 (1.0) years with 150,541 person-years of follow-up. Statistical Methods Baseline clinical features were calculated as mean (SD) values or percentages for each variable in relation to income, education, marital status, immigrant status, and sex. Survival Analyses Crude event rates were described as events per 1,000 person-years. Cox

3 1520 Report by the Swedish National Diabetes Register Diabetes Care Volume 38, August 2015 proportional hazards model was used to study relations between patients characteristics and the outcome. Two sequential models were developed to estimate the effect of socioeconomic factors on the risk of 1) fatal/nonfatal CVD, 2) fatal/ nonfatal CHD, 3) fatal/nonfatal stroke, 4) total death, 5) CV death and 6) diabetesrelated death. We adjusted for known and presumed risk factors and confounders for each outcome. The first model, which we refer to as the minimally adjusted model, was identical for all outcomes and included socioeconomic, demographic, and diabetes-related variables. The second model, which we refer to as the maximally adjusted model, was additionally adjusted for outcome specificcovariates. Minimally Adjusted Models Sex, immigrant status, age at inclusion, anddurationofdiabetesatinclusion were included in all models and held fixed. We used restricted cubic splines (five knots placed at the 5th, 27.5th, 50th, 72.5th, and 95th percentiles) to model the association between age and the outcome. Income quintile, marital status, and educational level were also included in all models as time-varying covariates. Maximally Adjusted Models Variables in the maximally adjusted models were entered as time-dependent covariates from baseline until an event, death, or the end of the study. Fatal/ nonfatal CVD was additionally adjusted for smoking, systolic blood pressure, HbA 1c, physical activity, egfr, albuminuria, total cholesterol to HDL ratio, antihypertensives, statins, and aspirin. Fatal/nonfatal CHD and fatal/nonfatal stroke were adjusted for the same covariates. All-cause death and CV death were additionally adjusted for smoking, HbA 1c, physical activity, egfr, CVD, albuminuria, and HF. Diabetes-related death was additionally adjusted for smoking status, systolic blood pressure, HbA 1c, physical activity, egfr, chronic kidney disease, and albuminuria. Given that sex is an important component of social position, we present hazard ratios (HR) for sex, using females as the reference group. For all-cause death, we calculated Cox adjusted survival curves according to socioeconomic categories. We adjusted for age and duration of diabetes. We assessed the proportional hazard assumption for all variables by using Schoenfeld residuals. No significant interactions were found between the socioeconomic variables and the covariates in the final model. Modeling Time-Dependent Covariates As explained above, some variables were modeled as time-dependent covariates. This is appropriate, since we had access to updated measurements of the covariates that change during follow-up. The course of the covariates might be more informative of the survival experience than their baseline values. Cox s model can be generalized to include time-dependent covariates and thus take all data into account. Technically, this is carried out by requiring that each time period for a patient appear as a separate observation in the data set. Missing Data We addressed missing data in two steps. Firstly, we used last observation carried forward to impute missing variables if previous measurements existed. Supplementary Table 1 presents the proportion of missing data after last observation carried forward. Secondly, we used multiple imputation by chained equations. Multiple imputation was performed according to guidelines (18) by means of the MICE (Multivariate Imputation by Chained Equations) algorithm. We imputed five data sets (using 10 