Open Access

Cross-country differences in the association between diabetes and disability

  • Shervin Assari1, 2Email author,
  • Reza Moghani Lankarani3 and
  • Maryam Moghani Lankarani4, 5
Journal of Diabetes & Metabolic Disorders201413:3

DOI: 10.1186/2251-6581-13-3

Received: 23 July 2013

Accepted: 14 October 2013

Published: 6 January 2014

The Erratum to this article has been published in Journal of Diabetes & Metabolic Disorders 2014 13:73

Abstract

Purpose

This study tested possible cross-country differences in the associations between diabetes and activities of daily living (ADLs), and possible confounding / mediating effects of socio-economic status, obesity, and exercise.

Methods

Data came from Research on Early Life and Aging Trends and Effects (RELATE). The study included a total number of 25,372 community sample of adults who were 40 years or older. We used data from community based surveys in seven countries including China, Mexico, Barbados, Brazil, Chile, Cuba, and Uruguay. Demographics (age and gender), socio-economic status (education and income), obesity, exercise, and ADL (bath, dress, toilet, transfer, heavy, shopping, meals) were measured. Self-reported data on physician diagnosis of diabetes was the independent variable. We tested if diabetes is associated with ADL, before and after adjusting for socio-economics, obesity, and exercise in each country.

Results

Based on Model I (age and gender adjusted model), diabetes was associated with limitation in at least one ADL in Mexico, Barbados, Brazil, Chile, Cuba, and Uruguay, but not China. Based on Model II that also controlled for education and income, education explained the association between diabetes and limitation in ADL in Mexico and Uruguay. Based on Model III that also controlled for exercise and obesity, in Cuba and Brazil, exercise explained the link between diabetes and limitation in performing ADLs. Thus, the link between diabetes and ADL was independent of our covariates only in Chile and Barbados.

Conclusions

There are cross-country differences in the link between diabetes and limitation in ADL. There are also cross-country differences in how socio-economic status, obesity, and exercise explain the above association.

Keywords

Diabetes Socio-economics Disability Exercise Obesity Cross country study

Introduction

About three hundred fifty million people have diabetes worldwide [1]. With a direct medical cost of 465 billion U.S. dollars in the year 2011, diabetes is responsible for more than 10% of total healthcare expenditures in adults [2].

People with diabetes are more likely to experience limitations in activities of daily living (ADL), mobility, and role functioning [35]. Given the growing rate of diabetes and its associated disability burden in the world [2], for different reasons, more research is still needed on the link between diabetes and ADLs [6].

First, research on disability among people with diabetes may suggest avenues for reducing the disability attributable to this common chronic disease [3]. Second, although diabetes has been consistently related to a broad spectrum of health outcomes, including quality of life, ADL and mobility disability [7], and lower extremity function [8], it is not clear if the association between diabetes and ADL remains significant after controlling for other factors, and also it is not clear if the same link exits in different countries [9]. Third, a large proportion of a major part of evidence on the link between diabetes and burden of mobility-related disability has originated from studies conducted on patients, or disabled individuals [9]. However, clinical samples and community individuals vary on many factors and there is a need to show the ADLs among individuals with diabetes in a community sample. Finally, more research is needed on cross-country differences in associations between diabetes and disabilities.

Although literature has frequently shown that diabetes has a role in the disablement of people, further research is needed to extend the current limited knowledge about this association [9]. The current study tested cross-country differences in association between diabetes and ADLs.

Methods

Data came from Research on Early Life and Aging Trends and Effects (RELATE), that included seven different studies [10]. This analysis included 25,372 individuals who were age 40 or older. These individuals were sampled in the following seven countries: China, Mexico, Barbados, Brazil, Chile, Cuba, and Uruguay. All surveys were fully in compliance with the Helsinki Declaration on ethical principles for medical research involving humans. Different institutional review boards approved participating surveys.

