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Keywords:

  • Diabetes;
  • glycaemic control;
  • social capital

Abstract

  1. Top of page
  2. Abstract
  3. What is known about this topic
  4. Introduction
  5. Methods
  6. Instrument
  7. Data collection
  8. Analysis
  9. Ethics
  10. Results
  11. Discussion
  12. Conclusion
  13. Acknowledgement
  14. Conflict of interests
  15. Author contributions
  16. References

Glycaemic control is an essential component in diabetes management. There is growing attention on the protective effects of social capital on health, where social capital comprises features of society that facilitate co-operation for mutual benefit. The aim of this study was to investigate its role as a social determinant of health in the glycaemic control of diabetes mellitus. A cross-sectional study was conducted in a diabetes care charity institute, Isfahan, Iran from July 2010 to September 2010. Based on the level of HbA1c, all patients were divided into two groups: HbA1c level ≤ 7 as controlled diabetes and HbA1c level > 7 as uncontrolled diabetes. Sixty patients were randomly selected from each group (controlled diabetes and uncontrolled diabetes) and all agreed to participate. Social capital was measured using the Integrated Questionnaire for the Measurement of Social Capital (SC-IQ). The mean age of participants in the controlled diabetes group was 51.3 (SD: 7.8) years and 50.1(SD: 7.2) in the uncontrolled group. The mean social capital score was 185.1 (CI 95% 181.4–188.6) in the controlled group and 175.4 (CI 95% 171.8–178.8) in the uncontrolled group. There was a significant negative correlation between empowerment and political action and trust and solidarity dimensions and the level of HbA1c. In multiple regression analysis, trust and solidarity and empowerment and political action were significant predictors of the HbA1c. The results of this study suggest that social participation, trust, and empowerment and political action may determine how effectively the patient’s diabetes has been managed. This initial finding warrants subsequent experimental investigations designed to identify strategies that can be used to foster the creation of social capital to improve diabetes control.


What is known about this topic

  1. Top of page
  2. Abstract
  3. What is known about this topic
  4. Introduction
  5. Methods
  6. Instrument
  7. Data collection
  8. Analysis
  9. Ethics
  10. Results
  11. Discussion
  12. Conclusion
  13. Acknowledgement
  14. Conflict of interests
  15. Author contributions
  16. References
  •  Social capital has a protective effect on risk-taking behaviours or corresponding disease outcomes.
  •  There is a strong association between social capital and management of chronic disease.

What this paper adds

  •  Social capital has a promising effect in the control of diabetes in Iran.
  •  Among social capital dimensions, ‘trust and solidarity’ and ‘empowerment and political action’, may determine how effectively that patient’s diabetes is managed.

Introduction

  1. Top of page
  2. Abstract
  3. What is known about this topic
  4. Introduction
  5. Methods
  6. Instrument
  7. Data collection
  8. Analysis
  9. Ethics
  10. Results
  11. Discussion
  12. Conclusion
  13. Acknowledgement
  14. Conflict of interests
  15. Author contributions
  16. References

Diabetes is the most prevalent metabolic condition with an impact on every aspect of life. The frequency of this disorder is dramatically increasing all over the world. More than 220 million people worldwide have diabetes and this figure is likely to be more than double by 2030 (Shaw et al. 2010). According to the World Health Organization (World Health Organisation, 2009), the number of people with diabetes in developing countries will increase by 150% in the next 25 years. The latest projection by Boyle et al. (2010) indicates that the annual diagnosed diabetes incidence (new cases) will increase from about eight cases per 1000 in 2008 to about 15 per 1000 in 2050. Considering its widespread prevalence and potentially debilitating impact, diabetes control becomes an increasingly important issue.

In recent years, there has been growing attention on the role of the social determinants of health and much of this work has focused on social capital. It is referred to as ‘the features of social organization, such as civic participation, norms of reciprocity, and trust in others, that facilitate cooperation for mutual benefit’ (Kawachi et al. 1997). Several studies indicate the protective effects of social capital in reducing stressful conditions, risk-taking behaviour, and psychological distress and may contribute to decreased mortality, as well as overall improved health status (Kawachi et al. 1999, Sullivan & Transue 1999, Lochner et al. 2003, Lindström 2004, Luthans & Youssef 2004, Luthans et al. 2004, Browning et al. 2006). In general, social capital is associated inversely with risk-taking behaviours or corresponding disease outcomes (Kawachi et al. 1996).