iterations) (19). RESULTS Baseline Clinical Features Immigrants and native Swedes were comparable in terms of both age and sex (Table 1). Immigrants, however, had lower income, were twice as likely to be smokers, were less physically active, and tended more to be married/ cohabiting. With respect to marital status, there were large differences in terms of age, rendering the baseline comparisons difficult to assess (Table 1). However, individuals who were married/cohabiting were fairly comparable with those who were divorced with regard to age and sex. Individuals who were divorced tended to be women, had higher HbA 1c, were twice as likely to be smokers, had albuminuria more often, and were less physically active. Individuals with years of education were comparable at baseline (considering distribution of age and sex) with those with a college/university degree (Table 2). Individuals with a college/university degree had higher income, had 5 mmol/mol lower HbA 1c, were more likely to be married/cohabiting, used insulin pump more frequently (17.5% vs. 14.5%), smoked less (5.8% vs. 13.1%), and had less albuminuria (10.8% vs. 14.2%). Income Q2, Q3, and Q4 were approximately of the same age. Individuals with high income were more likely to be married/cohabiting, had lower HbA 1c,and had lower rates of smoking as well as albuminuria (Table 2). With respect to sex, men and women were roughly the same age. Women had substantially lower income and higher education, were more often married, used insulin pump more frequently, had less albuminuria, and smoked more frequently than men (Supplementary Table 2). Crude Incidence Rates The number of events and the crude incidence rates are presented in Supplementary Table 3. Low income, low education, and being divorced or widowed were all associated with higher incidence rates of all outcomes. Refer to Supplementary Table 3 for incidence rates. Age- and Sex-Adjusted Survival Curves Cox adjusted survival curves for death (Supplementary Fig. 1A D) indicated that income, education, marital status, and immigrant status were significantly (all P, 0.05) associated with survival. Adjusted HRs for the Outcomes Marital Status Compared with being single (the reference category for marital status), being married/cohabiting did not affect the risk of fatal/nonfatal CVD, fatal/nonfatal CHD, or fatal/nonfatal stroke(fig.1). Being married/cohabiting was associated with 50 64% lower risk of all-cause death, CV death, and diabetes-related death (Fig. 2 [maximally adjusted models]). Being divorced increased the risk of fatal/ nonfatal CVD by 32%. The same tendency was noted for fatal/nonfatal CHD and stroke but without statistical significance (Fig. 1) (maximally adjusted models). Being divorced was associated with 40% lower risk of CV death compared with being single (Fig. 2) (maximally adjusted model). Being widowed was associated with

4 care.diabetesjournals.org Rawshani and Associates 1521 Table 1 Characteristics of the cohort at baseline, according to marital status and immigrant status Marital status Immigrant status Single Divorced Married/cohabiting Widowed Swedish native Immigrant Individuals, n 13,560 2,266 8, ,382 1,565 Males 7,863 (58.0) 1,058 (46.7) 4,411 (50.1) 71 (24.1) 12,659 (54.1) 756 (48.3) Age (years) (10.87) (10.58) (12.11) (11.22) (13.93) (12.74) Onset age (years) (7.41) (7.93) (7.82) (7.58) (7.67) (7.42) Income, n (%) Q1 4,036 (29.8) 265 (11.7) 1,430 (16.2) 64 (21.8) 5,242 (22.4) 553 (35.6) Q2 2,759 (20.3) 545 (24.1) 1,417 (16.1) 84 (28.6) 4,491 (19.2) 314 (20.2) Q3 2,604 (19.2) 487 (21.5) 1,669 (19.0) 39 (13.3) 4,505 (19.3) 294 (18.9) Q4 2,350 (17.3) 471 (20.8) 1,844 (21.0) 42 (14.3) 4,490 (19.2) 217 (14.0) Q5 1,811 (13.4) 498 (22.0) 2,440 (27.7) 65 (22.1) 4,637 (19.8) 177 (11.4) Education, n (%) #9 years 2,037 (15.2) 483 (21.4) 1,415 (16.1) 117 (40.2) 3,707 (15.9) 345 (23.3) years 7,507 (55.9) 1,210 (53.5) 