Association Between Neighborhood Deprivation and Heart Failure Among Patients With Diabetes Mellitus: A 10-Year Follow-Up Study in Sweden

ARTICLE IN PRESS Journal of Cardiac Failure Vol. 00 No. 00 2019

Association Between Neighborhood Deprivation and Heart Failure Among Patients With Diabetes Mellitus: A 10-Year Follow-Up Study in Sweden XINJUN LI, MD, PhD,1 JAN SUNDQUIST, MD, PhD,1,2,3 PER-OLA FORSBERG, MD,1 AND KRISTINA SUNDQUIST, MD, PhD1,2,3 Malm€ o, Sweden; New York, USA; and Shimane, Japan

ABSTRACT Background: Our aim was to study the potential effect of neighborhood deprivation on incident heart failure (HF) in patients with diabetes mellitus (DM). Methods: The study population included adults (n = 434,542) aged 30 years or older with DM followed from 2005 to 2015 in Sweden for incident HF. The association between neighborhood deprivation and the outcome was explored using Cox regression analysis, with hazard ratios (HRs) and 95% confidence intervals (95% CIs). All models were conducted in both men and women and adjusted for age, educational level, family income, employment status, region of residence, immigrant status, marital status, mobility, and comorbidities. DM patients living in neighborhoods with high or moderate levels of deprivation were compared with those living in neighborhoods with low deprivation scores (reference group). Results: There was an association between level of neighborhood deprivation and HF in DM patients. The HRs were 1.27, 95% CI 1.21 1.33, for men and 1.30, 95% CI 1.23 1.37, for women) among DM patients living in high deprivation neighborhoods compared with those from low deprivation neighborhoods. After adjustments for potential confounders, the higher HRs of HF remained significant: 1.11, 95% CI 1.06 1.16, in men and 1.15, 95% CI 1.09 1.21, in women living in high deprivation neighborhoods. Conclusions: Increased incidence rates of HF among DM patients living in deprived neighborhoods raise important clinical and public health concerns. These findings could serve as an aid to policy-makers when allocating resources in primary health-care settings as well as to clinicians who encounter patients in deprived neighborhoods. (J Cardiac Fail 2019;00:1 7) Key Words: Heart failure, diabetes mellitus, neighborhood, Sweden.

women with DM and a 2.4-fold increased risk in men.1 In a recent Swedish population-based study, the hazard ratio (HR) for HF was 1.45 in patients with DM compared with controls.2 In general, the incidence of HF varies by individual socioeconomic status (SES); higher income has previously been associated with a lower risk of developing HF.3,4 Moreover, risk factors for HF, such as hypertension and coronary heart disease (CHD), also vary with SES. 5 In addition to individuallevel socioeconomic factors, there are also neighborhood-level socioeconomic factors that could increase the risk of DM. Previous studies have shown that the prevalence of type 2 DM is higher in highly deprived than in less deprived or affluent neighborhoods.6 9 Furthermore, it is known that SES is associated with HF. However, the association between neighborhood deprivation and HF in patients with DM remains to be established. If established, such an association would help identify DM patients deemed to be at an increased risk of HF. Therefore, we sought to assess the association between neighborhood deprivation and incident HF in patients diagnosed with or medically treated for DM in a nationwide follow-up study. Our first aim was to investigate whether there is a difference in the risk of incident HF between patients with DM living in

In recent years, the awareness in the scientific community has steadily increased concerning the two-way association between diabetes mellitus (DM) and heart failure (HF) and has also gained more research interest.1,2 DM is an independent risk factor for HF, with a 5-fold increased risk of HF in From the 1Center for Primary Health Care Research, Lund University, Malm€ o, Sweden; 2Department of Family Medicine and Community Health, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, USA and 3Center for Communitybased Healthcare Research and Education (CoHRE), Department of Functional Pathology, School of Medicine, Shimane University, Shimane, Japan. Manuscript received November 15, 2018; revised manuscript received April 7, 2019; revised manuscript accepted April 29, 2019. Reprint requests: Xinjun Li, MD, PhD, Center for Primary Health Care  Research, Lund University, Jan Waldenstr€oms Gata 35, Skane University Hospital, 205 02 Malm€ o, Sweden. E-mail: [email protected] Funding: This work was supported by Crafoordska stiftelsen (20171054) and Stiftelsen Promobilia (15118), the Swedish Research Council, and Forte, ie, the Swedish Research Council for Health, Working Life and Welfare, and by the National Heart, Lung, And Blood Institute of the National Institutes of Health, Award R01HL116381 to K.S. 1071-9164/$ - see front matter © 2019 Elsevier Inc. All rights reserved. https://doi.org/10.1016/j.cardfail.2019.04.017