The following countries were participating in RELATE project and represented countries from a diverse range in national income levels: Barbados represents high income countries; Cuba, Uruguay, Chile, Brazil, and Mexico represent upper middle income countries; and China represents lower middle income countries [11].

Although most country-specific surveys had sampling weights, as recommended, the current study did not apply sampling weights. The main reason was that sampling weights were not applicable to data from China (China Health and Nutrition Study; CHNS).

Measures

Demographic characteristics

The study measured age (continuous variable) and gender (dichotomous variable of male and female) as two demographic variables.

Socio-economic status

The study measured education (four level categorical variable of 1) no schooling, 2) primary to elementary, 3) secondary to intermediate, and 4) higher), and income(continuous variable) as socio-economic status.

Obesity and exercise

The study measured obesity and exercise based on self reported data. BMI larger than 30 was considered as obesity.

Main outcome

Seven activities of daily living (ADLs) were measured by a modified Barthel index [1215]. ADL items that were included in this study included bath, dress, toilet, transfer, heavy, shopping, and meals. These items have been frequently used to assess ADLs in the community sample [16, 17].

Data analysis

Data analysis was conducted using SPSS for Windows. We used country specific logistic regressions to determine if diabetes is associated with ADLs, and if this link is independent of socio-economic factors (education, and income), obesity and exercise. In all models, at least one ADL limitation was our outcome. In Model I, we only adjusted for age and gender. In Model II, we also adjusted for education and income. In Model III, we also adjusted for obesity and exercise.

Results

Table 1 shows country differences by means of mean age, education, income, and ADLs. Mean age was lowest in China, and highest in Brazil. ADL limitation was highest in Chile, and lowest in Uruguay. Education was lowest in Brazil, and highest in Cuba. Income was highest in Uruguay and lowest in Cuba.
Table 1

Age, income, education, and activities of daily living in different countries among 25,372 community sample of adults

 

Min

Max

Mean

SD

China

    

Age

40

100

60.60

10.30

Income

0

500000

8645.24

15404.23

Education

1

4

2.18

0.85

Activities of daily living

0

7

0.63

1.35

Mexico

    

Age

40

97

68.81

8.41

Income

0

714285

11716.48

29825.89

Education

1

4

2.02

0.75

Activities of daily living

0

7

0.71

1.23

Barbados

    

Age

60

97

72.17

8.13

Income

0

804800

9321.03

37361.75

Education

1

4

2.22

0.56

Activities of daily living

0

7

0.32

0.84

Brazil

    

Age

60

100

72.94

8.26

Income

0

99900

3705.36

7495.84

Education

1

4

1.83

0.68

Activities of daily living

0

7

0.84

1.36

Chile

    

Age

60

99

71.44

8.00

Income

0

989232

279282.30

266840.20

Education

1

4

2.19

0.81

Activities of daily living

0

7

0.92

1.49

Cuba

    

Age

60

102

71.63

8.66

Income

0

109992

1456.25

5071.77

Education

1

4

2.46

0.70

Activities of daily living

0

7

0.69

1.28

Uruguay

    

Age

60

97

70.67

7.28

Income

0

960000

39607.53

71022.13

Education

1

4

2.37

0.78

Activities of daily living

0

7

0.46

0.95

Model I

Based on the first model, with an exception of China, diabetes was associated with ADL, and this link was independent of age and gender. Gender and age were associated with ADL in all countries (Table 2).
Table 2

Results of models I, with age and gender as covariates among 25,372 community sample of adults

      

95% C.I. for EXP(B)

 

B

S.E.

Wald

Sig.