4,292 (49.0) 124 (42.6) 12,466 (53.6) 667 (45.0) College/university 3,877 (28.9) 567 (25.1) 3,055 (34.9) 50 (17.2) 7,080 (30.4) 469 (31.7) Marital status, n (%) Single N/A N/A N/A N/A 12,967 (55.5) 593 (38.1) Divorced N/A N/A N/A N/A 2,047 (8.8) 219 (14.1) Married/cohabiting a N/A N/A N/A N/A 8,078 (34.6) 722 (46.4) Widowed N/A N/A N/A N/A 273 (1.2) 21 (1.4) Immigrant, n (%) 593 (4.4) 219 (9.7) 722 (8.2) 21 (7.1) N/A N/A CSII, n (%) 1,750 (14.7) 221 (11.3) 1,210 (15.5) 17 (6.5) 3,093 (14.9) 110 (9.1) HbA 1c (mmol/mol) (15.74) (14.39) (13.04) (11.68) (14.59) (16.54) egfr (ml/min) (27.69) (28.15) (24.87) (22.31) (26.95) (34.0) Systolic BP (mmhg) (14.53) (16.83) (16.26) (18.01) (15.77) (16.6) Chol-to-HDL ratio 3.18 (1.05) 3.10 (1.03) 3.04 (1.00) 2.89 (0.84) 3.11 (1.02) 3.30 (1.13) BMI (kg/m 2 ) (4.35) (4.38) (4.19) (4.87) (4.26) (4.98) Smoker, n (%) 1,670 (12.9) 429 (19.4) 801 (9.3) 43 (14.9) 2,657 (11.7) 288 (19.7) Exercise, n (%) Never 866 (9.1) 211 (13.5) 526 (8.5) 28 (14.6) 1,486 (9.0) 148 (14.0),1 time/week 1,248 (13.1) 233 (14.9) 789 (12.7) 24 (12.5) 2,127 (12.9) 170 (16.0) 1 2 times/week 2,663 (27.9) 377 (24.2) 1,810 (29.2) 41 (21.4) 4,645 (28.2) 248 (23.4) 3 5 times/week 2,708 (28.4) 334 (21.4) 1,529 (24.6) 40 (20.8) 4,379 (26.6) 238 (22.5) Daily 2,064 (21.6) 406 (26.0) 1,552 (25.0) 59 (30.7) 3,830 (23.3) 256 (24.2) Albuminuria, n (%) Microalbuminuria 1,473 (11.8) 389 (17.9) 1,318 (15.4) 55 (19.2) 3,036 (13.7) 202 (14.5) Macroalbuminuria 688 (5.5) 223 (10.3) 640 (7.5) 26 (9.1) 1,462 (6.6) 119 (8.6) Antihypertensives, n (%) 2,688 (20.5) 1,016 (45.6) 3,520 (40.5) 184 (63.4) 6,967 (30.5) 450 (29.9) Statins, n (%) 1,766 (13.5) 743 (33.5) 2,747 (31.8) 124 (43.1) 5,055 (22.2) 332 (22.2) Aspirin 889 (6.9) 525 (24.0) 1,706 (20.0) 108 (37.9) 3,031 (13.5) 201 (13.5) HF, n (%) 52 (0.4) 25 (1.1) 70 (0.8) 8 (2.7) 144 (0.6) 12 (0.8) AF, n (%) 29 (0.2) 14 (0.6) 75 (0.9) 10 (3.4) 114 (0.5) 14 (0.9) IHD, n (%) 30 (0.2) 32 (1.4) 91 (1.0) 9 (3.1) 148 (0.6) 14 (0.9) Data are crude baseline values as means 6 1 SD unless otherwise indicated. BP, blood pressure; Chol-to-HDL ratio, total cholesterol to HDL ratio; IHD, ischemic heart disease. a Cohabiting as registered partners. 56% greater risk of fatal/nonfatal CVD and more than twice the risk of fatal/ nonfatal CHD (HR 2.12, 95% CI ) (Fig. 1) (maximally adjusted models). Income Inverse relationships were found between income and the end points. Compared with the highest income quintile, individuals in the two lowest income quintiles had roughly twice the risk of fatal/nonfatal CVD, fatal/nonfatal CHD, and fatal/nonfatal stroke in the minimally adjusted model. This was somewhat attenuated in the maximally adjusted model (Fig. 1). Compared with the highest income quintile, the two lowest quintiles had roughly three times as great a risk of death, diabetesrelateddeath,andcvdeathinthe minimally adjusted model. The risk of all-cause death was still twice as much in the maximally adjusted model; the risk of CV death was three times as much, and the risk of diabetes-related death was twice as much (Fig. 2). Educational Level Compared with having #9 years of education, individuals with a college/ university degree had 31%, 26%, and 45% lower risk of fatal/nonfatal CVD, fatal/nonfatal CHD, and fatal/nonfatal stroke, respectively, in the minimally adjusted model. These differences were attenuated in the maximally adjusted

