Social Determinants of Health in Adults with Type 2 Diabetes – Contribution of Mutable and Immutable Factors

Abstract

Aims

Socioeconomic, psychosocial and neighborhood factors influence clinical outcomes and self-care behaviors in diabetes; however, few studies simultaneously assessed the impact of multiple social determinant of health factors on glycemic control. We used an explanatory model to examine the differential contribution of social determinants and clinical factors on glycemic control. Secondarily, we examined the contribution of mutable and immutable factors to identify meaningful future interventions.

Methods

615 adults with type 2 diabetes in the southeastern United States were recruited. A hierarchical model was run with HbA1c as the dependent variable and independent variables entered in blocks: demographics (block 1), socioeconomic (block 2), psychosocial (block 3), built environment (block 4), clinical (block 5), and knowledge/self-care (block 6).

Results

Significant associations for HbA1c included self-efficacy (β= −0.10, p<0.001), social support (β= 0.01, p<0.05), comorbidity (β= −0.09, p<0.05), insulin use (β= 0.95, p<0.001), medication adherence (β= −0.11, p<0.05), and being a former smoker (β= 0.34, p<0.05); accounting for 24.4% of the variance.

Conclusions

Important factors that drive glycemic control are mutable, and amenable to health interventions. Greater attention should be given to interventions that increase self-efficacy and social support, reduce the burden of comorbidities, and enhance medication adherence and smoking cessation.

1. INTRODUCTION

Type 2 diabetes affects more than 29 million people in the US, with an estimated 8.1 million individuals having undiagnosed type 2 diabetes and an estimated 86 million Americans diagnosed with prediabetes [1]. In 2012, costs associated with type 2 diabetes totaled $245 billion, including direct and indirect costs [1]. The remarkable cost of type 2 diabetes can be attributed to various complications and comorbid conditions associated with this chronic illness, including hypo- and hyperglycemia, high blood pressure, high LDL cholesterol, heart disease, stroke, blindness, kidney disease, and amputations [1]. The American Diabetes Association (ADA) recommends those diagnosed with type 2 diabetes receive diabetes self-management education and support, including training to improve diabetes knowledge and self-care behaviors as well as clinical care to support glycemic control [2]. Furthermore, the ADA emphasizes that diabetes education and support should address psychosocial and other external barriers to attaining optimal health outcomes, for example community factors, cultural barriers, and patient decision support [2,3]. Accordingly, social determinants of health have been shown to influence individuals with chronic illness [4], including those diagnosed with type 2 diabetes [5].

The CDC has characterized social determinants of health as economic and social conditions that influence the health of people and communities [6]. Socioeconomic factors have been shown to influence health, including outcomes and mortality in patients with diabetes. [7–9] In addition, psychosocial and neighborhood factors have been shown to impact health outcomes, including individuals diagnosed with type 2 diabetes [5]. Psychosocial factors address the interaction between individuals and their social environment, and have been shown to be a strong influence in diabetes. [10–11] Poor emotional coping to a diagnosis of type 2 diabetes affects an individual’s ability to adhere to self-care behaviors [12] and factors such as fatalism, depression, perceived stress, and serious psychological distress, and social support influence both self-care and diabetes outcomes. [13–17]. Additionally, external influences, such as neighborhood factors, have also been associated with health outcomes including food insecurity, perceived neighborhood problems, neighborhood safety, and walking/exercise environment [18–20]. Studies have found that neighborhood aesthetics and social support affect hemoglobin A1C [20] and residents in neighborhoods with higher income levels and neighborhood stability had better diabetes related self-care behaviors [21]. Neighborhood level socioeconomic factors, in addition to individual socioeconomic levels continue to be the most pervasive social determinant of health [19,22].