Exp(B)

Lower

Upper

China

       

Diabetes

.046

.057

0.636

.425

1.047

0.936

1.171

Age

.119

.002

4322.269

< .001

1.126

1.122

1.13

Female gender

.823

.038

474.844

< .001

2.277

2.115

2.452

Mexico

       

Diabetes

.294

.149

3.866

.049

1.342

1.001

1.798

Age

.073

.008

78.043

< .001

1.075

1.058

1.093

Female gender

1.087

.134

66.085

< .001

2.966

2.282

3.855

Barbados

       

Diabetes

.575

.151

14.59

< .001

1.778

1.323

2.389

Age

.064

.008

65.319

< .001

1.066

1.05

1.083

Female gender

.625

.143

19.177

< .001

1.869

1.413

2.472

Brazil

       

Diabetes

.298

.121

6.036

.014

1.347

1.062

1.708

Age

.075

.006

164.576

< .001

1.078

1.066

1.09

Female gender

.973

.098

98.472

< .001

2.646

2.183

3.207

Chile

       

Diabetes

.372

.176

4.494

.034

1.451

1.029

2.048

Age

.065

.008

69.835

< .001

1.067

1.051

1.083

Female gender

1.095

.134

66.678

< .001

2.99

2.299

3.89

Cuba

       

Diabetes

.378

.138

7.532

.006

1.459

1.114

1.91

Age

.062

.006

112.843

< .001

1.064

1.052

1.076

Female gender

1.009

.112

80.636

< .001

2.742

2.2

3.417

Uruguay

       

Diabetes

.323

.172

3.532

.060

1.382

0.986

1.936

Age

.06

.008

54.291

< .001

1.062

1.045

1.079

Female gender

.785

.133

34.711

< .001

2.193

1.689

2.847

Model II

Based on Model II that also controlled for education and income, education explained the association between diabetes and limitation in ADL in Mexico and Uruguay. Based on this model, gender and age were associated with ADL in all countries. However, education was not correlated with ADL in Barbados, Cuba, and Uruguay. Income was linked to ADL in China and Brazil (Table 3).
Table 3

Results of models II, with age, gender and socio-economic status as covariates among 25,372 community sample of adults

      

95% C.I. for EXP(B)

 

B

S.E.

Wald

Sig.

Exp(B)

Lower

Upper

China

       

Diabetes

.062

.059

1.081

.299

1.063

.947

1.194

Age

.118

.002

4050.944

.000

1.125

1.121

1.129

Female gender

.730

.042

305.224

< .001

2.075

1.912

2.252

Education

-.169

.026

41.884

< .001

.844

.802

.889

Income

.000

.000

4.355

.037

1.000

1.000

1.000

Mexico

       

Diabetes

.236

.151

2.438

.118

1.266

.942

1.701

Age

.070

.008

71.441

< .001

1.073

1.056

1.091

Female gender

1.045

.135

60.226

< .001

2.843

2.184

3.701

Education

-.250

.078

10.385

.001

.779

.669

.907

Income

.000

.000

.269

.604

1.000

1.000

1.000

Barbados

       

Diabetes

.666

.163

16.737

< .001

1.947

1.415

2.680

Age

.064

.009

52.773

< .001

1.066

1.047

1.084

Female gender

.588

.159

13.715

< .001

1.800

1.319

2.457

Education

-.137

.122

1.259

.262

.872

.687

1.107

Income

.000

.000

1.540

.215

1.000

1.000

1.000

Brazil

       

Diabetes

.290

.123

5.562

.018

1.336

1.050

1.701

Age

.070

.006

135.643

< .001

1.072

1.060

1.085

Female gender

.923

.099

86.452

< .001

2.516

2.071

3.056

Education

-.237

.069

11.764

< .001

.789

.689

.903

Income

.000

.000

18.748

< .001

1.000

1.000

1.000

Chile

       

Diabetes

.392

.180

4.750

.029

1.480

1.040

2.105

Age

.065

.008

67.121

< .001

1.067

1.051

1.084

Female gender

1.070

.137

60.798

< .001

2.917

2.229

3.817

Education

-.248

.062

16.089

< .001

.781

.692

.881

Income

.000

.000

1.143

.285

1.000

1.000

1.000

Cuba

       

Diabetes

.381

.138

7.578

.006

1.463

1.116

1.919

Age

.058

.006

93.065

< .001

1.059

1.047

1.072

Female gender

.980

.113

75.287

< .001

2.666

2.136

3.327

Education

-.222

.073

9.251

.002

.801

.694

.924

Income

.000

.000

3.320

.068

1.000

1.000

1.000

Uruguay

       