5 1522 Report by the Swedish National Diabetes Register Diabetes Care Volume 38, August 2015 Table 2 Characteristics of the cohort at baseline, according to income quintile and education Income quintiles (1 = lowest, 5 = highest) Educational attainment Q1 Q2 Q3 Q4 Q5 #9 years years College/university Individuals, n 5,795 4,805 4,799 4,707 4,814 4,052 13,133 7,549 Males 2,583 (44.6) 1,999 (41.6) 2,275 (47.4) 2,913 (61.9) 3,633 (75.5) 2,272 (56.1) 7,306 (55.6) 3,735 (49.5) Age (years) (16.35) (15.20) (12.42) (10.94) (10.65) (17.25) (13.39) (11.98) Onset age (years) (7.35) (7.74) (7.84) (7.71) (7.74) (7.79) (7.71) (7.64) Income, n (%) Q1 N/A N/A N/A N/A N/A 1,579 (39.0) 2,804 (21.4) 1,281 (17.0) Q2 N/A N/A N/A N/A N/A 927 (22.9) 2,770 (21.1) 1,067 (14.1) Q3 N/A N/A N/A N/A N/A 631 (15.6) 2,878 (21.9) 1,283 (17.0) Q4 N/A N/A N/A N/A N/A 565 (13.9) 2,592 (19.7) 1,548 (20.5) Q5 N/A N/A N/A N/A N/A 350 (8.6) 2,089 (15.9) 2,370 (31.4) Education, n (%) #9 years 1,579 (27.9) 927 (19.5) 631 (13.2) 565 (12.0) 350 (7.3) N/A N/A N/A years 2,804 (49.5) 2,770 (58.1) 2,878 (60.1) 2,592 (55.1) 2,089 (43.4) N/A N/A N/A College/university 1,281 (22.6) 1,067 (22.4) 1,283 (26.8) 1,548 (32.9) 2,370 (49.3) N/A N/A N/A Marital status, n (%) Single 4,036 (69.6) 2,759 (57.4) 2,604 (54.3) 2,350 (49.9) 1,811 (37.6) 2,037 (50.3) 7,507 (57.2) 3,877 (51.4) Divorced 265 (4.6) 545 (11.3) 487 (10.1) 471 (10.0) 498 (10.3) 483 (11.9) 1,210 (9.2) 567 (7.5) Married/cohabiting 1,430 (24.7) 1,417 (29.5) 1,669 (34.8) 1,844 (39.2) 2,440 (50.7) 1,415 (34.9) 4,292 (32.7) 3,055 (40.5) Widowed 64 (1.1) 84 (1.7) 39 (0.8) 42 (0.9) 65 (1.4) 117 (2.9) 124 (0.9) 50 (0.7) Immigrant, n (%) 553 (9.5) 314 (6.5) 294 (6.1) 217 (4.6) 177 (3.7) 345 (8.5) 667 (5.1) 469 (6.2) CSII, n (%) 764 (15.4) 553 (13.2) 633 (14.9) 620 (14.6) 628 (14.6) 324 (9.2) 1,689 (14.5) 1,173 (17.5) HbA1c (mmol/mol) (16.76) (15.40) (14.56) (13.33) (12.38) (15.72) (14.91) (13.07) egfr (ml/min) (32.35) (28.83) (25.94) (23.86) (24.18) (31.60) (27.09) (24.51) Systolic BP (mmhg) (16.56) (17.07) (15.11) (15.15) (14.70) (17.68) (15.72) (14.58) Chol-to-HDL ratio 3.16 (1.09) 3.12 (1.04) 3.08 (0.98) 3.13 (1.09) 3.09 (0.94) 3.23 (1.09) 3.17 (1.04) 2.97 (0.95) BMI (kg/m 2 ) (4.73) (4.72) (4.34) (4.00) (3.59) (4.72) (4.45) (3.75) Smoker, n (%) 772 (14.3) 734 (15.8) 602 (12.9) 515 (11.1) 320 (6.7) 833 (21.6) 1,663 (13.1) 425 (5.8) Exercise, n (%) Never 446 (11.3) 362 (10.9) 290 (8.7) 286 (8.4) 247 (7.0) 354 (12.8) 910 (9.8) 351 (6.6),1 time/week 505 (12.8) 447 (13.5) 438 (13.1) 437 (12.9) 467 (13.3) 404 (14.6) 1,247 (13.4) 628 (11.8) 1 2 times/week 966 (24.5) 858 (25.9) 950 (28.4) 980 (28.9) 1,137 (32.4) 684 (24.6) 2,560 (27.6) 1,625 (30.4) 3 5 times/week 1,056 (26.8) 814 (24.6) 892 (26.7) 889 (26.2) 960 (27.3) 620 (22.3) 2,363 (25.5) 1,592 (29.8) Daily 972 (24.6) 833 (25.1) 772 (23.1) 801 (23.6) 703 (20.0) 713 (25.7) 2,204 (23.7) 1,143 (21.4) Albuminuria, n (%) Microalbuminuria 598 (11.7) 684 (15.1) 671 (14.7) 647 (14.2) 635 (13.5) 679 (18.3) 1,754 (14.2) 787 (10.8) Macroalbuminuria 268 (5.2) 438 (9.7) 316 (6.9) 286 (6.3) 269 (5.7) 336 (9.1) 922 (7.5) 312 (4.3) Antihypertensives, n (%) 1,273 (23.1) 1,669 (35.6) 1,498 (31.8) 1,420 (30.7) 1,548 (32.5) 1,592 (40.4) 3,948 (30.9) 1,844 (24.9) Statins, n (%) 907 (16.5) 1,165 (24.9) 1,072 (22.9) 1,036 (22.4) 1,200 (25.3) 1,207 (30.7) 2,826 (22.2) 1,332 (18.1) Continued on p. 1523

6 care.diabetesjournals.org Rawshani and Associates 1523 Table 2 Continued Income quintiles (1 = lowest, 5 = highest) Educational attainment Q1 Q2 Q3 Q4 Q5 #9 years years College/university Aspirin, n (%) 589 (10.8) 742 (16.0) 654 (14.2) 572 (12.5) 671 (14.4) 804 (20.6) 1,660 (13.1) 752 (10.4) HF, n (%) 53 (0.9) 52 (1.1) 22 (0.5) 12 (0.3) 16 (0.3) 53 (1.3) 80 (0.6) 22 (0.3) AF, n (%) 27 (0.5) 40 (0.8) 16 (0.3) 16 (0.3) 29 (0.6) 39 (1.0) 59 (0.4) 28 (0.4) IHD, n (%) 47 (0.8) 55 (1.1) 27 (0.6) 14 (0.3) 19 (0.4) 48 (1.2) 89 (0.7) 23 (0.3) Data are crude baseline values as means 6 1 SD unless otherwise indicated. BP, blood pressure; Chol-to-HDL ratio, total cholesterol to HDL ratio. model and remained statistically significant only for fatal/nonfatal stroke (HR 0.55, 95% CI ) (Fig. 1). A similar trend was noted for those having years of education (Fig. 1). Likewise, for all-cause death, CV death, and diabetes death, a college/ university degree was protective in the minimally adjusted model, but the effect was