Cross-country variation in additive effects of socio-economics, health behaviors, and comorbidities on subjective health of patients with diabetes

Journal of Diabetes & Metabolic Disorders201413:36

DOI: 10.1186/2251-6581-13-36

Received: 15 October 2013

Accepted: 6 January 2014

Published: 21 February 2014

Abstract

Purpose

This study explored cross-country differences in the additive effects of socio-economic characteristics, health behaviors and medical comorbidities on subjective health of patients with diabetes.

Methods

The study analyzed data from the Research on Early Life and Aging Trends and Effects (RELATE). The participants were 9,179 adults with diabetes who were sampled from 15 countries (i.e. China, Costa Rica, Puerto Rico, United States, Mexico, Argentina, Barbados, Brazil, Chile, Cuba, Uruguay, India, Ghana, South Africa, and Russia). We fitted three logistic regressions to each country. Model I only included socio-economic characteristics (i.e. age, gender, education and income). In Model II, we also included health behaviors (i.e. smoking, drinking, and exercise). Model III included medical comorbidities (i.e. hypertension, respiratory disease, heart disease, stroke, and arthritis), in addition to the previous blocks.

Results

Our models suggested cross-country differences in the additive effects of socio-economic characteristics, health behaviors and comorbidities on perceived health of patients with diabetes. Comorbid heart disease was the only condition that was consistently associated with poor subjective health regardless of country.

Conclusion

Countries show different profiles of social and behavioral determinants of subjective health among patients with diabetes. Our study suggests that universal programs that assume that determinants of well-being are similar across different countries may be over-simplistic. Thus instead of universal programs that use one protocol for health promotion of patients in all countries, locally designed interventions should be implemented in each country.

Keywords

Subjective health Socio-economics Health behaviors Comorbidity Cross country study

Introduction

It has been consistently shown that individuals with diabetes report poorer well-being and subjective health, compared to people without diabetes [15]. A question that is not answered yet is whether poor subjective health of patients with diabetes is the consequence of diabetes - per se - or factors associated with diabetes. We know that low socio-economic status [6], health compromising behaviors [7] and chronic medical conditions [812] frequently co-occur with diabetes and also influence the well-being of individuals.

Low socio-economic status may be associated with poor subjective health [6]. The protective effect of high social class on well-being has been partially attributed to better access to financial and material resources available in the community [13]. Unfortunately, most of our knowledge about the effect of socio-economic status on health and well-being of individuals has originated from studies conducted within one country [14, 15]. Thus, it is not known if there are cross-country differences in the effect of socio-economic status on subjective health or not.

Comorbid conditions are associated with poor subjective health among patients with an index disease [6]. Patients who suffer from a higher number of chronic conditions tend to report lower physical and mental health related quality of life [1618]. In the United States, each comorbid chronic condition has been estimated to reduce 3–4 decrements in mental quality of life [19]. Chronic conditions are closely associated with deterioration in physical functioning, physical role, bodily pain, general health, vitality, social functioning, emotional role and mental health [20].

Although research has consistently shown cross-country differences in objective and subjective measures of health [2126], limited knowledge exists on causes of such variations. The World Values Survey, European Values Study, Eurobarometer, and Latinobarometer, have all reported cross-country variations in self-rated health and wellbeing of individuals [2133]. It is, however, not known if determinants of well-being also vary based on country. According to our knowledge, there are not many– if any- studies that have compared the effects of social and behavioral determinants of subjective health among individuals with an index chronic medical condition across countries.

The current study aimed to compare countries in the effects of socio-economic characteristics (i.e. age, gender, education and income), health behaviors (i.e. smoking, drinking and exercise), and comorbid conditions (i.e. hypertension, respiratory disease, heart disease, stroke, and arthritis) on the subjective health of a community sample of adults with diabetes.

Methods

Study design & participants

Research on Early Life and Aging Trends and Effects (RELATE) is a cross-national survey in 15 countries located in North America, South America, Asia, and Africa [34, 35]. The RELATE composed of the following national surveys: 1) China Health and Nutrition Study (CHNS), 2) Chinese Longitudinal Healthy Longevity Survey (CLHLS), 3) Costa Rican Study of Longevity and Healthy Aging (CRELES), 4) Puerto Rican Elderly: Health Conditions (PREHCO), 5) Study of Aging Survey on Health and Well Being of Elders (SABE), 6) WHO Study on Global Ageing and Adult Health (SAGE), and 7) Wisconsin Longitudinal Study (WLS). [34, 35] All studies were approved by an institutional review board. Written consent was provided by all participants. Data were collected in an anonymous fashion.

The current analysis included 9,179 adults with diabetes. Participants were sampled in the following 15 countries: China (n = 3,024), Puerto Rico (n = 1,197), the United States (n = 887), Mexico (n = 687), Costa Rica (n = 542), India (n = 478), Brazil (n = 380), South Africa (359), Russia (n = 350), Barbados (n = 325), Cuba (n = 290), Uruguay (n = 188), Chile (n = 173), Ghana (n = 167), and Argentina (n = 132).

The RELATE project represents countries from a diverse range in national income levels. The United States, Puerto Rico, and Barbados represent high income countries; Argentina, Cuba, Uruguay, Chile, Costa Rica, Brazil, Mexico, and Russia represent upper middle income countries; China and India represent lower middle income countries; and Ghana represents low income countries.

Measures

Socio-economic characteristics

The study measured socio-economic data such as age (continuous variable), gender (dichotomous variable), education level (a four level categorical variable composed of no schooling, primary to elementary, secondary to intermediate, and higher), and income (continuous variable).