Several mechanisms have been suggested for this association between social capital and health, including increasing healthy behaviours, such as exercise, improving access to services, degree of social isolation, social norms, accessibility to health care and health education services (Putnam 1993, Holtgrave & Crosby 2006).

Social capital may have similar association with chronic diseases such as cardiovascular disease, diabetes and obesity. Holtgrave & Crosby (2006) in an exploratory study identified a potentially strong relationship between social capital and obesity and diabetes in US adults. A recent study of black veterans with diabetes living in Philadelphia, USA showed that living in neighbourhoods where the population work together is associated with better glucose control in the low-income population (Long et al. 2010).

However, despite the important role of social capital in management of chronic disease, few studies have specifically investigated its actual effect. The aim of this study was to investigate if there is any relationship between the social capital and glycaemic control in diabetic patients in Iran.

Methods

  1. Top of page
  2. Abstract
  3. What is known about this topic
  4. Introduction
  5. Methods
  6. Instrument
  7. Data collection
  8. Analysis
  9. Ethics
  10. Results
  11. Discussion
  12. Conclusion
  13. Acknowledgement
  14. Conflict of interests
  15. Author contributions
  16. References

This was a cross-sectional study, which was conducted in a diabetes care charity institute in Isfahan, Iran from July 2010 to September 2010. The target population was patients with type 2 diabetes who had registered in this charity. More than 1200 diabetic patients have been registered in this charity and are receiving care from the attending faculty of Isfahan University of Medical Sciences and private internal medicine specialists. Inclusion criteria included persons aged 30 or above with a definitive diagnosis of type 2 diabetes, as confirmed by a physician with or without complication. Patients with any documented diagnosis of end-stage renal disease, psychotic disorder, dementia or blindness were excluded.

The information of all patients was extracted from the charity database. This database captures demographic, laboratory, imaging, billing and use, and follows up information for all patients.

According to the database and considering the level of HbA1c, all eligible patients were divided into two groups: HbA1c level ≤ 7 was considered as controlled diabetes and HbA1c level > 7 as uncontrolled diabetes. At the time of study, 553 controlled and 698 uncontrolled diabetic patients were registered in this charity. Patients were randomly sampled from each group using IBM SPSS Statistics for Microsoft Windows, Version 16. For a statistically significant level of 5%, a statistical power of 80%, a standard deviation of 16.5 (estimated as range/6) (Hozo et al. 2005) and an expected mean difference of 9.9 in total social capital score between the two groups, a sample size of 60 patients per group was required.

Instrument

  1. Top of page
  2. Abstract
  3. What is known about this topic
  4. Introduction
  5. Methods
  6. Instrument
  7. Data collection
  8. Analysis
  9. Ethics
  10. Results
  11. Discussion
  12. Conclusion
  13. Acknowledgement
  14. Conflict of interests
  15. Author contributions
  16. References

The Information Gathering Social Capital Questionnaire (SC-IQ) (Grootaert et al. 2003) used in this study reflects the six dimensions measured in the original World Bank survey tool: (i) groups and networks (the extent of the patient’s group membership and participation in formal and informal organisations), (ii) trust and solidarity (the level of trust that the patient feels towards various people within the community), (iii) collective action and co-operation (the extent to which patients work together and interact with each other to solve community problems), (iv) information and communication (the various information resources consulted by patients), (v) social cohesion and inclusion (whether there is social conflict within the community) and (vi) empowerment and political action (to the extent they have a measure of control over institutions and processes directly affecting their well-being). The modified Persian version of this questionnaire contains 46 items and is validated by the Social Development and Health Promotion Research Center.

Data collection

  1. Top of page
  2. Abstract
  3. What is known about this topic
  4. Introduction
  5. Methods
  6. Instrument
  7. Data collection
  8. Analysis
  9. Ethics
  10. Results
  11. Discussion
  12. Conclusion
  13. Acknowledgement
  14. Conflict of interests
  15. Author contributions
  16. References

After explaining the purpose and content of the study to the patients and following their agreement, the social capital questionnaire was administered by the researchers in face-to-face interviews. The demographic data such as age, gender, education, occupation and clinical characteristics were derived from medical records of the charity database.