invalidated in the maximally adjusted model (Fig. 2). Having years of education was associated with 48% higher risk of CV death compared with those having #9 years (Fig. 2). Immigrant Status The point estimates in all models indicated that immigrants had 10 40% lower risk of the outcomes compared with native Swedes (Figs. 1 and 2). This was statistically significant (in the maximally adjusted model) for fatal/nonfatal CVD, all-cause death, and diabetesrelated death. Sex Compared with women, males had 20 50% higher risk of all outcomes in the minimally adjusted models. This remained statistically significant in the maximally adjusted models for all-cause death, CV death, and diabetes-related death, with the following HRs (males vs. females): 1.44 (95% CI ), 1.63 (95% CI ), and 1.29 (95% CI ), respectively. Refer to Supplementary Table 4 for P values as well as HRs for fatal CVD and fatal/nonfatal acute myocardial infarction. CONCLUSIONS In this nationwide study of 24,947 patients with type 1 diabetes, we showed that low SES is a powerful predictor of CV morbidity and mortality in type 1 diabetes. To the best of our knowledge, this is the first study to establish these relationships. The effect of SES was striking despite rigorous adjustments for risk factors and confounders. Individuals in the two lowest income quintiles had two to three times higher risk of CV events and death than those in the highest income quintile. Compared with low educational level, having high education was associated with ;30% lower risk of stroke. Compared with being single, individuals who were married/cohabiting had.50% lower risk of death, CV death, and diabetes-related death. Immigrants had 20 40% lower risk of fatal/nonfatal CVD, all-cause death, and diabetes-related death. Additionally, we show that males had 44%, 63%, and 29% higher risk of all-cause death, CV death, and diabetes-related death, respectively. Despite rigorous adjustments for covariates and equitable access to health care at a negligible cost (20,21), SES and sex were robust predictors of CVD disease and mortality in type 1 diabetes; their effect was comparable with that of smoking, which represented an HR of 1.56 (95% CI ) for all-cause death. The fact that the excess risk was not mediated by known risk factors does not imply that risk factor control is less important. On the contrary, stringent risk factor control might be crucial in reducing morbidity and mortality among underprivileged groups, and clinicians should probably aim for rigorous risk factor control for socioeconomically vulnerable individuals with type 1 diabetes. The final solution to these disparities is, however, unlikely to emerge from conventional risk factor control. More individualized management and allocation of resources for clinics and clinicians are important measures, but they will not eliminate the gaps either. These socioeconomic disparities can only be overcome with health policy and societal reforms. Previous studies have shown that SES is associated with glycemic control and risk factors in type 1 diabetes (22 27), but very few studies have examined how SES relates to CVD and death (7 9). These studies reported either a modest effect or no significant effect of SES or they were inadequately adjusted to allow for reliable inferences. Gnavi et al. (8) examined type 1 diabetes and educational level. Individuals with low educational level were three to four times as likely to die as those with high educational level after adjustment for age and neighborhood. Secrest and colleagues studied 318 patients with type 1 diabetes and found that low educational level was associated with higher risk of death and CV complications, but this was not significant after adjusting for confounders and risk factors (7,9). Mühlhauser et al. (28) included 3,674 patients with type 1 diabetes who had participated in an intervention study.