Diabetes

.227

.183

1.541

.214

1.255

.877

1.798

Age

.052

.009

36.573

< .001

1.054

1.036

1.072

Female gender

.820

.142

33.140

< .001

2.270

1.717

3.000

Education

-.238

.074

10.441

.001

.788

.682

.911

Income

.000

.000

.178

.673

1.000

1.000

1.000

Model III

Based on Model III that also controlled for obesity and exercise, in Cuba and Brazil, exercise explained the link between diabetes and limitation in performing ADLs. Based on this model, the link between diabetes and ADL was independent of our covariates only in Chile and Barbados. Based on this model, gender and age were associated with ADL in all countries. Obesity was linked to ADL only in Barbados. Exercise was linked to ADL in all countries (Table 4).
Table 4

Results of models III, with age, gender, socio-economic status, exercise and obesity as covariates among 25,372 community sample of adults

      

95% C.I. for EXP(B)

 

B

S.E.

Wald

Sig.

Exp(B)

Lower

Upper

China

       

Diabetes

.036

.316

.013

.909

1.037

.558

1.926

Age

.103

.009

134.296

< .001

1.108

1.089

1.128

Female gender

.836

.136

37.870

< .001

2.307

1.768

3.011

Education

-.128

.054

5.520

.019

.880

.791

.979

Income

.000

.000

3.445

.063

1.000

1.000

1.000

Exercise

-.711

.160

19.680

< .001

.491

.359

.673

Obesity

-.390

.326

1.427

.232

.677

.357

1.284

Mexico

       

Diabetes

.185

.166

1.236

.266

1.203

.868

1.668

Age

.062

.009

42.516

< .001

1.064

1.044

1.084

Female gender

1.042

.153

46.221

< .001

2.836

2.100

3.830

Education

-.238

.089

7.106

.008

.788

.661

.939

Income

.000

.000

.003

.953

1.000

1.000

1.000

Exercise

-.569

.160

12.663

< .001

.566

.413

.774

Obesity

.079

.154

.260

.610

1.082

.800

1.463

Barbados

       

Diabetes

.438

.178

6.072

.014

1.550

1.094

2.196

Age

.053

.010

27.477

< .001

1.054

1.034

1.075

Female gender

.478

.176

7.411

.006

1.613

1.143

2.275

Education

-.038

.131

.084

.772

.963

.744

1.245

Income

.000

.000

2.459

.117

1.000

1.000

1.000

Exercise

-.602

.176

11.730

.001

.548

.388

.773

Obesity

.513

.185

7.716

.005

1.671

1.163

2.400

Brazil

       

Diabetes

.125

.137

.831

.362

1.133

.866

1.484

Age

.061

.007

78.208

< .001

1.063

1.048

1.077

Female gender

.974

.114

72.657

< .001

2.649

2.117

3.314

Education

-.210

.081

6.757

.009

.811

.692

.950

Income

.000

.000

9.882

.002

1.000

1.000

1.000

Exercise

-1.075

.143

56.711

< .001

.341

.258

.451

Obesity

.198

.132

2.235

.135

1.219

.940

1.580

Chile

       

Diabetes

.413

.188

4.848

.028

1.512

1.046

2.183

Age

.061

.008

52.554

< .001

1.063

1.045

1.080

Female gender

1.068

.145

54.130

< .001

2.911

2.190

3.870

Education

-.213

.064

11.033

.001

.808

.713

.916

Income

.000

.000

.587

.443

1.000

1.000

1.000

Exercise

-.764

.168

20.648

< .001

.466

.335

.648

Obesity

.012

.141

.007

.933

1.012

.767

1.335

Cuba

       