Though socioeconomic, psychosocial and neighborhood factors have been shown to influence clinical outcomes and self-care behaviors in diabetes, few studies have simultaneously assessed the impact of multiple social determinant of health factors on glycemic control. This study sought to consider both mutable and immutable factors that influence diabetes outcomes. The concept of mutable factors was first introduced by Aday and Anderson to describe health system factors that can be changed so patients could receive better access to medical care. [23] For the purposes of this study, we defined mutable factors as any influence that is amendable to change via intervention. The primary goal of this study was to use an explanatory model to better understand the differential role of social determinants of health, clinical factors and self-care on glycemic control in individuals with type 2 diabetes. The secondary goal was to understand the role of mutable and immutable factors and identify factors that should be targets for future interventions.

2. MATERIALS AND METHODS

2.1 Subjects

Two adult primary care clinics in the southeastern United States served as the site for recruitment of 615 patients with type 2 diabetes. All procedures were in accordance with ethical standards and approved by the Medical University of South Carolina institutional review board and the VA Research & Development committee prior to study enrollment. Eligibility included (1) ages 18 years or older, (2) diagnosis of type 2 diabetes in their medical record, and (3) able to communicate in English. Ineligibility was determined by interaction or chart documentation of cognitive impairment as a result of significant dementia or active psychosis.

Eligible patients were sent letters or approached in clinic, and those interested were provided an explanation of the study prior to being consented. Validated questionnaires collected demographic information, social determinants of health factors, self-care and clinical factors. Selected social determinant factors were included based on a modified version of the conceptual framework by Brown et al. [19] relating social determinants to health outcomes in patients with diabetes. Outcomes were abstracted from the electronic medical record, including blood pressure, cholesterol (LDL), and HbA1c.

2.2 Demographic Variables

Age was used as a continuous variable. Race was categorized as non-Hispanic Black, non-Hispanic White and Hispanic/other. Marital status was categorized as never married, married, separated/divorced, and widowed. Insurance was categorized as none, private, Medicare, Medicaid, VA and other.

2.3 Socioeconomic Variables

Previously validated items from the 2002 National Health Interview Survey [24] were used to capture household income, years of education and employment status. Household income was categorized into 10 income units and treated as a continuous variable. Years of education and hours worked per week were also treated as continuous variables.

2.4 Psychosocial Factors

Fatalism

Fatalism was assessed with the Diabetes Fatalism Scales (DFS); a 12-item scale where higher scores represent greater diabetes fatalism. [25] The DFS has a Cronbach’s alpha of 0.80. [25]

Depression

Depression was assessed with the PHQ-9; a 9-item scale based on the DSM-IV criteria for depression with higher scores indicating more severe depression. [26] Sensitivity is 88% and specificity is 88% for major depression. [27]

Diabetes Distress

Distress was assessed with the Diabetes Distress Scale (DDS); a 17-item measure with questions about disease management, support, emotional burden and access to care. (24) The sensitivity and specificity ranged from 0.85 to 0.97. [28]

Serious Psychological Distress

Serious Psychological Distress (SPD) was assessed with the K6; a 6-item scale with higher scores representing higher probability of severe mental illness. The scale has good precision and consistent psychometric properties across major sociodemographic samples. [29]

Self-Efficacy

Self-efficacy was assessed with the Perceived Diabetes Self-Management Scale (PDSMS); an 8-item measure where higher scores indicate higher self-efficacy. [30] It is a valid and reliable measure of diabetes self efficacy (Cronbach alpha = 0.83). [30]

Social Support

Social Support was assessed with the Medical Outcomes Study (MOS) Social Support Survey; a 19-item scale measuring tangible support, affection, positive social interaction, and emotional or informational support. The total scale has high internal consistency (α=0.97), good criterion and discriminant validity, and one-year test-retest reliability (0.72 to 0.76). [31]

Perceived Stress

Stress was assessed with the Perceived Stress Scale (PSS); a 4-item scale assessing the frequency over the previous month with which the respondent finds situations stressful. [32] The Cronbach alpha value is 0.69 and scores are highly correlated with stress, depression and anxiety. [33]

2.5 Built Environment Factors

Evidence from a prior validation study indicated six scales and four indices that can be used to assess the built environment [18]. For each of the scales and indices a higher score indicates more perceived problems in the neighborhood.