Analysis

  1. Top of page
  2. Abstract
  3. What is known about this topic
  4. Introduction
  5. Methods
  6. Instrument
  7. Data collection
  8. Analysis
  9. Ethics
  10. Results
  11. Discussion
  12. Conclusion
  13. Acknowledgement
  14. Conflict of interests
  15. Author contributions
  16. References

Descriptive statistics were used to present the demographic data and the social capital score. The Student’s t-test, Chi-square test for trend and Fisher’s exact test were used to determine the difference between the two groups in demographic variables. The bivariate relationship between social capital scales and outcome measure (HbA1c) was assessed by calculating Pearson correlation coefficients. Predictor variables achieving bivariate significance were placed into a linear regression model. Multiple linear regression analysis was performed to measure the association between social capital subscale scores and HbA1c after controlling potential confounders. The normal distribution of dependent variable and residuals was tested using visual inspection of data plots, and the Kolmogorov–Smirnov test. Collinearity diagnostics were performed by means of the variance inflation factors (VIF) for each independent variable entered in the regression equations; a VIF > 10 was considered as positive multicollinearity (Kleinbaum et al. 2007). The level of significance was set at P < 0.05, and all tests were two-tailed. Data were analysed using SPSS (Release 16) for Windows.

Ethics

  1. Top of page
  2. Abstract
  3. What is known about this topic
  4. Introduction
  5. Methods
  6. Instrument
  7. Data collection
  8. Analysis
  9. Ethics
  10. Results
  11. Discussion
  12. Conclusion
  13. Acknowledgement
  14. Conflict of interests
  15. Author contributions
  16. References

The design of the study was approved by the Ethics committee of the Vice Chancellor for Research, Isfahan University of Medical Sciences (project No. 387197). Written and informed consent was obtained from all patients. The confidentiality of all information was managed by researchers in accordance with the approved Ethics Committee protocol.

Results

  1. Top of page
  2. Abstract
  3. What is known about this topic
  4. Introduction
  5. Methods
  6. Instrument
  7. Data collection
  8. Analysis
  9. Ethics
  10. Results
  11. Discussion
  12. Conclusion
  13. Acknowledgement
  14. Conflict of interests
  15. Author contributions
  16. References

All patients approached agreed to participate and 60 patients were recruited in each group (controlled diabetes and uncontrolled diabetes). The response rate was 100% in both groups. The mean age of participants was 51.3 (SD: 7.8) years in the controlled group and 50.1(SD: 7.2) in the uncontrolled group. The difference in mean age between the two groups was not statistically significant (P = 0.372). The duration of disease was 24.2 months in the controlled group and 23.6 months in the uncontrolled one. There was no statistically significant difference between the two groups related to duration of disease or gender (P > 0.5). The demographic data of patients in the two groups are summarised in Table 1. The controlled group had a higher level of education and occupation, in comparison with the uncontrolled group, and this difference was statistically significant (< 0.001 for education and P = 0.034 for occupation).

Table 1.   Demographic characteristics of diabetic patients (n = 120)
VariableGroup n (%) P value
Controlled diabetes = 60Uncontrolled diabetes = 60
  1. HbA1c level ≤ 7: controlled diabetes. HbA1c level > 7 uncontrolled diabetes.

Gender
 Female47 (78.3)51 (85.0)0.345
 Male13 (21.7)9 (15.0)
Education
 Basic22 (36.7)51 (85.0)<0.001
 Intermediate32 (53.3)9 (15.0)
 Higher education6 (10.0)0 (0.0)
Occupation
 Housewife40 (66.7)49 (81.7)0.031
 Retired2 (3.3)6 (10.0)
 Governmental12 (20.0)4 (6.7)
 Other6 (10.0)1 (1.6)
Treatment
 Oral medications44 (73.3)45 (75.0)0.525
 Insulin therapy11 (18.3)7 (11.7)
 Diet5 (8.3)7 (11.7)
 Oral medication + insulin0 (0.0)1 (1.6)

Most patients were treated with oral hypoglycaemic agents either alone or along with insulin and there was no statistically significant difference, except for fasting blood sugar, between two groups (P = 0.652). Also, the difference between biochemical parameters of these two groups was not statistically significant (Table 2).

Table 2.   Biochemical parameters of two groups (controlled and uncontrolled diabetic patients)
Biochemical blood parametersGroupMeanSD P value
  1. HbA1c level ≤ 7: controlled diabetes. HbA1c level > 7 uncontrolled diabetes.

TriglycerideControlled169.598.50.652
Uncontrolled194.1108.4
CholesterolControlled169.248.20.143
Uncontrolled241.738.2
High density lipoproteinControlled45.59.10.331
Uncontrolled43.510.4
Low density lipoproteinControlled95.129.90.933
Uncontrolled110.426.3
Fasting blood sugarControlled122.124.5<0.001
Uncontrolled152.346.3

The mean of social capital score in the controlled group was 185.1 (CI 95% 181.4–188.6) compared with 175.4 (CI 95% 171.8–178.8) in the uncontrolled diabetes group. Higher scores were detected in empowerment and political action, trust and solidarity, and the total social capital score in the controlled group (Table 3).