Diabetes

.278

.149

3.472

.062

1.320

.986

1.768

Age

.047

.007

50.995

< .001

1.048

1.035

1.061

Female gender

.956

.124

59.358

< .001

2.601

2.040

3.318

Education

-.132

.080

2.736

.098

.876

.749

1.025

Income

.000

.000

1.888

.169

1.000

1.000

1.000

Exercise

-.729

.149

23.980

< .001

.483

.361

.646

Obesity

.263

.154

2.907

.088

1.301

.961

1.760

Uruguay

       

Diabetes

.155

.197

.623

.430

1.168

.794

1.718

Age

.047

.009

25.884

< .001

1.049

1.030

1.068

Female gender

.724

.156

21.482

< .001

2.062

1.518

2.801

Education

-.196

.081

5.878

.015

.822

.701

.963

Income

.000

.000

.382

.536

1.000

1.000

1.000

Exercise

-1.413

.282

25.015

< .001

.244

.140

.424

Obesity

.112

.145

.594

.441

1.118

.842

1.485

Discussion

Our study showed at least four important cross-country differences in the pattern of association between diabetes and disability; 1) in most (i.e. Mexico, Barbados, Brazil, Chile, Cuba, and Uruguay) but not all countries (China), diabetes seems to be associated with ADL, 2) in some countries (i.e. Mexico and Uruguay), education explains the association between diabetes and ADL limitation, 3) in some countries (i.e. Cuba and Brazil), exercise explains the link between diabetes and ADLs, and 4) in some countries (i.e. Chile and Barbados), the link between diabetes and ADL seems to be independent of our covariates/mediators.

Only in Chile and Barbados was the link between diabetes and ADL independent of all study covariates/mediators. Based on a study in Japan, history of diabetes, bone fractures, and heart diseases contributed to some specific aspects of ADL disabilities, however, cerebrovascular disease influenced all aspects of ADL [18].

We showed that in Cuba and Brazil, physical activity might be a mechanism behind the link between diabetes and ADL. According to the literature, at least a part of the limitations in ADLs of patients with diabetes might be due to function in extremities. A study examined how hand disorders contribute to ADLs among elderly men with diabetes, and showed that limited joint motion (measured by prayer sign and Dupuytren's contracture) was more common in individuals with diabetes, compared to non-diabetics. Vibrotactile sense was impaired symmetrically in the index and little fingers in diabetics [19].

Peripheral artery disease and peripheral nerve dysfunction are known causes of diabetes -related disability [9, 20], and may explain more than 30% of the association of diabetes with disability related dysfunction in physical activity. Physical activity is particularly important for people with diabetes, because being physically active can improve the body's ability to use insulin and facilitate weight loss [2125]. In the United States, for example, only one-third of individuals with diabetes and obesity are physically active [26, 27]. Interventions need to enhance physical activity of patients, and this may break the cycle by which diabetes causes limitation in performing ADLs.

The current study failed to show mediating or confounding effects of obesity on the association between diabetes and ADLs. However, obesity had an independent association with ADLs in one of the seven countries (Barbados). Literature suggests that obesity and overweight may mediate or confound the link between diabetes and disability. In a survey among obese diabetic individuals, ADL was linked to current exercise, using exercise programs, and self-reported weight history [28]. In a qualitative study among obese (body mass index [BMI] > 30 kg/m2) individuals with diabetes, patients believed that their performance of daily activities would improve with weight loss of 5–10% of body weight [29].

The ability to remain physically active is an essential aspect of quality of life and is critical for the preservation of independence among patients with diabetes. Lower extremity function is a strong predictor of poor health outcomes, including disability, hospitalization, and death among patients [30, 31]. Recently, a search for potentially modifiable conditions associated with impaired mobility and lower extremity function identified several socio-demographic and behavioral characteristics, along with acute and chronic medical conditions [32, 33]. Among the latter group, diabetes has been shown to be consistently a correlate of poor extremity performance [34, 35].

Several impairments and comorbidities are involved in the disablement process associated with diabetes. Obesity, visual impairment, and cardiovascular diseases may mediate the association between diabetes and disability among diabetic patients. These conditions are important causes of different aspects of physical dysfunction in older individuals [36, 37].