Neighborhood Characteristics

Aesthetic quality consisted of 7 items; walking/exercise environment and consisted of 11 items; access to healthy foods and consisted of 11 items; crime/safety (safety had 3 items and crime had 4 items); and social cohesion and consisted of 5 items. The scales included items with response categories ranging from 1 (strongly agree) to 5 (strongly disagree). The violence scale response options ranged from 1 (often) to 4 (never). The recreation facilities index included eight items on the presence of recreational facilities in the neighborhoods (yes/no) and rating of the facilities from 1 (excellent) to 4 (poor condition).

Neighborhood Participation Index

12 items that measure a person’s participation in civic and political activities with their neighbors. This index counts a “yes” response as 1 point and then summed the responses to indicate a person’s participation.

Neighborhood Problems Index

16 items measuring neighborhood characteristics such as presence of trash and litter. The response categories range from 1 to 3, with 1 indicating that the neighborhood attribute was not a problem, 2 it was somewhat of a problem, and 3 it was a big problem. Responses were summed to construct the neighborhood problems index score.

2.6 Clinical Factors

Duration of diabetes was collected in years and treated as a continuous variable. Body mass index (BMI) was calculated based on the most recent height and weight from the electronic medical record. Comorbidity was calculated using the Charlson comorbidity index and reported as a continuous score. [34] Participants reported use of insulin, kidney problems, or eye problems, and results were dichotomized as yes or no. Self-reported health status was reported on a scale of 1 to 5 with 1 being low and 5 being high. [20]

2.7 Diabetes Knowledge and Self-Care

Diabetes Knowledge

Diabetes knowledge was assessed with the Diabetes Knowledge Questionnaire (DKQ); a 24-item scale where the final score is based on the percentage of correct scores. [35]

Self-Reported Medication Adherence

Medication adherence was assessed with the Morisky Medication Adherence Scale (MMAS); an 8-item scale with higher values indicating poorer adherence. [36]

Behavioral Skills

Diabetes behavior was assessed with the Summary of Diabetes Self-Care Activities (SDSCA) scale; an 11-item scale measuring frequency of self-care activity in the last 7 days for general diet (follow healthy diet), specific diet (ate fruits/low fat diet), exercise, blood glucose testing, and foot care. [37] Smoking was self-reported as never smoked, former smoker or current smoker.

2.8 Outcome Measure

Hemoglobin A1c was abstracted from the electronic medical record using values within the previous 6 months relative to date of completed survey.

2.9 Statistical Analyses

Sample Size

The sample size provides 80% power to detect an association of at least ρ=0.3, where ρ represents the population correlation between the HbA1c and each primary independent variable. In multivariate analyses, the sample size provides 80% power to detect an increment of at least 10% in R² for a given primary independent variable, while controlling for covariates. As a result, this sample size has power to detect a small or moderate effect (2% to 13% of the variance).

Analysis

Three sets of analyses were conducted to provide information on the individual and collective contribution of mutable and immutable factors on glycemic control. First, means and percentages for all variables were calculated and normality was assessed. Second, pairwise correlation (for continuous variables) and one-way ANOVA (for categorical variables) were used to test the association among HbA1c and demographics, social determinant of health variables, clinical factors, and knowledge/self-care. Third, a hierarchical multiple linear regression model was run with variables entered in blocks based on theoretical relationships: demographics (block 1), socioeconomic factors (block 2), psychosocial factors (block 3), built environment factors (block 4), clinical factors (block 5), and knowledge/self-care (block 6). All analyses were performed with STATA Version13. A two-tailed alpha of 0.05 was used to assess for significance.