7 1524 Report by the Swedish National Diabetes Register Diabetes Care Volume 38, August 2015 Figure 1 Adjusted HRs for CV events among patients with type 1 diabetes. Two models were computed for each outcome. The minimally adjusted models were identical for each outcome and controlled for age, sex, immigrant status, and duration of diabetes. Fatal/nonfatal CVD was additionally adjusted for smoking, systolic blood pressure, HbA 1c, exercise level, egfr, albuminuria, total cholesterol to HDL ratio, antihypertensive medications, statins, and aspirin. Fatal/nonfatal CHD and fatal/nonfatal stroke were adjusted for the same covariates. Note that the category married includes individuals who were cohabiting (registered as partners). educ., education. The authors constructed a socioeconomic score based on educational level and/or occupation and reported that it was significantly associated with death after adjusting for nephropathy, smoking, diabetes duration, cholesterol, age, sex, and systolic blood pressure. Thus, these studies indicated that education is significantly associated with morbidity and mortality. However, our study shows that the effect of education is much weaker after controlling for income. Our study shows that men with type 1 diabetes are at greater risk of CV events and death compared with women. This should be viewed in the light of a recent meta-analysis of 26 studies, which showed higher excess risk in women compared with men. Overall, women had 40% greater excess risk of all-cause mortality, and twice the excess risk of fatal/nonfatal vascular events, comparedwithmen(29).thus,whereas the excess risk (i.e., the risk of patients with diabetes compared with the nondiabetic population) of vascular disease is higher in women with diabetes, we show that men with diabetes are still at substantially greater risk of all-cause death, CV death, and diabetes death compared with women with diabetes. Other studies are in line with our findings (10,11,13,30 32). Immigrants had lower risk of CVD and death. This contrasts with findings for type 2 diabetes, where immigrants are at higher risk of death (6,33). This might be explained by the healthy immigrant effect (34) either due to healthy behaviors before migrating to a (Western) country with less healthy behaviors or due to immigrant self-selection where healthier and stronger individuals are more likely to migrate. One-third of the immigrants came from a Nordic country, one-third from another European country, and one-third from a non-western country. Unexpectedly, income Q1 had lower HRs for fatal/nonfatal CVD, CHD, and stroke in the minimally as well as maximally adjusted models. This is difficult to explain, and we did not observe this phenomenon for all-cause death, CV death, or diabetes death. However, it suggests that individuals in the lowest income quintile appear to be protected against CVD, CHD, and stroke compared with people having slightly higher income. It is possible that individuals in Q1 receive support from the social support system that alleviates some of the stress associated with low income. It follows that more individuals in Q2 arelikelytobeemployedandexperience work-related stress. It is also possible that individuals in the lowest quintile are at such high risk that they have developed disease early (before

8 care.diabetesjournals.org Rawshani and Associates 1525 Figure 2 Adjusted HRs for death from any cause and death from specific causes among patients with type 1 diabetes. Two models were computed for each outcome. The minimally adjusted models were identical for each outcome and controlled for age, sex, immigrant status, and duration of diabetes. All-cause death and CV death were additionally adjusted for smoking, HbA 1c, exercise level, egfr, CVD, albuminuria, and HF. Diabetesrelated death was additionally adjusted for smoking status, systolic blood pressure, HbA 1c, exercise level, egfr, chronic kidney disease, and albuminuria. Note that the category married includes individuals who were cohabiting (registered as partners). educ., education. start of inclusion) and thus been ineligible for inclusion in our study. Regardless, it is clear from our analyses that income is a strong predictor of morbidity and mortality. Type 1 diabetes demands a great deal of self-care and substantial lifestyle changes, as well as personal, financial, social, and community resources. The causal mechanisms by which SES affects health in type 1 diabetes are diverse. Socioeconomic deprivation triggers a range of chronic stressors, such as unemployment, marital