Comorbid conditions

We measured five different chronic medical conditions including hypertension, respiratory disease, heart disease, stroke, and arthritis, using self-report of physician diagnoses. Agreement between self-report and physician diagnosis of comorbid conditions has been shown to be high (kappa: 0.74-0.92) [36].

Main outcome

The outcome was a single item measure of subjective health. Overall perceived health was measured using a five-item Likert scale (i.e. very bad, bad, moderate, good, and very good). Single items have been frequently used to measure subjective health and well-being [27, 28, 3742]. The test retests reliability of single items for measuring subjective health range from 0.7 to 0.8 [41]. Results of these single item measures of subjective health are highly correlated with standard scales [41, 43]. Single item measures of subjective health have shown high predictive validity for prediction of mortality, even after controlling for other risk factors [29].

Data analysis

Data analysis was conducted using SPSS 20.0 for Windows. We transformed our five-item Likert scale to a dichotomous outcome, as poor health (i.e. very bad health and bad health) versus good health (i.e. moderate health, good health, and very good health). Odds Ratios (OR) and 95% confidence intervals (95% CI) were reported. P less than 0.05 was considered as significant.

We fitted country specific logistic regressions to determine if the associations between socio-economic factors (i.e age, gender, education, and income), health behaviors (i.e. smoking, drinking, and exercise) and chronic conditions (i.e. hypertension, respiratory disease, heart disease, stroke, and arthritis), and subjective health vary across countries. Although most country specific surveys had sampling weights, sampling weights were not applicable to surveys from the United States (Wisconsin) and China (CHNS). Thus, the current study did not apply sampling weights.

We took a hierarchical approach for our regression analysis. Model I only included socio-economic characteristics (i.e. age, gender, education and income). In Model II, health behaviors (i.e. smoking, drinking, and exercise) were added to the model. Model III also included comorbidities (i.e. hypertension, respiratory disease, heart disease, stroke, and arthritis).

Changes in the odds ratios from Model I (socio-economic factors) to Model II (socio-economic factors and health behaviors) suggest that health behaviors may mediate the effect of socio-economic factors on subjective health. Changes in the odds ratios from Model II (socio-economic factors and health behaviors) to Model III (full model) suggest that comorbid conditions may mediate the effect of socio-economic factors and health behaviors on subjective health.

Results

This study included 9,179 adults with diabetes. Participants were sampled in the following 15 countries: China (n = 3,024), Puerto Rico (n = 1,197), the United States (n = 887), Mexico (n = 687), Costa Rica (n = 542), India (n = 478), Brazil (n = 380), South Africa (359), Russia (n = 350), Barbados (n = 325), Cuba (n = 290), Uruguay (n = 188), Chile (n = 173), Ghana (n = 167), and Argentina (n = 132).

Model I (socio-economics)

With the exception of Costa Rica, the United States, Mexico, Brazil, and South Africa, in all 10 other countries, female patients had significantly poorer subjective health than male patients [Table 1].
Table 1

Socio-economic predictors of poor subjective health among patients with diabetes in 15 countries

 

B

S.E.

Wald

Sig.

Exp (B)

95% C.I. for EXP (B)

      