3. RESULTS

Patient characteristics for this sample of 615 adults with type 2 diabetes are summarized in Table 1. The majority of the population were non-Hispanic Blacks (64.9%) and men (61.6%). The average age was 61.3 years, with an average of 13.4 years of education and 12.5 hours worked per week. Participants had been diagnosed with type 2 diabetes for an average of 12.3 years.

Table 1.

Sample demographic characteristics (n=615)

	% or Mean ± standard deviation
Age (years)	61.3 ± 10.9
Education (years)	13.4 ± 2.8
Employment (hours worked per week)	12.5 ± 18.9
Diabetes Duration (years)	12.3 ± 9.1
Comorbidity (Charlson score)	25.7 ± 2.2
Health Status (score)	3.4 ± 0.9
Gender
Women	38.4
Men	61.6
Race/Ethnicity
Non-Hispanic Black	64.9
Non-Hispanic Whites	33.0
Hispanic/Other	2.1
Marital Status
Never Married	11.2
Married	49.7
Separated/Divorced	28.2
Widowed	10.9
Site of Care
Non-VAMC	51.2
VAMC	48.8
Annual income level
<$10,000	20.2
$10,000–$14,999	11.3
$15,000–$19,999	10.1
$20,000–$24,999	10.4
$25,000–$34,999	14.7
$35,000–$49,999	13.8
$50,000–$74,999	10.1
$75,000+	9.4

Table 2 shows the correlation between glycemic control and mutable and immutable factors. This model shows that glycemic control has a significant inverse relationship with age (0.15, p<0.001), education (−0.08, p<0.05), general diet (−0.12, p<0.01), exercise (−0.10, p<0.05), medication adherence (0.03, p<0.001), self-efficacy (−0.34, p<0.001), social support (−0.09, p<0.05), and perceived neighborhood problems (−0.01, p<0.05). Contrarily, a significant positive correlation existed between employment status (0.09, p<0.05), health status (0.04, p<0.05), blood sugar testing (0.09, p<0.05), fatalism (0.08, p<0.05), depression (0.16, p<0.001), diabetes distress (0.27, p<0.001), serious psychological distress (0.13, p<0.01), perceived distress (0.12, p<0.01), neighborhood aesthetics (0.10, p<0.05), walking/exercise environment (0.09, p<0.05), neighborhood violence (0.12, p<0.01), neighborhood crime (0.13, p<0.01), self-rating of neighborhood (0.15, p<0.001), neighborhood comparison (0.10, p<0.05), food insecurity (0.15, p<0.001), and diabetes duration (0.16, p<0.001) and glycemic control. Additionally, we found that glycemic control differed significantly by gender (women 7.7% ± 1.8 vs. men 8.1% ± 1.9, p<0.05), site of care (non-VAMC 7.6% ± 1.7 vs. VAMC 8.3% ± 1.9, p<0.001), use of insulin (yes 8.6% ± 1.9 vs no 7.3% ± 1.5, p<0.001), diabetes-related kidney problems (yes 8.3% ± 1.9 vs no 7.9% ± 1.8, p<0.05), and diabetes-related eye problems (yes 8.2% ± 1.8 vs no 7.8% ± 1.8, p<0.001).

Table 2.

Pearson’s Correlation for Association between HbA1c and demographics, social determinant of health variables, clinical factors, and knowledge/self-care