Table 3.   Social capital dimensions in two groups (controlled and uncontrolled diabetic patients)
Social capital itemGroupMeanSD95% Confidence interval for mean
Lower boundUpper bound
  1. HbA1c level ≤ 7: controlled diabetes. HbA1c level > 7 uncontrolled diabetes.

Empowerment and political actionControlled46.96.645.448.4
Uncontrolled44.06.242.545.5
Trust and solidarityControlled46.65.545.248.1
Uncontrolled43.14.641.744.5
Social cohesion and inclusionControlled36.14.535.037.3
Uncontrolled35.44.133.336.6
Groups and networksControlled25.44.924.126.7
Uncontrolled24.14.322.825.3
Information and communicationControlled17.63.516.818.4
Uncontrolled16.72.416.017.5
Collective action and co-operationControlled12.21.811.712.7
Uncontrolled11.81.611.312.2
Total social capital scoreControlled185.113.8181.4188.6
Uncontrolled175.413.1171.8178.8

The Pearson correlation showed a statistically significant negative correlation in all social capital subscales except ‘collective action and co-operation’ and ‘social cohesion and inclusion’. Significant variables were included in the linear regression model (Table 4).

Table 4.   Pearson correlations between social capital dimensions and HbA1c
 HbA1c
Correlation coefficient P value
Empowerment and political action−0.374<0.001
Trust and solidarity−0.385<0.001
Social cohesion and inclusion−0.0790.389
Groups and networks−0.2630.004
Information and communication−0.2060.024
Collective action and co-operation−0.0880.337
Total social capital score−0.464<0.001

As education and occupation were correlated with both the main independent variable (social capital dimensions) and the outcome variable (diabetes control), they were included in the multivariable analysis as confounder variables. In multiple regression analyses, after evaluating the correlations among the independent variables, no multicollinearity problem was detected and the residuals appeared to have a normal distribution with homogeneous variance. There was a negative significant association between dimensions of trust and solidarity (unstandardised regression coefficient B = −0.260, P = 0.004), empowerment and political action (B = −0.188, P = 0.027) and the level of HbA1c (Table 5).

Table 5.   Relationship between social capital dimensions and HbA1c using multiple linear regression
 HbA1c level
Standardised coefficient BUnstandardised coefficient95% CI for unstandardised coefficient P value
Lower boundUpper bound
Empowerment and political action−0.188−0.050−0.094−0.0060.027
Trust and solidarity−0.260−0.055−0.092−0.0170.004
Groups and networks−0.118−0.003−0.009−0.0010.342
Information and communication−0.176−0.004−0.0100.0020.219

Discussion

  1. Top of page
  2. Abstract
  3. What is known about this topic
  4. Introduction
  5. Methods
  6. Instrument
  7. Data collection
  8. Analysis
  9. Ethics
  10. Results
  11. Discussion
  12. Conclusion
  13. Acknowledgement
  14. Conflict of interests
  15. Author contributions
  16. References

The aim of this study was to investigate the effect of social capital as a social determinant of health on the level of HbA1c. The total score of social capital was higher in controlled diabetes and this difference was statistically significant. Consistent with theory, social capital was negatively correlated with the control of diabetes and this association was significant in ‘empowerment and political action’ and ‘trust and solidarity’ dimensions. The results revealed that social capital appears to impact on the control of diabetes.

It seems that among persons with diabetes, higher levels of trust and empowerment and political action have been associated with more civic engagements and resulted in better self-management, including adherence to recommended diet and exercise regimens and better glycaemic control (Garay-Sevilla 1995, Toljamo & Hentinen 2001). This protective effect of social capital had been investigated in several studies.

Long et al. (2010) in a study of US black veterans with diabetes found that living in neighbourhoods where people work together is associated with better glycaemic control. Holtgrave & Crosby (2006) in their exploratory study suggested that greater levels of social capital are protective against obesity and diabetes among the US population. This might reflect the effect of norms and social cohesion on prevention and controlling of disease.