The association between diabetes and ADLs did not stay significant in the multivariable models in most countries. The literature is also not informative about the question of whether or not the link between diabetes and disability is independent of other factors that contribute to disability. We know that several conditions that may contribute to the impairment in ADLs are more prevalent in diabetic patients than controls. These conditions may be the actual mechanisms by which diabetes is associated with physical disability. Some of these conditions include cardiovascular diseases, peripheral neuropathy, obesity, and visual deficits [35].

Our findings are important for different reasons. As very few studies have shown exact mechanisms by which disability is seen among patients with diabetes [35], our knowledge about the mechanisms underlying the disability among diabetic patients is limited. Further research is needed to know which complication of the disease plays a more important role in the disablement process. Further research is also needed on possible synergistic effects of different conditions associated with diabetes [38]. In addition, prevalence of diabetes is expected to increase considerably in the next decades [38]. And knowledge from similar studies may be important for planning strategies aimed at preventing or slowing functional decline in older persons and for tertiary prevention in subjects with diabetes [9].

Our study has strengths and limitations. This was a cross-national survey composed of different surveys conducted in seven countries [10]. Research on Early Life and Aging Trends and Effects (RELATE) did not collect data on duration of diabetes, or mental health of the participants. Duration of diabetes is known to be strongly linked to disability among patients with diabetes. We also did not collect data on degree of metabolic imbalance, as we know loss of control of glucose may be a mediator for the disabling effect of diabetes [9]. Based on the literature, high prevalence of depression among diabetic patients may explain a substantial proportion of the excess risk of disability associated with diabetes. Depression is common among patients with diabetes [3941]. Longitudinal studies are warranted to elucidate the role of depression in the pathway from diabetes to disability. Our study is one of very few studies that contribute to our understandings of cross – country differences in morbidity related to diabetes. To study cross – country differences in mental health associates of diabetes, one study used data from World Mental Health, and showed that with a consistent pattern, in all countries, mood and anxiety disorders occurred with somewhat greater frequency among persons with diabetes than those without diabetes, and the strength of association did not differ significantly across countries [42]. It is essential to conduct more research to test if the association between diabetes and disability is independent of other chronic conditions or not [9].

Our study sheds light on cross – country differences in factors associated with well-being. Considerable differences in morbidity, life satisfaction, and well-being have been shown across the globe [43, 44]. The World Values Survey, European Values Study, Eurobarometer, and Latinobarometer, have consistently shown that life expectancy, physical health, all-cause mortality, and also subjective well-being vary across countries [4548].

Conclusion

To conclude, our study showed considerable differences in the association between diabetes and disability across countries. The study also suggests that there might be some cross-country differences in the factors that may explain this link.

Notes

Declarations

Acknowledgement

Research on Early Life and Aging Trends and Effects (RELATE) was funded by United States Department of Health and Human Services, National Institutes of Health, and National Institute on Aging (K25AG027239). This fund was used to create the RELATE dataset from existing data sources. The following individuals and Principal Investigators also deserve recognition for being instrumental in the release of the first public version of RELATE: Drs. George Alter, Barry Popkin, David Weir, Yi Zeng, Luis Rosero-Bixby, Ana Luisa Dávila, Alberto Palloni, Somnath Chatterji, Paul Kowal, Pamela Herd, and Bob Hauser. A full detail of funding sources for each of the country specific studies are available in the appendices for the RELATE data [10].

Authors’ Affiliations

(1)
Department of Health Behavior and Health Education, University of Michigan School of Public Health
(2)
Center for Research on Ethnicity, Culture and Health, School of Public Health, University of Michigan
(3)
Tehran University of Medical Sciences
(4)
Medicine and Health Promotion Institute
(5)
Universal Network for Health Information Dissemination and Exchange (UNHIDE)

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© Assari et al.; licensee BioMed Central Ltd. 2014

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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