	Correlation coefficient
Age ***	−0.15
Education *	−0.08
Income	−0.06
Employment *	0.09
Diabetes duration ***	0.16
BMI	−0.03
Comorbidity	0.04
Health status *	0.10
Diabetes knowledge	0.05
Self-care
General Diet **	−0.12
Specific Diet	−0.07
Exercise *	−0.10
Blood Sugar Testing *	0.09
Foot Care	0.03
Medication Adherence ***	−0.20
Psychosocial Factors
Fatalism *	0.08
Depression ***	0.16
Diabetes Distress ***	0.27
Serious Psychological Distress **	0.13
Self-efficacy ***	−0.34
Social Support *	−0.09
Perceived Stress **	0.12
Neighborhood Factors
Neighborhood Aesthetics *	0.10
Walking/Exercise Environment *	0.09
Neighborhood Safety	0.07
Access to Healthy Foods	0.06
Social Cohesion	−0.04
Neighborhood Violence **	0.12
Neighborhood Crime **	0.13
Self-rating of Neighborhood ***	0.15
Neighborhood Comparison *	0.10
Recreational Facilities	0.06
Neighborhood Activity Index	0.03
Perceived Neighborhood Problems *	−0.01
Food insecurity ***	0.15

^*p<0.05,

^**p<0.01,

^***p<0.001

Results of the hierarchical multiple linear regression model that included demographics, social determinants, clinical factors and self-care behaviors on glycemic control are shown in Table 3. In the first model assessing patient demographics, age (β= −0.02, p<0.01) and receiving care at the VAMC (β= 0.75, p<0.01) were significant. Age (β= −0.02, p<0.05) and receiving care at a VAMC (β= 0.75, p<0.01) remained significant after socioeconomic variables (block 2) were added. Psychosocial factors were added as a third block. We found that receiving care at a VAMC (β= 0.50, p<0.05), diabetes distress (β= 0.30, p<0.05), self-efficacy (β= −0.11, p<0.001), and social support (β= 0.01, p<0.05) were significant. The fourth block included factors of the built environment and significant relationships remained for receiving care at a VAMC (β=0.48, p<0.05), diabetes distress (β= 0.31, p<0.01), self-efficacy (β= −0.11, p<0.001), and social support (β= 0.01, p<0.05). When clinical factors (block 5) were added to the regression we found Medicaid insurance (β= −0.66, p<0.05), diabetes distress (β= 0.29, p<0.05), self-efficacy (β= −0.09, p<0.001), social support (β= 0.01, p<0.05), and insulin use (β= 1.01, p<0.001) were significant. The final model added diabetes knowledge and self-care variables (block 6) and showed, accounting for all factors, the factors that remained significant were self-efficacy (β= −0.10, p<0.001), social support (β= 0.01, p<0.05), comorbidity (β= −0.09, p<0.05), insulin use (β= 0.95, p<0.001), medication adherence (β= −0.11, p<0.05), and being a former smoker (β= 0.34, p<0.05). The final model accounted for 24.4% of the variance observed in glycemic control in the sample population.

Table 3.

Hierarchical regression models for influence of social determinants, clinical factors and self-care on glycemic control