Lower

Upper

China

Female

.183

.028

41.441

<.001

1.201

1.136

1.269

Age

−.016

.001

334.036

<.001

.984

.982

.986

Education

−.211

.016

176.776

<.001

.810

.785

.835

Income

.000

.000

178.850

<.001

1.000

1.000

1.000

Costa Rica

Female

.121

.083

2.116

.146

1.129

.959

1.328

Age

−.014

.004

12.238

<.001

.986

.978

.994

Education

−.378

.068

31.278

<.001

.685

.600

.782

Income

.000

.000

10.246

.001

1.000

1.000

1.000

Puerto Rico

Female

.487

.075

42.085

<.001

1.628

1.405

1.886

Age

−.004

.005

.630

.427

.996

.987

1.005

Education

−.462

.050

85.795

<.001

.630

.572

.695

Income

.000

.000

17.886

<.001

1.000

1.000

1.000

United States

Female

−.105

.082

1.636

.201

.901

.767

1.057

Age

.060

.055

1.198

.274

1.062

.953

1.183

Education

−.517

.102

25.588

<.001

.596

.488

.728

Income

.000

.000

23.914

<.001

1.000

1.000

1.000

Mexico

Female

.105

.080

1.691

.193

1.110

.948

1.300

Age

.016

.005

12.286

<.001

1.016

1.007

1.025

Education

−.305

.054

32.476

<.001

.737

.664

.819

Income

.000

.000

17.668

<.001

1.000

1.000

1.000

Argentina

Female

.363

.155

5.494

.019

1.438

1.061

1.949

Age

−.013

.010

1.718

.190

.987

.967

1.007

Education

−.763

.104

53.394

<.001

.466

.380

.572

Income

.000

.000

2.467

.116

1.000

1.000

1.000

Barbados

Female

.407

.120

11.421

.001

1.502

1.186

1.901

Age

.041

.007

31.863

<.001

1.042

1.027

1.057

Education

−.290

.099

8.624

.003

.748

.617

.908

Income

.000

.000

4.121

.042

1.000

1.000

1.000

Brazil

Female

.040

.090

.192

.661

1.040

.872

1.241

Age

.001

.005

.045

.832

1.001

.991

1.012

Education

−.279

.063

19.373

<.001

.756

.668

.856

Income

.000

.000

17.582

<.001

1.000

1.000

1.000

Chile

Female

.351

.125

7.875

.005

1.421

1.112

1.816

Age

.003

.008

.153

.696

1.003

.988

1.018

Education

−.326

.063

26.812

<.001

.722

.638

.817

Income

.000

.000

.016

.899

1.000

1.000

1.000

Cuba

Female

.531

.103

26.484

<.001

1.701

1.389

2.082

Age

−.005

.006

.623

.430

.995

.983

1.007

Education

−.317

.075

18.155

<.001

.728

.629

.842

Income

.000

.000

1.871

.171

1.000

1.000

1.000

Uruguay

Female

.387

.124

9.774

.002

1.472

1.155

1.876

Age

−.001

.008

.005

.945

.999

.984

1.015

Education

−.404

.070

32.948

<.001

.667

.581

.766

Income

.000

.000

1.744

.187

1.000

1.000

1.000

India

Female

.176

.069

6.487

.011

1.192

1.041

1.364

Age

.047

.003

193.134

<.001

1.048

1.041

1.055

Education

−.213

.041

26.517

<.001

.808

.746

.877

Income

.000

.000

17.654

<.001

1.000

1.000

1.000

Ghana

Female

.263

.105

6.257

.012

1.301

1.059

1.598

Age

.055

.005

135.610

<.001

1.056

1.047

1.066

Education

−.129

.055

5.598

.018

.879

.789

.978

Income

.000

.000

.132

.716

1.000

1.000

1.000

South Africa

Female

.057

.102

.306

.580

1.058

.866

1.293

Age

.025

.005

24.866

<.001

1.025

1.015

1.035

Education

−.061

.034

3.120

.077

.941

.880

1.007

Income

.000

.000

2.535

.111

1.000

1.000

1.000

Russia

Female

.277

.099

7.854

.005

1.319

1.087

1.602

Age

.074

.005

214.090

<.001

1.077

1.067

1.088

Education

−.261

.073

12.717

<.001

.771

.668

.889

Income

.000

.000

16.061

<.001

1.000

1.000

1.000

In six countries (i.e. Mexico, Barbados, India, Ghana, South Africa, and Russia), older patients had poorer subjective health than younger patients. In China and Costa Rica, older patients reported better subjective health. In the other seven countries (i.e. Puerto Rico, the United States, Brazil, Chile, Cuba, Argentina, and Uruguay), age was not associated with subjective health [Table 1].

In all countries other than South Africa, high education was associated with better subjective health. This association was marginally significant in South Africa [Table 1].

In six countries (i.e. Argentina, Chile, Cuba, Uruguay, Ghana, and South Africa), high income was not associated with subjective health. High income was predictive of better subjective health in the other nine countries [Table 1].

Model II (socio-economics and health behaviors)

In all countries but Mexico, exercise was predictive of better subjective health. In Mexico, exercise was associated with worse subjective health [Table 2].
Table 2

Socio-economics, behaviors, and number of chronic conditions as predictors of poor subjective health among patients with diabetes in 15 countries

 

B

S.E.

Wald

Sig.

Exp (B)

95% C.I. for EXP (B)

      