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ARTICLE IN PRESS 2 Journal of Cardiac Failure Vol. 00 No. 00 2019 deprived neighborhoods and patients with DM living in less deprived/affluent neighborhoods. The second aim was to investigate whether this possible difference remains after accounting for individual-level sociodemographic characteristics (age, marital status, family income, education, employment status, immigration status, region of residence, mobility, and comorbidities). Methods Data used in this study were retrieved from nationwide, comprehensive registers that contain individual-level information on all people in Sweden, including age, sex, SES, geographical region of residence, hospital diagnoses and dates of hospital admissions (1964 2015), date of emigration, and date and cause of death. The unique datasets for this study were constructed using several national Swedish data registers including the Total Population Register, the Swedish Hospital Register (ie, In-Patient Register), and Prescription Register (available between 2005 and 2015). Diagnoses were reported according to the International Classification of Diseases (ICD). All linkages were performed using the national 10-digit civic registration number, which is assigned to each person in Sweden upon birth or immigration to the country. This number was replaced by serial numbers to ensure the integrity of all individuals. The nationwide prescription register was used to identify all individuals aged 30 years and older with medically treated DM. This register includes all medical prescriptions that were retrieved at any pharmacy in Sweden between July 1, 2005 and December 31, 2015. All individuals that were prescribed insulin or oral antidiabetic agents or picked up a prescription for insulin or oral antidiabetic agents during the entire time period between July 1, 2005 and December 31, 2015 were included in the study population. The ATC-codes A10 were used to identify the patients from the prescription register. In addition, we used the main diagnoses for DM recorded in the In-Patient Register. In the present study, the first-time hospital admission for DM was defined as an incident event according to ICD-10 E10-E14 during the study period. We identified unique 466,322 DM patients during the study period and excluded 11,875 (2.6%) individuals who had previously been diagnosed with HF (1997 2004) and 9125 individuals (2.0%) who were diagnosed with HF before the first diagnosis of DM during the study period. To remove possible coding errors, we also excluded 10,790 (2.3%) individuals who had a reported emigration date before the HF diagnosis. A total of 434,542 DM patients (93.2% of the original cohort) remained suitable for inclusion in the study. Outcome variables: the Swedish Hospital Discharge/InPatient register was used to identify the outcome variable of HF, ICD-10 I50, incident HF. Incident HF was defined as the first hospitalization for HF during the study period, after excluding individuals with preexisting disease. Explanatory Variables

All individual-level variables were assessed on December 31, 2005. Separate analyses were conducted for