	Model 1 (Demographics)	Model 2 (Socioeconomic)	Model 3 (Psychosocial)	Model 4 (Built Environment)	Model 5 (Clinical)	Model 6 (Knowledge and Self-care)
Age	−0.02 **	−0.02 *	−0.01	−0.01	−0.01	−0.07
Gender
Female (ref)	---	---	---	---	---	---
Male	0.04	−0.07	0.13	0.15	0.20	0.19
Race
Non-Hispanic White (ref)	---	---	---	---	---	---
Non-Hispanic Black	0.15	−0.01	0.09	−0.04	−0.13	−0.15
Hispanic/Other	0.10	0.05	−0.21	−0.24	−0.52	−0.61
Marital Status
Never married (ref)	---	---	---	---	---	---
Married	0.01	0.08	0.01	0.01	−0.01	−0.01
Separated/Divorced	0.07	0.05	0.06	0.10	0.12	0.13
Widowed	−0.04	−0.12	−0.18	−0.21	0.03	−0.03
Insurance
None (ref)	---	---	---	---	---	---
Private	0.16	0.10	0.12	0.12	−0.16	−0.10
Medicare	−0.04	−0.13	−0.12	−0.09	−0.26	−0.21
Medicaid	−0.15	−0.32	−0.33	−0.41	−0.66 *	−0.63
VA	−0.28	−0.27	−0.36	−0.33	−0.43	−0.37
Other	0.03	−0.17	−0.17	−0.15	−0.18	−0.17
Site
Non-VAMC (ref)	---	---	---	---	---	---
VAMC	0.75 **	0.75 **	0.50 *	0.48 *	0.24	0.20
Education		−0.05	−0.03	−0.03	−0.02	−0.02
Income		−0.04	−0.12	−0.001	−0.03	−0.04
Employment		0.01	0.01	0.005	0.01	0.01
Fatalism			−0.01	−0.01	−0.02	−0.01
Depression			−0.01	−0.01	0.01	0.004
Diabetes Distress			0.30 *	0.31 *	0.29 *	0.27
Serious Psychological Distress			0.01	0.01	0.005	0.003
Self-efficacy			−0.11 ***	−0.11 ***	−0.09 ***	−0.10 ***
Social Support			0.01 *	0.01 *	0.01 *	0.01 *
Perceived Stress			−0.02	−0.03	−0.03	−0.04
Neighborhood Aesthetics				0.05	0.03	0.04
Walking/Exercise Environment				0.01	0.01	0.001
Neighborhood Safety				−0.01	0.003	−0.008
Access to Healthy Food				−0.01	−0.01	−0.01
Social Cohesion				−0.04	−0.04	−0.04
Neighborhood Violence				0.01	0.02	0.02
Neighborhood Crime				0.18	0.14	0.17
Self-rating of Neighborhood				0.01	0.08	0.10
Neighborhood Comparison				0.005	−0.08	−0.04
Recreational Facilities				0.02	0.03	0.03
Neighborhood Activity Index				−0.04	−0.03	−0.03
Neighborhood Problems				0.03	0.03	0.03
Food Insecurity				0.07	0.07	0.07
Duration of diabetes					0.01	0.01
BMI					−0.01	−0.01
Comorbidity					−0.07	−0.09 *
Insulin use
No (ref)					---	---
Yes					1.01 ***	0.95 ***
Kidney problems
No (ref)					---	---
Yes					−0.04	−0.09
Eye problems
No (ref)					---	---
Yes					−0.05	0.08
Health status					−0.01	−0.05
Diabetes knowledge						0.01
General diet						0.05
Specific diet						0.01
Exercise						−0.05
Blood sugar testing						0.04
Foot care						0.03
Medication adherence						−0.11 *
Smoking
Never smoke (ref)						---
Former smoker						0.34 *
Current smoker						−0.02
Adjusted R2	0.0393	0.0446	0.1529	0.1541	0.2334	0.2442

^*p<0.05,

^**p<0.01,

^***p<0.001,

ref = reference group

Adjusted beta estimates are given for final multiple linear regression model for influence on glycemic control, variables were entered in blocks based on theoretical relationships: demographic factors (block 1), socioeconomic factors (block 2), psychosocial factors (block 3), built environment (block 4), clinical factors (block 5), and knowledge/self-care (block 6).

4. DISCUSSION

This study sought to identify the mutable and immutable factors independently associated with glycemic control by using an explanatory model to detect incremental effect. After accounting for demographic factors, socioeconomic status, psychosocial factors, the built environment, clinical factors, and knowledge/self-care, the most significant influences were self-efficacy, social support, comorbidity, insulin use, being a former smoker, and medication adherence. As a result, this study found that the majority of factors driving glycemic control are mutable, and amenable to interventions by the healthcare system.

By elucidating the most significant independent factors influencing glycemic control, these results show that mutable social determinants of health are critical to address in patients with type 2 diabetes. These factors most appropriately represent mutable enabling (self-efficacy and social support) and need (comorbidity, insulin use, smoking cessation, medication adherence) components of Aday and Anderson’s model for access to health care. [23] These factors, in turn, influence both healthcare utilization and consumer satisfaction, but as all are mutable, they are amenable to policy changes and interventions at the clinical level. [23] The findings of this study suggests that there is need to shift attention from traditional focus on education and skills training in diabetes to broader strategies that incorporate interventions that target important social determinants of health such as social support. There is also a need to develop more effective interventions that reduce the high comorbidity burden in diabetes, enhance smoking cessation and improve medication adherence.