Lower

Upper

China

Female

.139

.037

13.854

<.001

1.149

1.068

1.236

Age

−.016

.001

284.715

<.001

.985

.983

.986

Education

−.203

.017

139.722

<.001

.817

.790

.844

Income

.000

.000

192.184

<.001

1.000

1.000

1.000

Smoking

.106

.038

7.674

.006

1.112

1.031

1.198

Drinking

−.153

.035

18.984

<.001

.858

.802

.919

Exercising

−.377

.031

146.203

<.001

.686

.645

.729

Costa Rica

Female

.029

.109

.071

.790

1.030

.831

1.276

Age

−.019

.004

20.334

<.001

.981

.973

.989

Education

−.394

.069

32.336

<.001

.674

.588

.772

Income

.000

.000

8.779

.003

1.000

1.000

1.000

Smoking

.011

.099

.011

.915

1.011

.833

1.226

Drinking

−.010

.109

.009

.924

.990

.799

1.226

Exercising

−.590

.105

31.737

<.001

.554

.452

.681

Puerto Rico

Female

.461

.084

29.913

.000

1.585

1.344

1.870

Age

−.011

.005

5.302

.021

.989

.980

.998

Education

−.401

.051

62.523

<.001

.669

.606

.739

Income

.000

.000

14.095

.000

1.000

1.000

1.000

Smoking

.283

.086

10.753

.001

1.327

1.120

1.571

Drinking

−.336

.102

10.931

.001

.714

.585

.872

Exercising

−.448

.078

32.801

<.001

.639

.548

.745

United States

Female

−.054

.097

.306

.580

.948

.784

1.146

Age

.049

.066

.557

.455

1.051

.923

1.196

Education

−.333

.116

8.273

.004

.717

.571

.899

Income

.000

.000

12.963

<.001

1.000

1.000

1.000

Smoking

.604

.102

35.374

<.001

1.830

1.500

2.233

Drinking

−.703

.097

52.461

<.001

.495

.409

.599

Exercising

−1.056

.200

28.031

<.001

.348

.235

.514

Mexico

Female

.023

.100

.055

.815

1.024

.841

1.246

Age

.017

.005

11.807

.001

1.017

1.007

1.026

Education

−.291

.055

27.461

<.001

.748

.671

.834

Income

.000

.000

16.775

<.001

1.000

1.000

1.000

Smoking

.462

.096

23.380

<.001

1.588

1.316

1.915

Drinking

−1.108

.099

125.824

<.001

.330

.272

.401

Exercising

.546

.102

28.673

<.001

1.727

1.414

2.109

Argentina

Female

.374

.182

4.222

.040

1.453

1.017

2.075

Age

−.014

.011

1.657

.198

.986

.966

1.007

Education

−.756

.108

49.389

<.001

.470

.380

.580

Income

.000

.000

2.127

.145

1.000

1.000

1.000

Smoking

.415

.172

5.853

.016

1.515

1.082

2.120

Drinking

−.528

.160

10.903

.001

.590

.431

.807

Exercising

−.622

.243

6.541

.011

.537

.333

.865

Barbados

Female

.330

.147

5.028

.025

1.390

1.042

1.855

Age

.032

.008

17.359

<.001

1.032

1.017

1.048

Education

−.273

.103

7.082

.008

.761

.622

.931

Income

.000

.000

3.758

.053

1.000

1.000

1.000

Smoking

.154

.160

.921

.337

1.166

.852

1.597

Drinking

−.564

.143

15.517

<.001

.569

.429

.753

Exercising

−.503

.124

16.409

<.001

.605

.474

.771

Brazil

Female

.012

.108

.012

.913

1.012

.819

1.250

Age

−.007

.006

1.470

.225

.993

.982

1.004

Education

−.196

.065

9.063

.003

.822

.723

.934

Income

.000

.000

11.466

.001

1.000

1.000

1.000

Smoking

.397

.104

14.675

<.001

1.488

1.214

1.823

Drinking

−.788

.105

56.162

<.001

.455

.370

.559

Exercising

−.680

.111

37.302

<.001

.507

.407

.630

Chile

Female

.253

.136

3.475

.062

1.288

.987

1.682

Age

.001

.008

.021

.885

1.001

.986

1.016

Education

−.323

.064

25.809

<.001

.724

.639

.820

Income

.000

.000

.000

.989

1.000

1.000

1.000

Smoking

.179

.128

1.943

.163

1.196

.930

1.537

Drinking

−.395

.130

9.271

.002

.674

.523

.869

Exercising

−.408

.146

7.809

.005

.665

.499

.885

Cuba

Female

.472

.119

15.580

<.001

1.603

1.268

2.025

Age

−.008

.006

1.389

.239

.992

.980

1.005

Education

−.264

.076

12.158

<.001

.768

.662

.891

Income

.000

.000

1.217

.270

1.000

1.000

1.000

Smoking

.251

.115

4.785

.029

1.285

1.026

1.609

Drinking

−.434

.127

11.570

.001

.648

.505

.832

Exercising

−.382

.119

10.371

.001

.682

.541

.861

Uruguay

Female

.201

.149

1.805

.179

1.222

.912

1.639

Age

−.006

.008

.581

.446

.994

.978

1.010

Education

−.366

.072

25.639

<.001

.693

.602

.799

Income

.000

.000

.887

.346

1.000

1.000

1.000

Smoking

.180

.140

1.668

.197

1.198

.911

1.575

Drinking

−.682

.132

26.538

<.001

.506

.390

.656

Exercising

−.809

.194

17.446

<.001

.445

.305

.651

India

Female

.293

.080

13.231

<.001

1.340

1.145

1.569

Age

.040

.004

129.415

<.001

1.041

1.034

1.048

Education

−.205

.042

23.824

<.001

.814

.750

.884

Income

.000

.000

15.854

<.001

1.000

1.000

1.000

Smoking

.337

.072

21.774

<.001

1.401

1.216

1.614

Drinking

.166

.095

3.037

.081

1.181

.980

1.423

Exercising

−.613

.077

63.331

<.001

.542

.466

.630

Ghana

Female

.284

.119

5.655

.017

1.328

1.051

1.679

Age

.052

.005

115.199

<.001

1.053

1.043

1.063

Education

−.188

.056

11.171

.001

.829

.742

.925

Income

.000

.000

.160

.689

1.000

1.000

1.000

Smoking

.236

.135

3.037

.081

1.266

.971

1.651

Drinking

.165

.109

2.307

.129

1.180

.953

1.460

Exercising

−.587

.108

29.316

<.001

.556

.449

.687

South Africa

Female

.064

.108

.348

.555

1.066

.863

1.316

Age

.025

.005

22.845

<.001

1.025

1.015

1.035

Education

−.052

.036

2.075

.150

.950

.885

1.019

Income

.000

.000

2.049

.152

1.000

1.000

1.000

Smoking

.156

.122

1.643

.200

1.169

.921

1.484

Drinking

.219

.131

2.816

.093

1.245

.964

1.608

Exercising

−.665

.179

13.800

<.001

.515

.362

.731

Russia

Female

.372

.131

8.002

.005

1.450

1.121

1.876

Age

.070

.005

175.456

<.001

1.073

1.062

1.084

Education

−.256

.075

11.785

.001

.774

.669

.896

Income

.000

.000

14.406

<.001

1.000

1.000

1.000

Smoking

.417

.140

8.907

.003

1.518

1.154

1.996

Drinking

−.146

.111

1.725

.189

.864

.695

1.074

Exercising

−.746

.118

40.223

<.001

.474

.377

.597

In India and South Africa, drinking was marginally associated with poor subjective health. In Ghana, and Russia, drinking was not associated with subjective health. In all other 12 countries, drinking was associated with better subjective health [Table 2].

In Ghana, smoking was marginally associated with poor subjective health. In Costa Rica, Barbados, Chile, Uruguay, and South Africa, smoking was not associated with subjective health. In all other nine countries, smoking was associated with poor subjective health [Table 2].

Model III (socio-economics, health behaviors and comorbidities)

With no exception, comorbid heart disease was associated with poor subjective health in all countries. With an exception of South Africa, in all other countries, comorbid hypertension was associated with poor subjective health. Arthritis was associated with poor subjective health in all countries but Ghana. In countries other than China and Ghana, comorbid lung disease was associated with poor subjective health. With an exception of China, Argentina and Ghana, in all other countries, stroke was associated with poor subjective health. In Ghana, the association between stroke and subjective health was marginally significant [Table 3].
Table 3

Socio-economics, behaviors and chronic conditions as predictors of poor subjective health among patients with diabetes in 15 countries

 

B

S.E.