women and men. Age was used as a continuous variable from age 30 years. Marital status was divided into 2 groups: 1) married/cohabitating, and 2) never married, widowed, or divorced. Educational attainment was divided into 3 groups based on: completion of compulsory school or less (<9 years), practical high school or some theoretical high school (10 12 years), or theoretical high school and/ or college (>12 years). Immigration status was divided into 2 groups: 1) born in Sweden and 2) born outside Sweden. Mobility (moved) was based on the length of time lived in the neighborhood, categorized as <5 years or 5 years. Region of residence was divided into 3 groups: large cities (Stockholm, Gothenburg, and Malm€o), middle-sized towns, and small towns/rural areas. Employment status was divided into 2 groups: employed or unemployed. Comorbidities were identified from the Swedish inpatient and outpatient registers as follows: hypertension (I10 I15); CHD (I20 I25); obesity (E65 E68); chronic obstructive pulmonary disease (COPD; J40 J47); alcoholism and related liver disorders (F10 and K70); depression (F32); and stroke (I60-I69). Family income was based on the annual family income divided by the number of people in the family, ie, individual family income per capita. This variable was provided by Statistics Sweden (the Swedish Governmentowned statistics bureau). The income parameter also took into consideration the ages of people in the family and used a weighted system whereby small children were given lower weights than adolescents and adults. The calculation procedure was performed as follows: the sum of all family members’ incomes was multiplied by the individual’s consumption weight divided by the family members’ total consumption weight. Neighborhood-Level Variable. The home addresses of all Swedish adults have been geocoded to small geographic administrative units that have boundaries defined by homogeneous types of buildings. These neighborhood units, which are called small area market statistics (SAMS), have an average of 1000 2000 people and were used as proxies for neighborhoods, as has been done in previous research. 10 Neighborhood Deprivation Index:a summary measure was used to characterize neighborhood-level deprivation. We identified deprivation indicators used by past studies to characterize neighborhood environments and then used a principal components analysis to select deprivation indicators in the Swedish national database.11 The following 4 variables were selected for those aged 25 64: low educational status (<10 years of formal education); low income (income from all sources, including that from interest and dividends, defined as <50% of individual median income)12; unemployment (not employed, excluding full-time students, those completing compulsory military service, and early retirees); and social welfare assistance. The neighborhood deprivation index was calculated based on the population aged 25 64 years because this age group (ie, the working population) was considered to be more socioeconomically active than other age groups. The study population, however, consisted of individuals aged 30 years and older. Each of the 4

ARTICLE IN PRESS Neighbourhood Deprivation and Heart Failure  LI et al 3 deprivation variables loaded on the first principal component with similar loadings (+.47 to +.53) and explained 52% of the variation between these variables. A z score was calculated for each SAMS neighborhood. The z scores, weighted by the coefficients for the eigenvectors, were then summed to create the index.13 The index was categorized into 3 groups: below 1 SD from the mean (low deprivation), >1 SD from the mean (high deprivation), and within 1 SD of the mean (moderate deprivation). Higher scores reflect more deprived neighborhoods. Using this categorization, 1383 neighborhoods were categorized as low deprivation (13.3% of the study population), 4791 as moderate (67.4% of the study population), and 1093 as high deprivation neighborhoods (19.3% of the study population; Supplementary Table S1). Statistical Analysis. Person-years were calculated from the start of follow-up until first hospitalization for HF, death, emigration, or the end of the study on December 31, 2015. The associations between the individual variables and HF were analyzed with Cox regression models. Cox proportional hazard models are used to study the association between certain variables and the time it takes for a specified event to happen, in this case the first/incident event of HF. The stratified Cox proportional hazards model provides a HR for HF that is adjusted for the individual variables. First, a univariate Cox regression was performed for each variable. Next, a multivariate Cox regression model including all variables was calculated. Interaction tests were performed to examine whether the association between neighborhood deprivation and HF among DM patients was affected by any of the individual variables. All statistical analyses were performed using SAS 9.3. Ethical Considerations. This study was approved by the Ethics Committee at Lund University. Results Table 1 shows the study population comprising a total of 434,542 DM patients, number of HF incident events, and incidence of HF in the DM patients by neighborhood-level deprivation. During the follow-up (mean follow-up = 6 years), there were 26,511 and 20,772 HF incident events among the men and women with DM, respectively. There was an apparent gradient for the incidence rate; the HF incidence became higher by increasing neighborhood-level deprivation. The same pattern appeared in most subgroups. The proportion of patients affected with HF increased among individuals living in high-deprivation neighborhoods. Fig. 1 shows the Kaplan Meir curves for the duration of survival until the first incident HF by different levels of neighborhood deprivation. A graded effect appeared with a poorer prognosis for those with a high level of neighborhood deprivation. Table 2 shows the HRs for HF in men. The results indicate the presence of a gradient where HF incidence became greater with increasing neighborhood deprivation. For men, the HRs were 1.14 (95% CI = 1.10 1.19) and 1.27 (95% CI = 1.21 1.33) in moderate and high deprivation neighborhoods, respectively. The results of the full