Another study found that social networks were associated with good self-rated health in type 2 diabetic patients. They considered social networks as providing a ‘buffer effect’, and suggest that those who are exposed to a high level of burden might benefit most from it (Eller et al. 2008). Evidence has suggested similar results between trust and health and also between social participation and mental health (Poortinga 2006, Mohseni & Lindstrom 2007, Lindstrom & Mohseni 2009).

Our study showed no statistically significant difference in ‘groups and networks’, ‘collective action and co-operation’, ‘information and communication’ and ‘social cohesion and inclusion’ dimensions of social capital. This may be due to the transitional nature of Iranian society in recent decades. Our society has experienced a distortion in traditional networks and social cohesion patterns adopting new structures of a more developed society. It is expected that new patterns of social action and networks will be developed in the future to build a novel social cohesion in our community.

Furthermore, our results showed that there is a statistically significant difference in the control of diabetes in terms of education and occupation. Patients with better-controlled diabetes had higher levels of education. Educational level creates differences between people in terms of occupation and as well as income level. It has several important consequences on the service utilisation rate as well as self-care (Van Der Meer & Mackenbach 1999, Muller 2002, Schillinger et al. 2002). This study showed that patients with occupations which required more civic engagements had lower level of HbA1c in comparison with housekeepers and retired ones. This finding is in line with earlier studies (Davey Smith et al. 1998, Weijman et al. 2003). These results have some theoretical implications for the effectiveness of social support and encourage us to include them in prevention programmes, aiming to provide more trust and civic engagement for patients.

Our study, while having many strengths, involved some limitations that should be considered. The cross-sectional nature of this study makes it impossible to draw inferences about the causality with certainty. Additionally, our participants were selected from a charity clinic in one city. The small sample size prevents more elaborate subgroups analyses. Broader geographical samples may prove more or different effects. An additional limitation concerns the fact that the responses were based on interviews and could have introduced a prestige bias where the participants’ answers are an overestimation or underestimation depending on which they perceived as being socially acceptable.

Conclusion

  1. Top of page
  2. Abstract
  3. What is known about this topic
  4. Introduction
  5. Methods
  6. Instrument
  7. Data collection
  8. Analysis
  9. Ethics
  10. Results
  11. Discussion
  12. Conclusion
  13. Acknowledgement
  14. Conflict of interests
  15. Author contributions
  16. References

To sum up, the results of this study suggest that social participation, trust and empowerment and political action, may determine how effectively the patient’s diabetes is managed. Social capital is likely to have broad implications across a variety of health and social conditions especially in management of chronic diseases such as diabetes. More research, particularly longitudinal, is warranted to direct the causation between social capital and diabetes control. Furthermore, there is an obvious need to explore strategies to foster the creation of social capital in the regions of Iran with high prevalence of diabetes.

Acknowledgement

  1. Top of page
  2. Abstract
  3. What is known about this topic
  4. Introduction
  5. Methods
  6. Instrument
  7. Data collection
  8. Analysis
  9. Ethics
  10. Results
  11. Discussion
  12. Conclusion
  13. Acknowledgement
  14. Conflict of interests
  15. Author contributions
  16. References

This study was conducted by research chancellor of Isfahan University of Medical Sciences. Our heartfelt thanks are extended to all the patients who graciously agreed to participate in this study. We thank Dr Azita Kheiltash who kindly provided us the translated Social Capital questionnaire. Also, we are greatly thankful of Ms. Taki and all the staff of the Diabetic charity of Isfahan for their support throughout the project. We sincerely thank Dr Amir Loghmani for his contribution to this study. The authors thank the anonymous reviewers for their helpful comments and constructive suggestions for improving this manuscript.

Author contributions

  1. Top of page
  2. Abstract
  3. What is known about this topic
  4. Introduction
  5. Methods
  6. Instrument
  7. Data collection
  8. Analysis
  9. Ethics
  10. Results
  11. Discussion
  12. Conclusion
  13. Acknowledgement
  14. Conflict of interests
  15. Author contributions
  16. References

ZF, the main investigator, analysed the data and wrote the manuscript. NJ assisted in designing the study, contributed to the analysis and helped in writing the final manuscript. SN contributed to the study design, data analysis and writing the manuscript. MK helped in design of study and AZ contributed to study design and writing the final manuscript. All authors read and approved the final version of manuscript.

References

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  2. Abstract
  3. What is known about this topic
  4. Introduction
  5. Methods
  6. Instrument
  7. Data collection
  8. Analysis
  9. Ethics
  10. Results
  11. Discussion
  12. Conclusion
  13. Acknowledgement
  14. Conflict of interests
  15. Author contributions
  16. References
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