Wald

Sig.

Exp (B)

95% C.I. for EXP ( B)

      

Lower

Upper

China

Female

.145

.046

9.782

.002

1.156

1.056

1.267

Age

−.003

.001

6.835

.009

.997

.994

.999

Education

−.185

.026

50.921

<.001

.831

.790

.875

Income

.000

.000

87.633

<.001

1.000

1.000

1.000

Smoking

.217

.047

21.218

<.001

1.242

1.133

1.362

Drinking

−.156

.043

12.915

<.001

.856

.786

.932

Exercising

−.563

.040

196.258

<.001

.570

.527

.616

Hypertension

.232

.045

26.124

<.001

1.261

1.154

1.378

Lung Disease

.048

.057

.712

.399

1.049

.939

1.172

Heart Disease

.527

.055

91.014

<.001

1.694

1.520

1.888

Stroke

−.054

.070

.585

.445

.948

.826

1.087

Arthritis

.431

.046

86.767

<.001

1.539

1.406

1.685

Costa Rica

Female

−.074

.114

.421

.517

.929

.743

1.161

Age

−.021

.004

21.239

<.001

.980

.971

.988

Education

−.448

.072

38.379

<.001

.639

.554

.736

Income

.000

.000

6.267

.012

1.000

1.000

1.000

Smoking

−.027

.102

.070

.791

.973

.797

1.189

Drinking

.016

.113

.020

.888

1.016

.814

1.267

Exercising

−.497

.108

21.128

<.001

.608

.492

.752

Hypertension

.272

.088

9.463

.002

1.312

1.104

1.560

Lung Disease

.485

.117

17.282

<.001

1.624

1.292

2.041

Heart Disease

.501

.131

14.612

<.001

1.650

1.276

2.133

Stroke

.375

.191

3.871

.049

1.456

1.001

2.116

Arthritis

.433

.119

13.354

<.001

1.542

1.222

1.946

Puerto Rico

Female

.277

.090

9.399

.002

1.319

1.105

1.575

Age

−.019

.005

13.947

<.001

.981

.971

.991

Education

−.407

.053

58.032

<.001

.666

.599

.739

Income

.000

.000

15.183

<.001

1.000

1.000

1.000

Smoking

.242

.091

7.089

.008

1.274

1.066

1.523

Drinking

−.184

.107

2.959

.085

.832

.674

1.026

Exercising

−.353

.083

18.283

<.001

.702

.597

.826

Hypertension

.664

.080

68.161

<.001

1.943

1.660

2.275

Lung Disease

.576

.183

9.964

.002

1.779

1.244

2.545

Heart Disease

.826

.123

45.129

<.001

2.285

1.796

2.908

Stroke

.590

.212

7.753

.005

1.805

1.191

2.734

Arthritis

.818

.083

97.363

<.001

2.265

1.926

2.665

United States

Female

.020

.108

.033

.855

1.020

.825

1.260

Age

.033

.071

.220

.639

1.034

.900

1.187

Education

−.273

.124

4.809

.028

.761

.596

.971

Income

.000

.000

12.312

<.001

1.000

1.000

1.000

Smoking

.417

.110

14.458

<.001

1.517

1.224

1.881

Drinking

−.527

.106

24.865

<.001

.590

.480

.726

Exercising

−1.086

.212

26.201

<.001

.337

.223

.511

Hypertension

.489

.104

21.986

<.001

1.630

1.329

1.999

Lung Disease

.759

.118

41.048

<.001

2.135

1.693

2.693

Heart Disease

1.361

.109

157.177

<.001

3.902

3.154

4.827

Stroke

1.035

.195

28.045

<.001

2.816

1.920

4.131

Arthritis

.685

.104

43.091

<.001

1.984

1.617

2.435

Mexico

Female

−.201

.107

3.517

.061

.818

.663

1.009

Age

.013

.005

6.592

.010

1.013

1.003

1.024

Education

−.310

.058

28.134

<.001

.734

.654

.823

Income

.000

.000

15.857

<.001

1.000

1.000

1.000

Smoking

.385

.101

14.426

<.001

1.469

1.205

1.792

Drinking

−1.192

.105

129.940

<.001

.303

.247

.373

Exercising

.587

.106

30.569

<.001

1.799

1.461

2.215

Hypertension

.349

.089

15.502

<.001

1.418

1.192

1.687

Lung Disease

.734

.161

20.753

<.001

2.083

1.519

2.857

Heart Disease

.285

.137

4.331

.037

1.329

1.017

1.738

Stroke

.443

.189

5.485

.019

1.557

1.075

2.256

Arthritis

1.018

.111

84.795

<.001

2.768

2.229

3.438

Argentina

Female

.172

.201

.732

.392

1.188

.801

1.760

Age

−.024

.012

4.206

.040

.976

.954

.999

Education

−.736

.116

40.039

<.001

.479

.381

.602

Income

.000

.000

2.969

.085

1.000

1.000

1.000

Smoking

.446

.187

5.701

.017

1.562

1.083

2.251

Drinking

−.519

.173

9.005

.003

.595

.424

.835

Exercising

−.394

.259

2.302

.129

.675

.406

1.122

Hypertension

.548

.161

11.643

.001

1.729

1.263

2.369

Lung Disease

1.283

.289

19.658

<.001

3.607

2.046

6.358

Heart Disease

.956

.194

24.405

<.001

2.603

1.781

3.804

Stroke

.428

.383

1.248

.264

1.534

.724

3.248

Arthritis

.999

.169

34.967

<.001

2.716

1.950

3.782

Barbados

Female

.021

.161

.016

.898

1.021

.744

1.400

Age

.032

.008

15.068

<.001

1.032

1.016

1.049

Education

−.283

.108

6.806

.009

.754

.610

.932

Income

.000

.000

4.073

.044

1.000

1.000

1.000

Smoking

.023

.171

.018

.894

1.023

.732

1.430

Drinking

−.503

.152

10.996

.001

.605

.449

.814

Exercising

−.372

.132

7.919

.005

.690

.532

.893

Hypertension

.565

.129

19.170

<.001

1.759

1.366

2.264

Lung Disease