model show that the HRs decreased, after adjustment for the individual-level variables; the HRs in the full model remained, however, significant in both moderate-deprivation neighborhoods (HR = 1.08, 95% CI = 1.04 1.12) and high-deprivation neighborhoods (HR = 1.11, 95% CI = 1.06 1.16). Table 3 shows the HRs for HF in women; the corresponding figures of HF for women were 1.16 (95% CI = 1.10 1.21) and 1.30 (95% CI = 1.23 1.37). The results of the full model show that the HRs decreased, after adjustment for the individuallevel variables; the HRs in the full model remained, however, significant in both moderate-deprivation neighborhoods (HR = 1.10, 95% CI = 1.05 1.16) and high-deprivation neighborhoods (HR = 1.15, 95% CI = 1.09 1.21). There was a clear and consistent positive association between neighborhood deprivation and HF in all socioeconomic groups, ie, any moderation by individual SES ought to be minor and the potential interactions do not seem to be clinically meaningful. Some of the individual-level variables were significantly associated with HF in the full models. The HRs for HF were higher for men and women with low education, low family income, country of birth outside Sweden, or those who had moved or had a hospitalization for comorbidities (Supplementary Table S2). Discussion The main finding of this study was that the risk of incident HF is higher among patients with DM living in deprived neighborhoods than among patients with DM living in less deprived/affluent neighborhoods. This difference was attenuated but remained significant, after adjustment for the individual-level sociodemographic variables and traditional cardiovascular risk factors (COPD, alcoholism, and related liver disorders, diabetes, obesity). It is important to note that a significant number of individuals changed their place of residence, and level of neighborhood deprivation, during the follow-up. Many of the individuals were, however, elderly and it was to be expected that some of them would downsize by moving from their larger house to an apartment, such as in the case of being widowed. We adjusted our analyses for the move of participants to a neighborhood of differing level of deprivation, and the relationship between neighborhood deprivation and HF remained significant in the DM patients. Living in highly deprived neighborhoods has been shown to be associated with an increased risk of morbidities, such as that of coronary heart disease,11 and DM.14 In our study, we found that the incidence rates of HF in DM patients increased with the level of neighborhood deprivation. The causal pathways between neighborhood deprivation and cardiovascular health outcomes are, however, not fully understood.10,15 17 Several possible mechanisms could, however, explain our findings. One possible mechanism is the potential differences between socioeconomic groups in knowledge, attitudes, and beliefs that could lead to

ARTICLE IN PRESS 4 Journal of Cardiac Failure Vol. 00 No. 00 2019 Table 1. Distribution of Population, Number of Incident HF, Cumulative Rates (%) of Incident HF in Diabetes Patients, 2005 2015, Sweden Population No. Total population Total incident HF Gender Males Females Age (years) 30 39 40 49 50 59 60 69 70 79 80 Education attainment (years) 9 10 12 >12 Family income Middle-high income Middle-low income Low income High income Region of residence Large cities Southern Sweden Northern Sweden Marital status Married/cohabiting Not married Mobility Not moved Moved Employment status Yes No Hospitalization of COPD No Yes Hospitalization of alcoholism and related liver disorders No Yes Hospitalization of CHD No Yes Hospitalization of obesity No Yes Hospitalization of hypertension No Yes Hospitalization of depression No Yes Hospitalization of stroke No Yes

%

Incident HF No.