A number of interventions currently exist to address the mutable factors found influential in this study. Psychosocial factors such as self-efficacy and social support can be addressed through psychological interventions aimed at improving glycemic control through therapies such as cognitive behavioral therapy and problem-solving. [38,39] A study by Alam et al. showed that generalists could deliver these psychological interventions with equal effectiveness if trained. [39] Expanding on current recommendations to consider family, social and community environment in clinical care [40], ADA and health systems should consider provision of training to clinicians to allow delivery of less intensive psychological interventions in a primary care setting. Use of psychological interventions to address self-efficacy and social support will allow additional psychosocial factors to be taken into account, such as depression and stress. Individuals with more depressive symptoms were shown to have greater gains in self-efficacy following self-management training. [41] Additionally, obstructive family behaviors were shown to exacerbate the association between both stress and depression with medication non-adherence. [42] These findings show the importance of considering a comprehensive view of treatment, which includes clinical, behavior, and psychosocial factors together.

Additionally, early and aggressive treatment of diabetes should be considered to limit the impacts of comorbidities resulting from uncontrolled diabetes. Randomized controlled trials show that early and aggressive treatment focused on improving glycemic control are important in addressing progression of diabetes and reducing diabetes-related complications. [43] Effective strategies to enhance medication adherence and smoking cessation need to be employed early in disease onset, as they have been shown to lower HbA1c, improve lipid profiles, and reduce diabetes complications. [44] A number of pharmacist-based intervention models have shown effectiveness, including the pharmacy care model where pharmacists partner with patients to develop a therapeutic plan, and physician/pharmacist collaborative models where pharmacists are part of team-based care. [45,46] Additionally, in a recent meta-analysis it was shown that pharmacist-led interventions using patient activation showed significant improvement in HbA1c. [47] A meta-analysis of pharmacist interventions delivered as part of general practice found significant improvements in blood pressure, HbA1c, and cholesterol. [48] Taken together, this evidence suggests the collaborative use of pharmacists in an effort to aggressively treat diabetes may lead to significant improvement in glycemic control, improved medication adherence, and a reduction in comorbidity burden.

Strengths of this study include the relatively large sample size, detailed information on key mutable and immutable factors including extensive data on different components of social determinants of health, and the use of an explanatory model in which variables were entered into the model sequentially based on theoretical relationships from prior research. Limitations include the cross-sectional nature of the data, which precludes discussion of causation and the regional sample from the southeastern United States. In addition, most recent HbA1c values were collected from medical records, rather than taking a new sample at the time of completion of the questionnaire.

Based on this comprehensive analysis of the socioeconomic, psychosocial, and built environment determinants of health, clinical factors, and knowledge/self-care behaviors, it appears interventions that target self-efficacy and social support, reducing the burden of comorbidities, and supporting patients in medication adherence and smoking cessation will lead to greater improvements in glycemic control. Use of psychological and pharmacist interventions in an effort to provide aggressive clinical treatment may lower the burden of disease, and address mutable social determinants. While the collaborative model of care is gaining momentum, these results provide data-driven information on important factors to consider when planning collaborative care teams for patients with type 2 diabetes.

HIGHLIGHTS.

• Examined differential contribution of social determinants and clinical factors on HbA1c.

• Explanatory model with variables entered in blocks (n=615)

• Variables: demographics, SES, psychosocial, environment, clinical, and knowledge/self-care.

• HbA1c associated with self-efficacy, social support, comorbidity, and medication adherence

• Important social determinants are mutable and amenable to health interventions.