1.248

.349

12.774

<.001

3.482

1.757

6.903

Heart Disease

.641

.208

9.530

.002

1.898

1.263

2.850

Stroke

.918

.313

8.587

.003

2.504

1.355

4.628

Arthritis

.810

.129

39.233

<.001

2.247

1.744

2.895

Brazil

Female

−.101

.116

.750

.386

.904

.720

1.135

Age

−.009

.006

2.442

.118

.991

.979

1.002

Education

−.218

.068

10.133

.001

.804

.704

.920

Income

.000

.000

9.953

.002

1.000

1.000

1.000

Smoking

.392

.109

12.853

<.001

1.481

1.195

1.835

Drinking

−.709

.111

40.701

<.001

.492

.396

.612

Exercising

−.555

.117

22.487

<.001

.574

.457

.722

Hypertension

.560

.097

33.052

<.001

1.751

1.447

2.120

Lung Disease

.494

.151

10.666

.001

1.638

1.218

2.203

Heart Disease

.622

.127

24.113

<.001

1.862

1.453

2.386

Stroke

.514

.197

6.777

.009

1.672

1.135

2.461

Arthritis

.676

.106

40.333

<.001

1.965

1.595

2.421

Chile

Female

.080

.148

.290

.590

1.083

.811

1.447

Age

−.012

.008

2.218

.136

.988

.972

1.004

Education

−.332

.066

25.001

<.001

.717

.630

.817

Income

.000

.000

.110

.740

1.000

1.000

1.000

Smoking

.100

.135

.547

.460

1.105

.848

1.441

Drinking

−.328

.137

5.737

.017

.721

.551

.942

Exercising

−.417

.155

7.280

.007

.659

.487

.892

Hypertension

.699

.129

29.203

<.001

2.012

1.561

2.592

Lung Disease

.911

.227

16.179

<.001

2.488

1.596

3.879

Heart Disease

.360

.139

6.658

.010

1.433

1.090

1.883

Stroke

.656

.298

4.838

.028

1.928

1.074

3.460

Arthritis

.627

.148

17.868

<.001

1.873

1.400

2.505

Cuba

Female

.080

.133

.362

.548

1.083

.835

1.404

Age

−.006

.007

.826

.363

.994

.980

1.008

Education

−.292

.082

12.576

<.001

.747

.636

.878

Income

.000

.000

.665

.415

1.000

1.000

1.000

Smoking

.207

.124

2.785

.095

1.230

.964

1.570

Drinking

−.386

.139

7.770

.005

.680

.518

.892

Exercising

−.483

.129

13.956

<.001

.617

.479

.795

Hypertension

.550

.118

21.728

<.001

1.733

1.375

2.183

Lung Disease

.794

.192

17.158

<.001

2.211

1.519

3.219

Heart Disease

1.150

.158

53.301

<.001

3.158

2.319

4.300

Stroke

.512

.226

5.134

.023

1.669

1.072

2.598

Arthritis

1.068

.114

87.228

.000

2.909

2.325

3.639

Uruguay

Female

.092

.164

.314

.575

1.096

.795

1.512

Age

−.011

.009

1.567

.211

.989

.971

1.006

Education

−.396

.078

26.041

<.001

.673

.578

.784

Income

.000

.000

.278

.598

1.000

1.000

1.000

Smoking

.166

.150

1.230

.267

1.181

.880

1.584

Drinking

−.592

.142

17.408

<.001

.553

.419

.731

Exercising

−.660

.206

10.220

.001

.517

.345

.775

Hypertension

.491

.131

13.954

<.001

1.634

1.263

2.113

Lung Disease

1.212

.221

30.110

<.001

3.362

2.180

5.183

Heart Disease

.807

.151

28.710

<.001

2.241

1.668

3.010

Stroke

1.012

.332

9.282

.002

2.752

1.435

5.278

Arthritis

.749

.132

32.109

<.001

2.114

1.632

2.739

India

Female

.147

.093

2.491

.115

1.158

.965

1.390

Age

.035

.004

71.757

<.001

1.035

1.027

1.044

Education

−.271

.049

31.218

<.001

.762

.693

.839

Income

.000

.000

9.795

.002

1.000

1.000

1.000

Smoking

.349

.083

17.567

<.001

1.418

1.204

1.669

Drinking

.029

.112

.068

.794

1.030

.826

1.283

Exercising

−.695

.091

58.313

<.001

.499

.417

.596

Hypertension

.460

.093

24.401

<.001

1.585

1.320

1.902

Lung Disease

.785

.156

25.286

<.001

2.193

1.615

2.978

Heart Disease

.705

.083

71.269

<.001

2.023

1.718

2.383

Stroke

.670

.210

10.211

.001

1.954

1.296

2.946

Arthritis

.555

.087

40.684

<.001

1.742

1.469

2.065

Ghana

Female

.331

.128

6.678

.010

1.392

1.083

1.789

Age

.055

.005

116.455

<.001

1.057

1.046

1.068

Education

−.182

.061

9.080

.003

.833

.740

.938

Income

.000

.000

.131

.717

1.000

1.000

1.000

Smoking

.288

.144

3.991

.046

1.333

1.005

1.768

Drinking

.177

.115

2.371

.124

1.193

.953

1.494

Exercising

−.530

.115

21.227

<.001

.588

.470

.737

Hypertension

.373

.144

6.766

.009

1.453

1.096

1.925

Lung Disease

−.097

.659

.021

.883

.908

.250

3.301

Heart Disease

.391

.150

6.814

.009

1.479

1.102

1.985

Stroke

.526

.270

3.792

.052

1.691

.997

2.871

Arthritis

−.208

.145

2.057

.152

.812

.611

1.079

South Africa

Female

.045

.116

.151

.698

1.046

.833

1.314

Age

.023

.006

16.941

<.001

1.023

1.012

1.034

Education

−.054

.038

2.024

.155

.947

.879

1.021

Income

.000

.000

1.156

.282

1.000

1.000

1.000

Smoking

.068

.130

.273

.601

1.070

.829

1.381

Drinking

.299

.140

4.564

.033

1.349

1.025

1.776