%

434,542 47,283

Rate (%) of HF by neighborhood deprivation Low

Moderate

High

P Value

57,890 (13.3%) 5357 (11.3%)

292,812 (67.4%) 32,423 (68.6%)

83,840 (19.3%) 9503 (20.1%) .9810

239,567 194,975

55.1 44.9

26,511 20,772

56.1 43.9

9.7 8.6

11.3 10.8

11.3 11.3

24,192 49,390 95,456 119,581 93,204 52,719

5.6 11.4 22.0 27.5 21.4 12.1

235 1192 4476 11,397 17,435 12,548

0.5 2.5 9.5 24.1 36.9 26.5

0.7 2.0 3.6 7.9 16.9 22.9

0.9 2.4 4.7 9.4 18.7 23.9

1.2 2.8 5.5 11.3 19.9 24.0

188,398 131,177 114,967

43.4 30.2 26.5

27,110 11,366 8807

57.3 24.0 18.6

13.4 8.2 6.6

14.6 8.6 7.7

14.2 9.1 8.6

108,017 109,096 109,064 108,365

24.9 25.1 25.1 24.9

11,974 15,075 12,090 8144

25.3 31.9 25.6 17.2

10.5 11.4 10.0 6.9

11.5 14.1 11.2 7.6

10.0 14.1 11.5 8.2

111,226 229,331 93,985

25.6 52.8 21.6

11,393 25,573 10,317

24.1 54.1 21.8

9.1 9.4 9.1

10.5 11.3 11.1

10.3 12.0 12.0

231,230 203,312

53.2 46.8

23,050 24,233

48.7 51.3

8.5 10.6

10.2 12.1

10.5 12.1

306,420 128,122

70.5 29.5

29,426 17,857

62.2 37.8

8.0 12.6

9.7 14.4

10.4 13.2

161,007 273,535

37.1 62.9

6163 41,120

13.0 87.0

8.7 10.6

10.6 12.3

5.6 10.6

399,091 35,451

91.8 8.2

38,971 8312

82.4 17.6

8.3 21.7

10.0 23.4

10.0 24.4

420,106 14,436

96.7 3.3

46,018 1265

97.3 2.7

9.2 10.9

11.2 8.5

11.5 8.5

328,138 106,404

75.5 24.5

20,162 27,121

42.6 57.4

5.0 23.6

6.3 25.6

6.4 26.4

410,897 23,645

94.6 5.4

44,570 2713

94.3 5.7

9.2 10.8

11.1 11.4

11.3 12.0

259,162 175,380

59.6 40.4

20,370 26,913

43.1 56.9

6.3 13.3

8.1 15.4

8.0 16.5

419,437 15,105

96.5 3.5

45,822 1461

96.9 3.1

9.2 9.5

11.1 10.1

11.5 8.8

380,176 54,366

87.5 12.5

37,465 9818

79.2 20.8

8.2 17.3

10.1 18.1

10.3 18.5

<.001

<.001

<.001

<.001

<.001 <.001 <.001 .0326 <.001

<.001 <.001 <.001 .1680 .0528

differences in lifestyle in DM patients; these differences may partly explain differences in morbidity risk across socioeconomic strata.16,18,19 For instance, a United Kingdom study showed that cardiovascular disease risk factors, including obesity and smoking, were more common among patients with DM living in deprived neighborhoods than

among those living in less deprived/affluent neighborhoods.18 Similar results were found in another neighborhood study of cardiovascular disease risk factors among patients with DM.19 A Swedish study showed that cardiovascular disease risk factors, including physical inactivity, obesity, and smoking, were more common among individuals living in

ARTICLE IN PRESS Neighbourhood Deprivation and Heart Failure  LI et al 5

Fig. 1. The Kaplan Meier curves for the duration of survival until first diagnosis of/incident HF in DM patients by different levels of neighborhood deprivation.

deprived neighborhoods than among those living in less deprived/affluent neighborhoods.16 It is possible that sociocultural norms regarding diet, smoking, and physical activity could vary between neighborhoods and affect the health of the residents and the consequent risk for disease. HF is one of the most common comorbidities of DM. Glucose-lowering therapies that can prevent HF or improve outcomes in patients with established HF are of critical importance among patients with DM.20 Although Sweden

has a universal health care system, it is possible that there still are differences between neighborhoods regarding the access of glucose-lowering therapies affecting HF risk of DM. These differences could be related both to individual socioeconomic differences that may affect people’s possibilities to buy prescribed medicine21 and poorer access to primary health care in deprived neighborhoods.22 The findings of previous studies together with the findings of the present study illuminate the need for improving health in