disruption, and financial difficulties. Long-standing socioeconomic disadvantage exhausts coping abilities and causes adverse health behaviors (35,36). Having less education might make it hard to adopt healthy behaviors, partly due to lack of knowledge about their beneficial effects. It may also aggravate attempts to put in place the control mechanisms requiredtoleadahealthylifestyle. Adoption of healthy habits and avoidance of unhealthy ones can be a neverending battle. We found that income appears to have a greater effect than education in type 1 diabetes. The reason might be the immediate opportunities generated by financial privilege: the ability to pay for fitness clubs, enroll in health programs, buy expensive produce, etc., might counteract the hazards that people with low educational levels face (37). Socioeconomically disadvantaged individuals live in communities that often fail to motivate and facilitate healthy behaviors; their neighborhoods feature abundant fast food restaurants and shops that sell cigarettes and alcohol. Lack of social support, cohesion, or positive peer pressure aggravates the situation even further (38). But these factors are modifiable through targeted interventions. To the best of our knowledge, this is the largest study to have explored the association between social factors and serious outcomes in type 1 diabetes. We evaluated extensive clinical data, including 24,947 individuals in Sweden for a mean follow-up of 6 years. We had access to detailed clinical and socioeconomic data with time-updated information. All individuals in our study had equal access to heavily subsidized health care because access to and use of health care in Sweden are extremely equitable (20,21). Insulin treatment is

9 1526 Report by the Swedish National Diabetes Register Diabetes Care Volume 38, August 2015 free of charge, and the social benefits provide almost complete economic coverage for remaining health care and medications, with fixed copays up to a ceiling. These factors are therefore unlikely to have confounded our results more than to a limited extent. We also used time-updated socioeconomic data, which allows for more accurate predictionsofses.measuresfromasingle point in time might not capture actual SES and therefore not be representative of the follow-up. This consideration is particularly important in the kind of young population that we studied (39,40). Limitations of the study deserve mention. Causal inference from observational data are always open to question. The effects associated with socioeconomic factors might be mediated by other factors that are not included in our analyses. We did, however, challenge our models by including other comorbid conditions such as psychiatric diseases and cancer as well as to include profession as a covariate, but no results changed materially. The income variables measured the individuals disposable annual income. It is possible that this variable does not capture the financial resources available to an individual, particularly not household income. To some extent, this should be adjusted for by marital status, which defined individuals who had a partner. In this nationwide study in Swedend where access to and use of health care is very equitable and diabetes care is virtually free of charge and management is well developeddlow income and low educational level were associated with two to three times as great a risk of serious CV events and death in type 1 diabetes. Being male, divorced, single, or widowed was also associated with substantially higher risk of poor outcomes. Although controlling for conventional risk factors did not eliminate these disparities, stringent risk factor control and new approaches to these challenges are warranted. Acknowledgments. The authors thank the various regional NDR coordinators, as well as contributing nurses, physicians, and patients. The authors also thank George Lappas (University of Gothenburg) for invaluable statistical advice. Funding. This study was funded by the Swedish NDR. The Swedish Association of Local Authorities and Regions funds the NDR. The Swedish Diabetes Association and the Swedish Society of Diabetology support the NDR. Duality of Interest. No potential conflicts of interest relevant to this article were reported. Author Contributions. A.Ra., A.-M.S., A.Ro., B.E., and S.G. developed the study concept and design. A.Ra. performed statistical analyses. A.Ra., A.-M.S., A.Ro., B.E., and S.G. interpreted data. A.Ra. wrote the manuscript. 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