Exercising

−.663

.192

11.969

.001

.515

.354

.750

Hypertension

.028

.118

.057

.812

1.029

.816

1.297

Lung Disease

1.205

.267

20.325

<.001

3.335

1.976

5.631

Heart Disease

.706

.162

18.995

<.001

2.026

1.475

2.783

Stroke

1.279

.236

29.486

<.001

3.594

2.265

5.702

Arthritis

.738

.120

37.630

<.001

2.092

1.653

2.649

Russia

Female

.331

.159

4.314

.038

1.392

1.019

1.903

Age

.050

.006

60.756

<.001

1.051

1.038

1.064

Education

−.277

.088

10.041

.002

.758

.638

.900

Income

.000

.000

16.812

<.001

1.000

1.000

1.000

Smoking

.509

.169

9.072

.003

1.664

1.195

2.318

Drinking

−.306

.132

5.355

.021

.737

.569

.954

Exercising

−.670

.138

23.611

<.001

.512

.390

.670

Hypertension

.296

.128

5.345

.021

1.344

1.046

1.727

Lung Disease

.376

.137

7.508

.006

1.456

1.113

1.905

Heart Disease

1.140

.119

91.247

<.001

3.126

2.474

3.949

Stroke

.846

.206

16.849

<.001

2.330

1.556

3.490

Arthritis

.690

.114

36.867

<.001

1.993

1.595

2.490

Discussion

The purpose of this study was to explore cross-country differences in the associations between socio-economic characteristics, health behaviors and comorbid medical conditions with subjective health among individuals with diabetes. The study showed that low socio-economic status, smoking, lack of exercise, and medical comorbidities are predictive of poor subjective health of patients with diabetes in most countries. The study, however, documented several cross–country differences in the links between socio-economics, health behaviors and chronic conditions, and subjective health of individuals with diabetes. The only factor with a consistent effect on subjective health of patients with diabetes was comorbid heart disease. These findings suggest that the link between social and behavioral determinants of health and subjective health may vary across countries.

With exception of the United States, Costa Rica, Mexico, Brazil, and South Africa, in all ten other countries, female gender was associated with poor subjective health among individuals with diabetes. According to another study among the general population, in 6 of 15 countries (i.e. China, Costa Rica, Puerto Rico, Barbados, Cuba and Uruguay) women reported poorer subjective health than men [44]. Among individuals with at least one chronic medical condition in Uruguay, Ghana and South Africa, female gender was associated with worse subjective health. Gender was not associated with subjective health in other countries [45]. These findings explain the complex role of gender in shaping the well-being of individuals. These studies collectively suggest that there are variations in the effect of gender on well-being between various populations, and sometimes even within a single country. The effect of gender on health and well-being among patients with medical conditions may be different from gender's effects among the general population. Interestingly, the role of gender on the well-being of patients with medical conditions may depend on type of chronic illness.

Literature suggests that women tend to report a higher number of self-reported chronic medical conditions and poorer self-reported health [46]. Women also report worse subjective health and well-being, compared to men [46]. Due to gender differences in longevity, a larger part of a woman’s life is spent with illness and disabilities [47]. Although women require more care later in life than men, women tend to have less access to health resources [48, 49]. In Ghana and Uruguay, among individuals with one chronic medical condition, women were more vulnerable to the effect of education on subjective health [45]. In a study on patients with chronic heart disease from Iran, women were more prone to the effect of income and education on sleep quality [50].

Pinquart and Sörensen proposed a number of mechanisms that may explain gender differences in subjective well-being. First, due to gender inequities and gendered social power, women may have lower material resources. In several countries, the gendered labor market may result in a lower level of stable employment among women [51]. Even among those who are employed, women’s pensions may be lower than men’s [52]. Among elderly, women more frequently live in poverty compared to men [53]. In addition, older women are more likely to be widowed than men [53]. In the United States, nearly four times as many older women than men live alone [49]. Finally, gender differences in response sets may explain worse self-reported health among women, as women may have more tendencies to report negative feelings and emotions [54].