Table 2. HR and 95% CI for Incident HF in Men; Results of Cox Regression Models Model 1

Neighborhood deprivation (ref. Low) Moderate High Age Family income (ref. Highest quartiles) Middle-high income Middle-low income Low income Education attainment (ref. 12 years) 9 years 10 11 years Country of origin (ref. Sweden) Marital status (ref. Married/cohabiting) Region of residence (ref. Large cities) Southern Sweden Northern Sweden Mobility (ref. Not moved) Employment status (ref. Yes) Hospitalization of COPD (ref. Non) Hospitalization of alcoholism and related liver disorders (ref. Non) Hospitalization of obesity (ref. Non) Hospitalization of depression (ref. Non) Hospitalization of hypertension (ref. Non) Hospitalization of CHD (ref. Non) Hospitalization of stroke (ref. Non)

HR

95% CI

1.14 1.27 1.07

1.10 1.21 1.07

1.31 1.34 1.26

Model 2 HR

95% CI

1.19 1.33 1.08

1.08 1.12 1.06

1.04 1.07 1.06

1.26 1.29 1.22

1.36 1.38 1.30

1.11 1.09 1.10

1.25 1.20 1.10 1.20

1.21 1.16 1.06 1.17

1.29 1.25 1.14 1.23

0.95 0.94 1.64 1.59 2.45 1.41 2.50 1.27 1.68 3.36 1.25

0.92 0.90 1.60 1.53 2.37 1.33 2.37 1.19 1.64 3.27 1.21

0.98 0.97 1.68 1.65 2.53 1.50 2.64 1.37 1.72 3.44 1.29

Model 3 HR

95% CI

1.12 1.17 1.06

1.08 1.11 1.06

1.04 1.06 1.06

1.12 1.16 1.06

1.07 1.05 1.06

1.16 1.13 1.14

1.16 1.11 1.09

1.11 1.07 1.05

1.21 1.15 1.13

1.19 1.15 1.02 1.12

1.15 1.11 0.98 1.09

1.23 1.19 1.06 1.14

1.14 1.10 0.98 1.17

1.11 1.07 0.94 1.14

1.18 1.14 1.02 1.20

0.96 0.94 1.60 1.48

0.93 0.90 1.56 1.43

0.99 0.97 1.65 1.54

0.99 0.95 1.63 1.26 2.02 1.20 1.95 1.01 1.39 3.06 1.09

0.96 0.91 1.59 1.21 1.96 1.13 1.85 0.94 1.35 2.98 1.05

1.02 0.98 1.67 1.31 2.09 1.28 2.06 1.08 1.42 3.14 1.12

Model 1, Univariate model, adjusted for age; Model 2, Adjusted for individual characteristics; Model 3, Full model.

ARTICLE IN PRESS 6 Journal of Cardiac Failure Vol. 00 No. 00 2019 Table 3. HR and 95% CI for Incident HF in Women; Results of Cox Regression Models Model 1

Neighborhood deprivation (ref. Low) Moderate High Age Family income (ref. Highest quartiles) Middle-high income Middle-low income Low income Education attainment (ref. 12 years) 9 years 10 11 years Country of origin (ref. Sweden) Marital status (ref. Married/cohabiting) Region of residence (ref. Large cities) Southern Sweden Northern Sweden Mobility (ref. Not moved) Employment (ref. Yes) Hospitalization of COPD (ref. Non) Hospitalization of alcoholism and related liver disorders (ref. Non) Hospitalization of obesity (ref. Non) Hospitalization of depression (ref. Non) Hospitalization of hypertension (ref. Non) Hospitalization of CHD (ref. Non) Hospitalization of stroke (ref. Non)