Our results suggested that age and subjective well-being of patients with diabetes may be differently linked across countries. While in a number of countries (i.e. Mexico, Barbados, India, Ghana, South Africa, and Russia) high age is predictive of poor subjective health, age may not be associated with subjective health of patients with diabetes in other countries (i.e. Puerto Rico, United States, Brazil, Chile, Cuba, Argentina, and Uruguay). Interestingly, in China and Costa Rica, high age was associated with better subjective health among patients with diabetes. A recent study of general populations showed that in three countries (i.e. China, Costa Rica and Argentina), high age may predict better subjective health, while in four countries (i.e. Barbados , India, South Africa and Russia), high age was associated with low subjective health. Based on that study, in seven countries (i.e. Puerto Rico, United States, Mexico, Brazil, Chile, Cuba and Uruguay), a linear association between age and subjective health of elderly individuals in the general population could not be found [44]. Among individuals with at least one chronic medical condition, high age was associated with better subjective health in China, Costa Rica, Puerto Rico, Brazil and Argentina. In that study, high age was associated with poor subjective health in India, Ghana, South Africa and Russia. Age and subjective health were not significantly associated in other countries [45]. There are studies suggesting that there is an improvement in well-being as age increases among older individuals [55, 56]. A study among patients with heart disease showed that patients older than 65 years had better health-related quality of life than those younger [45].

Based on Model I, low education was consistently associated with higher risk of poor subjective health among patients with diabetes. Based on a recent study among general populations, education was not associated with subjective health in the United States, Ghana or South Africa [44]. Among patients with chronic conditions, education was not associated with subjective health in the United States, Mexico, Barbados, Brazil, Uruguay, Ghana, South Africa, or Russia. [45] The effect of education on health and well-being might be due to income or marital status [57]. Other reasons that highly educated people may stay healthier include social support and health protective behaviors [57].

Based on our study, in nine countries, income had an effect on subjective health of patients with diabetes, above and beyond the effect of education and other socio-economic factors. In Argentina, Chile, Cuba, Uruguay, Ghana, and South Africa, income did not have an effect on subjective health of patients with diabetes while the effect of education was controlled. Similar results were reported on the residual effect of income after controlling education in nine of 15 countries by a study that included a general population [44]. Among patients with at least one chronic medical condition, income was not predictive of poor subjective health in Argentina, Chile, Cuba, India, Ghana, or South Africa [45]. In India, the effect of income on subjective health of patients with chronic medical conditions was larger among women than men [45]. In Iran, among patients with chronic heart disease, the effect of income on well-being was larger for women than men [50]. These findings suggest that the links between country, gender, education, income and well-being are very complex.

A recent study suggested that the complex interplay between socio-economic status, chronic conditions and subjective health varies from setting to setting. In the United States, chronic conditions may explain the effect of marital status on health, while in Puerto Rico, the effect of income on subjective health was attributed to chronic conditions. In Costa Rica, Argentina, Barbados, Cuba, and Uruguay, chronic conditions explained gender disparities in subjective health. In China, Mexico, Brazil, Russia, Chile, India, Ghana and South Africa, the effect of socio-economic status was not due to chronic conditions [44].

Based on our study, comorbid heart disease was consistently predictive of poor subjective health among patients with diabetes. The effects of other chronic conditions on subjective health, however, were moderated by country. A study among 21,133 individuals on the association between number of chronic somatic conditions and quality of life showed an association between presence of a chronic condition and lower well-being across all domains of subjective health including physical function, fatigue, pain, emotional distress, and social function. Presence of two or more conditions was associated with larger decrements in quality of life, compared to a single condition [58]. Another large study among adults showed that after adjustments for socio-economic status and health behaviors (i.e. smoking, alcohol consumption, and physical activity), people with 3 or more chronic medical conditions were more likely to report poor general health, mental distress, physical distress, and activity limitations compared to individuals who had one or two chronic conditions [59, 60].

Our study may have important implications for global public health policy and practice. As countries show different sets of determinants of subjective health among individuals, we suggest that country should be considered as the context that shapes social and behavioral determinants of health. Comorbid heart disease, however, has a consistent effect and should be universally diagnosed and treated among patients with diabetes. Thus, we do not recommend universal programs for health promotion of patients with diabetes across countries. Based on our findings, tailored health promotion programs should be designed specific to each country.

Universal programs focusing on comorbid heart disease among patients with diabetes may be important. In addition, our results suggested clusters of countries with similar patterns of social and behavioral determinants of health. Patients in such countries may benefit from similar health promotion interventions. Our findings discourage policy makers and public health practitioners from implementing universal programs that assume social and behavioral determinants of well-being are the same across different settings. Our results may also explain why the same programs may have different effects on well-being of patients with diabetes across countries. Locally designed interventions may be superior to such rigid programs.

Limitations

The current study had several limitations. Due to the cross sectional design, causative associations are not plausible from this study. In addition, cross–country differences in the validity of self-report of subjective health and chronic conditions cannot be ruled out. The study did not measure glucose control, type of diabetes, or mental health as other factors associated with subjective health of participants with diabetes. The study also ignores duration or complications of diabetes.

Conclusion

Our study revealed major cross-country differences in social and behavioral determinants of well-being among patients with diabetes. Only comorbid heart disease was consistently associated with poor subjective health across all countries. The findings advocate for design and implementation of country–specific health promotion programs for patients with diabetes. Further research is needed on causes and consequences of cross-country variations in social and behavioral determinants of well-being among patients with chronic conditions.

Declarations

Acknowledgment

Research on Early Life and Aging Trends and Effects (RELATE): A Cross-National Study (ICPSR 34241) was conducted by Mary McEniry, who serves as a Research Affiliate at the University of Michigan’s Population Studies Center and as the Director of the DSDR project at ICPSR. The RELATE study compiles several cross-national surveys.

Authors’ Affiliations

(1)
Center for Research on Ethnicity, Culture, and Health (CRECH), Department of Health Behavior and Health Education, University of Michigan School of Public Health

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