HR

95% CI

1.16 1.30 1.08

1.10 1.23 1.07

1.23 1.34 1.18

Model 2 HR

95% CI

1.21 1.37 1.08

1.11 1.17 1.06

1.06 1.11 1.06

1.16 1.27 1.11

1.30 1.42 1.25

1.08 1.13 1.08

1.25 1.11 1.16 1.19

1.20 1.06 1.12 1.15

1.30 1.17 1.21 1.22

0.98 0.96 1.63 1.93 2.63 1.39 2.14 1.28 1.65 3.18 1.36

0.95 0.92 1.58 1.82 2.54 1.22 2.01 1.19 1.60 3.09 1.32

1.01 1.00 1.68 2.05 2.72 1.59 2.27 1.38 1.69 3.27 1.41

Model 3 HR

95% CI

1.16 1.24 1.06

1.10 1.15 1.06

1.05 1.09 1.06

1.16 1.21 1.06

1.01 1.06 1.01

1.14 1.20 1.15

1.10 1.10 1.05

1.03 1.04 0.98

1.17 1.17 1.12

1.19 1.10 1.10 1.13

1.14 1.05 1.05 1.10

1.25 1.15 1.15 1.17

1.12 1.01 1.04 1.14

1.07 0.96 1.00 1.11

1.17 1.06 1.09 1.18

1.00 0.99 1.63 1.86

0.97 0.95 1.58 1.75

1.04 1.03 1.68 1.98

1.03 0.99 1.64 1.51 2.17 1.11 1.74 1.05 1.37 2.79 1.19

0.99 0.95 1.59 1.42 2.10 0.97 1.64 0.98 1.34 2.71 1.15

1.07 1.03 1.69 1.60 2.25 1.27 1.85 1.14 1.41 2.87 1.23

Model 1, Univariate model, adjusted for age; Model 2, Adjusted for individual characteristics, Model 3: Full model.

low resource settings, which is underway in Europe.23 For example, a United Kingdom study showed social differences in both the prevalence of DM as well as impaired glucose regulation.24 There are also other potential mechanisms behind our findings. The levels of social capital, which in turn are related to social norms, beliefs, and attitudes, are lower in deprived neighborhoods.25 The crime levels are also higher in deprived neighborhoods,26 which could increase psychosocial stress and reduce physical activity due to fear of going outside. It has also been suggested that neighborhood goods, services, and resources are poorer in deprived neighborhoods. However, a previous study of ours showed that the availability of potentially health-promoting goods, services, and resources is higher, not lower, in deprived neighborhoods.27 Our study has a number of strengths. Our large cohort included practically all patients with diabetes (30 years and older) in Sweden during the study period, which increases the generalizability of our results. Another strength is the personal identification number that is assigned to each individual in Sweden. This gave us the opportunity to follow the patients without any loss to follow-up. The outcome data were based on clinical diagnoses, registered by physicians, rather than self-reported data, which eliminated any recall bias. An additional key strength was the access to SAMS units that defined geographic boundaries of our study neighborhoods. The SAMS units were small (in the order of 1000 2000 persons) and each unit consisted of relatively homogenous types of buildings. In previous research, small neighborhoods have been shown to correspond well with

how the residents define their neighborhoods.28 Moreover, our data were highly complete; only 0.6% of the patients with diabetes were excluded because of missing SAMS codes. We were able to link clinical data from individual patients to national demographic and socioeconomic data. The national demographic and socioeconomic data were highly complete; less than 1% of the data were missing. Our study also has some limitations. We had no data on several risk factors for HF, such as smoking, high-caloric diet, or physical inactivity. However, some prior works on SES and HF risk have adjusted for smoking and physical inactivity and still found an independent association.3,29 We had no data on quality of health care in the neighborhood. Finally, the outcome variable (HF) was based only on hospitalizations. Conclusions The findings of the present study are useful for healthcare workers encountering patients with DM and particularly those living in deprived neighborhoods. Understanding the pathways between neighborhood factors (independent of individual factors) and various health outcomes is challenging. Future research could focus on the specific pathways between neighborhood environments and HF, and how to reduce differences in HF among patients with DM living in various types of neighborhood environments. Such research is needed in order to identify those mechanisms that may result in efficient preventive strategies and health policies in the future.

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